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Urban Expansion, Land Cover and

Soil Ecosystem Services
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More than half of the world population now lives in cities, and urban expansion
continues as rural people move to cities. This results in the loss of land for other
purposes, particularly soil for agriculture and drainage. This book presents a
review of current knowledge of the extension and projected expansion of urban
areas at a global scale.
Focusing on the impact of the process of ‘land take’ on soil resources and the
ecosystem services that they provide, it describes approaches and methodolo-
gies for detecting and measuring urban areas, based mainly on remote sensing,
together with a review of models and projected data on urban expansion. The
most innovative aspect includes an analysis of the drivers and especially the
impacts of soil sealing and land take on ecosystem services, including agriculture
and food security, biodiversity, hydrology, climate and landscape.
Case studies of cities from Europe, China and Latin America are included.
The aim is not only to present and analyse this important environmental chal-
lenge, but also to propose and discuss solutions for the limitation, mitigation
and compensation of this process.

Ciro Gardi works in the Animal and Plant Health Unit of the European Food
Safety Authority, Parma, Italy. Previously, he was a Senior Scientist at the Land
Resource Management Unit of the Joint Research Center of the European
Commission and Professor of Soil Science at the University of Parma. He has
served as an independent expert and consultant for the European Commission,
World Bank, OECD and several NGOs and is currently a member of the
Scientific Advisory Committee of the Global Soil Biodiversity Initiative,
repre­senting it in the Global Soil Partnership (FAO).
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 Urban Expansion, Land Cover
and Soil Ecosystem Services

Edited by Ciro Gardi

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 First published 2017
by Routledge
2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
711 Third Avenue, New York, NY 10017
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2017 Ciro Gardi, selection and editorial material; individual
chapters, the contributors
The right of the editor to be identified as the author of the editorial
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material, and of the authors for their individual chapters, has been
asserted in accordance with sections 77 and 78 of the Copyright,
Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or
reproduced or utilised in any form or by any electronic, mechanical,
or other means, now known or hereafter invented, including
photocopying and recording, or in any information storage or
retrieval system, without permission in writing from the publishers.
Trademark notice: Product or corporate names may be trademarks
or registered trademarks, and are used only for identification and
explanation without intent to infringe.
The positions and opinions presented in this article/book are those
of the authors alone and are not intended to represent the views or
scientific works of the European Food Safety Authority (EFSA).
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging in Publication Data
Names: Gardi, Ciro, editor.
Title: Urban expansion, land cover and soil ecosystem services /
edited by Ciro Gardi.
Description: London ; Boston : Routledge, 2017. | Includes
bibliographical references and index.
Identifiers: LCCN 2016042613| ISBN 9781138885097 (hbk) |
ISBN 9781315715674 (ebk)
Subjects: LCSH: Urban ecology (Biology) | Urbanization—
Environmental aspects. | Urbanization—Case studies. |
Soil ecology.
Classification: LCC QH541.5.C6 U72 2017 | DDC 577.5/6—dc23
LC record available at https://lccn.loc.gov/2016042613

ISBN: 978-1-138-88509-7 (hbk)

ISBN: 978-1-315-71567-4 (ebk)

Typeset in Bembo
by Swales & Willis Ltd, Exeter, Devon, UK
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To Sofia and Maria Isabella, to stimulate them

struggling for a better world.
Downloaded by [University of California, San Diego] at 23:51 15 May 2017
 Contents
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List of figures x
List of tables xiii
Notes on contributors xv
Foreword, by Fernanda Guerrieri xxviii
Acknowledgements xxx

PART I
Introducing and understanding the process 1

1 Is urban expansion a problem? 3

CIRO GARDI

2 Measuring and monitoring land cover: methodologies

and data available 19
MICHELE MUNAFÒ AND LUCA CONGEDO

3 Measuring and monitoring the extent of human

settlements: from the local to the global scale 33
DANIELE EHRLICH, ANETA J. FLORCZYK, ANDREEA JULEA,
THOMAS KEMPER, MARTINO PESARESI AND VASILEIOS SYRRIS

4 Modelling and projecting urban land cover 59

CARLO LAVALLE, FILIPE BATISTA E SILVA, CLAUDIA BARANZELLI,
CHRIS JACOBS-CRISIONI, ANA LUISA BARBOSA, JEAN-PHILIPPE
AURAMBOUT, RICARDO BARRANCO, MERT KOMPIL,
INE VANDECASTEELE, CAROLINA PERPIÑA CASTILLO AND
PILAR VIZCAINO

5 Drivers of urban expansion 85

STEFAN FINA
 viii Contents
PART II
Impact of land take and soil sealing on soil-related
ecosystem services 121

6 The effects of urban expansion on soil health and

ecosystem services: an overview 123
MITCHELL PAVAO-ZUCKERMAN AND RICHARD V. POUYAT
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7 Impact of land take on global food security 146

CIRO GARDI

8 Hydrological impact of soil sealing and urban land take 157

ALBERTO PISTOCCHI

9 Impact of land take and soil sealing on biodiversity 169

GEERTRUI LOUWAGIE, MIRKO GREGOR, MANUEL LÖHNERTZ,
ECE AKSOY, CHRISTOPH SCHRÖDER AND ERIKA ORLITOVA

10 Impacts of land take and soil sealing on soil carbon 181

KLAUS LORENZ AND RATTAN LAL

11 Urban sprawl, soil sealing and impacts on local climate 193

LUIGI PERINI, ANDREA COLANTONI, GIANLUCA RENZI
AND LUCA SALVATI

12 Impacts of urban sprawl on landscapes 204

MARIE CUGNY-SEGUIN

PART III
Case studies 215

13 Soil consumption monitoring in Italy 217

MICHELE MUNAFÒ AND LUCA CONGEDO

14 Urban land expansion and its impacts on cultivated

land in the Pearl River Delta, China 231
XIAOQING SONG AND ZHIFENG WU

15 Urbanization in Latin America with a particular

emphasis on Mexico 238
RENÉ R. COLDITZ, MARÍA ISABEL CRUZ LÓPEZ, ADRIAN GUILLERMO
AGUILAR MARTÍNEZ, JOSÉ MANUEL DÁVILA ROSAS AND
RAINER A. RESSL
 Contents ix
16 Monitoring built-up areas in Dar es Salaam using
free images 258
MICHELE MUNAFÒ AND LUCA CONGEDO

PART IV
Policy and good practices 263

17 The European approach: limitation, mitigation and

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compensation 265
GUNDULA PROKOP AND STEFANO SALATA

18 Policy, strategy and technical solutions for land

take limitations 276
STEFANO SALATA

19 Soil sealing and land take as global soil threat: the

policy perspective 291
LUCA MONTANARELLA

20 Conclusions 296
CIRO GARDI

Index 298
 Figures
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1.1 Dynamics of urban and rural populations of the world 7

3.1 Alger and settlements surrounding Alger over a
52 × 40 km2 area 40
3.2 City of Bangalore as seen from Landsat imagery 41
3.3 Multi-temporal representation of four cities: Sao Paolo (a),
Washington DC (b), Guate (c) and Riyadh (d) 48
3.4 Density of built-up depicted by the European Settlement
Map for the city centre of Genoa, Italy, and its surroundings 49
3.5 Comparison of the settlement maps derived from Landsat (b)
and SPOT 5 (c) for a selected rural area in South Africa (a) 50
3.6 Detail of the two settlement maps from Figure 3.5 derived
from Landsat (a) and SPOT 5 (b) 51
4.1 Changes in resident population in the periods 2010–2030
and 2010–2050 66
4.2 Population density, 2010 68
4.3 Population density: absolute changes in percentage between
2010 and 2030 69
4.4 Built-up area per inhabitant, 2010 70
4.5 Changes in built-up area per inhabitant, 2010–2030 71
4.6 Urban sprawl, 2010 72
4.7 Changes in urban sprawl, 2010–2030 73
4.8 Network efficiency, 2010 74
4.9 Changes in network efficiency, 2010–2030 75
4.10 Potential accessibility, 2010 75
4.11 Changes in potential accessibility, 2010–2030 76
4.12 Population growth in Functional Urban Areas, 1961–2011 77
4.13 Average share of built-up surface in FUAs per country 78
4.14 Annual average land taken per inhabitant in the periods
2010–2030 and 2030–2050 79
4.15 Population growth vs. built-up growth, 2010–2050 80
 List of figures xi
4.16 Land use intensity in EU-28, 1990–2050 81
5.1 Conceptual framework for an analysis of land use change 86
5.2 Urban land use configurations in European metropolitan regions 89
5.3 Drivers of urban sprawl 90
5.4 Economic growth and urban expansion for EU countries 94
5.5 Transnational land acquisitions, 2005–2009 95
5.6 Transport policy and economic growth 99
5.7 Tax relief on debt financing cost of homeownership, 2009 103
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5.8 Passenger-km per year per capita in 2000 and projected

for 2050, and projected car ownership rates in 2050 107
5.9 Land take by road and rail infrastructure for the 2001
European Union member states 108
5.10 Distribution of Europe’s sprawling and compact cities 110
5.11 Trends and outlook: land use and soil functions 112
6.1 Direct and indirect effects of urbanization influence on both the
natural capital of soils and the generation of ecosystem services 125
6.2 Two paradigms for policy and management related to
urbanization influences on soils and ecosystem services 135
7.1 Distribution of intrinsically fertile soils 147
8.1 Conceptual graphs of ecological and human value of water 160
8.2 Ante-operam runoff coefficient based on direct runoff from
LISFLOOD model simulations 163
8.3 Compensation volume estimated for the compensation of
new urban areas built between 2000 and 2006 165
9.1 Capacity of soils to serve as a biodiversity pool 173
9.2 Percentage decline (per NUTS 3 area) of land with good
soil biodiversity potential 174
9.3 Land take in and near nature areas protected by Natura
2000 status 176
9.4 Land take in and near Natura 2000 areas for selected
NUTS 3 areas 177
9.5 Spatial pattern of land take in and near a Natura 2000 area
in a NUTS 3 region defined as a hotspot 178
10.1 Map of urban structures in Europe in unparalleled precision
based on data acquired by radar satellites 182
10.2 Soil carbon losses by land take and soil sealing processes in
urban areas 183
11.1 Urban heat island profile 194
11.2 Correlation coefficient of mean monthly temperatures
between urban and rural environmental contexts in
Milan and Rome, Italy 197
 xii List of figures
12.1 Landscape fragmentation per 1 km² grid in 2009 207
12.2 Urban profile in Europe 210
13.1 High resolution land cover map of Italy 220
13.2 Very high resolution layer of built-up area 221
13.3 Comparison of resolutions for orthoimagery, HRL
and VHRL 222
13.4 LCPI at the provincial level 223
13.5 RMPS at the provincial level 223
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13.6 ED at the provincial level 224

13.7 Urban Sprawl Index at the provincial level 224
13.8 Classes of urban development 225
13.9 Distribution of provincial capitals according to the
landscape metrics and classes of urban development 226
13.10 Mean Patch Area calculated at the provincial level 227
13.11 Patch Density calculated at the provincial level 227
13.12 Percentage of Like Adjacencies calculated at the
provincial level 228
13.13 Shannon Diversity Index calculated at the provincial level 228
13.14 Mean Shape Index calculated at the provincial level 228
14.1 Mechanisms of urban land use transition and cultivated
land use transition in the Pearl River Delta, China 236
15.1 Urban population proportion and urban area for the World,
Latin America and its regions and countries 241
15.2 A: Potential land cover change around urban areas, 2008.
B: Urban population proportion, 2015 243
15.3 Urban population proportion and urban area for the
states of Mexico 245
15.4 Gain and loss of urban area for bi-annual comparisons,
2005–2011 247
15.5 Location and population of urban areas of
Mexico in 2010 247
15.6 Urban expansion of Mexico City 249
17.1 Overview of most common surfaces 271
 Tables
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1.1 Past, actual (2014) and predicted population of the world’s 28

megacities 6
1.2 Population dynamics in some Asian urban agglomerations 8
1.3 The top 30 fastest growing urban agglomerations 10
1.4 Extension, population and population density of the
30 largest urban agglomerations 11
2.1 Band characteristics of Landsat 4 and 5 23
2.2 Band characteristics of Landsat 7 23
2.3 Band characteristics of Landsat 8 23
2.4 Band characteristics of Sentinel-2 24
2.5 Schematization of an error matrix 27
3.1 Examples of global observing missions and selected technical
specifications 36
3.2 Examples of optical space-borne missions 37
3.3 Examples of hyperspectral platforms 38
3.4 Built-up and built-up change statistics over the Alger
metropolitan area 40
3.5 Built-up area per time period for the Bangalore case study 42
3.6 Selected global datasets relevant for urban area mapping 44
3.7 European datasets relevant for urban area mapping 45
3.8 CORINE Land Cover products 46
3.9 Landsat imagery and GHSL processing time 48
4.1 List of indicators used to assess urban development and
accessibility according to the LUISA EU Reference
Scenario 2014 67
5.1 Classification of the public sector in relation to the level
of control of urban development 100
5.2 Strength of land use controls in European countries 101
5.3 Aggregate homeownership rates in selected OECD countries 103
7.1 Absolute and relative yearly agricultural land take in
21 EU countries 153
 xiv List of tables
9.1 Effects of landscape fragmentation on flora and fauna 172
10.1 Maximum relative changes in soil organic and inorganic
carbon stocks 183
11.1 An example of Local Climatic Zones for the analysis of
urban contexts 198
13.1 Soil consumption in Italy 218
13.2 Percentage of soil consumption in Italian regions 218
13.3 Landscape metrics calculated at the provincial level 226
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13.4 Soil consumption at the municipal level for the top 20

municipalities, 2012 229
14.1 Change in urban land parcels with different sizes in
1980–2010, Pearl River Delta 233
14.2 Change in cultivated land with different sizes in 1980–2010,
Pearl River Delta 234
14.3 Change in the ratio of area of dry farmland to area of paddy
fields with different sizes in 1980–2010, Pearl River Delta 234
15.1 Area, total population and urban population (selected years)
for the World, Latin America and its regions and Mexico 240
16.1 Land cover classification results 259
16.2 Fuzzy error matrix calculated for land cover classification
based on Landsat images of 2011 260
16.3 The accuracies of user and producer 260
16.4 The accuracies of user and producer 260
17.1 Comparison of benefits and limitations of most common
permeable surfaces 272
 Contributors
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Adrian Guillermo Aguilar is a Senior Researcher at the Institute of

Geography and Professor for Urban Geography in the graduate studies of
the National Autonomous University of Mexico (UNAM). He conducts
research on the areas of urban-regional analysis, medium-sized cities and
metropolitan development, globalization and megacities, and urban sustain-
ability. He has co-authored 11 books and numerous articles and chapters in
different books. He received a PhD from the University of London, UK.
Ece Aksoy is an interdisciplinary researcher with a BSc in Urban and Regional
Planning, an MSc on GIS and a PhD in Agricultural and Soil Science. She
worked as a scientific officer in the European Commission, Joint Research
Centre (JRC), Institute for Environment and Sustainability (IES) from 2011
to 2014, where she strengthened her experiences on EU agricultural and
environmental policies and agricultural technologies in the EU. She has
worked on numerous applied research projects in Turkey on the environ-
ment, land management, agricultural information systems and soil studies at
different scales (farm, watershed, urban–rural). She also worked as an inter-
national consultant of FAO-UN and developed a National Geospatial Soil
Fertility and Soil Organic Carbon Information System for Turkey. She has
worked in the European Topic Centre on Urban, Land and Soil Systems
(ETC-ULS of EEA) as a senior expert since 2014, dealing with digital soil
mapping, urban sustainability studies, quantitative assessment of land deg-
radation, potentials of soil biodiversity functions, impact analysis on land
resource productivity and providing support to biodiversity strategy targets –
CAP greening actions by assessing multi-mono functionality of the soils.
Jean-Philippe Aurambout holds an agronomy engineering degree from
ENSAIA, France (2000), an MSc (2002) and PhD (2005) in Natural
Resources and Environmental Sciences from the University of Illinois at
Urbana-Champaign, USA. Between 2005 and 2013 he worked as a Senior
Research Scientist with the Spatial Information Sciences group of the
Victorian Department of Primary Industries in Melbourne, Australia. He is
currently working as s Scientific Officer at the European Commission, Joint
Research Centre in Italy. His research interests focus on the modelling of
complex dynamic systems at the spatial scale.
 xvi Notes on contributors
Claudia Baranzelli is from Milan, Italy, where she graduated in Environmental
Engineering in 2005, with particular focus on Environmental Modelling
and Territorial Planning, and completed a PhD in Urban, Regional and
Environmental Planning in 2010. From 2006 to 2010 she worked at the
Politecnico di Milano as researcher and teaching assistant, with particular
focus on urban and regional planning, and assessment methods applied to
buildings and urban areas. In 2010 she joined the Joint Research Centre and
since then she has been working on the development and application of the
LUISA Territorial Modelling Platform in several fields, mainly related to
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energy, agriculture and the urban environment.

Ana Luisa Barbosa graduated in Geography (2005) and has a Master of
Geographic Information System and Science (2009). With over eight years
of experience working for European Institutions (European Topic Centre –
European Environmental Agency and the Joint Research Centre at the
European Commission), she participated in several initiatives to support
the impact assessments studies of EU regional and environmental policies.
As a scientific officer at the JRC (2011–2015) she has been involved in
the development of the LUISA Territorial Modelling Platform, a platform
used for the ex-ante evaluation of EC policies that have a direct territorial
impact. She specializes in scenarios development, land use modelling and
the development of indicators for impact assessment.
Ricardo Barranco graduated in 2008 in Environmental Engineering
at University of Algarve, Portugal, doing his final Master’s thesis at
Wageningen University and Research Centre, the Netherlands, focusing
on the use of Geographical Information Systems (GIS) on urban areas. From
2008 to 2012 he worked in Genova, Italy at IREN’s Environmental and
Technological Services department. From 2012 to the present he has been
part of the LUISA Territorial Modelling Platform project. Working mainly
on urban indicators development, more recently he has been collaborating
on energy-related projects and data visualization techniques.
Filipe Batista e Silva graduated in 2006 in Geography, and was later awarded
a Master’s in Geographic Information Systems and Spatial Planning. He
worked in the Department of Geography at the University of Porto in
Portugal until 2010, lecturing and contributing to urban and regional plan-
ning projects. He then joined the Joint Research Centre of the European
Commission and has since been involved in the development and applica-
tion of the LUISA Territorial Modelling Platform. He is particularly inter-
ested in population and activity mapping, demography–economy–land use
interactions, regional modelling, spatial analysis and downscaling methods.
He is finalizing his PhD at the VU University Amsterdam, the Netherlands.
Carolina Perpiña Castillo received her PhD in Cartography and Geodesy
Engineering in 2012 from the Universitat Politecnica de Valencia, Spain,
as applied to biomass logistics and transport optimization using spatial tech-
niques. As a researcher, she worked at the Institute of Energy Engineering
 Notes on contributors xvii
from 2006 to 2012 (funded by the Spanish Ministry of Science and
Innovation), specifically in the area of renewable energies. Additionally,
she earned an MSc in Land Use Planning and Transport at the same uni-
versity. Since 2012, Carolina has been working at EC-JRC (Ispra, Italy),
focused on the development of the LUISA Modelling Platform, especially
on the impact assessment of food, feed and energy production in Europe.
Other contributions to the modelling platform have included various
technical improvements and the integration of an economic evaluation of
agricultural land.
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Andrea Colantoni is a Researcher at the Department of Agriculture and

Forestry Science, Tuscia University (Viterbo, Italy). He has a PhD in
Agriculture Mechanization. He is a member of: the scientific committee
of PhD Engineering for Energy and Environment at Tuscia University;
the Italian Association of Agricultural Engineers, which is part of the
European Society of Agricultural Engineers (EurAgEng); the International
Commission of Agricultural Engineering (CIGR); and the Italian Association
of Scientific Agricultural Societies (AISSA). His scientific activity is mainly
focused on: the mechanization of harvesting of tree crops (hazelnut), indus-
trial crops (tomato) and fibre plants; safety and quality in the agro-food and
agro-industrial chains; ergonomics and analysis of working loads in agro-
forestry; safety and risk assessment in agro-industrial workplaces; and the use
of renewable energy sources in agriculture.
René R. Colditz has been a Remote Sensing Specialist at the National
Commission for the Knowledge and Use of Biodiversity (CONABIO),
Mexico, since 2007. His main fields of research are land cover classifica-
tion and change detection, time series generation and analysis, and multi-­
resolution analysis using medium to coarse (30–1000 m) spatial resolution
satellite data. In 2008 he received his PhD in Geography and Remote
Sensing from the University of Würzburg, Germany, in cooperation with
the German Aerospace Center (DLR).
Luca Congedo has a Master’s degree in Environmental Engineering. His
major fields of specialization are remote sensing and GIS analysis. He has
conducted several studies related to the semi-automatic classification of
remote sensing images such as the land cover classification of Dar es Salaam
(Tanzania) in the context of the ACC Dar Project, and the mapping of
asbestos-cement roofing in the Latium Region (Italy) using MIVIS hyper-
spectral images. He has been involved in the verification and enhancement
of the Copernicus High Resolution Layers 2012, and cooperated with
the Italian National Institute for Environmental Protection and Research
(ISPRA) for the report on soil consumption monitoring.
María Isabel Cruz López works as Head of the Remote Sensing Division
at the National Commission for the Knowledge and Use of Biodiversity
(CONABIO), Mexico. Since 1998 she has coordinated and developed
projects on ecosystem monitoring and early warning systems using remote
 xviii Notes on contributors
sensing data. Her main research fields are forest fires, land cover changes
and vegetation monitoring with remote sensing and geographic information
system (GIS). She holds a Master’s degree in Geography from the National
Autonomous University of Mexico (UNAM).
Marie Cugny-Seguin is Project Manager for ‘Urban and Territorial Issues’
at the European Environment Agency, Copenhagen, Denmark. She has
professional experience in environmental reporting at city, regional and
national level. Her interests are focused on the way to develop more
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resource-efficient cities, the analysis of green infrastructure inside and

around cities, and the integrative analysis of cities taking into account the
complexity of urban systems and the diversity of cities.
José Manuel Dávila Rosas worked for seven years in the private sector
before joining the National Commission for the Knowledge and Use of
Biodiversity (CONABIO), Mexico, as Head of Geographical Information
Systems in 2012. His main interests are development of GIS software,
automation of geo-data processing, digital cartography and geographic
analysis for biodiversity in urban areas. He graduated as Geographer from
Autonomous University of the State of Mexico.
Daniele Ehrlich holds a PhD from the University California at Santa Barbara,
USA. He is a senior staff member of the Joint Research Centre (JRC),
European Commission, Ispra, Italy. He has over 25 years of experience in
remote sensing and GIS applied to a variety of disciplines including crop area
estimation, tropical forest mapping, crisis management with a focus on dam-
age assessment and humanitarian assistance. His current research focuses on
quantifying the extent and the dynamics of settlements using high-resolution
satellite imagery. He uses derived settlement information for a systematic
analysis of the global built-up environment, for population estimations and
for generating physical exposure databases for global disaster risk assessments.
Stefan Fina, PhD in Geography, is a land use planning researcher at the
Institute of Spatial and Regional Planning at the University of Stuttgart,
Germany. He has an international research focus on issues of land use
change and urban sprawl, both in academia and planning practice. His dis-
sertation and a range of research articles are about indicators and quanti-
fication methods, as well as topics of demographic change, infrastructure
planning, environmental impacts, and segregation studies. As a planner he
worked for a number of years in transport and as an integrated planning
analyst for a council in Auckland, New Zealand, and as a private consultant
in the development of planning support systems and land use modelling. His
teaching is mainly about planning tools and assessment methods in national
and international Master’s programmes at the University of Stuttgart. Stefan
currently extends his research interests into areas of health-related urban
development and participatory planning approaches.
 Notes on contributors xix
Aneta Jadwiga Florczyk received a PhD in Computer Science from the
University of Zaragoza, Zaragoza, Spain, in 2012. In November 2007, she
started to collaborate with the Advanced Information Systems Laboratory
(IAAA), University of Zaragoza, Spain. Currently, she works as a Scientific
Project Officer (Post-doctoral Grant Holder) with the Joint Research
Centre (JRC), European Commission, Ispra, Italy. Her research interests
include remote sensing, GIScience, image processing, machine learning,
statistics, high-performance computing and big data analytics.
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Ciro Gardi is currently working in the Plant Health team of the European
Food Safety Authority, Parma, Italy. Agronomist, soil scientist/ecologist,
with a PhD in Crop Science, he has a deep knowledge of agricultural sys-
tems and of the interactions between land management, soil quality and
ecosystem service provision. He is actively involved in all aspects related
to soil degradation, from research to policy support and awareness raising.
He taught soil science at the University of Parma, Italy, and in interna-
tional Master’s and other courses. His main research activities are on the
relationships between land use, agronomic management and soil quality,
with particular emphasis on soil degradation processes and their relationships
with soil organic matter and soil biodiversity. He is experienced in GIS,
remote sensing and soil survey research, which he has carried out in Italy
and abroad. He has been a consultant and served as an independent expert
for the European Commission, World Bank, OECD and several NGOs and
he is currently a member of the Global Soil Partnership (FAO).
Mirko Gregor holds a Diploma degree in Applied Physical Geography
(2003) from the University of Trier, Germany, with a particular focus on
geomorphology, remote sensing and climatology. After working as a GIS
and remote sensing expert for the Luxembourg-based private company
GIM (2003–2008), he became a project manager at space4environment
(formerly GeoVille Environmental Services), Luxembourg. Since then, he
has been actively involved and leading space4environment’s activities on
land resource efficiency and urban sustainability assessments in the frame-
work of the European Topic Centre on Urban, Land and Soil Systems
(ETC-ULS). He is also working on urban biodiversity topics in the context
of the European Topic Centre on Biodiversity (ETC-BD) and as Technical
Project Manager for the ESA project Earth Observation in support of the
City Biodiversity Index.
Fernanda Guerrieri holds an MSc in Agronomy with honours from the
University of Bologna, Italy, and a Diploma in Watershed Management
from the International Institute for Aerial Survey and Earth Science (ITC),
Enschede, the Netherlands, with distinctions. Ms Guerrieri started her
career in 1982 at the University of Bologna, Italy, working on soil conserva-
tion and land evaluation/watershed management. She joined the Food and
Agriculture Organization of the United Nations (FAO) in 1988 as Associate
 xx Notes on contributors
Project Operations Officer on the Latin American and Caribbean Desk,
Forestry Department, FAO Headquarters. From 1990 to 1995, she served as
Programme Officer/Deputy FAO Representative in Equatorial Guinea, and
then, from 1992 to 1995, in Côte d’Ivoire and Mozambique. In 1995, she
returned to FAO Headquarters as Project Analyst, Technical Cooperation
Programme (TCP). In 1998, she was appointed FAO Representative in
Vietnam. She returned to FAO Headquarters in 2002 as Chief, Emergency
and Rehabilitation Operations Service. In August 2008, she was appointed
Deputy Regional Representative, Subregional Coordinator for Central and
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Eastern Europe in Budapest, Hungary. From May 2010 until January 2013,
she served as Assistant Director-General and Regional Representative for
Europe and Central Asia, in Budapest, Hungary. From January 2013 until
July 2015, she served as Assistant Director-General/Directeur de Cabinet,
Office of the Director-General in Rome.
Chris Jacobs-Crisioni is from Amsterdam, the Netherlands, where he gradu-
ated in Urban Planning at the University of Amsterdam and is currently
finalizing a PhD in Spatial Economics at VU University Amsterdam. His
main research interest is in the expansion processes of transport networks
and the subsequent land use and sustainability effects of these processes. He
has published in reputed international scientific journals such as Environment
and Planning A and the Journal of Geographical Systems. Between 1999 and
2013, he worked for various municipalities, transport consultancy agency
Goudappel Coffeng and VU University Amsterdam. In those years he
learned many facets of geographic data gathering, geographical information
systems and spatial decision support systems. In 2013, he joined the Joint
Research Centre’s LUISA team, where he is occupied with the further
development of the LUISA model.
Andreea Julea received a PhD in Electronics, Telecommunication and
Computer Science from the Polytechnic University of Bucharest, Romania,
and Grenoble University (Savoy University, Annecy), France, in 2011.
Between 2005 and 2013, she was a scientific researcher with the Institute
of Space Science, Magurele-Bucharest, Romania. Since 2013, she has
been Scientific Project Officer (Post-doctoral Grant Holder) with the Joint
Research Centre (JRC), European Commission, Ispra, Italy. Her main
research interests are in the areas of knowledge discovery in databases, data
mining, image processing and remote sensing applications.
Thomas Kemper received a PhD in Geosciences from the University of Trier,
Germany, in 2003. He is a Scientific Officer with the Joint Research Centre
(JRC), European Commission, Ispra, Italy. From 2004 to 2007, he worked
with the German Aerospace Center (DLR), Cologne, Germany, where
he helped in setting up the Center for Satellite-Based Crisis Information
(ZKI), which provides rapid mapping information after natural disasters.
Since 2007, he has been working on the analysis of human settlements, in
particular informal settlements such as slums and IDP/refugee dwellings.
 Notes on contributors xxi
Mert Kompil is Scientific/Technical Project Officer in the LUISA Modelling
Platform group at the Joint Research Centre, Italy. He worked as postdoc-
toral researcher on transport policy analysis and modelling at the Institute
for Prospective Studies of the Joint Research Centre based in Spain. Before
that, he worked as a research and teaching assistant at the Department of
City and Regional Planning under the Izmir Institute of Technology in
Turkey. He completed his PhD in the same department with a specializa-
tion on travel demand analysis. His main areas of research include spatial
interaction models, trip distribution models, travel demand analysis, acces-
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sibility, land use modelling and territorial impact assessment.

Rattan Lal, PhD, is a Distinguished University Professor of Soil Science at
the Ohio State University, USA, and was Senior Research Fellow with the
University of Sydney, Australia (1968–1969), and Soil Physicist at IITA,
Ibadan, Nigeria (1969–1987). His research focus is on climate-resilient
agriculture, soil carbon sequestration, sustainable intensification, enhanc-
ing the use efficiency of agroecosystems, and the sustainable management
of soils of the tropics. He was included in Thomson Reuters 2014 list of
World’s Most Influential Scientific Minds (2002–2013). He was President
the Soil Science Society of America (2005–2007), is President Elect of
the International Union of Soil Sciences, Vienna, Austria (2014–), and is
Chair of the Advisory Committee to UNU-FLORES, Dresden, Germany
(2014–). He has mentored 105 graduate students, 54 postdoctoral research-
ers and 145 visiting scholars. He has authored/co-authored more than 1940
research publications including 761 refereed journal articles and 421 book
chapters, and has written 16 and edited/co-edited 58 books.
Carlo Lavalle, has over 25 years of experience in applied geophysics and inte-
grated modelling. Since 1990 he has been with the Joint Research Centre
of the European Commission, with involvement in dossiers related to envi-
ronment, energy, urban and regional development. He is coordinating the
development of the LUISA Territorial Modelling Platform.
Manuel Löhnertz holds a Diploma degree in Applied Environmental Science
(2006) from the University of Trier, Germany, with a special focus on soil sci-
ence, physical geography and remote sensing. From 2006 to 2008 he worked as
GIS and Remote Sensing expert for the private company GIM in Luxembourg.
Since 2009 he has been working for space4environment, Luxembourg (formerly
GeoVille Environmental Services) as a GIS and remote sensing expert. Having a
strong background in remote sensing applications, application development and
database management, his current focus is spatial data processing and control in
the framework of space4environment’s activities for the European Topic Centre
on Urban, Land and Soil Systems (ETC-ULS) and Biodiversity (ETC-BD).
Klaus Lorenz, PhD, is Assistant Director/Research Scientist at the Carbon
Management and Sequestration Center, Ohio State University, USA.
From 2011 to 2013, he was Chief Soil Scientist/Research Fellow at the
IASS Institute for Advanced Sustainability Studies in Potsdam, Germany.
 xxii Notes on contributors
He studied biology at University of Freiburg, Germany, and obtained his
PhD in Agricultural Sciences from University of Hohenheim, Germany.
His research focuses on agricultural, forest and urban soil use and man-
agement to enhance soil organic carbon sequestration for climate change
adaptation and mitigation. He has written the book Carbon Sequestration in
Forests Ecosystems (co-author Rattan Lal), authored/co-authored 33 peer-
reviewed journal articles and 18 book chapters.
Geertrui Louwagie graduated as Bio-Engineer in Land and Forest Management,
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Soil and Water Management at Ghent University, Belgium (1995), and has
a PhD in Science (earth sciences) from Ghent University (2004). She started
her career as an academic assistant (in soil science and land evaluation) at
Ghent University, where she also did research on land evaluation and palaeo-
environment reconstruction and provided policy support in the area of archae-
ological site management. In 2005 she joined University College Dublin as
a postdoctoral researcher in an EU research consortium, developing meth-
ods for the evaluation of agri-environmental schemes in different EU con-
texts. From 2008 to 2011, she was a postdoctoral researcher at the European
Commission’s Joint Research Centre (Institute for Prospective Technological
Studies, Spain) and coordinated policy support projects on sustainable agri-
culture and soil conservation in the EU, as well as on rural development in
the Western Balkans. Since 2012, she has been the Project Manager ‘Soil
Assessments and Reporting’ at the European Environment Agency, Denmark.
Luca Montanarella has been working as a scientific officer in the European
Commission since 1992. He has been leading the Soil Data and Information
Systems (SOIL Action) activities of the Joint Research Centre in support
of the EU Thematic Strategy for Soil Protection and numerous other soil-
related policies, like the Common Agricultural Policy (CAP), the UNCCD,
UNFCCC, CBD, among others. He is responsible for the European Soil
Data Centre (ESDAC), the European Soil Information System (EUSIS) and
the European Soil Bureau Network (ESBN). More recently he has been
in charge of supporting the establishment of the Global Soil Partnership at
FAO. He is currently the Chair of the Intergovernmental Technical Panel
on Soils (ITPS) of the GSP and the Co-chair of the Intergovernmental
Platform for Biodiversity and Ecosystem Services (IPBES) Land Degradation
and Restoration Assessment (LDRA). He has more than 300 publications,
books and reports, and has received numerous awards and memberships.
Michele Munafò has been at ISPRA since 2000. He is currently head of the
Environmental Monitoring and Pressures Database Unit and of the Italian
Land Take monitoring group. He has a PhD in Urban Planning. He is tem-
porary Professor in Regional and Urban Planning, Strategic Environmental
Assessment and Geographical Information Systems at the University of Rome
Sapienza, Italy. He is a member of the scientific committee for PhDs in
Landscape and Environment. He is responsible for enhancement and valida-
tion activities for the national Copernicus Land Monitoring High Resolution
 Notes on contributors xxiii
Layers, National Reference Centre of the European Environment Information
and Observation Network (European Environment Agency), involved in the
production of Italian Corine Land Cover, and project manager for several
projects regarding land monitoring and environmental information.
Erika Orlitova graduated in Automatic Control Systems at the Czech
Technical University in Prague and postgraduate study in GIS Application
at Technical University of Ostrava. She is a senior data manager experienced
in spatial analysis, thematic mapping, satellite data processing and interpreta-
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tion and geo-information assessment, taking part in various domestic and

international projects related to spatial data processing. She was involved
in the MARSOP3 project supporting the JRC-AGRI4CAST and JRC-
FOOD-SEC and has experience working with EEA since 2004 within the
ETC-LUSI framework on various data related tasks.
Mitchell Pavao-Zuckerman is an Assistant Professor in the Department of
Environmental Science and Technology and Institute for Sustainability in
the Built Environment at the University of Maryland, College Park, MD,
USA. Mitch is an ecosystem ecologist focusing on the responses of eco-
systems to urbanization and land-use change. He has research focuses on:
(1) the ecosystem services and biogeochemical cycling of green infrastruc-
ture; (2) the influence of urbanization on the ecohydrology and physiologi-
cal ecology of soils and plants; (3) the resilience of urban socio-ecological
systems to climate and land-use change, and (4) interdisciplinary approaches
to studying urban ecosystems, including connections to environmental
design, planning, policy, and governance.
Luigi Perini, at CREA (the Italian Council for Research in Agriculture),
Rome, Italy, since 2004, is currently Director of the Research Institute of
Climatology and Meteorology applied to Agriculture (CREA-CMA). Gradu­
ating in agronomy, he obtained subsequent specializations in meteorology
and agrometeorological modelling. He was involved in various scientific pro-
jects and institutional collaborations. He participated to found the National
Agrometeorological Service for the Italian Ministry of Agriculture and
contributed professionally to constitute several regional agrometeorological
services. He collaborated with the National Commission to combat drought
and desertification (CNLSD) in order to identify the vulnerabilities of the
Italian territory. He is a member of the technical group on long-term forecast
for the Italian Department of Civil Protection and he is the Italian component
of the Agricultural Commission for the World Meteorological Organization
(ONU-WMO). Currently he is involved in experimentation in land moni-
toring techniques by unmanned aerial vehicles (UAV).
Martino Pesaresi graduated in Town and Regional Planning, University of
Venice, Italy, in 1992. He pursued research activities on remote sensing, spa-
tial statistics and urban analysis with the Centre d’Analyse et de Mathématique
Sociales of the Ecole des Haute Etudes en Sciences Sociales (EHESS), Paris,
France, in 1991–1992, with the Laboratoire d’Informatique Appliquée,
 xxiv Notes on contributors
ORSTOM, Paris, France, in 1992–1993 and for the Department of Urban
and Regional Planning, University of Venice until 1995. From 1997 to 2000,
he was on a postdoctoral research contract on urban analysis using satellite
data with the EC Joint Research Centre, Space Applications Institute. Since
2004, he has been working with the Joint Research Centre (JRC), European
Commission, Ispra, Italy, contributing to several programmes dealing with
the use of space technologies for automatic image information retrieval and
decision support systems. Since 2014, he has been scientifically responsible
for the GLOB-HS and E-URBAN projects exploiting remote sensing tech-
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nologies for fine-scale systematic analysis of human settlements, respectively,

at the global and European levels.
Alberto Pistocchi, PhD, is a chartered environmental engineer and land
planner and an Associate Professor of Land and Urban Planning at the Joint
Research Center of the European Commission, Ispra, Italy. He is a scien-
tific officer and project leader at the European Commission DG JRC since
2013. Since 1997 he has been working as a professional hydraulic engineer
and land planner, focusing on river basin management, flood risk man-
agement, groundwater resources, water supply and distribution networks,
environmental assessment and project appraisal. His scientific interests con-
cern hydrological and water quality modelling, and spatial decision support
systems. He has authored/co-authored several scientific publications and
the book GIS Based Chemical Fate Modeling: Principles and Applications (2014).
Richard V. Pouyat received his PhD in Ecology from Rutgers University,
USA, in 1992 and an MSc in Forest Soils and BSc in Forest Biology at the
College of Environmental Science and Forestry, State University of New
York, USA, in 1983 and 1980, respectively. He is the National Program
Lead for Air and Soil Quality Research for Research and Development at
the Washington DC headquarters of the United States Forest Service. He is
currently on a detail to the White House Office of Science, Technology, and
Policy (OSTP). He is an original co-principal investigator of the Baltimore
Ecosystem Study – a Long Term Ecological Research site funded by the
National Science Foundation.
Gundula Prokop (MSc, MBA) is a senior expert and project manager at
the Austrian Environment Agency (Umweltbundesamt). Since 1996 she
has been with the Austrian Federal Environment Agency, mainly provid-
ing consultancy to the European Environment Agency and working as
co-ordinator or project partner in EU Research and Territorial Co-operation
Projects. Furthermore, she has been working under contracts for the
European Commission, EUROSTAT, the World Bank and the Joint
Research Centre. She has been in charge of developing an indicator for
contaminated sites for the EEA (CSI015, LSI003), indicators for land take
and soil sealing for the European Commission and land use indicators for
regional surveys on behalf of EUROSTAT. She is an active networker and
member of several national and international working groups, including the
 Notes on contributors xxv
National Reference Centre for Land Use (EEA), member of the UNECE-
ITU Technical Advisory Board for the Sustainable Development Goal 11
‘sustainable cities and communities’. Gundula is the author and editor of
national and international publications related to contaminated land man-
agement, brownfield recycling and land take.
Gianluca Renzi, having graduated in Agricultural Science and Technology,
obtained a subsequent specialization PhD in Sciences and Technologies for
Forest and Environmental Management. He has worked at CREA (the Italian
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Council for Research in Agriculture), Rome, Italy, since 2009.

Rainer A. Ressl is the General Director of Geomatics at the National
Commission for the Knowledge and Use of Biodiversity (CONABIO),
Mexico. His main research interests include satellite remote sensing for eco-
system monitoring, development of operational monitoring systems based
on remote sensing and GIS data, e.g. for forest fire, land cover, and ocean
products and GIS applications related to biodiversity. He graduated with
a PhD in Geography and Remote Sensing from the Ludwig Maximilians
University of Munich, Germany in cooperation with the German Aerospace
Center (DLR) in 1999.
Stefano Salata has a PhD in Territorial Government and Urban Design XXVI
Cycle at the Department of Architecture and Urban Studies (DAStU),
Politecnico di Milano. He is a contract Professor of Urban and Territorial
Analysis at Politecnico di Milano and works on the project LIFE SAM4CP
with the Interuniversity Department of Regional and Urban Studies and
Planning, Politecnico di Torino. Graduated in Urban Planning and Territorial
Policies he is involved in research activities on land take analysis and its envi-
ronmental effects. He is a member of Ecosystem Service Partnership (ESP) and
a member of the National Research Centre on Land Take – ITALY (CRCS).
He is the co-editor of CRCS’ Italian National Reports on land take (2010,
2012, 2014 and 2016). He is also the co-author of the book L’insostenibile
consumo di suolo (2013) and author of many international publications on land
take and its related effect on ecosystem services.
Luca Salvati has two degrees (Ecology: 2000; Demography and Social
Sciences: 2004), a Master’s degree in Economic Statistics, a specialization
degree in Geography and Environment and a PhD in Economic Geography.
He is adjunct Professor of Cartography and GIS, Multivariate Statistics, and
Strategic Environmental Assessment at Third University of Rome. He col-
laborates with University of Rome ‘La Sapienza’ in the field of urban and
rural geography. He is currently permanent researcher at the Italian Council
of Agricultural Research and Economics (CREA). He has also collaborated,
as a research fellow, with various research institutions in the framework
of both national and European projects on the following themes: deser-
tification, sustainable agriculture, land use, climate change, urban sprawl
and polycentric development at the regional scale, with reference to the
 xxvi Notes on contributors
Mediterranean basin. He is the author of more than 300 scientific articles in
English, 20 books, essays and cartographical atlases.
Christoph Schröder graduated in Geography and is a GIS specialist and
environmental researcher at the University of Malaga, Spain, where he
works for the European Topic Centre on Urban, Land and Soil Systems
to support land and soil related activities of the European Environment
Agency. His main field of research is the application of Geographic
Information Systems to land use/cover change analysis from local to global
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scale with particular interest in the Mediterranean. Over the past couple of
years he has been involved in analysing the impact of land cover changes
on soil functions at a European scale.
Song Xiaoqing, PhD, is a Lecturer on Geographical Sciences, Guangzhou
University, China, and was a visiting scholar of the Cluster of Excellence on
Integrated Climate System Analysis and Prediction (CliSAP) at Hamburg
University, Germany. His research focus is on urbanization, land use transi-
tion and multifunctional land management. He has authored more than 15
refereed journal articles.
Vasileios Syrris received a PhD in Computational Intelligence from Aristotle
University of Thessaloniki, Thessaloniki, Greece, in 2010. He has worked as
an Assistant Lecturer/Tutor with the Automation and Robotics Laboratory,
Aristotle University of Thessaloniki, and the Department of Informatics
and Electronics, Technological Educational Institute of Thessaloniki,
Thessaloniki. Currently, he works as a researcher with the Global Security
and Crisis Management Unit, Institute for the Protection and Security of
the Citizen, Joint Research Centre, European Commission, Ispra, Italy. His
research interests include high-performance computing, machine learning,
robotics, automation, computer vision, remote sensing, control engineer-
ing, statistics and big data analytics.
Ine Vandecasteele has a Master’s degree in Hydrogeology (2007) and
Conflict and Development (2008), both from Ghent University, Belgium.
She has been working at the JRC linking land use modelling with hydro-
logical management since 2011. She completed her PhD on this subject area
in 2014 with the Vrije Universiteit Brussel.
Pilar Vizcaino received her Bachelor and MSc degrees as a Forest Engineer
from the Polytechnic University of Madrid, Spain, in 2001. She worked
for several years for the Hydrology Department of her university, and is
co-founder of a spin-off company that provides consultancy on the man-
agement of ecosystems. She has worked in the Joint Research Centre of
the European Commission between 2006 and 2009 and since 2013, where
she has mainly worked on the development and application of models inte-
grated in geographical information systems. Her fields of interest are spatial
analysis of growth and development indicators and application of quantita-
tive methods (machine learning techniques, statistics, and optimization) to
spatial problems.
 Notes on contributors xxvii
Zhifeng Wu, PhD, is Professor of Geographical Sciences, Guangzhou
University, China. He plays important roles in landscape ecology, urban
studies, remote sensing and GIS in China. His research focuses on
Anthropocene landform processes, urban remote sensing, the urbaniza-
tion spatial process and its environmental-ecological effects. He also has
organized many international co-researches. He is the committee member
of the Sino-EU Panel on Land and Soil (SEPLS) and Vice-Director of the
International Association for Landscape Ecology, China. He has supervised
25 graduate students and nine postdoctoral researchers. He has authored/
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co-authored more than 1940 research publications including 267 refereed

journal articles and five book chapters (in Chinese).
 Foreword
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An anonymous saying states ‘whatever mankind does, we depend on 10 centimetres

of soil and a few drops of water’.
Soil is a finite resource, meaning its loss and degradation is not recov-
erable within a human lifespan. As a core component of land resources,
agricultural development and ecological sustainability, the soil is the base
for food, feed, fuel and fibre production and for many critical ecosys-
tem services. Current demographic trends and predicted growth in global
population (to exceed 9 billion by 2050) are estimated to result in a global
60 per cent increase in demand for food, feed and fibre by 2050 (FAO,
IFAD and WFP, 2015). By 2050, 70 per cent of the world population will
live in urban areas. Cities will expand dramatically and mainly in Africa
and other low- and middle-income areas. Demographic changes create a
series of challenges that differ from one country to another, such as ageing
populations, shrinking or rapidly expanding cities or intense processes of
suburbanization. The population in some areas of Europe has increased
significantly in recent years while other areas have depopulated (Piirto
et al., 2010), and as life expectancy increases, the average age of the popu-
lation will rise. Overall, this means more people to house, with higher
expectations of the size and appropriate location of their homes. Despite
a notable decrease in the average number of people in a household, in
high-income countries the recent changes in the society such as lifestyles
and consumption patterns demand more land and often good agricultural
land. The pressure on soils for urbanization and the subsequent soil sealing
phenomenon have never been higher.
In the late Anthropocene (Crutzen and Stoermer, 2000) there is often a
general lack of appreciation as to the value of soil (and landscape), which is not
recognized as a limited and non-renewable resource. This is a cause of serious
concern, because soil formation is a very slow process, often taking centuries
to build up even a centimetre.
The objective of this book is to create awareness on soil sealing due to the
expansion of urbanization. The book proposes best practices to limit, mitigate
or compensate the phenomenon. These best practice examples may be of
 Foreword xxix
interest to competent national authorities, professionals dealing with land plan-
ning and soil management, and stakeholders in general, but individual citizens
may also find them useful.

Fernanda Guerrieri
FAO, Rome, Italy

References
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Crutzen, P. J., and Stoermer, E. F. (2000). The Anthropocene IGBP Newsletter, 41.
Stockholm: Royal Swedish Academy of Sciences.
FAO, IFAD and WFP (2015) The State of Food Insecurity in the World 2015. Meeting
the 2015 International Hunger Targets: Taking Stock of Uneven Progress. Rome: FAO.
Piirto, J., Johansson, A., and Lang, V. (2010). Europe in Figures: Eurostat Yearbook 2010.
Luxembourg: Publications Office of the European Union.
 Acknowledgements
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I express my gratitude to Tandra Fraser, James Cottrell and Martha Dunbar for
the revision of texts. A special thanks go to Arwyn Jones for inspiring me with
the ‘Apple’ concept of soil as limited resource. And of course I acknowledge the
authors, for trusting me since the proposal for this book was just a fuzzy idea in
my mind.

Ciro Gardi
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Part I

Introducing and
understanding the process
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 1 Is urban expansion a problem?
Ciro Gardi
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Introduction
Every mark traced on the territory by a new road or highway assumes the
meaning of the ditch ploughed by Romulus, imposing the boundaries of the
rising ancient Rome: another piece of land to be filled by buildings, generally
low-quality buildings lacking aesthetics. Our approach to the use of land is
much the same as the American pioneer, even 200 years later, even in crowded
continents like Europe or China.
The evident environmental failure of liberal, and also communist, economic
systems is caused essentially by the limits of the monetary aspects involved in
the production of goods and services, ignoring the externalities (or, in the best
case, under evaluating them). The globalization process that was announced as
the panacea for these problems resulted in an unlimited amplification of envi-
ronmental issues.
It is evident that if we consider only the direct costs (production and
transport for instance) associated with the production of a good in China,
for example (with labour and social costs one tenth that in Europe or North
America), the market competition will be very unfair. To perform an environ-
mentally correct evaluation, in addition to the evident, direct costs, we should
add the impact associated to the extraction, production and use of fuel needed
for the transport of the goods. This disproportional competition, and incorrect
evaluation of environmental costs, has resulted in unsustainable development
processes and severe environmental impacts.
The uncontrolled, and often unmotivated urban sprawl is an example of
this inaccurate evaluation of the environmental consequences of our actions
and decisions. The ‘flooding’ of concrete and asphalt is progressing, with little
consideration of the irreversible consequences of these practices. Degradation
of the landscape, increase in traffic and air pollution, flooding events, the loss
of agricultural and natural areas, have been ineffective in raising awareness
and stimulating action to protect one of our most precious resources and our
collective identity: our land. In addition to the local impacts, we then have to
consider the cumulative consequences of our local actions at the global scale.
 4 C. Gardi
Soil is becoming, more and more, a limited and strategic resource; increases
in population and food demand and the production of biofuels, are driving an
increase in biomass demand, and consequently demands on agricultural lands.
At the same time, urban expansion and the intensification of agricultural prac-
tices, are causing the degradation of agricultural soils and pushing agriculture
onto marginal lands or into natural habitats.
What is happening to the soil is a representation of the frequent ‘tragedies
of commons’ (Hardin, 1968), that are replicated every day, in every corner of
the planet, for water, biodiversity, climate, etc.
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There is, however, increasing attention to these global environmental issues

in the public opinion (still too limited), driving also changes in the interna-
tional and local political agenda.
New concepts, such as ‘climate justice’ (Robinson and Miller, 2009), or
‘soil security’ (McBratney et al., 2014), have been introduced, and a new deal
and renovated environmental commitment by the Catholic Church, started
by Pope Francesco, are marked by the recent encyclical Laudato si (Francesco,
Pope, 2015), in which the Pope is waking humanity up to care for the planet.
Analysis of global land cover maps, e.g. Global Land Cover 2000, indicates
that urban areas were covering 0.2 per cent of the Earth’s land surface at the
end of the previous millennium. This number can be considered relatively
small and, therefore, not necessarily a major threat to our planet’s ecosystem
services. Let us focus, however, on one of the most pertinent ecosystem ser-
vices, at least from an anthropocentric point of view: food production. If we
consider the potential threat represented by urban expansion on food produc-
tivity, it becomes clear that the topic merits a more in-depth assessment.
With regard to estimating the extent of urban areas/artificial surfaces, it
is possible to ascertain values from other global land cover maps, most of
them derived from satellite images. Schneider et al. (2009) produced a land
cover map, derived from Modis images, and focused on urban areas. Based
to this map, they provided a very accurate estimate that the extent of urban
areas in 2000 was 657,000 km2. Values obtained from other data sources,
reviewed in the same paper, ranged between 276,000 km2 up to 3,524,000
km2. These values highlight the importance of reliable data in understanding
urban growth processes.

Urban expansion dynamics in the world

Over the past few decades, human activities have reached such intensity that
they represent the most significant factor modifying our planet. Among human
activities, the processes related to urbanization most certainly play a major role.
Urbanization can be determined by several factors: population growth, a
positive balance between immigration and emigration, the economic growth
of a given area, and speculation processes where the expansion of built-up areas
is not related to the needs of increasing residential, commercial or industrial
infrastructures.
 Is urban expansion a problem? 5
At the global level, especially with regard to developing countries, demographic
pressures and the migration of rural populations towards urban centres are the
main drivers of urban expansion.
The global urban population increases by 200,000 every day, amounting
to 70 million people every year that become part of the Earth’s urban centres.
Currently, the urban population represents approximately 53 per cent of the
total human population, and it is expected that by 2050 this proportion will
reach 70 per cent. It is considered that 60 per cent of urban growth is deter-
mined by the demographic growth of urban populations, while 40 per cent is
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determined by processes of migration and reclassification of land uses (with the

expansion of urban areas).
We know that the first urban centres were associated with the introduction
of agricultural practices, in the fertile crescent,1 and with our ability to produce
surplus food that would allow the maintenance of a certain proportion of the
population not directly engaged in agriculture. This is how the first cities in
the Middle East, the Mediterranean, Asia and South America were established.
The earliest signs of human aggregation, in the form of rural villages, dates back
to 8500 bc, and the first city that we know of is Jericho, whose construction
is attributed to 8000 bc.
Following the first signs of urbanization we enter the modern era, which is
commonly divided into three phases:

•• The first phase of urban growth in the modern era coincides with the
important innovations in energy production technologies and, there-
fore, with the industrial era. From 1750 to 1950 Europe, North America
and some areas of Asia were the centres of attention. Since then we
have witnessed the birth of a new urban and industrial society involv-
ing significant population growth. In 1950 there were two megacities in
the world, with more than 10 million inhabitants (New York–Newark,
USA, and Tokyo, Japan).
•• The second phase is represented by the rapid growth of urban areas in
developing nations, where population growth and urbanization are usually
accompanied by economic growth. This second phase, which is currently
underway, is developing at a much faster rate than the previous one.
•• The third phase is characterized by extremely rapid growth of urban areas,
occurring in countries with fast growing economies, where rapid growth
of the Gross Domestic Product (GDP) is associated with urbanization pro-
cesses and/or demographic growth.

As of 2014, there were 488 cities in the world with a population of more than
1 million inhabitants, and 28 urban areas classified as megacities, as character-
ized by a population of over 10 million inhabitants (Table 1.1). It is expected
that within 15 years, 13 additional cities will be added to the list of megaci-
ties: Ahmadabad, Bangalore, Chennai, Hyderabad (India), Bangkok (Thailand),
Bogota (Columbia), Chengdu (China), Dar es Salaam (United Republic of
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Table 1.1 Past, actual (2014) and predicted population of the world’s 28 megacities
Country City Population (× 1,000) Average annual rate of change (%)
1970 1990 2014 2030 1970–1990 1990–2014 2014–2030

Japan Tokyo 23,298 32,530 37,833 37,190 1.67 0.63 -0.11

India Delhi 3,531 9,726 24,953 36,060 5.07 3.93 2.30
China Shanghai 6,036 7,823 22,991 30,751 1.30 4.49 1.82
Mexico Mexico City 8,831 15,642 20,843 23,865 2.86 1.20 0.85
Brazil Sao Paolo 7,620 14,776 20,831 23,444 3.31 1.43 0.74
India Mumbai (Bombay) 5,811 12,436 20,741 27,797 3.80 2.13 1.83
Japan Osaka 15,272 18,389 20,123 19,976 0.93 0.38 -0.05
China Beijing 4,426 6,788 19,520 27,706 2.14 4.40 2.19
United States New York–Newark 16,191 16,086 18,591 19,885 -0.03 0.60 0.42
Egypt Cairo 5,585 9,892 18,419 24,502 2.86 2.59 1.78
Bangladesh Dhaka 1,374 6,621 16,982 27,374 7.86 3.92 2.98
Pakistan Karachi 3,119 7,147 16,126 24,838 4.15 3.39 2.70
Argentina Buenos Aires 8,105 10,513 15,024 16,956 1.30 1.49 0.76
India Kolkata (Calcutta) 6,926 10,890 14,766 19,092 2.26 1.27 1.61
Turkey Istanbul 2,772 6,552 13,954 16,694 4.30 3.15 1.12
China Chongqing 2,237 4,011 12,916 17,380 2.92 4.87 1.86
Brazil Rio de Janeiro 6,791 9,697 12,825 14,174 1.78 1.16 0.62
Philippines Manila 3,534 7,973 12,764 16,756 4.07 1.96 1.70
Nigeria Lagos 1,414 4,764 12,614 24,239 6.08 4.06 4.08
United States Los Angeles 8,378 10,883 12,308 13,257 1.31 0.51 0.46
Russia Moscow 7,106 8,987 12,063 12,200 1.17 1.23 0.07
China Guangdong 1,542 3,072 11,843 17,574 3.45 5.62 2.47
Democratic Republic of Congo Kinshasa 1,070 3,683 11,116 19,996 6.18 4.60 3.67
China Tianjin 3,318 4,558 10,860 14,655 1.59 3.62 1.87
France Paris 8,208 9,330 10,764 11,803 0.64 0.60 0.58
China Shenzhen 22 875 10,680 12,673 18.44 10.42 1.07
United Kingdom London 7,509 8,054 10,189 11,467 0.35 0.98 0.74
Indonesia Jakarta 3,915 8,175 10,176 13,812 3.68 0.91 1.91
Source: UN (2015).
Note: Cities are ranked according to the actual population.
 Is urban expansion a problem? 7
Tanzania), Johannesburg (South Africa), Lahore (Pakistan), Lima (Peru), Luanda
(Angola), and Thành Pho Ho Chí Minh (Vietnam).
The process of growth of urban agglomerations, also known as urban sprawl,
can be analysed from many points of view: social, economic, environmental;
however, the aim of this discussion is limited to the assessment of urbanization
with regard to its direct impacts on a limited and non-renewable resource,
such as soil.
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The global picture

In 2008, for the first time in history, the urban population reached 50 per cent
of the Earth’s total human population (Figure 1.1). It could be argued that a
limit, which is not only psychological, has been exceeded.
From the graphic in Figure 1.1 we can see that the projections indicate
that, while from 2020 onwards the rural population will begin to decline, the
urban population will continue to grow and by 2050 the rural population will
represent just one-third of the total (United Nations Department of Economic
and Social Affairs, 2008). It is clear that the environmental impacts resulting
from such a radical change will be enormous, albeit difficult to assess and pre-
dict with accuracy. Considering only the flows of matter and energy necessary
to sustain an urban ecosystem, and the consequent production of waste and
disposal thereof, we have an indication of the dimensions and the type of prob-
lems that will need to be addressed in the future.
If we consider the organic matter cycle, it is easy to see how much more
balanced and sustainable a widespread system (rural area) would be, in which

Figure 1.1 Dynamics of urban and rural populations of the world

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Table 1.2 Population dynamics in some Asian urban agglomerations

City Country Population (thousands) Urbanized area (km2) Population density (pop./ha)
1990 2000 Var. (%) 1990 2000 Var. (%) 1990 2000 Var. (%)

Anging China 1,003 1,055 0.5 54 78 3.6 186 135 -3.0

Bacolod Philippines 462 510 1.3 13 33 12.3 343 155 -9.8
Bandung Indonesia 2,942 3,628 2.2 109 182 5.4 271 199 -3.1
Cebu Philippines 1,118 1,524 3.0 54 66 1.9 206 231 1.1
Changzhi China 1,160 1,254 1.2 104 156 6.4 111 104 -1.1
Coimbatore India 552 613 1.1 99 156 4.7 56 39 -3.4
Guangzhou China 7,712 13,156 5.5 452 979 8.1 171 134 -2.4
Hyderabad India 4,888 5,708 1.3 167 302 5.1 293 189 -3.6
Jaipur India 2,116 2,779 2.5 59 141 8.3 360 197 -5.4
Jalna India 445 556 2.1 11 25 7.5 395 223 -5.0
Kanpur India 1,124 1,442 2.3 34 60 5.4 110 79 -2.9
Kolkata India 6,646 7,834 1.7 288 484 5.3 231 162 -3.5
Kuala Lumpur Malaysia 2,733 4,959 5.0 383 805 6.2 71 62 -1.2
Leshan China 608 670 0.9 75 146 6.4 81 46 -5.1
Manila Philippines 14,044 17,335 2.4 444 660 4.5 316 263 -2.0
Mumbai India 14,224 17,070 2.1 344 451 3.1 413 378 -1.0
Pune India 3,510 4,042 2.1 93 191 11.0 379 211 -8.1
Rajshahi Bangladesh 491 600 1.8 11 20 5.8 452 296 -3.8
Saidpur India 503 596 1.4 9 16 5.5 564 366 -3.9
Songkhla Thailand 220 244 1.0 14 19 3.0 159 129 -1.9
Vijayawada India 981 1,117 1.3 40 62 4.5 244 179 -3.0
Yigang China 1,108 1,135 0.5 49 100 14.7 227 114 -12.4
 Is urban expansion a problem? 9
the production of biomass (food, fibre, biomass for energy production) and
its decomposition take place in a distributed way throughout the territory. In
contrast, in a megalopolis, fluxes of thousands of tons of organic matter enter-
ing the urban ecosystem are then disposed through artificial processes (landfills,
incineration, anaerobic digestion) in a concentrated way.
From the point of view of land use, processes of urban growth and the
urbanization of rural populations involve a net increase in impervious sur-
faces (sealed areas), increasing the consumption of land through a phenomenon
known as ‘land take’.
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If we consider, for example, areas with strong economic growth in some

Asian cities, we can observe a much higher expansion of urban areas, which
is not correlated to the increase in the resident population (Table 1.2). The
growth of city populations considered increased at an average of 23 per cent in
the period 1990–2000, while the average growth of the urban areas during the
same period amounted to 80 per cent.
It seems clear then that the growth of urbanized areas is not only determined
by a demand for more living space, which would lead to a linear correlation
between the two variables. Trigger processes, that are only partly related to the
hypothetical improvement of living conditions, but are largely linked to specu-
lation, are occurring. The space requirements of urban growth are not only
determined by the housing spaces for the new inhabitants, which in some cases
can legitimately aspire to having a larger home compared to their previous dwell-
ings, but are determined mainly by the need for new transport infrastructure,
manufacturing, commercial and entertainment facilities, logistical infrastruc-
tures and areas for the treatment and disposal of waste. To all this, which is a
functional requirement of the urban ecosystem, we should add the speculative
drivers that result in the proliferation of business and shopping centres, hotels,
residential settlements, not always necessary, but rather seen as a potential invest-
ment. In an article published in Urban Studies, Julian Marshall (2007) analysed
the relationship between population growth and the growth of urban areas; the
author noted that this relationship can be expressed by the following equation:

∝ = Pn

The urban areas (A) are growing proportionally to the number of inhabitants (P),
to the power (n); n assumes, in several of the cases analysed by Marshall, a value
close to 2, indicating that on average recently established citizens tend to use a
larger amount of land, compared to their predecessors (i.e. a 3 per cent popula-
tion increase will determine approximately a 9 per cent increase in area).
If these processes have been, somehow, managed and controlled in Western
countries, thanks to the tradition in land and urban planning, the same pro-
cesses can be devastating when they occur in territories or countries without
any process of land use planning, management and control.
Among the 30 cities characterized by the highest demographic growth, there
is only one Western city (Table 1.3). In Africa for instance, at the beginning
 10 C. Gardi
Table 1.3 The top 30 fastest growing urban agglomerations
Country Urban area Annual growth %

China Beihai 10.58

India Ghaziabad 5.20
Yemen Sana’a 5.00
India Surat 4.99
Afghanistan Kabul 4.74
Mali Bamako 4.45
Nigeria Lagos 4.44
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India Faridabad 4.44

Tanzania Dar es Salaam 4.39
Bangladesh Chittagong 4.29
Mexico Toluca 4.25
Congo Lubumbashi 4.10
Uganda Kampala 4.03
Bolivia Santa Cruz 3.98
Angola Luanda 3.96
India Nashik 3.90
Congo Kinshasa 3.89
Kenya Nairobi 3.87
Bangladesh Dhaka 3.79
Madagascar Antananarivo 3.73
India Patna 3.72
India Rajkot 3.63
Guinea Conakry 3.61
India Jaipur 3.60
Mozambique Maputo 3.54
Somalia Mogadishu 3.52
Pakistan Gujranwala 3.49
India Delhi 3.48
India Pune 3.46
United States Las Vegas 3.45
Source: City of Mayors, www.citymayors.com/.

of the previous century 95 per cent of the population was living in rural areas
and only 5 per cent was settled in urban areas. In 1960 the percentage of
city dwellers reached 20 per cent, in 2010 it was 43 per cent and it is estimated
to reach 50 per cent by 2030. The growth of several African cities has been
tumultuous and chaotic: Nairobi, Dar es Salaam, Lagos, Kinshasa grew seven-
fold in the period between 1950 and 1980 (Brundtland, 1987). The pressures
on the environment, also in terms of land take, made by ‘citizens’ are much
larger than the pressures of the rural populations. The problems of water supply,
solid waste and waste water production and management increased dramati-
cally, without having adequate infrastructures for water provision and plants
for waste treatment.
Around the world there are already more than 17 urban agglomerations with
areas larger than 1,000 km2 (Table 1.4). Often without any system or process
of urban planning, urban growth occurs at the expense of the most fertile and
 Is urban expansion a problem? 11
productive agricultural lands. Among the possible examples to be mentioned,
one of the provinces of the People’s Republic of China, the Shandong province,
where the expansion of urban areas and the growth of transport infrastructures
occurred between 1996 and 2003, was built on agricultural lands (Hong, 2007).
According to other data sources, however (Demographia, 2015), the number
of urban agglomerations larger than 1,000 km2 were more than 100 in 2014.
These discrepancies depend on the definition of urban agglomeration.
In Europe the situation is quite heterogeneous: we have countries like
Spain, where the urban area is lower than 2 per cent, while small, densely
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populated countries like Belgium or the Netherlands have urbanized areas that
are close to 20 per cent of the entire national territory.

Table 1.4 Extension, population and population density of the 30 largest urban

agglomerations
Country City Area (km2) Population Population density
(pop./km2)

United States New York 11,642 20,955,600 1,800

Japan Tokyo– 8,547 37,606,800 4,400
Yokohama
United States Los Angeles 6,299 15,117,600 2,400
Russia Moscow 4,403 15,850,800 3,600
China Beijing 3,497 18,184,400 5,200
United States Phoenix 3,276 3,931,200 1,200
Brazil Sao Paolo 3,173 20,624,500 6,500
France Paris 2,845 10,811,000 3,800
Indonesia Jakarta 2,784 26,726,400 9,600
Thailand Bangkok 2,331 14,452,200 6,200
Malaysia Kuala Lumpur 1,943 6,606,200 3,400
Egypt Cairo 1,658 15,087,800 9,100
United Kingdom London 1,623 9,575,700 5,900
Philippines Manila 1,437 21,267,600 14,800
India Kolkata 1,204 14,568,400 12,100
Russia St Petersburg 1,191 4,883,100 4,100
Spain Barcelona 1,075 4,622,500 4,300
Ghana Accra 945 3,969,000 4,200
Nigeria Lagos 907 12,063,100 13,300
Belgium Brussels 751 1,952,600 2,600
Mexico Guadalajara 699 4,543,500 6,500
Brazil Brasilia 673 2,422,800 3,600
Greece Athens 583 3,498,000 6,000
Tanzania Dar es Salaam 570 3,705,000 6,500
Kenia Nairobi 557 4,456,000 8,000
India Mumbai 546 17,308,200 31,700
New Zealand Auckland 544 1,305,600 2,400
Singapore Singapore 518 5,283,600 10,200
Finland Helsinki 492 1,180,800 2,400
Denmark Copenhagen 453 1,223,100 2,700
Source: UN-Habitat, www.unhabitat.org.
 12 C. Gardi
During the last two decades (1990–2008), however, the less urbanized
countries in Europe have also experienced a boom in the real estate sector,
driven mainly by speculative processes. Especially in countries such as Spain
and Ireland during that period, there was a proliferation of new houses, build-
ings and commercial centres that largely exceeded the real demand. In fact,
in these countries the rapid economic growth was the driving factor for the
explosion of the building sector during that period.
If we consider the case of Ireland, where thanks to a tempting fiscal policy
during the last few decades it was possible to attract capital, and to boost the
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economy, creating jobs and also promoting an increase in prices. This posi-
tive economic situation prompted demographic growth, causing a increase in
demand for residential and productive infrastructures on one side, but on the
other side the rising prices of real estate were the driving factor behind specula-
tive processes. In Dublin, for instance, it is foreseen that there will be an increase
of 250,000 inhabitants during next years; part of this population increase will
be determined by immigration from eastern EU countries (Poland, the Baltic
states), and will largely determine changes to the structure of the city: new
houses, schools, hospitals, roads, supermarkets, etc. The problem is that, during
these large immigration processes, for instance, for 1,000 ha of new buildings
in Dublin, it will not be possible to dismantle or de-seal the same amount of
land in the Netherlands or the Baltic states. This vicious cycle, associated with
unlimited economic growth, will cause a progressive and continuous depletion
of soil resources.
Let us consider the case of Germany: during the last few decades it has
reached the level of 100–120 ha day-1 of land take for urban expansion (60
ha day-1 was the average in the period 1990–2000 according to Corine Land
Cover). Germany has decided to reduce the rate of this process, with a target
value of 30 ha day-1 for the near future. This would be a great achievement,
but in any case would represent a continuous dissipation of a finite resource,
such as the land.
In addition to the urban growth associated with the needs of growing popu-
lations, growing economies that require new industrial or commercial districts,
we have luxury and unessential goods: we need only read on-board magazines
to be fatally attracted by a luxurious, small villa within a golf resort in the
Algarve, at the same price that we would pay for a car garage or a car-box in
one of the largest European towns. In Italy for instance, until a few years ago,
it was common at the entrance to highways to see advertisements for very nice
little houses or villas in (probably not in the most renowned) touristic areas at
very cheap prices.

Soil: a strategic and limited resource

The occupation and destruction of soil caused by urban expansion should be
considered an irreversible event in the human time scale. Several thousands of
years are necessary to form a deep, fertile and mature soil, such as the soils of
 Is urban expansion a problem? 13
the US corn belt, Argentinian pampas or Russian steppe region. The develop-
ment and the economic models that we are adopting often represent, from a
soil perspective, a non-return option: an area of urban expansion, occupied by a
commercial centre or a logistic facility, in the case of economic crisis or displace-
ment of economic activities, can be converted to different uses, but could never
be re-used as an agricultural area (unless after extremely expensive operations of
de-sealing). Every time a new shed, parking area or road is built, we are wasting
a non-renewable, finite natural capital, cared for and valued by our predecessors,
to constitute heritage for the future generations. To seal a piece of land with
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asphalt or concrete means not only preventing for our selves and for future gen-
erations the provision of ecosystem services, but also to lose the rent associated
with the economic productivity that soil can ensure over the long term.
The increasing rate of agricultural land take, the lack of care taken when
disposing of land, and the absence of critical voices and opposition to the
irreversible transformations of land, make evident the progressive loss of com-
petitiveness by agriculture with respect to other economic activities more
profitable in the short term, but also the limited strategic value that society
attributes to this natural capital. In practice we are misusing and wasting agri-
cultural soils, as if they were extremely abundant and indefinitely replaceable
resources. A quick look at land use and food consumption at the global scale,
however, demonstrates the weakness of this assumption.
At a global scale, agricultural soils, and in particular fertile and resilient soils, are
already limited resources, destined to become even more limited in the future.
According to the UN’s Millennium Ecosystem Assessment (2005), agri-
cultural systems cover a quarter of continental lands, which is already the
vast majority of soils suitable for agriculture (UN, 2005a). From these agro-
ecosystems we obtain more than 90 per cent of the carbohydrates and proteins
needed by the human population. After the Second World War, agricultural
mechanization and the ‘green revolution’ made it possible to increase agricul-
tural productivity by several folds, at rates much higher than the increase in
human population (Alés and Solbrig, 2001; UN, 2005a). If we consider the
average values at the global scale, the amount of available per capita calories has
steadily increased, but despite this benefit it was not evenly distributed among
the human population. According to current UN estimates, about 793 million
people are undernourished globally (FAO, 2015).
Recent global data on agricultural production and food consumption, however,
indicate a possible trend reversal, shifting from a global market of food commodi-
ties characterized by surplus, to one characterized by scarcity (Brown, 2005).
According to the Millennium Ecosystem Assessment scenarios, by 2050 the
demand for food will increase between 70 and 85 per cent, compared to cur-
rent values – this takes into account the combined effect of demographic and
economic growth. In the next 35 years, an additional 2.5 billion people will live
on the planet, in addition to the current 6.5 billion, with increments of 70 mil-
lion per year; more than 9 billion people by 2050 (UN, 2005b). The increased
food demand, generated solely by the increased number of people living on
 14 C. Gardi
the Earth, will be added to the relevant increase in food demand generated by
the improved economic conditions reached by hundreds of millions people as
a result of economic growth. The effect of improved economic conditions is
generally associated with a shift from purely subsistence diets, based on direct
consumption of cereals, to more diverse diets, richer in fat and animal pro-
teins. This transition towards higher trophic levels in the food pyramid will
determine a relevant increase in the per capita needs of agricultural, productive
land. Compared to a pure subsistence diet, the increased consumption of meat,
cheese and other milk derivatives, and alcoholic beverages, will require two to
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three times more agricultural land (Gerbens-Leenes and Nonhebel, 2002).

The same factors determining economic growth and, consequently, the rise
in food demand will be the driving factors of urban expansion and land take, in
order to create new residential, industrial and commercial structures, as well as
roads, railways, waste disposal sites, mining sites, etc., thereby threatening the
agricultural production capacity. It is estimated that every day approximately
4,000 ha of land, most of which is agricultural, is covered by asphalt for roads,
highways and parking lots. If we consider that two of the most rapidly grow-
ing economies, China and India, are primarily adopting transport on wheels
for transporting goods, it becomes clear how this process can directly affect
food security at a local and global scale. In order to understand the potential
impact associated with road transport, it is sufficient to consider that in China
and India the motorization rate (number of vehicles per 1,000 people) was 6.7
and 6.0, respectively, in 2002, and already 91 and 20 in 2013 (OICA, 2013).
Considering that every car implies an asphalt coverage ranging between 0.02
and 0.07 m2, if these growing countries reach the motorization rate of Europe
(565) or the USA (790), we can easily calculate the impact on agricultural land.
According to Lester Brown of the Earth Policy Institute, considering only the
population growth, more than 3 million ha, most of them agricultural, will be
taken. Furthermore the urban growth, a consequence of population and eco-
nomic growth, will take place over the most fertile soils of the planet, where
towns are typically settled. In addition to the competition for land, agriculture
and cities will also compete in the future for water, a fundamental resource for
irrigated crops and for ensuring high productivity in many areas of the world.
It is very unlikely that in the future we will be able to tackle the increased
demand for food, and the reduction of available agricultural land, simply
by increasing the productivity of the remaining agricultural areas. It is true
that during the Green Revolution it was possible within 40 years to triple
(even quadruple in some cases) crop yields, but this type of increase seems
very difficult to achieve in the future (Hawken et al., 1999). Despite some
very ambitious programmes that aim to further increase the productivity of
some crops (e.g. ‘wheat 20:20’), it is more realistic to think that, at least in
Western agriculture, the selection of new crop varieties, the use of mechani-
zation, fertilizers, growth regulators and pesticides has already exploited most
of its potential. Furthermore, genetically modified organism (GMO) crops,
independent of the issues concerning consumer and ecosystem safety, cannot
 Is urban expansion a problem? 15
be exempted from the limits imposed by crop physiology and by the efficiency
of photosynthetic processes. Biotechnology has mainly contributed, so far, to
developing GMO crops characterized by resistance to pests or to specific her-
bicides, or better adapted to grow and produce in unfavourable pedoclimatic
conditions (Brown, 2005).
We must also consider that the significant increase in crop productivity
achieved during the Green Revolution was obtained using large quantities of
water and energy, often with negative impacts on the environment. Among the
impacts associated with intensive, conventional agriculture, we have eutrophi-
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cation of surface waters (especially where there are excessive phosphorus and
nitrogen loads), and the dispersion in the environment of toxic and persistent
chemicals that have accumulated throughout trophic levels. Often, increases
in crop yield, in the short term, have been obtained by compromising natural
capital and concomitant ecosystem services.
In the most vulnerable agricultural areas, after an initial promise to increase
crop productivity, severe and often irreversible declines in yields have
occurred as a consequence of soil degradation processes (soil erosion, com-
paction, organic matter decline, salinization, etc.) or of the depletion of water
resources. In some extreme cases this type of intensive exploitation has caused
desertification processes, resulting in the complete loss of soil fertility, thus
preventing any possibility of agricultural or pastoral activities. Examples of
these processes are common in the Sahel or in north-western China, where
the desert expands over hundreds of thousands of hectares every year, pro-
moting the formation of dust storms or dust bowls, that tarnish the sun over
Beijing and other areas of the country.
The solution to the increasing food demand will not be found in the oceans.
The quantity of fish caught that increased fivefold between 1950 and 1990,
has been declining since the end of the 1990s, and the possibility of reversing
this decline is very unlikely due to the overexploitation of fish stock of several
species in several areas. However, the reduction in fish catch has been com-
pensated by the coastal and off-shore pisciculture. This type of activity, albeit
very efficient for protein production, does not represent a realistic alternative
to agricultural food production because it is essentially based on the use of feed
obtained in terrestrial agricultural systems
In order to satisfy the future food demand, in absence of significant
increases in crop yields and without any possible alternative to food derived
from agriculture, it would be necessary to extend, where the pedoclimatic
conditions are suitable, the arable and pastoral lands. This extension of agri-
cultural land would allow us to compensate for the land taken by the urban
and infrastructure expansion, but would cause loss of natural or semi-natural
ecosystems, and the associated impacts on biodiversity and ecosystem ser-
vices. The process will eventually progress, including marginal lands, where
risks of desertification and soil degradation are higher, and where the inputs
(water, fertilizers, energy) required for sustaining productivity will be higher.
In the future, the conversion of natural habitats into agricultural systems will
 16 C. Gardi
represent one of the most important processes, resulting in environmental
degradation and biodiversity losses on Earth (Vitousek, 1994; Tilman et al.,
2001; Foley et al., 2005; UN, 2005a). The Millennium Ecosystem Assessment
scenarios show that a percentage ranging between 10 and 20 per cent of cur-
rent grasslands and forested areas will be converted into agricultural land.
This land use change will occur mainly in tropical and sub-tropical areas,
among the most biodiverse ecosystems on the planet. At the same time the
intensification of existing agricultural systems, and the increased impacts asso-
ciated with the use of chemicals, will cause changes and deterioration of the
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provision of ecosystem services. Despite all these changes and environmental

impacts, there is no certainty that by 2050 we can achieve global food security
and the eradication of malnutrition.
The environmental changes and crises and the food security crisis described
by the Millennium Ecosystem Assessment are, unfortunately, realistic and very
much occurring. From the 1980s onwards, global grain production (a good
indicator of food security), started to increase at a lower rate compared to
population growth. Between 2000 and 2007, grain production was lower than
consumption in seven out of eight years, causing the progressive depletion of
global grain reserves. In 2008 the lowest level of the last 35 years was reached.
In 2008 the quantity of grain stocks was sufficient to cover only 54 days of
global consumption. These changes have been caused by several combined
processes: some of the traditional grain exporters became importers, such as
China which in 2004 was the largest importer with more than 8 million tonnes
of grain. Between 1998 and 2003 China lost 18 per cent of its cereal produc-
tion, mainly as a consequence of the decline in cultivated areas. In order to
tackle the rapidly growing demand for protein, directly connected with its
economic growth and dietary changes, China also became the largest soybean
importer (22 million tonnes), while up until 1997 it was self-sufficient.
Soybeans represent the most important protein source for animal feed.
Soybean production has been booming since the 1970s, driven by the constant
increase in meat consumption at the global scale, and by the progressive reduc-
tion of alternative sources of proteins, such as fishmeal. In Brazil, the worlds
leading soybean exporter, the production of this commodity increased from
1 to 66 million tonnes in 35 years. This astonishing result was obtained by
extending the cultivation area by 23 million hectares. Half of this expansion
took place after 1996, by converting the Cerrado (a mixed bush–low forest
ecosystem) into arable systems at the rate of 1.5 million ha year-1, but also part
of the Amazon rainforest. The environmental impacts of this large-scale land
use change are huge, and at the same time very difficult to assess: from the bio-
diversity losses, to severe modification of the hydrological cycle, from the soil
degradation to the release in the atmosphere of enormous quantities of carbon
dioxide. The theoretical possibility to go further, and convert 75 million ha of
Cerrado into agricultural systems may allow for tripling soybean production,
but it would also represent an environmental catastrophe without precedent,
with a high probability of severe degradation of the soils of the area.
 Is urban expansion a problem? 17
The soybean represents a compensation for the loss of productive lands
in other areas of the planet. Not only for China, but also for the extremely
intensive dairy and meat production systems in Europe. Imported soybeans
represent the classical case of indirect land use change: the loss of agricultural
and forage production areas in Europe (due also to urban expansion), is com-
pensated for by the import of proteins from elsewhere (Brazil, Argentina, etc.),
where cultivated lands are expanding, pushing (as is the case of Argentina) the
pasture land towards marginal and less productive areas.
In other words, we can consider that the effect of urban expansion occur-
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ring in China, Europe, and North America, is directly responsible for the
deforestation of at least part of the Cerrado or the Amazon basin and for the
loss of natural grasslands in Argentina.

Note
1 Middle East–Northern African area, also known as the Cradle of Civilization.

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 2 Measuring and monitoring
land cover
Methodologies and data available
Michele Munafò and Luca Congedo
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Introduction
Land Cover Change (LCC) and its environmental consequences are global
challenges, as pointed out by IPCC (2001): climate processes are indirectly
affected by land surfaces and the materials on the ground, and soil has a major
role in carbon fluxes and greenhouse gas emissions. Moreover, soil provides
ecosystem services that are fundamental for humanity and environmental
sustainability, such as food and timber production, biodiversity and habitat
support, carbon sequestration and climate regulation (IPCC, 2001; Lal, 2005;
TEEB, 2010; Munafò et al., 2015). Furthermore, soil has a major role in the
mitigation of and adaptation to floods or droughts and extreme events in gen-
eral (European Commission, 2014).
One of the main drivers of LCC is urban development, especially in the
form of soil consumption that is the conversion from natural to artificial land
cover. In Europe, soil consumption is a major issue related to the demand
for residential, industrial, commercial infrastructures, and transportation, with-
out a direct correlation to demographic growth (Indovina, 2006; European
Commission, 2006).
During the last decade, soil consumption has been addressed by various
institutions, especially in Europe, with the major objective of ensuring soil
protection (European Environmental Agency, 2006). The European Thematic
Strategy for Soil Protection (European Commission, 2006) defined good
practices for reducing negative impacts of urban development. In 2012, the
European Commission described the implementation of the Soil Thematic
Strategy, highlighting the importance of raising awareness about soil, sup-
porting research projects and monitoring soil at regular intervals (European
Commission, 2012a).
Several studies have demonstrated the utility of remote sensing and
Geographic Information Systems (GIS) for monitoring the built-up expansion
and for mapping land cover in general (Brook and Davila, 2000). The results
of land cover monitoring are fundamental for developing effective policies for
sustainability and adaptation to environmental change (Cardona et al., 2012).
 20 M. Munafò and L. Congedo
Basic definitions about land and soil monitoring
It is worth pointing out the main definitions related to land and soil monitoring.
Soil is the top layer of the earth’s crust that has a crucial role in ecosystems,
especially considering that it is a non-renewable resource with an extremely
slow process of formation (European Commission, 2006).
Soil sealing is the process of permanently covering the soil surface with
impervious and artificial materials, separating soil from other ecosystem com-
partments; however, there are alternative definitions, depending on the study
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approach, that highlight: the loss of functions associated to soil sealing; or the
change of soil natural characteristics causing soil to behave as an imperme-
able medium (Burghardt et al., 2004). A wider concept is soil consumption,
which is the increase of artificial land cover, defined as the physical material
at the ground, which for instance is vegetation, bare soil, water, asphalt, etc.
(Fisher and Unwin, 2005). Artificial land cover includes soil sealing (in terms
of impervious surfaces) and other artificial surfaces that may be permeable but
alter the natural soil, such as dumps, quarries and railways (Munafò et al., 2015).
The land consumption (land take) phenomenon is the increase of artificial
land use at the expense of natural and semi-natural land use, therefore it includes
sealed and unsealed areas such as urban green areas (European Environmental
Agency, 1997). It usually does not include impervious surfaces in natural, semi-
natural and agricultural areas, such as greenhouses.
It is worth highlighting that land take and land consumption refer to
the use of soil, while soil consumption refers to land cover. However, soil
sealing, soil consumption and land consumption are highly interrelated
(Huber et al., 2008).

Land cover monitoring in the land system science

Considering the complexity of interactions between land cover and envi-
ronmental issues, it is useful to identify a framework that helps discern these
relations, bearing in mind the aim of sustainability; in this sense, the focus of
this chapter is on land cover.
During the last few years, land system science has attempted to understand
the relationship between urban development and ecosystems (at the local and
global scale) by integrating several disciplines, from ecology and social science,
to remote sensing (Verburg et al., 2013).
Within this framework, land cover change is considered both cause and
effect of climate change, highlighting the importance of land cover monitoring
for soil protection, and improving sustainable policies and planning processes
(Verburg et al., 2013).
Land cover monitoring can provide the spatial data required for assessing
ecosystem services and supporting decision making (Maes et al., 2012). The use
of remote sensing is a valuable approach for the efficient monitoring of land
cover, especially for the built environment, and the estimation of impacts on
ecosystem services (Chen et al., 2013).
 Methodologies and data available 21
In this context, Europe has developed several initiatives aimed at land cover
monitoring; Copernicus (previously GMES), is a European programme of
earth observation with the purpose of environmental protection and civil secu-
rity. This is a complex system of data acquisition from multiple satellites and
data integration with field surveys that aims also at ensuring the independence
of Europe in environmental monitoring.
In the Copernicus framework, services are provided for several thematic areas
(e.g. atmosphere, security, emergency) and in particular for the land theme;
these data and services can foster environmental protection (e.g. management
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of urban areas, regional and local planning, agriculture, forest management,

etc.) and increase awareness about soil consumption and environmental change
(European Commission, 2014).
The sources of information are fundamental in land cover monitoring: the
techniques and tools used for monitoring land cover change affect the results
in terms of resolution and accuracy. In fact, the incorrect understanding of data
acquisition, or interpretation of the classification system can lead to the misin-
terpretation of results.
Environmental analyses that are based on the study of land use and land
cover change allow for the assessment of environmental evolution in time;
nevertheless, data are characterized by technical and semantic features (such as
the reference system, classification system and legend) that must be considered
for the accurate estimation of soil consumption (Munafò et al., 2010; CRCS,
2012). In particular, different systems of classifications can lead to substantial
dissimilarities of results, for instance because of various definitions of homo-
geneous area, minimum mapping unit, or the mix of land use and land cover
characteristics that define classes.
Moreover, the majority of databases is created for specific purposes (e.g.
agricultural controls, land planning, environmental assessment, statistical
report), having classification systems that are not suitable for soil consumption
monitoring (ISPRA, 2013).
Land cover monitoring can be based on two main approaches: mapping
and sampling. The former is particularly useful because the output is a spatial
product that can be used as input for spatial modelling and the assessment of
ecosystem services; the latter is more reliable from the statistical point of view,
allowing for greater flexibility of use, ease and speed of data update.
It is fundamental that land cover monitoring is based on the integration of
mapping and sampling approaches.

An overview of Earth observation satellites

Remote sensing is the science and technology that allows for the identifica-
tion or measure of object characteristics without direct contact (JARS, 1993).
In general, the energy that emanates from the Earth’s surface is measured by
sensors, distinguished in: passive remote sensing, if the source of the measured
energy is the sun; active remote sensing, if the source of the measured energy
is emitted from the sensor platform (Richards and Jia, 2006).
 22 M. Munafò and L. Congedo
Sensors are placed on board airplanes or satellites, measuring the electromagnetic
radiation at specific ranges (i.e. spectral bands). In particular, sensors measure the
radiance that corresponds to the brightness in a given direction toward the sensor; it
is worth mentioning the definition of reflectance that is the ratio of reflected versus
total power energy.
Measured energy is quantized and converted into a digital image, where
each picture element (i.e. pixel) has a discrete value in units of Digital Number
(i.e. DN) (NASA, 2013).
The characteristics of sensors (i.e. resolutions) are defined as:
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•• Spatial resolution ‘is the resolving power of an instrument needed for the
discrimination of features and is based on detector size, focal length, and
sensor altitude’ (NASA, 2013); spatial resolution is also referred to as geo-
metric resolution or IFOV (Instantaneous Field Of View), and it is usually
measured in pixel size.
•• Spectral resolution is the number and location in the electromagnetic spec-
trum (defined by two wavelengths) of the spectral bands (NASA, 2013) in
multispectral sensors, for each band corresponds to an image.
•• Radiometric resolution, usually measured in bits (binary digits), is the
range of available brightness values, which in the image correspond to the
maximum range of DNs; for example an image with 8 bit resolution has
256 levels of brightness (Richards and Jia, 2006).
•• The temporal resolution, related to satellites’ sensors, is the time required
for revisiting the same area of the Earth (NASA, 2013).

Satellite resolutions deeply influence applications in environmental studies,

therefore the evaluation of satellite capabilities on which the objectives of the
studies are based is fundamental. The following section examines the charac-
teristics of the main high-resolution satellites.

High-resolution satellites

Landsat
Landsat is a set of multispectral satellites developed by NASA (National
Aeronautics and Space Administration) since the early 1970s, which are
widely used in environmental research on land cover and soil consumption
(Vogelmann et al., 1998; Lu et al., 2011).
The resolutions of Landsat 4 and 5, Landsat 7 and Landsat 8 are reported in
Tables 2.1, 2.2 and 2.3 respectively (http://landsat.usgs.gov/band_designations_
landsat_satellites.php, accessed 27 January 2016).
Landsat temporal resolution is 16 days, which allows for frequent image
acquisitions and land cover change analyses (NASA, 2013). At the moment,
only Landsat 7 and Landsat 8 are operational, but a vast archive of Landsat images
 Methodologies and data available 23
Table 2.1 Band characteristics of Landsat 4 and 5
Landsat 4, Landsat 5 bands Wavelength (micrometres) Resolution (metres)

Band 1 – Blue 0.45-0.52 30

Band 2 – Green 0.52-0.60 30
Band 3 – Red 0.63-0.69 30
Band 4 – Near Infrared (NIR) 0.76-0.90 30
Band 5 – Shortwave Infrared (SWIR) 1.55-1.75 30
Band 6 – Thermal Infrared 10.40-12.50 120 (resampled to 30)
Band 7 – Shortwave Infrared (SWIR) 2.08-2.35 30
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Table 2.2 Band characteristics of Landsat 7

Landsat 7 bands Wavelength (micrometres) Resolution (metres)

Band 1 – Blue 0.45–0.52 30

Band 2 – Green 0.52–0.60 30
Band 3 – Red 0.63–0.69 30
Band 4 – Near Infrared (NIR) 0.77–0.90 30
Band 5 – Shortwave Infrared (SWIR) 1.57–1.75 30
Band 6 – Thermal Infrared 10.40–12.50 60 (resampled to 30)
Band 7 – Shortwave Infrared (SWIR) 2.09–2.35 30
Band 8 – Panchromatic 0.52–0.90 15

Table 2.3 Band characteristics of Landsat 8

Landsat 8 bands Wavelength Resolution (metres)
(micrometres)

Band 1 – Coastal aerosol 0.43-0.45 30

Band 2 – Blue 0.45-0.51 30
Band 3 – Green 0.53-0.59 30
Band 4 – Red 0.64-0.67 30
Band 5 – Near Infrared (NIR) 0.85-0.88 30
Band 6 – Shortwave Infrared 1 (SWIR 1) 1.57-1.65 30
Band 7 – Shortwave Infrared 2 (SWIR 2) 2.11-2.29 30
Band 8 – Panchromatic 0.50-0.68 15
Band 9 – Cirrus 1.36-1.38 30
Band 10 – Thermal Infrared (TIRS) 1 10.60-11.19 100 (resampled to 30)
Band 11 – Thermal Infrared (TIRS) 2 11.50-12.51 100 (resampled to 30)

is freely available for the past decades at the USGS EROS (http://earthexplorer.
usgs.gov/, accessed 27 January 2016).
The numerous Landsat bands allow for environmental analyses such as
land cover and urban areas (Bagan and Yamagata, 2012), vegetation and
ecosystem monitoring (Yang et al., 2012) and land surface temperature using
the thermal infrared (Sobrino et al., 2004). In particular, the visible bands
 24 M. Munafò and L. Congedo
(blue, green and red) are useful for the visualization of urban features, and
the near infrared bands allows for the identification of healthy vegetation and
the calculation of vegetation indices (Rouse et al., 1973).
Moreover, the new Landsat 8 characteristics allow for new and enhanced
applications in agriculture, coastal water and change detection (Roy et al., 2014).
Spatial resolution of multispectral bands (i.e. 30 m) is a constraint because the
detection of small objects (e.g. isolated buildings) is difficult, therefore Landsat is
mainly used for studies at the regional scale (Patino and Duque, 2013).
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Sentinel-2
The European initiative Copernicus includes the development of earth obser-
vation satellites. In particular, the Sentinel-2 satellite (launched in June 2015) is
designed to provide high-resolution images for several spectral bands (Drusch
et al., 2012). It is worth noting that Sentinel-2 images are provided for free by
the European Space Agency (ESA).
Table 2.4 (https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/
resolutions/radiometric, accessed 27 January 2016) summarizes the characteristics
of the Sentinel-2 sensor, which is comparable to Landsat sensors.
Sentinel-2 bands have different spatial resolutions depending on the spec-
tral range; however, the visible and near infrared bands have a resolution
of 10 m, which is remarkable if compared to Landsat pixel size (i.e. 30 m).
Therefore, several applications are possible using the numerous spectral
bands of Sentinel-2 that allow for the accurate identification of land cover
classes, especially for vegetation; in fact, the vegetation red edge bands are
very useful for deriving vegetation indices and assessing the state of crops
(Clevers and Gitelson, 2013).

Table 2.4 Band characteristics of Sentinel-2

Sentinel-2 bands Central wavelength (micrometres) Resolution (metres)

Band 1 – Coastal aerosol 0.443 60

Band 2 – Blue 0.490 10
Band 3 – Green 0.560 10
Band 4 – Red 0.665 10
Band 5 – Vegetation Red Edge 0.705 20
Band 6 – Vegetation Red Edge 0.740 20
Band 7 – Vegetation Red Edge 0.783 20
Band 8 – NIR 0.842 10
Band 8b – Vegetation Red Edge 0.865 20
Band 9 – Water vapour 0.945 60
Band 10 – SWIR – Cirrus 1.375 60
Band 11 – SWIR 1.610 20
Band 12 – SWIR 2.190 20
 Methodologies and data available 25
Other satellites
Several satellites (especially the commercial ones) are useful for land cover
monitoring, offering high- and very high-resolution images. It is worth illus-
trating the main characteristics of the available satellites, in particular for:

•• SPOT
•• RapidEye
•• QuickBird
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•• IKONOS
•• WorldView.

SPOT is a system of satellites designed and developed by the French space

agency Centre National d’Études Spatiales. SPOT 4 and 5 are multispectral
satellites, acquiring four spectral bands with a 10 m spatial resolution: green,
red, near infrared (NIR) and short-wave infrared (SWIR). The ESA provides
most SPOT images for free to research studies, and considering their spatial
resolution SPOT data are valuable for land cover and urban studies (Kong
et al., 2012).
RapidEye is a commercial satellite providing five bands (blue, green, red,
NIR and red edge) with 5 m pixel size. These bands have proved to be use-
ful for urban studies (Munafò et al., 2015), especially for the identification of
vegetation (Tigges et al., 2013).
QuickBird is a satellite providing very high-resolution images (panchro-
matic at 61 cm) and 2.44 m resolution multispectral bands (i.e. blue, green,
red and NIR) (www.digitalglobe.com/sites/default/files/QuickBird-DS-QB-
Prod.pdf, accessed 21 November 2013). IKONOS is similar to QuickBird,
having 82 cm resolution for panchromatic and 3.2 m resolution for multi-
spectral bands (i.e. blue, green, red and NIR) (www.digitalglobe.com/sites/
default/files/DG_IKONOS_DS.pdf, accessed 21 November 2013). These
two commercial satellites allow for urban studies at the local scale, detecting
very small objects on the Earth’s surface (Myint et al., 2011).
WorldView is a family of multispectral satellites with very high spatial reso-
lution (panchromatic less than 50 cm and multispectral less than 2 m) with
several bands ranging from visible to infrared (www.digitalglobe.com/sites/
default/files/DG_WorldView3_DS_forWeb_0.pdf, accessed 21 November
2013); the very high resolution allows for the identification of small features,
which is very useful in urban areas (Belgiu et al., 2014).

Land cover classification using remote sensing images

The use of remote sensing for land cover classifications has proved to be reli-
able and affordable for the detection of impervious surfaces (Brook and Davila,
2000; Fan et al., 2007).
 26 M. Munafò and L. Congedo
Classification methodologies rely on image resolutions producing different
results in terms of land cover classes and accuracy (Richards and Jia, 2006);
consequently, several approaches have been developed for land cover classifica-
tions depending on the scale of the study.

Methodologies of land cover classification

According the image processing approach, methodologies of classification can
be defined as:
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•• unsupervised classification
•• supervised classification
•• Object Based Image Analysis
•• photo-interpretation.

Unsupervised classification is a per-pixel methodology (i.e. based on spectral

characteristics of single pixels, often using clustering methods) where spec-
tral classes are assigned without foreknowledge of the existence of classes.
Therefore the definition of classes is performed after the classification (Richards
and Jia, 2006). This method is relatively quick to execute although the a pos-
teriori definition of classes can be difficult.
Supervised classifications (also semi-automatic classifications) are per-pixel
processing techniques that use class foreknowledge for the identification of
materials, based on the spectral properties (i.e. spectral signatures) of the materi-
als on the ground (Richards and Jia, 2006). There are several algorithms in this
category of classification that have been widely used with remote sensing images,
such as: the Maximum Likelihood algorithm, which calculates the probability
distributions (assumed in the form of multivariate normal models) for the classes,
related to Bayes’ theorem, estimating if a pixel belongs to a land cover class
(Strahler, 1980; Richards and Jia, 2006); the Spectral Angle Mapping algorithm
calculates the spectral angle between spectral signatures of image pixels and class
spectral signatures defined a priori (Kruse et al., 1993; Fiumi et al., 2014).
With the increasing availability of very high-resolution images, the Object
Based Image Analysis (OBIA) has been developed, based on image segmenta-
tion, for exploiting the spatial properties of objects on the ground (Blaschke
et al., 2014). This kind of classification is particularly useful for the classification
of urban land cover (Myint et al., 2011).
Finally, photointerpretation is the visual inspection of images that allows for
very high accuracy levels (Richards and Jia, 2006). For instance, photointer-
pretation is used for classification of samples of the Italian monitoring network
of soil consumption (ISPRA, 2013).

Classification accuracy
The accuracy assessment of a land cover classification is a fundamental step of
the monitoring process, in order to identify and measure map errors and at the
same time evaluate the coherence between the classification and reality.
 Methodologies and data available 27
Table 2.5 Schematization of an error matrix
Ground truth 1 Ground truth 2 ... Ground truth k Total

Class 1 a11 (correct) a12 (error) ... a1k (error) a1+

Class 2 a21 (error) a22 (correct) ... a2k (error) a2+
... ... ... ... ... ...
Class k ak1 (error) ak2 (error) ... akk (correct) ak+
Total a+1 a+2 ... a+k n
Note: k is the number of classes identified in the land cover classification; n is the total number
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of collected sample units.

In general, classification accuracy is calculated with an error matrix (also

referred to as confusion matrix), which is a table that compares map informa-
tion with reference data (i.e. ground truth data) for a number of sample units
(Congalton and Green, 2009).
The structure of an error matrix has ground truth classes (i.e. reference data) in
columns and thematic map classes (i.e. classification data) in rows (see Table 2.5).
Therefore, correctly classified samples are located in the major diagonal of the matrix,
while errors are in the other elements of the matrix (Richards and Jia, 2006).
The selection of sample units should be random and, depending on classi-
fication spatial resolution, sample units can be a single pixel, a cluster of pixels
or a polygon (Congalton and Green, 2009). The reference data are produced
by field survey, or by the photo interpretation of images having higher spatial
resolution than classification.
It is possible to calculate several accuracy statistics using the error matrix. In
particular, the Overall Accuracy is the ratio between the number of samples
that are correctly classified and the total number of sample units (Congalton
and Green, 2009):

Overall Accuracy = asum / n

Where:

•• asum = the sum of the major diagonal

•• n = total number of sample units

In addition, it is possible to calculate the User’s Accuracy and the Producer’s

Accuracy for each class, defined as (Congalton and Green, 2009):

User’s Accuracy = aii/Ri

Where:

•• aii = samples classified correctly for class i

•• Ri = sum of row i, which is the number of samples belonging to class i in
the classification
 28 M. Munafò and L. Congedo
Producer’s Accuracy = aii/Ci

Where:

•• aii = samples classified correctly for class i

•• Ci = sum of column i, which is the number of samples belonging to class
i in reality

Generally, a classification is considered good if class accuracy is at least 85 per cent

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(European Environmental Agency, 2012).

Case studies about land cover monitoring

During the last few decades, Europe has been developing several initiatives in
order to provide land cover information to users in the field of environmental
and other terrestrial applications. In particular, the GMES/Copernicus pro-
gramme developed a series of projects, starting from the GMES Fast Tracking
Services (European Commission, 2005), through the INSPIRE Directive
(2007/2/EC) that aims to create a European spatial data infrastructure.
The European Environmental Agency has been particularly active in
monitoring land cover since the 1990s, in particular with the Land Cover
project of the CORINE programme (i.e. Coordination of Information on the
Environment).
EUROSTAT has developed a monitoring network (LUCAS, Land Use
and Cover Area Frame Survey) that monitors land use and cover change since
2006 in the European Union, based on an in situ survey of samples; this survey
allows for the production of homogeneous statistics for European Countries.
Now, at the European level, the activities of land cover monitoring rely on
Copernicus products and services such as the High Resolution Layers (HRLs).
At the regional and local level, land cover monitoring requires higher reso-
lution data, acquired systematically according to the pace of growth of urban
areas. For these reasons, Copernicus products still require additional informa-
tion and parallel studies in order to correctly assess soil consumption.
Moreover, for non-European countries where Copernicus data are not
available, land cover monitoring must rely only on affordable and efficient
methodologies, mainly based on remote sensing. For instance at the global
level, the ESA has developed a global land cover map having 300 m spatial
resolution.
In Chapters 13 and 16 two case studies are described: the first one illus-
trates land cover monitoring in Italy, addressed by ISPRA (the Italian National
Institute for Environmental Protection and Research), which in 2015 pub-
lished the second National Report on Soil Consumption (Munafò et al., 2015);
the second case study is the assessment of land cover change in Dar es Salaam
(Tanzania) using free Landsat data, in the frame of the European project ACC
Dar (Adapting to Climate Change in Coastal Dar es Salaam).
 Methodologies and data available 29
Conclusions
At the European level, soil consumption is a major issue that in the past decades
has become a priority for environmental policies; the European Commission
(2012b) published the guidelines on best practice to limit, mitigate or compen-
sate soil sealing which are the key strategies for reducing soil consumption and
land cover change. The pace of land cover change is important in light of the
European objectives for 2020 (European Commission, 2011).
Therefore, land cover monitoring is becoming a crucial activity in order
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to assess soil consumption and consequently adapt current policies at the vari-
ous administrative levels. The Copernicus initiative, which aims at monitoring
the earth surface and environmental changes, is becoming the main point of
reference at the European and national levels, especially with the production
of HRLs.
However, considering the complexity of environmental effects caused by
soil consumption at the local level, it is fundamental that land cover monitoring
could assess even little changes. In Italy, ISPRA has developed several method-
ologies and products at very high resolution with the purpose of assessing soil
consumption with a high level of precision.
The VHRL will be useful for local administrations in order to assist decision
making and keep the database of land cover up to date. This valuable informa-
tion, homogenous and complete for the whole country, could improve urban
planning and policy making from the local to the national level.

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 3 Measuring and monitoring the
extent of human settlements
From the local to the global scale
Daniele Ehrlich, Aneta J. Florczyk, Andreea Julea,
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Thomas Kemper, Martino Pesaresi and

Vasileios Syrris

Introduction
Population increase and urbanisation are fuelling the growth of cities and
human settlements. This growth is often at the expense of valuable agricultural
land from which societies draw their food base. It is also at the expense of forest
and other natural land that provides timber or other ecosystem services such
as clean water and fresh air. This process of growth modifies the land uses and
the land cover and most importantly seals soil with built material. The extent
of the growth of the built environment is much talked about but rarely quanti-
fied. This is often because of a lack of semantics and measurement technologies
issues that are also briefly addressed in this chapter.
This chapter addresses the measurement of the spatial extent of human set-
tlements and their changes in time. This measure can be used as a proxy value
for the loss of soils. Settlements are part of the landscape that includes buildings,
roads and transport networks that are also referred to as built-up environment.
In its simpler term, we can define a settlement as any form of human habita-
tion, which ranges from a single dwelling to a large city. Settlements’ building
blocks are three dimensional constructions typically referred to as buildings
used for residential or other societal activities. Settlements differ in aspect and
function from other land cover types. While vegetation is still found as parks
and lawns, the cover is by and large dominated by concrete, asphalt and other
man-made covers. It is thus completely different from other (semi-)natural
land cover types.
Quantifying changes in human settlement is not trivial. Measuring settle-
ment requires an unambiguous definition of a built-up area and changes in a
built-up area, and assumes standardisation of measurement (measurement scale)
and standardisation in processing or modelling information on built-up areas.
This work uses the building in its different uses (i.e. as residential, commercial,
industrial) as the characterising element of the built-up environment (Pesaresi
et al., 2008). Other constructions, roads or parking lots can be included in the
built-up. However, the building is the only characterising element with the
density of built-up as a measure. Density is defined as the area identified by
 34 D. Ehrlich et al.
the building footprint over a given spatial reporting unit (Pesaresi et al., 2013),
typically the grid cell. The changes can thus be measured as changes of density
within that reporting unit (Gueguen et al., 2011). The changes can be coded
as no-change, when both images show either non-built-up or the same amount
of built-up, positive change and negative change when the percentage of built-up
increases or decreases respectively between the two dates.
Human settlements are studied using aerial photography and satellite imagery,
also referred to as remote sensing. The most valuable characteristic of satellite
remote sensing is its ability to provide a synoptic overview that allows us to
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outline the extent of a settlement, its size, its form and the complexity of the
urban fabric. In addition, when analysed over time, imagery allows measuring
the change in size and form of settlements.
Remotely sensed data are available globally at different resolutions, and offer
a multi-temporal representation of the Earth. Each sensor provides unique
opportunities, either spatial precision, or temporal coverage, or spectral charac-
teristics to be used in the detection of the built environment. Each can provide
information that, when combined, can provide a useful measure of the increase
in the built environment.
The following sections provide an overview of remote sensing technology
and its use for measuring changes in the built environment. First, we list the
type of satellite imagery that has been used and that potentially can be used to
derive information on the built environment. Second, we provide examples
of analysis of urban growth from different sensors and using different proce-
dures. We then show two examples of change at the city level, assuming only
the city’s change of interest. We then provide examples of global and regional
processing that are conducted in an automatic way. Finally, we discuss the
challenges in combining imagery and image processing products at different
resolution.

Satellite imagery
This section summarises the types of remotely sensed satellite images used in
civilian applications and provides an outlook on future missions. We con-
sider both the open source imagery and the commercial imagery used for the
analysis of the built environment. The unique characteristic of satellite remote
sensing is its ability to collect imagery globally. The data acquired by a satellite
is stored in large imagery archives, which allow temporal comparison, and thus
urban change analysis, even at global scale. Remote sensing has been widely
recognised as the most economic and feasible approach to derive land cover
information over large areas (Cihlar, 2000). Today, the continuous remotely
sensed observations of the Earth’s land surface offer unique opportunities to
perform multi-temporal analysis of global phenomena. Satellite programmes
continue to proliferate. Civilian, military/intelligence and commercial com-
munities enjoy the imaging capabilities of polar-orbiting satellites. Since the
first Earth Observation satellite was launched by the USA in 1972, almost 200
 From the local to the global 35
satellites have been launched with a global land cover mission; and at the end
of 2013, 50 per cent of them were still operating (Belward and Skien, 2015).
The most relevant long-term missions that offer a major data source for
developing continental to global scale land cover and change products at spatial
resolutions necessary for many surface phenomena are the Landsat,1 MODIS,2
SPOT Vegetation3 and Sentinel4 missions. The Landsat mission is an ideal
source of data because of its 40-year acquisition legacy (Markham and Helder,
2012), which provides long-term inventory of global land cover change
at a sub-hectare resolution (30–80 m). Currently, the Global Land Survey
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(GLS) datasets (Gutman et al., 2013), i.e. collections of orthorectified, cloud-

minimised Landsat-type satellite images, aim at providing mosaics of near
complete coverage of the global land area and are available per decade since
the early 1970s, centred on 1975, 1990, 2000, 2005, and 2010. The Landsat
record will continue to grow with the currently operational Landsat-8 and the
planned Landsat-9 in 2023.
Another source of long-term land cover observations is the MODerate reso-
lution Imaging Spectrometer (MODIS) on board the Earth Observing System
(EOS) Terra and Aqua satellites (Ardanuy et al., 1991). In particular, Terra’s
MODIS, in operational mode from 2002, is specifically designed to monitor
land properties at global scales, and acquires multispectral data with medium
resolution but high temporal frequency (almost daily).
In Europe, the SPOT Vegetation programme offers long-term imagery
that is relevant for observing and analysing the evolution of land surfaces and
understanding land changes over large areas (Henry et al., 1996; Mucher and
de Badts, 2002). The 1 km SPOT Vegetation (VGT) data have been acquired
by SPOT 4–5 satellites from 1998 till 2015 on a daily basis. Recently, the
European Space Agency (ESA) has launched the first satellite of the Sentinel
2 constellation, which is designed to provide systematic global acquisition of
high-resolution, multispectral images allied to a high revisit frequency (Berger
et al., 2012; Malenovský et al., 2012). These observation data will be the base
for the next generation of operational products, such as land cover maps,
land change detection maps and geophysical variables. Sentinel 2 is one of
the Sentinel missions developed by ESA within the European programme
Copernicus for the establishment of a European capacity for Earth observation
beyond 2025 (Aschbacher and Milagro-Pérez, 2012).
Additional sources for mapping human presence from space at global scale
are the long-term time series of night light imagery. There is, for example,
the imagery produced by the Defense Meteorological Satellite Program
(DMSP) Operational Linescan System (OLS) (Croft and Colvocoresses, 1979;
Imhoff et al., 1997) and the Suomi-NPP satellite that carries a panchromatic
Day/Night Band (DNB) radiometer, namely VIIRS (Miller et al., 2013).
However, the night light imagery includes also temporary light sources such
as wild fires and volcanic eruptions that have to be taken into account. In
addition, they indicate human economic activities. Hence, settlements with-
out illumination will be neglected.
 36 D. Ehrlich et al.
Table 3.1 presents a comparison of Landsat, MODIS and Sentinel with
other selected sources. We can differentiate between panchromatic (PAN),
multispectral and synthetic aperture radar (SAR) sensors. A panchromatic
sensor is sensitive to all visible colours and usually has the highest spatial reso-
lution. Some sensors, such as MODIS on EOS-Terra, are able to provide
data at two or more spatial resolutions. Also, some missions carry more than
one sensor, which capture data at different resolutions. SAR missions, such as
ESA Sentinel 1, offer variable resolution image acquisitions through different
operational modes.
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Other examples of a SAR mission can be ESA Envisat (ASAR instrument)

(ESA, 1993), ESA ALOS (PALSAR instrument) (Henderson and Lewis, 1998)

Table 3.1 Examples of global observing missions and selected technical specifications.

Repeat cycle in days/minutes depends on latitude, cloud conditions
(in case of optical sensors) and constellation configuration (e.g. Sentinel 2
using the full two-satellite constellation configuration)
OWNER Operational Spatial resolution Revisiting time Main objective
Platform (Sensor) period (platform) (m) (total in days (d) or (operational purpose)
number of bands) minutes (min)

NASA 1999–. . . (T) 250m (2) 1–2 d Multiple

Terra/Aqua 2002–. . . (A) 500m (5)
(MODIS) 1km (28)
NASA/USGS 2012–2017 15m (PAN) 16 d Land cover
Landsat 8 30m (9) (continuity)
(OLI/TIRS) 100m (2)
NASA/USGS 1999–2003 15m (PAN) 16 d Land cover
Landsat 7 30m (6) (continuity)
(ETM+) 60m (1)
NASA 1982–2001 (4) 80/30m (4/6) 16 d Land cover
Landsat 4–5 1984–2013 (5) 120m (1) (continuity)
(MSS/TM)
NASA 1972–1978 (1) 80m (4) 18 d Land cover
Landsat 1–3 1975–1982 (2)
(MSS) 1978–1983 (3)
CNES 2002–2015 2.5 or 5m 2–3 d Land cover
SPOT 5 (PAN) (continuity)
10m (4)
20m (1)
Spot Image 2012–2023 (6) 1.5m (PAN) 1–3 d Land cover
SPOT 6–7 2014–2023 (7) 6m (4) (continuity)
ESA 2015–2025 (A) 300m–1km 27 d Global ocean and
Sentinel 3 2017–2025 (B) land monitoring
A/B/C 2022–2025 (C) (continuity
ENVISAT,
SPOT VEG)
ESA 2014–2020 (A) 10m (3) 2–5 d (with High-resolution and
Sentinel 2 2016–2022 (B) 20m (6) 2 satellites) optical imaging
A/B/C 2021–2025 (C) 60m (3) for land services
(continuity SPOT
and Landsat MT)
 From the local to the global 37
ESA 2014–2020 (A) 5m (Strip Map <1–3 d All-weather, day
Sentinel 1 2015–2021 (B) and wave and night radar-
A/B/C 2021–2025 (C) modes) imaging for land
(SAR) and ocean services
NASA/NOAA 2011–. . . 750m (PAN 102 min continuity with
Suomi-NPP DNB) MODIS, DMSP-
(VIIRS) 750m (21) OLS and NOAA
AVHRR
US DoD 1972–2013 2.7km (daily) 101 min Meteorology
DMSP
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(OLS)

or the German Aerospace Center (DLR) TerraSAR-X (Eineder and Runge,

2002). Since a SAR sensor scans the Earth’s surface with the help of micro-
waves, it has advantages over optical instruments, i.e. it works also at night and
despite cloud cover, and it can be used to obtain reliable geophysical measure-
ments (e.g. backscatter constants, distances).
Global Earth Observation missions aim at improving spatial and radiometric
(i.e. quantisation) resolution of the provided imagery (see Table 3.2). In general,
we can observe an increment in spatial and spectral resolution. The third dimen-
sion of improvement focuses on the spectral resolution, which is referred to as
hyperspectral images. So far, hyperspectral sensors are operated mainly on air-
borne remote sensing platforms such as the Airborne Visible InfraRed Imaging
Spectrometer (AVIRIS). There are also space borne missions (Bioucas-Dias
et al., 2013), such as the Italian Space Agency (ASI) Hyperspectral Precursor and
application mission (PRISMA) or the DLR EnMap (Environmental Mapping
and Analysis Program) (Table 3.3).

Table 3.2 Examples of optical space-borne missions (in orbit, approved and planned)
grouped based on resolution nomenclature commonly used in the
Copernicus programme (i.e. Low Resolution (LR), Medium Resolution
(MR), High Resolution (HR), Very High Resolution (VHR)) according
to the highest resolution on board.
LR MR1/MR2 HR2 HR1 VHR2 VHR1
(>300m) (30–300m) (10–30m) (4–10m) (4–1m) (<1m)

DMSP Terra MODIS Sentinel 2 RapidEye 5, SPOT 4–7 WorldView 1–2

1972–2013; 1999; 2014 Follow-on 1998; 2007;
Envisat Aqua MODIS 2008; IKONOS GeoEye 1
AATSR/ 2002; Sentinel 1 1999; 2008;
MERIS PROBA-V 2014 Seosat/ Pleiades 1–2
2002–2013; 2013–2015 Ingenio 2 2011;
Sentinel 3 2014 Deimos 2
2015 2014;
DMC3
2015
Note: Italic text indicates commercial missions.
 38 D. Ehrlich et al.
Table 3.3 Examples of hyperspectral platforms
Platform Launch Spatial Spectral resolution of Comments
(Sensor) year resolution (total hyperspectral bands
number of bands)

Space-borne 2018 30m (228) 6.5nm in a range German mission to

DLR of 420–1000nm support ecosystem
EnMap (VNIR); 10nm applications
in a range of Status: planned
900–2450nm Revisit: 4–27 days
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(SWIR)
Space-borne 2015 20–30m (238); 10nm in a range Italian mission of
ASI 2.5–5m (PAN) of 400–2500nm demonstrative/
PRISMA (VNIR and technological and
SWIR regions) pre-operational
nature supporting
multiple applications
Status: planned
Airborne 1987 20m (224) 10nm in a range Climate change (not
NASA/JPL of 380–2500nm limited to)
ERS (AVIRIS) Status: operational

Change analysis at city level

This section provides two examples on change detection at the city level using
two different sensors. The first example illustrates automatic built-up map-
ping and change detection by combining recent VHR SPOT imagery and
older HR SPOT imagery over Alger. SPOT data are commercial, thus with
limited access. The second case relies on open access Landsat imagery from
archives that date back to the mid-1970s. An analysis using Landsat images over
Bangalore is briefly presented. The two case studies use concepts and proce-
dures that can be applied to detect changes at continental scale.

Alger case study

The Alger case study uses SPOT 1 images from 8 July 1986 and SPOT 5
from 9 February 2009, which are available as panchromatic bands at 10 and
2.5 m resolution, respectively. SPOT imagery is used to detect changes in the
built environment due to its fine spatial resolution that captures, by and large,
most of the built-up structures. Some authors have also attempted to measure
the changes only for the built environment by comparing built-up maps pro-
duced at different moments in time (Tiede et al., 2012). This work follows a
new research trend where changes are measured by directly comparing features
computed from the imagery (Ehrlich and Bielski, 2011; Gueguen et al., 2013).
The conceptual and methodological issues on change detection and this specific
case study are fully reported in Ehrlich et al. (2015).
 From the local to the global 39
This change detection method uses imagery from two different sensors
and two different spatial resolutions. The processing procedure consists in six
main steps: (1) pre-processing imagery for geometric correction; (2) calcula-
tion of built-up presence index (BUPI) features (Pesaresi et al., 2008) for each
image; (3) combining (stacking and resampling) the BUPI features into a sin-
gle two-band image in order to perform principal component (PC) analysis;
(4) processing the PC2 as a change feature; (5) thresholding the change fea-
ture into a built-up change map; (6) modelling the change feature according
to a regular grid. This last step associates the BUPI features with the desired
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information ‘built-up’ or ‘not built-up’ and ‘built-up change’ or ‘not built-up

change’ as described in Ehrlich et al. (2015).
For the sake of simplicity, we provide below a descriptive summary of pro-
cessing steps and the results. The main processing relates to the computation of
a texture-related feature (Pantex) as described in Pesaresi et al. (2008). These
derived texture measurements have been shown to be highly correlated with
the presence of built-up land. In fact, the 2009 texture image is used to gen-
erate a binary built-up map, which is produced by simply thresholding the
texture values.
The change analysis is based on identifying changes in textures, and thus
changes in built-up. The changes are quantified using principal component
(PC) analysis between texture measures computed for the images collected in
1986 and 2009. PC 1 captures the region of the image with similar texture and
is not used in the analysis. PC 2 captures the changes in texture between the
1986 and 2009 images and thus the changes in built-up and is thus our change
information layer. We analysed only the positive changes as reported in PC2, that
is a change from low texture (i.e. agricultural land) into landscapes with high
texture (i.e. built-up).
In order to make the changes in built-up information more explicit, we have
applied a threshold on the PC2 obtaining the built-up change map of interest.
Finally, the change information was aggregated at a spatial unit of 100 × 100 m
to provide the gridded change map. This aggregation into grids of 100 m allows
the fine tuning of changes by removing unwanted artefacts corresponding to
small patches or to those whose change signal is of low magnitude. The artefacts
are inevitable given the technical characteristics of imagery.
Thresholding the texture information into a change map and generalising
the information into a gridded change map is crucial to interpret the results.
The threshold, applied to the change map, simplifies the density of change
information into binary change information. This simplification is justified by
the inability to obtain fine density changes due to the relatively coarse spatial
resolution of SPOT 1 imagery, but inevitably comes with a loss of information.
The aggregating of the change into 100 m grid cells has also some generali-
sation drawbacks. Each 100 m grid cell is labelled ‘built-up’ and/or ‘built-up
change’ irrespective of the density of built-up within the cell. One cell with a
fraction of built-up is treated similar to the cell entirely covered by built-up and
this influences the change detection statistics. This generalisation also amplifies
 40 D. Ehrlich et al.
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Figure 3.1 Alger and settlements surrounding Alger over a 52 × 40 km2 area. The
settlement maps are labeled red for built-up before 1986 and yellow for
built-up after 1986 (modified from Ehrlich et al., 2015)

the statistics of built-up and/or its change – especially for low densities – and
should be taken into account when interpreting the results below.
The processing generates two information layers used for change analysis: a
gridded change map and a built-up map for 2009, both shown in Figure 3.1 as
yellow and red zones, respectively.
The 2009 built-up map and change maps have not been validated quanti-
tatively due to a lack of reference data both for 1986 and for 2009. However,
they have been visually inspected against the imagery from which the change
was produced (Table 3.4). The analysis shows that 339.69 km2 are measured as
built-up in 1986. In only 23 years, 173.80 km2 are added to the built-up land
of 1986, corresponding to an increase of 50 per cent of the built-up area. The
total built-up land over this area increases from just over 26 per cent in 1986 to
nearly 40 per cent in 2009. The statistics are based on the analysis of built-up
land computed over 100 m grid cells. In fact, different density thresholds or

Table 3.4 Built-up and built-up change statistics over the Alger metropolitan area
Date 1986 2009

Built up area (km2) 339.69 513.49

Urban change (km2) 173.80
Percentage of built-up area over total (%) 26.39 39.89
Urban change (%) 13.50
 From the local to the global 41
different grid cell sizes used in the analysis may provide results that differ from
those provided herein. In addition, statistics may be confirmed only through a
thorough validation protocol.

Bangalore case study

A similar analysis can be performed using other imagery, for example Landsat.
Here, the collections of nominal temporal signature 1975, 1990, 2000 and 2014
were used to produce a global change map. The image processing method is
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described in the section ‘Global processing’, below. In this section we describe

the change analysis performed on Bangalore, the capital of the Indian state
of Karnataka. Since the city population has increased from 4.3 to 8.4 million
between 2001 and 2011 (ORGI, 2015), it is an interesting case study.

Figure 3.2 City of Bangalore as seen from Landsat imagery: 27 Feb. 1973 (a), 14 Jan.
1992 (b), 27 Nov. 2000 (c) and 31 Mar. 2014 (d). The urban change maps
encode the urban area detected in 1975 (e), 1990 (f), 2000 (g) and 2014 (h)
 42 D. Ehrlich et al.
Table 3.5 Built-up area per time period for the Bangalore case study, as derived from
analysis of Landsat imagery collections
Time period 1975 1990 2000 2014

Built up area (km2) 139 251 329 520

Urban change (km2) 112 78 191
Percentage of built-up 8.66 15.62 20.52 32.42
area over total
Urban change (%) 6.96 4.9 11.9
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All four Landsat collections were processed to generate four information

layers on built-up presence. Then the layers were merged into one urban
change map, which is a classification grid of 38.22 m resolution. There are
four classes of urban area, which represent urban areas that appear in one of the
epochs (i.e. 1975, 1990, 2000 or 2014). A class is assigned to a cell according to
first occurrence of the built-up within the multi-temporal layers.
The study area is a square (40 × 40 km) that covers the present-day extent
of the city of Bangalore. Figure 3.2 shows the study area on Landsat false col-
our images and the mapped change per epoch. Table 3.5 gathers the calculated
extent of the built-up area per epoch. It can be observed that the major change
occurred between 2000 and 2014 but also between 1975 and 1990. Also, here
we do not have proper validation data, and we rely on visual analysis of the
images. However, if assuming a positive correlation between urban extent
change and population growth, the census information indicates a relevant
population increase between 1970 and 1990 as well (ORGI, 2015).

Global urban area mapping

Methods for mapping urban extension or studying its morphology and growth
have radically changed with the arrival of remote sensing technology. However,
urban remote sensing is very challenging due to the heterogeneity of urban areas.
In some cases, the discrimination of vegetative land cover may help in the deline-
ation of urban areas, a theory that was tested by exploiting the near-infrared band
of multispectral imagery (SPOT) in Gao and Skillcorn (1998). Some researchers
have used hyperspectral imagery for mapping a narrow range of urban materials
(Salu, 1995; Ben-Dor et al., 2001) or for analysing morphological characteris-
tics of urban areas (Benediktsson et al., 2005). Other approaches to urban area
detection exploit spectral and spatial characteristics of Landsat (Guindon et al.,
2004; Guindon and Zhang, 2009), while Platt and Goetz (2004) found that
hyperspectral AVIRIS holds advantages over Landsat ETM+ for the classifica-
tion of heterogeneous and vegetated land uses (for the tested urban-rural fringe).
Furthermore, SAR data (i.e. ENVISAT) have been tested as a baseline for urban
mapping by means of a textural analysis (Ban et al., 2015) or for urban change
detection (Yousif, 2015); and night lights from DMSP-OLS data have been
studied to map urban areas (Imhoff et al., 1997; Small et al., 2005).
 From the local to the global 43
Currently, the data fusion for urban area characterisation has become a com-
mon approach, and it may be done at different levels, namely multi-sensor,
multiresolution (scale-space) or multi-temporal (Gamba et al., 2005). There are
studies that explore the integration of hyperspectral and SAR imagery for urban
mapping (Hepner et al., 1998; Gamba and Houshmand, 2001), the combination
of DMSP-OLS data with vegetation indexes derived from MODIS (Schneider
et al., 2003), SPOT (Cao et al., 2009) or Landsat (Zhang et al., 2015). Multi-
temporal Landsat data have been used to extract impervious surface time series
for multi-temporal settlement mapping on Java Island (Patel et al., 2015), and
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the analysis of annual urban dynamics in Beijing city (Li et al., 2015).
Most of the methods for detecting urban areas from remote sensing imagery
have been tested on some selected areas. However, it is a challenge to produce
a dataset at the global scale. Currently, there are several global datasets relevant
for mapping the urban extent (Elvidge et al., 2009; Poterea et al., 2009). There
is also an ongoing project, the DLR Global Urban Footprint (GUF), which
aims at mapping settlements globally at around 12 m using SAR imagery (Esch
et al., 2013). Additionally, there is a research team that attempts to predict the
future change of urban extent for selected cities (Angel et al., 2011).
In this work, we provide some details on selected datasets, namely MODIS
500m Global Urban Extent5 (MODIS 500m), Global Land Cover 20006
(GLC2000), GlobCover7 and GlobeLand308 (Table 3.6). Other global data-
sets fall in the following categories: population maps (e.g. LandScan (Bhaduri
et al., 2002) or WorldPop (WorldPop, 2015)), soil sealing surfaces (e.g. Global
Density of Constructed Impervious Surface Areas (ISA) (Elvidge et al., 2007)),
nightlight-derived urban maps (Zhou et al., 2015) or place-name databases
(GeoNames, 2015).
MODIS 500m dataset has been created by exploiting spectral and temporal
information in one year of MODIS observations (Schneider et al., 2010). The
global training database was created by the stratification of urban ecoregions, which
have been defined via natural, physical and structural elements of urban areas. The
product validation focused on 140 cities, because the method targets relatively
extended settlements while neglecting sparsely urbanised areas, mainly due to the
coarse resolution of the input imagery. However, MODIS 500 is an improvement
over MODIS 1km, which was produced using MODIS data, DMSP-OLS dataset
(1 km mosaic) and gridded population data (about 5 km). Both MODIS datasets
were used to detect changes in urban areas (Mertes et al., 2015).
GLC2000 is a harmonised global land cover classification database based
on SPOT VGT data, created by an international partnership of 30 institutions
(Bartholomé et al., 2002; Bartholomé and Belward, 2005). The ‘urban’ class has
been derived with the help of nightlight data; however, the performed validation
using Landsat 7 has not targeted the ‘urban’ class (Bicheron et al., 2008). The
lessons learnt contributed to the ESA GlobCover initiative, which have deliv-
ered two global composite and land cover maps that use the same classification
nomenclature. Also here, authors admit that the ‘urban’ class has a low accuracy,
as the urban areas are underestimated and the class is not well represented in the
validation dataset (i.e. points) (Bontemps et al., 2011).
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Table 3.6 Selected global datasets relevant for urban area mapping
MODIS 500m GlobCover2005/ GLC2000 GlobeLand30 2000/2010
GlobCover2009

Producer University of Wisconsin-Madison ESA JRC (coordinator) National Geomatics Center of

China
Purpose Urban area Land cover Land and inland water Land cover to support sustainable
biodiversity development goals
Input satellite MODIS ENVISAT/MERIS SPOT 4/Vegetation LandsatTM/ETM+ /
data LandsatTM/ETM+ and Chinese
environmental and disaster
satellite (HJ-1)
Time consistency 2001–2002 2004–2006 / 1999–2000 2000
2009 2010
Type of data Classification Classification Classification Classification
(1 class) (22 classes) (22 classes) (10 major classes)
Urban area class Areas dominated by built Artificial surfaces Artificial surfaces and Artificial cover (settlement place,
environment (>50%), including and associated associated areas (urban industrial and mining area,
non-vegetated, human- areas (urban areas areas >50%) traffic facilities)
constructed elements, with >50%)
minimum mapping unit >1 km2
Production Supervised classification Unsupervised Mainly unsupervised POK-based approach;
method (ensemble decision-tree) classification; classification; a supervised pixel-based
(*specific for supervised classification an ad hoc classification classification (using spectral and
urban class) for urban and algorithm using texture characteristics), then
wetlands areas auxiliary data segmentation, and finally visual
(e.g. DMSP) verification and correction
Stratification Urban ecoregions Equal-reasoning areas Continental-like N/A
Urban coverage 4.16% 0.22% / 0.19% 0.95%
0.20%
Resolution 500m 300m 1km 30m
 From the local to the global 45
Recently, a Chinese global land cover classification, GlobeLand30, has
been released at 30 m as a result of a four-year effort (Chen et al., 2015).
The applied operational approach, called POK (the pixel-object-knowledge),
mixes automatic classifiers and interactive processes (in cases of classification
in complex areas and for quality control). First, each class is identified in an
a priori sequence, by applying pixel- and object-based classification. Then,
the results are merged through a knowledge-based interactive verification (i.e.
experts using auxiliary datasets). Urban areas fall into one of the difficult cases.
When evaluating data for potential usage, we should also consider regional
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datasets. For example, there are multiple datasets hosted by the European
Environment Agency (EEA) that can be used for urban analysis in Europe.
The main datasets are HR Soil Sealing (SSL) (EEA, 2015), multi-temporal
CORINE Land Cover (CLC) (EEA, 2012), and Urban Atlas (UA) (European
Commission, 2011) (see Tables 3.7 and 3.8). Most of the urban area in CLC
is encoded within the ‘artificial surfaces’ class. However, the sparse built-up
structures are ignored, especially in agricultural or (semi-)natural areas (i.e. the
units smaller than 25 ha are included in the dominant land cover type around or
grouped in polygons labelled as ‘heterogeneous’). Also, SSL underrepresents or
completely omits small and dispersed rural settlements (Hurbanek et al., 2010).
UA offers a far more accurate picture of urban sprawl in the fringe of urban
zones than CLC but it does not offer full European coverage. In practice, many
research studies combine those datasets to mitigate their mutual limitations.

Table 3.7 European datasets relevant for urban area mapping

CLC SSL UA

Input data Multiple SPOT 4–5 Satellite imagery (SPOT 5, ALOS

imagery IRS P6 LISS P/XS, RapidEye XS and
III QUICKBIRD), SSL, road network,
topographic and cartographic maps
(different scales), other ancillary data
(e.g. local digital/paper maps, Bing)
Method Photo- Automatic Photo-interpretation (MMA of
interpretation image ‘artificial surfaces’: 0.25ha)
(MMA: 25ha) analysis
Type of data Land cover Thematic Land use (extension of CLC
gradient nomenclature)
Urban area ‘Artificial Soil sealing Several classes of ‘artificial surfaces’
definition surfaces’ class degree category
Time Around 1990, 2006 2005–2007
consistency 2000, 2006
and 2012
Spatial Europe Europe European urban areas (>100,000
coverage inhabitants)
Spatial Vector 20 and 100m Vector
resolution
 46 D. Ehrlich et al.
Table 3.8 CORINE Land Cover products
CLC1990 CLC2000 CLC2006 CLC2012

Satellite data Landsat 5 Landsat 7 SPOT 4–5, IRS IRS P6 LISS III
P6 LISS III RapidEye
Time consistency 1986–1998 2000 +/- 1 2006 +/- 1 2011–2012
Number of countries involved 26–27 30–35 38 39
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The cost of human interaction (e.g. photo-interpretation) is usually very

high when producing most global urban maps. Since there is large variety of
local landscapes in different areas, there is an issue for supervised and unsu-
pervised methods for urban areas mapping. Most classifiers are tuned into
the local study area, and if the same settings are applied to other areas, the
accuracy of detection may decrease significantly. Therefore, experienced
researchers are needed for parsing the results, which can be very time con-
suming and expensive. In case of supervised methods, this cost is even higher,
because training samples will be gathered per scene. Therefore, automatic
methods are the best option, especially for producing time series maps at the
global scale. However, existing automated classification methods have been
deemed ineffective because of the low classification accuracy achievable at
the global scale and at HR2 resolution, as tested at 30 m Landsat imagery in
Gong et al. (2013).
Recently, an alternative automatic method for urban area extraction has
been successfully applied at global and continental scales. The European
Settlement Map9 (ESM) was developed jointly by the Joint Research Centre
(JRC) and the Directorate General for Regional Policy (DG REGIO) of the
European Commission. The fundamental methodological choices followed in
the processing chain are coherent with the Global Human Settlement Layer
(GHSL) paradigm. The next section will outline the GHSL methodology and
its applications.

Global and regional processing

The GHSL methodology has been developed to provide an automatic
image processing method for extracting built-up surfaces from remote sens-
ing images and produce information layers of high resolution at the global
scale (Pesaresi et al., 2013). The methodology is able to process imagery
with varying characteristics (such as diversity in spatial/spectral resolution,
spatial/temporal coverage, spatial displacement errors; quality degradation
that makes the calibration impossible; seasonality). As such, it qualifies as
an example for the processing of remotely sensed big data (Ma et al., 2014),
in terms of the data volume, diversity and complexity. This is achieved
through a fully automatic and computationally efficient method that is
robust and general enough.
 From the local to the global 47
The main characteristic of the developed methodology is a scene-based
processing and automatic image feature extraction by applying a multiscale
learning paradigm. The multiscale learning relies on low resolution auxiliary
data. The traditional approach to urban area detection for continental and
global coverage (Schneider et al., 2010) is based on searching for an ‘urban’
spectral signature (i.e. homogeneous and dominant combination of spectral
characteristics) within an a priori defined local region. This approach has strong
limitations when used with decametric or metric spatial resolution image data.
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Global processing
The multi-temporal medium resolution GHSL is the first geographic data-
set that describes the spatial evolution of the human settlements at the global
scale and along a time interval covering 40 years (from 1975 to the present).
Producing spatio-temporal built-up layers is a demanding task requiring several
processing steps and sophisticated modelling. Prototyping and production were
fraught with several challenges such as: (1) size, diversity and quality of the
input/output datasets, (2) parameterisation and fine-tuning of the information
extraction and fusion techniques and (3) computational complexity.
In order to deal with this complexity in an efficient way, a new methodol-
ogy has developed that is able to cope with (1) a large number of data granules
(scenes), (2) imagery captured by heterogeneous sensors and (3) morphological
diversity spread over different geographical areas and at different time spans.
The new approach treats the image values as symbols and attempts to build
associations between sequences of symbolic objects and target class values that
represent the land cover semantics. The sequences can be formed by informa-
tion derived either from the image bands directly or from features extracted
through data-driven (statistical) or model/assumption-based (analytical) meth-
ods. Typically, this information retains a spatial consistency, yet potentially can
span to time domain. The so-called Symbolic Machine Learning associative
classifier, which has been defined in this context, is a supervised-learning tech-
nique that maximises the within-class similarity of the symbolic objects based
on their frequent appearance in each of the classes. The classifier is controlled
by very few, easily tunable parameters, and the processing chain can be modu-
lated smoothly to any low to moderate computational infrastructure.
In the specific application of the multi-temporal medium resolution GHSL,
the information was extracted from Landsat image records organised in four
collections10 corresponding to the epochs 1975, 1990, 2000 and the present
time. Table 3.9 shows the type of imagery we used and quantifies the volume
of data and the respective processing time. The fourth collection is composed
by a set of Landsat 8 images from the years 2013 and 2014.
Both feature extraction and image classification have been implemented at the
original resolution of the input images. At the final stage, the images were warped
to the WGS84 Web Mercator projection at 38.22 m. The outcome of the pro-
cessing is a multiclass geographic layer with the following notation: no-data (0),
 48 D. Ehrlich et al.
Table 3.9 Landsat imagery and GHSL processing time
GLS1975 GLS1990 GLS2000 Landsat-8

Number of scenes 7,588 7,375 8,756 4,426 (2013)

4,663 (2014)
Number of bands 4 6 6 9
Working resolution 60m 30m 15m 15m
Total processing time 300 680 750 1980
per image (sec)
Indicative machinery: Intel(R) Xeon(R) CPU E7420 @ 2.13GHz, 8~10GB RAM
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Figure 3.3 Multi-temporal representation of four cities: Sao Paolo (a), Washington

DC (b), Guate (c) and Riyadh (d)

water bodies (1), land classified as non-built-up (2), 2013/14 built-up (3), 2000
built-up (4), 1990 built-up (5) and 1975 built-up (6). Data and cloud masks are
also available for each collection. Figure 3.3 shows the derived urban area change
in the example of four cities.

Continental processing for Europe

Another example of successful application of the GHSL methodology is the
high-­resolution regional ESM. It is produced as a built-up density map released
at 100 m for the general public. It has been produced from the pan-European
Copernicus (Core 003) dataset (Burger et al., 2012), an image collection pro­
duced in support to the UA project, which includes multispectral SPOT 5
 From the local to the global 49
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Figure 3.4 Density of built-up depicted by the European Settlement Map for the city
centre of Genoa, Italy, and its surroundings

and some SPOT 6 scenes of 2.5 m and 1.5 m spatial resolution, respectively.
Although the pre-processing (i.e. pan-sharpening, histogram stretching) has
caused significant spectral degradation in the data (Burger et al., 2012), the
method proved to be robust enough to extract meaningful information in
an automatic way. In total, 2,900 SPOT images have been processed at
2.5 m resolution.
The information layer has been produced using an automatic image process-
ing workflow (see Florczyk et al., 2015). Several auxiliary datasets have been
used in the production, the main being SSL, CLC and OpenStreetMap. The
method combines radiometric, textural and morphological analysis in order
to detect built-up structures. The produced 10 m and 100 m ESM datasets
offer built-up density maps, and each pixel (i.e. 100 m2 and 10,000 m2 cells,
respectively) represents a percentage of built-up structure within the spatial
domain (i.e. cell). This approach enables a quantitative analysis of the urban
area. Figure 3.4 presents an example of the city of Genoa.

National processing for South Africa

Since 2006, the South African National Space Agency (SANSA) has been
acquiring the national SPOT 5 imagery annually to support various aspects of
government planning and monitoring, including mapping and monitoring of
human settlements. In South Africa, the proportion of people living in urban
areas increased from 52 per cent in 1990 to 62 per cent in 2011, and about 8.2
per cent of the population was living in informal settlements (Statistics South
 50 D. Ehrlich et al.
Africa, 2011). In addition to natural population growth and the migration of
people from rural areas to cities, urbanisation is also influenced by the migra-
tion of people from neighbouring and other parts of Africa (Statistics South
Africa, 2011). Both cities and smaller towns are experiencing high growth
rates, together with the proliferation of informal settlements around them.
To support the efforts of the government, SANSA and the JRC have
developed a dedicated and fully automated workflow for the processing of
SANSA’s SPOT 5 imagery, based on multiscale textural and morphological
image features extraction (Kemper et al., 2015). In total, 485 scenes (a 2.5 m
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panchromatic and four 10 m multispectral bands) were processed. The SPOT 5

imagery increases the spatial detail compared to the Landsat roughly by a factor
of 10 (from 30 m to 2.5 m). Such a significant improvement in spatial reso-
lution is crucial for the monitoring of informal dwellings and scattered rural
settlements. Figure 3.5 highlights the effect of the increased spatial resolution.
While it is possible to detect well the dense settlements in the south and east of
the settlement map derived from Landsat data, the SPOT 5-based maps show
a much higher density in the scattered settlements in the central and western
part of the maps.

Figure 3.5 Comparison of the settlement maps derived from Landsat (b) and SPOT
5 (c) for a selected rural area in South Africa (a). The SPOT 5 map shows
building densities
 From the local to the global 51
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Figure 3.6 Detail of the two settlement maps from Figure 3.5 derived from Landsat
(a) and SPOT 5 (b). The SPOT 5 settlement map (b) includes building
points derived from a visual interpretation of the imagery. Note the good
match between the SPOT 5 settlement map (black) and the building
points (red)

A closer look at these scattered settlements (Figure 3.6) confirms this

observation. The settlement map derived from SPOT 5 data is able to outline
also the built-up area of the scattered settlements, which is only partly mapped
by the Landsat data. This example illustrates clearly the need for an integrated,
multiscale concept (such as the GHSL) that is able to provide consistent infor-
mation at global, regional and local scales.
In general, the GHSL datasets will provide an excellent basis for measuring
changes in built-up in the future. Also, they will be the baseline that will be
used to hindcast built-up change across large areas.

Discussion
Mapping urban area change from optical remotely sensed data at the global
scale poses several challenges. The first challenge in processing the Landsat GLS
collections was cloud coverage. For example, 12.6 per cent and 6.6 per cent
of the land masses were not covered in GLS1975 and GLS1990 respectively
due to cloud cover. Furthermore, 5 per cent of the GLS2000 images processed
had more than 10 per cent of cloud cover (Gutman et al., 2013). For this
reason, the images that made up one collection in time were actually selected
from images covering a wider temporal range than the nominal year. The
second challenge to be addressed is vegetation seasonality that prevents obtain-
ing a stable information extraction algorithm. In fact, it may cause false land
cover ‘change’ that is just a change in vegetation cover. The recent Landsat
 52 D. Ehrlich et al.
8 mission’s objectives include, among others, affording seasonal coverage of the
global land mass for a period of no less than five and three years for reflective
and thermal multispectral image data respectively (Irons et al., 2012).
Another challenge is the definition of built-up when using imagery col-
lected with different measurement scales. For example, medium resolution
imagery allows identifying constructed land often referred to as impervious
surface (Elvidge et al., 2007). This land includes roads, parking lots, buildings,
driveways, sidewalks and other man-made surfaces. Imagery with higher spa-
tial resolution, for example the imagery that was used to produce the ESM,
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allows separation between buildings and open spaces that require yet another
definition of built-up and another set of information extraction algorithms.
The lack of appropriate and consistent multi-temporal reference datasets makes
the training of algorithms and the evaluation of the precision of the produced
built-up map very difficult. In fact, no proper protocol for validating built-up
maps globally is available today.
The two case studies on change detection at city level test a conceptual
model for quantifying built-up areas and changes in time from multi-resolution
remote sensing. It is tested on VHR2/HR and MR imagery. The processing
aims to provide features that are related to the building density. The features
can then be modelled to provide density of built-up and, when compared in
time, changes of built-up. The selection of the input datum, the processing
techniques and the modelling procedure (i.e. the area at which the density is
computed and the spatial rules used to compute the density) will determine the
final outcome.
The visual analysis of urban area change using multi-resolution imagery
is also challenging due to the difference in resolution. The visual analysis of
2.5 m imagery (i.e. SPOT 5) confirms that built-up land can be measured,
because the building structure can be enumerated and the spatial arrangement
of buildings can be assessed. At 10 m resolution (i.e. SPOT 1), only large
buildings may be identified and mapped. With Landsat imagery, at resolution
coarser than 15 m, the majority of the built-up structures cannot be identified
and it is rather the density of constructed land that is detected visually.
The automatic procedure may be better suited to detect the building struc-
tures from multi-resolution imagery. However, challenges remain mostly
due to the wide variety of built-up patterns (i.e. different sizes and spatial
arrangements of built-up structures). The challenges in detecting built-up areas
are multiplied when changes in built-up are analysed. The change detection
techniques perform relative unambiguous results when the change in built-up
occurs through the encroachment of built-up land into other land cover. That
is when natural land is converted in dense built-up land. However, the small
density changes are difficult to assess due to the characteristics of the data and
to the absence of reference data.
Further refinement of the techniques and interpretation is needed. The
ultimate goal of this work is to walk through the conceptual change model,
test the process rather than the technique and the result. The final map and the
 From the local to the global 53
final statistics have to evaluate based on the input imagery and the processing
procedure used. Also, the map statistics need to be checked against reference
data that often is not available. Each step of the procedure will be further
evaluated to better understand the information content of the imagery, the
techniques used to measure changes and the eventual outcome to be used in
urbanisation studies.
Finally, Mertes et al. (2015) indicate that an urban area (or an impervious
surface) is a relatively stable land type over a long period of time, and typically
only positive changes occur – from natural land into built-up land. However,
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due to the dynamics of urban change globally, the future methods for urban
area change detection should also consider negative changes, from built-up to
other land cover types.

Conclusions
This chapter addresses some of the challenges in quantifying the growth of
human settlement at local, continental and global scales. It is also about seman-
tics and terminology that are indispensable for understanding what is being
mapped and for training image processing procedures. Especially in cases of
continental and global change mapping, the data availability, handling large
datasets, processing huge data volume in an automatic way are the main issues.
The work shows that using different satellite sensors we will produce dif-
ferent results, simply due to the precision of sensors’ measurements. The key
is to understand the limitation and the advantages of each sensor and to define
transfer functions that allow comparing one with the other. VHR satellite
imagery provides enough detail to map changes in the built-up environment
in a systematic and thorough way. Medium resolution satellite imagery pro-
vides the unique global coverage and, most importantly, the historical records
of Earth’s landscape. Although the detail might not be desirable for urban
change detection analysis, it can be useful for studying global change trends.
Since long-term changes can only be obtained from archived imagery col-
lections, which are the only record of past urban extent, we need to use
multi-resolution change detection techniques.
This work shows examples from a medium resolution global built-up
layer and derived changes computed globally over a time span of 40 years.
Comparison with finer resolution SPOT datasets measured over part of
South Africa shows examples of the opportunities that the SPOT GHSL
product can provide. In fact, SPOT Europe provides the detail that can be
used for a systematic high-resolution built-up analysis of Europe’s built-up
landscapes. The new forthcoming satellite imagery, such as that provided
by the Copernicus service, will empower us to measure the built-up extent
globally and with high quality.
The GHSL project has put in place the infrastructure that will allow us
to process future satellite images collected by the Sentinel sensors and other
free and open data sources and thus provide a true opportunity to monitor
 54 D. Ehrlich et al.
changes in built-up and therefore also in the loss of soil and natural landscape.
The information is particularly relevant in view of the development of the
composite indicators that will be used to monitor the targets of international
frameworks such as the Sendai framework for disaster risk reduction and the
sustainable development goals.

Notes
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1 http://landsat.usgs.gov/.
2 http://modis.gsfc.nasa.gov/.
3 www.spot-vegetation.com/index.html.
4 https://sentinels.copernicus.eu/web/sentinel/home.
5 https://nelson.wisc.edu/sage/data-and-models/schneider.php.
6 http://forobs.jrc.ec.europa.eu/products/glc2000/glc2000.php.
7 http://due.esrin.esa.int/page_globcover.php.
8 www.globallandcover.com/GLC30Download/index.aspx.
9 http://land.copernicus.eu/pan-european.
10 To download the images (http://landsat.usgs.gov/science_GLS.php), USGS pro-
vides the tool EarthExplorer at http://earthexplorer.usgs.gov/.

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 4 Modelling and projecting urban
land cover
Carlo Lavalle, Filipe Batista e Silva, Claudia
Baranzelli, Chris Jacobs-Crisioni, Ana Luisa
Barbosa, Jean-Philippe Aurambout, Ricardo
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Barranco, Mert Kompil, Ine Vandecasteele,

Carolina Perpiña Castillo and Pilar Vizcaino

Introduction
As previous chapters in this book have shown, urban expansion is an ongoing
process with considerable impacts on the environment, the economy and qual-
ity of life. Europe, with its largely urban population, is no exception. To curtail
the negative impacts and foster the positive effects of ongoing urban expansion,
policies will have to be adjusted and harmonised. To do so an outlook of future
land use and urbanisation trends is indispensable. Such an analysis of evolutions
and functional profiles of European cities requires evaluating the impacts of
continent-wide drivers and, at the same time, the effect of national and local
strategies with their own priorities and plans.
The Directorate General Joint Research Centre (DG JRC) of the European
Commission (EC) is contributing to the analysis of European regions and cit-
ies with the LUISA Territorial Modelling Platform, the aim of which is to
provide an integrated methodology based on a set of spatial tools that can be
used for assessing, monitoring and forecasting the development of urban and
regional environments. LUISA allows quantitative and qualitative comparisons
at pan-European level, among areas subject to transformation due to policy
intervention. A further characteristic is that it adopts a methodology that simul-
taneously addresses the EU perspective on the one hand, and the regional/local
dimension on the other. These features allow investigating and understanding
territorial dynamics in a wider continental dimension while considering local
and regional driving forces.
This chapter illustrates how European cities are evolving in the period
2010–2050, according to the reference configuration of the LUISA platform.
The second section provides a sketch of the burgeoning academic field of urban
land use models, while the third summarises the main technical structural char-
acteristics of LUISA. The fourth section presents the key trends governing land
use evolution in Europe for the future decades and how these influence urban
developments by looking into a few key indicators. A review of conclusions
and future improvements concludes the chapter.
 60 C. Lavalle et al.
The role of land use modelling for urban applications:
review, opportunities and limitations
Cities are complex structures characterised by specific dynamic elements that
can hardly be captured with simple linear representations. Complex modelling
can often be an efficient way to understand the mechanisms of urban dynamics,
to evaluate current urban systems and to provide support in urban management
(Schaldach and Priess, 2008). Since urban land use dynamics are the direct con-
sequence of the action of individuals, public and private corporations acting
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simultaneously in time over the urban space, advanced land use models may
help to build future growth scenarios and to assess possible impacts (Lambin
and Geist, 2006).
Several reviews, e.g. by Berglund1 (2014), INSIGHT (2014), Simmonds
et al. (2013), Silva and Wu (2012), Haase and Schwarz (2009) and Schaldach
and Priess (2008), present exhaustive and critical appraisals of approaches and
techniques concerning directly or indirectly land use modelling for urban
applications. The variety and population of such models are continually grow-
ing, hence any compilation will be necessarily incomplete. These constant
developments guarantee that almost all aspects related to modelling have been
or will soon be tackled by researchers and/or practitioners, hence greatly open
the perspectives and potential for urban applications.
Following the classification suggested by Silva and Wu (2012), models can be
described according to the following key characteristics:

•• modelling approaches: mathematical or statistical, geographical, cellular

automata, agent-based, rule-based and integrated
•• spatial scales: regional scale, metropolitan scale, local scale and multi-scale
models
•• temporal resolution/span: long-term, medium-term and short-term models
•• spatial emphasis: spatially or not-spatially explicit
•• thematic application: land use planning, urban growth, transportation, envi-
ronmental protection, impact assessment, scenario-based modelling, etc.

While there is an overall increasing acceptance of model results for the

management and planning of urban areas, the applicability and usefulness of
a model depends very much on the nature of the questions to be answered
(Triantakonstantis and Mountrakis, 2012). Typically, urban land use models
are adopted to investigate behaviours that have strictly local characteristics,
but also when related to global issues, such as climate change, since the air,
soil and waste emissions that occur in cities are quantified as having a direct
impact on local drivers. Furthermore, because urbanisation might go along
with potential environmental consequences, urban growth modelling appears
to have a key role in urban planning to assist in decisions related to sustain-
able urban development. Very seldom urban models are employed to assess
 Modelling and projecting urban land cover 61
the impact of urban growth beyond the strict delineation of the urban areas
of concern, such as spill-over effects or gravitational attractiveness between
urban agglomerations.
A further key issue when modelling urban systems concerns the availability
(in terms of both quality and quantity) of data to be fed as input into models
and also to be used for calibration and validation purposes. Although this is a
common and overall concern for all modelling domains, from global change
to micro-economic and behavioural applications, it is particularly pronounced
when, as in the urban field, there is the need to cross-correlate data from many
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sectors (e.g. housing, transport, environment, etc.), in many different formats

(statistics, maps, surveys, time-series, etc.) and often with a varying range of
accuracy and precision.
The methodological approach at the basis of the LUISA Territorial
Modelling Platform hereinafter described, aims to tackle some of the above
mentioned issues, in particular for what concerns the capability to resolve local
features while still providing a holistic vision of continental patterns of urban
development.

Description of the LUISA Territorial Modelling Platform

Overview
The LUISA platform has been specifically designed to assess territorial impacts
of European policies (EC, 2002, 2013) by providing a vision of possible futures
and quantitative comparisons between policy options. The platform accom-
modates multi-policy scenarios, so that several interacting and complementary
dimensions of the EU are represented. At the core of LUISA is a computa-
tionally dynamic spatial model that allocates activities and services based on
biophysical and socio-economic drivers. This model receives direct input from
several external models covering demography, economy, agriculture, forestry
and hydrology, which define the main macro assumptions that drive the model.
LUISA is also compliant with given energy and climate scenarios, which are
modelled further upstream and link directly to economy, forestry or hydrology
models. The model was initially based on other land-use models, namely the
Land Use Scanner and CLUE models (Hilferink and Rietveld, 1999; Dekkers
and Koomen, 2007; Verburg and Overmars, 2009), but in its current form
LUISA is the result of a continuous development effort by the JRC (Lavalle
et al., 2011a). The model projects future land/use cover changes, accessibility
maps and gridded population distribution at the relatively fine spatial resolution
of 1 hectare (100 × 100 metres) (Batista et al., 2013b; Batista et al., 2013c) for
the time period 2010–2050, with the most relevant groups of land use/cover
types being represented. LUISA is usually run for all EU countries, but can be
used for more detailed case studies or, on the contrary, be expanded to cover
pan-European territory.
 62 C. Lavalle et al.
In contrast to many other land-use models LUISA incorporates additional
information on ‘land functions’. Those land functions are a new concept for
cross-sector integration and for the representation of complex system dynam-
ics. They are instrumental to better understand land use/cover change processes
and to better inform on the impacts of policy options. LUISA simulates future
land use changes, and land functions related to the resulting land use patterns
are then inferred and described by means of spatially explicit indicators. A land
function can, for example, be physical (e.g. related to hydrology or topogra-
phy), ecological (e.g. related to landscape or phenology), social (e.g. related to
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housing or recreation), economic (e.g. related to employment or production

or to an infrastructural asset) or political (e.g. consequence of policy decisions).
Commonly, one portion of land is perceived to exercise many functions. Land
functions are temporally dynamic, depend on the characteristics of land parcels,
and are constrained and driven by natural, socio-economic and technological
processes. Since it is centred on this novel concept, LUISA is far beyond a
single, stand-alone model. It can be best described as a platform with a land use
model at its core, linked to other upstream and downstream models. LUISA
was designed to yield, ultimately, a comprehensive, consistent and harmonised
analysis of the impacts of environmental, socio-economic and policy changes
in Europe.
As with many modelling tools, LUISA is not a forecasting model. The most
meaningful and useful way to use it is by simulating two or more comparable
scenarios. Typically, a ‘baseline’ scenario captures the policies already in place,
assuming the most likely socio-economic trends and ‘business-as-usual’ dynam-
ics (i.e. as observed in the recent past). Such a baseline serves as a benchmark
to compare other scenarios in which future conditions or policies are assumed
to change. This approach to impact assessment provides relevant elements to
structure discussion and debate in a decision-making process. Two elements
are crucial when performing an assessment with the LUISA integrated model-
ling framework: (1) the definition of a coherent multi-sector baseline scenario
to be used as a benchmark for the evaluation of alternative options, (2) a con-
sistent and comprehensive database covering socio-economic, environmental
and infrastructural themes.
The baseline scenario provides the basis for comparing policy options and
should ideally include the full scope of relevant policies at the European level.
A comprehensive baseline integrated in a modelling platform such as LUISA
serves to capture the aggregated impact of the drivers and policies that it covers.
Sensitivity analysis can be helpful to identify linkages, feedbacks, mutual ben-
efits and trade-offs between policies. The definition of the baseline should be
the result of agreements between the main stakeholders and experts involved.
Ideally, the baseline’s assumptions should be shared and used by different mod-
els in integrated impact assessment. Since 2013, LUISA has been configured
and updated to be in line with the EC’s ‘Reference Scenario’ (Lavalle et al.,
2013; Baranzelli et al., 2014), which has been used as a baseline in subsequent
impact assessments. Various aspects of the model, such as sector forecasts and
land suitability definitions, are updated whenever pertinent.
 Modelling and projecting urban land cover 63
The second element refers to the wealth of data that are needed to cope
with the European-wide coverage and multi-thematic nature of a territorial
impact assessment. The principal input datasets required by LUISA must com-
ply with the following set of characteristics:

•• EU-wide (ideally pan-European) coverage

•• geographically referenced to bring information together and infer relation-
ships from diverse sources
•• consistency of data nomenclature, quality and resolution to allow cross-
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country/region comparison.

LUISA is structured into three main modules: a ‘demand module’, a ‘land

use allocation module’ and an ‘indicator module’. The main, final output of
the allocation module is a land use map. Potential accessibility and population
distribution maps are also endogenously computed by the model as a result
of the simulation, and are themselves important factors for the final projected
land use map. From these outputs, and in conjunction with other modelling
tools that have been coupled with LUISA, a number of relevant indicators can
be computed in the indicator module. The indicators capture policy-relevant
information from the model’s outputs for specific land use functions, such as
water retention or accessibility. When computed for various scenarios, differ-
ences in the indicators can be geographically identified, sensitive regions can
be pinpointed and impacts can be related to certain driving factors assumed in
the definition of the scenarios. In the next sections LUISA’s demand and land
allocation modules are elaborated upon.

The demand module

The demand module captures top-down or macro drivers of land use change
that limit the regional quantities of the modelled land use types. The demands
for different land use categories are modelled by specialised upstream models.
For example, regional land demands for agricultural commodities are taken
from the CAPRI model (Britz and Witzke, 2008), which simulates the conse-
quences of the Common Agricultural Policy (Lavalle et al., 2011b); demographic
projections from Eurostat (Eurostat, 2010) are used to derive future demands
for additional residential areas in each region; and land demands for industrial
and commercial areas are driven primarily by the growth of different economic
sectors (Batista e Silva et al., 2014). It is clear that LUISA is linked to sev-
eral thematic models, and thus it also inherits the scenario configurations and
assumptions of those models. Special care is therefore taken when integrating
the input data from multiple source models to ensure that inputs are mutually
consistent in terms of scenario assumptions.
In the case of urban, industrial and commercial areas the link between
macro driving forces and land demands are modelled within LUISA’s demand
module. Urban land use demands are obtained from combining demand for
residences and tourist accommodations. The demand for residential urban areas
 64 C. Lavalle et al.
is a function of the number of households and a land use intensity parameter
that indicates the number of households per hectare of residential urban land.
The number of households is a function of the regional population and of an
average household size that is assumed to converge across European regions.
The land use intensity parameter can either be extrapolated from observed
past trends in a business-as-usual approach, or can be modified to depend on
specific urban policies. The demand for touristic land use is a function of the
number of beds in a region and another land use intensity parameter that indi-
cates the number of beds in tourist accommodations per hectare of touristic
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urban land. The number of beds is a function of the projected number of

tourist arrivals, which are in turn obtained from the United Nations World
Tourism Organization. Finally, demand for industrial, commercial and services
land use (ICS) is a function of economic growth in those three sectors of activ-
ity, and, again, a land use intensity parameter that in this case indicates gross
value added per hectare of ICS land (Batista e Silva et al., 2014). Here the land
use intensity parameter responds to GDP per capita because it has been found
that economic land use intensity depends foremost on that factor.

The land allocation module

The land use allocation module is based on the principle that competing
land use classes vie for most suitable locations, given available land and the
demand for various land use classes. Given that assumption, the actual alloca-
tion of land uses to space is governed by a land use optimisation approach, in
which discrete land use transitions per grid cell occur in each discrete time-
step. The suitability of locations for various land use types is based on both
rules and statistically inferred transition probabilities that are derived from
the following factors: terrain factors such as slope, orientation and elevation;
socio-economic factors such as potential accessibility, accessibility to towns
and distance to roads; and neighbourhood interactions between land use. The
association between these factors and each land use type is obtained from past
land use observations by means of statistical regressions. In addition to exoge-
nous suitability factors, spatial planning, regulatory constraints (e.g. protected
areas) and exogenous incentives influencing specific land use conversions can
also be taken into account in the model. Furthermore, two matrices govern
the occurrence of land use transitions. A ‘transition cost matrix’ informs the
model on the likelihood of pair-wise transitions. This transition cost matrix
is obtained from observed land use transitions recorded in the CLC time-
series (1990–2006); for example indicating that in general a land use transition
from agriculture to urban is more likely than from forest to urban. An ‘allow
matrix’ informs the model on which transitions are permitted, and can also be
specified to define the number of years required for a transition to take place.
Both matrices can be used either as calibration or scenario parameters, and
contribute, in addition to the above mentioned factors, to the overall suitability
of grid cells for each land use type.
 Modelling and projecting urban land cover 65
Recent developments are shifting LUISA from traditional, land-cover
based modelling approaches (LC) to activity-based modelling. The fore-
most developments entail the endogenous computation of accessibility levels
and population distributions for each grid cell as part of the land use model-
ling exercise; this is explained exhaustively in Batista e Silva et al. (2013a).
Essentially these developments add that for each year, potential accessibility
levels are computed given a road network and population distribution (Jacobs-
Crisioni et al., 2014); while the population allocation module in the model
allocates people (newcomers and internal migrants) across each region based on
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a range of factors. With regard to population distributions the model assumes

that people are, amongst others, driven by high accessibility, vicinity to other
people (a proxy for economies of scale) and preferences to build housing on
certain land use. A proxy for housing supply at each location limits the amount
of people that can be accommodated without further development. Finally,
whether one grid cell will be urban no longer depends on the discrete land use
allocation process, but instead is obtained from population distributions given
straightforward threshold rules.

Urban evolutions for a baseline scenario in Europe

The baseline configuration

LUISA has been configured to project a baseline scenario of land use changes
up to 2050, assuming likely socio-economic trends, business as usual urbani-
sation processes and the effect of established European policies with direct
and/or indirect land-use impacts. This baseline configuration is defined as the
‘LUISA EU Reference Scenario 2014’ and is described in detail in Baranzelli
et al. (2014). Variations to that reference scenario may be used to estimate
impacts of specific policies, or of alternative macro-assumptions.
LUISA includes a set of procedures that capture top-down or macro drivers
of land-use change (taken from a set of upstream models) and transform them
into actual regional quantities of the modelled land-use types. Regional land
demands for agricultural commodities are taken from the CAPRI, which sim-
ulates market dynamics using nonlinear regional programming techniques to
forecast the consequences of the Common Agricultural Policy. Demographic
projections from Eurostat and tourism projections from the United Nations
World Tourism Organization (UNWTO) are used to derive future demand
for urban areas in each region; land demand for industrial and commercial areas
are driven primarily by the economic growth as projected by the Directorate-
General for Economic and Financial Affairs of the European Commission (DG
ECFIN); and the demand for forest is determined by extrapolating observed
trends of afforestation and deforestation rates reported under the scheme of
the United Nations Framework Convention on Climate Change (UNFCCC).
The demand for the different land use types is ultimately expressed in terms of
acreage and defined yearly and regionally (NUTS 2).
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Figure 4.1 Changes in resident population in the periods 2010–2030 (left) and 2010–2050 (right)
 Modelling and projecting urban land cover 67
In the LUISA Reference Scenario 2014, the economic and demographic
assumptions are consistent with the 2012 Ageing Report (EC, 2012). The
demographic projections, hereinafter referred as EUROPOP2010, were pro-
duced by Eurostat, whereas the long-term economic outlook was undertaken
by DG ECFIN and the Economic Policy Committee. The actual economic
figures used in LUISA were taken from the GEM-E3 model, which modelled
the sector composition of future economy (GVA per sector) consistently with
the DG ECFIN’s projections (EC, 2014). Both projections are mutually con-
sistent in terms of scenario assumptions.
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To compute the travel times that inform accessibility, a road network from
the Trans-Tools transport model is used.

Overall trends to 2030: main regional indicators

Demographic trends are amongst the main drivers of land use/cover changes,
in particular for urban areas. According to the EUROPOP2010 projections,
clear patterns of changes in the net population will appear in Europe in the next
decades, as shown in Figure 4.1 for the periods 2010–2030 and 2010–2050. A
decrease of resident population is predicted to occur in wide central and east-
ern areas of the European Union. Also, spots of increases are evident in some
metropolitan areas, although it is worth remarking that absolute changes as
those reported in Figure 4.1 are necessarily higher in densely populated areas.
The analysis of the wide picture of the evolutions of the European territory in
response to such demographic patterns, and coherent economic projections, will
hereinafter focus on the period 2010–2030, with a set of indicators related to urban
development and accessibility at the regional level. The analysis covers variability
over space and time. For this purpose, the indicators are presented at NUTS 2
level and display values for the year 2010 as well as absolute or relative changes
between 2010 and 2030. A detailed list of the indicators is presented in Table 4.1.

Table 4.1 List of indicators used to assess urban development and accessibility

according to the LUISA EU Reference Scenario 2014
THEME INDICATOR UNIT

POPULATION Population density in 2010 Person per m2

Relative changes between 2010 and 2030 %
URBAN Built-up area per inhabitant in 2010 m2 per person
DEVELOPMENT Absolute changes between 2010 and 2030 m2 per person
Urban sprawl in 2010 UPU/m2
Absolute changes between 2010 and 2030 UPU/m2
ACCESSIBILITY Network efficiency Dimensionless
Relative changes between 2010 and 2030 %
Potential accessibility Dimensionless
Relative changes between 2010 and 2030 %
 68 C. Lavalle et al.
Population density
The indicator of population density is calculated as the total number of
inhabitants divided by the land area in m2 and is used as an ancillary indica-
tor intended to compare the regions based on similar figures. The higher the
density, the higher the concentration of population living in a specific region.
The number of people is derived from EUROPOP2010 at NUTS 2 level
(Eurostat, 2010). The land area corresponds to the total area of the region
at NUTS 2 level (EuroBoundaryMap v81 – see Eurogeographics website,
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www.eurogeographics.org/).
Figure 4.2 shows the population density in 2010 and Figure 4.3 the rela-
tive changes between 2010 and 2030. According to the population projections
used, Europe will diverge in terms of population density, with clear winners
and losers. The change in population density also shows a high degree of
autocorrelation, with large concentrations of regions with either increasing or
decreasing trends.
Regions with decreasing trends in population are mostly concentrated
in Central and Eastern Europe, particularly in Romania, Bulgaria, Croatia,

Figure 4.2 Population density, 2010

 Modelling and projecting urban land cover 69
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Figure 4.3 Population density: absolute changes in percentage between 2010 and 2030

the Baltic countries and Germany. In Western Europe, only the northwest
of the Iberian Peninsula is projected to show a decrease in population over
the next couple of decades.
The projected population decline in most of Germany, for instance, is pri-
marily due to negative natural growth, with immigration levels insufficient
to balance population decline. In Romania and Bulgaria, on the other hand,
emigration contributes to further overall population decline. However, inter-
national migration flow projections are highly uncertain due to their high
volatility over time and space.
For what concerns most of the other parts of Europe, overall population
growth is expected to be positive. In addition, regions with capital cities tend
to stand out in terms of population growth, even in Eastern Europe. If such a
scenario holds, the resulting substantial changes in regional population might
generate non-negligible impacts on economy, landscape and urban dynamics.

Built-up area per inhabitant

One indicator, built-up area per inhabitant, measures land consumption by
expressing the relation between population and the size of built-up areas as the
 70 C. Lavalle et al.
square meters of land per person. The built-up area per inhabitant is a useful tool
to monitor the growth of the built-up areas and assess changes in the efficiency
of land use in Europe in the period 2010–2030 according to the EU Reference
Scenario 2014. The total area and changes in ‘built-up areas’ (i.e. land take) is a
key indicator that reflects human intervention in the environment. The lower the
consumption per capita of land the more efficient the use of the built-up areas
In Europe, cities use land most efficiently and population densities tend
to decline the further away from city centres. This general trend can be
explained by the price of land and its use, which varies with distance from
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the city centre (EC, 2014).

In the EU-28, the available built-up area per person in 2010 was on average
391 m2 (Figure 4.4). In 2030, the model forecasts the amount of land con-
sumed per person will increase by 6 per cent between 2010 and 2030. This
implies that on average the EU population in 2030 will consume more land
than in 2010.
According to the modelling results the amount of land consumed per person
in 2010 is lower in the regions located in the southern part of Europe (with the
exception of Cyprus). The regions with the highest land use intensity correspond

Figure 4.4 Built-up area per inhabitant, 2010

 Modelling and projecting urban land cover 71
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Figure 4.5 Changes in built-up area per inhabitant, 2010–2030

to the city capitals where the land use intensity is among the highest in Europe
(Figure 4.4). This pattern changes the further north one goes in Europe, with
an increase in the amount of land consumed per person (Kasanko et al., 2006).
Concerning the changes between 2010 and 2030, the majority of regions
show an increase in the amount of land consumed per inhabitant, meaning
that land use efficiency is declining over time. In this sense, the use of land
will be less efficient in 2030. Countries that follow this trend are, for example,
the Scandinavian countries and the eastern part of Europe (shown in red and
orange hues in Figure 4.4).
There are also a few regions in the EU-28 that are expected to use land
more efficiently over time. Countries that follow this trend are foreseen to
decrease the land consumed per person as compared to the baseline year (red
and orange hues). This is the case, for instance, in Ireland and some regions in
the United Kingdom (blue hues in Figure 4.5).

Urban sprawl
Weighted Urban Proliferation (WUP) is an index to quantify urban sprawl,
proposed by Jaeger and Schwick (2014) and implemented in LUISA (Barranco
et al., 2014). It is based on the following definition of urban sprawl:
 72 C. Lavalle et al.
the more area built over in a given landscape (amount of built-up area) and
the more dispersed this built-up area in the landscape (spatial configuration),
and the higher the uptake of built-up area per inhabitant or job (lower utili-
sation intensity in the built-up area), the higher the degree of urban sprawl.

The WUP is calculated as a combination of three different elements taking

into consideration (1) the degree of urban penetration (incorporating the dis-
tance between built-up cells), (2) the building density of built-up area and
(3) the population present in this built-up area. The urban sprawl is expressed
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in Urban Permeation Unit (UPU) per square metre (UPU/m2). The higher
the UPU, the higher the urban sprawl.
In 2010, the average WUP, aggregated at NUTS 2 level for the EU-28,
was 1.10 UPU/m2. Much higher values were reached in capital cities such as
London, Paris, Brussels and Budapest (Figure 4.6). The average WUP was pro-
jected to increase to 1.22 UPU/m2 in 2020 and 1.36 UPU/m2 in 2030. This
increasing and accelerating trend indicates a general increase in urban sprawl
across Europe but most significantly around Brussels, Prague, Vienna, London
and Bucharest. This can most likely be attributed to migrations of population
settling at the periphery of urban centres (Figure 4.7). In contrast, less sprawl-
ing regions can be seen all over Europe, particularly in Spain, Italy, Greece,
Ireland, Scotland and in the Scandinavian countries. Some of these regions

Figure 4.6 Urban sprawl, 2010

 Modelling and projecting urban land cover 73
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Figure 4.7 Changes in urban sprawl, 2010–2030

also registered a significant increase in urban sprawl between 2010 and 2030,
in particular in the southeast part of Spain and Ireland, most likely due to the
population growth during this period (Figure 4.7).

Accessibility
Two indicators are shown here that measure the effects of transport network
improvements on accessibility: relative network efficiency (Figures 4.8 and 4.9)
and potential accessibility (Figures 4.10 and 4.11). These can be loosely linked
to specific policy objectives: network efficiency measures the effectiveness of
transport networks (López et al., 2008); and potential accessibility measures
economic opportunity (López et al., 2008; Stepniak and Rosik, 2013). Both
indicators are implemented in LUISA (Jacobs-Crisioni et al., 2016) and are
based on the shortest travel times between two municipalities and population
distribution at the destination. The road network data used to obtain travel
times describes the current (2006) and the expected future (2030) network;
the latter takes into account the expected network improvements enabled by
EU policy funding.
The analysis of accessibility maps yields common findings: for both
indicators, north-western Europe has the best spatial linkages, the best
 74 C. Lavalle et al.
network efficiency and a clearly dominant place in terms of economic
opportunity. The modelled changes in accessibility levels are caused by
two processes: on the one hand, changes in municipal populations mod-
elled by LUISA; on the other hand, changes in travel times induced by
transport network investments, which are taken into account in LUISA
(see Batista e Silva et al., 2013a; Jacobs-Crisioni et al., 2016). In particular
new member states are assumed to receive such network improvements.
One may expect that network investments increase the accessibility pro-
visions in currently underprovided regions. Unfortunately, as the results
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presented here partially show, in some cases the effects of network invest-
ments are offset by the fact that population numbers in the target regions
are declining, often also with migration to more central regions that ben-
efit from even higher accessibility levels through population growth. The
results of these processes can, for example, be seen in lower network effi-
ciency in the west of France, in the UK and in Helsinki in Finland, and
poorer potential accessibility in a number of regions in the eastern part of
Europe and Greece.

Figure 4.8 Network efficiency, 2010

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Figure 4.9 Changes in network efficiency, 2010–2030

Figure 4.10 Potential accessibility, 2010

 76 C. Lavalle et al.
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Figure 4.11 Changes in potential accessibility, 2010–2030

Evolutions of European Functional Urban Areas

For the purpose of analysing the evolution of individual ‘urban areas’, we
herein adopt the definition of a city and its commuting zone as given by the
OECD and the European Commission (Barranco et al., 2014).
This definition identifies 828 (greater) cities with an urban centre of at least
50,000 inhabitants in the EU, Switzerland, Croatia, Iceland and Norway and
allows for comparability of cross-country analysis of cities, otherwise not pos-
sible with other definitions.
Half of the European cities included in the definition are relatively small
with a centre between 50,000 and 100,000 inhabitants. Only two are global
cities (London and Paris). These cities host about 40 per cent of the EU
population. Each city is part of its own commuting zone or a polycen-
tric commuting zone covering multiple cities. These commuting zones
are significant, especially for larger cities. The cities and commuting zones
together (called ‘Functional Urban Area’, FUA) account for 60 per cent of
the EU population.
Figure 4.12 shows the annual average population growth in the Functional
Urban Areas for the period 1961–2011. With the exception of several
 Modelling and projecting urban land cover 77
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Figure 4.12 Population growth in Functional Urban Areas, 1961–2011

cities in eastern Germany and few in northern UK, all areas present positive
annual increase.
In future projections, the European Functional Urban Areas present a
rather diverse picture. According to the LUISA Reference scenario, the over-
all increase of built-up areas in the EU for years 2030 and 2050 is 8 per cent
and 13 per cent respectively when compared to the level of 2010, in spite of a
population growth of respectively 4.1 per cent and 4.4 per cent. Built-up area
per inhabitant sees increases of 3 per cent in 2030 and 8 per cent in 2050.
The average share (percentage vs. total surface) of built-up (i.e. artificial)
surface of all FUAs per country represent a measure of the level of urbanisation
around the cities, Since it does not include processes of artificial development
in rural areas. Figure 4.13 presents the value for the years 2010, 2030, 2050
for the 28 member states of the European Union. Bulgaria, Croatia, Germany,
Hungary and Greece have an increase of less than 0.5 per cent while Italy,
Lithuania, Slovakia, Cyprus, UK, Luxembourg, Malta, Romania and Belgium
have increases higher than 1 per cent, with Belgium scoring for both periods
more than 2.5 per cent. With few exceptions, the projections confirm that
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35.00%

30.00%

25.00%

20.00%

15.00%

10.00%

5.00%

0.00%
AT BE BG CY CZ DE DK EE EL ES FI FR HR HU IE IT LT LU LV MT NL PL PT RO SE SI SK UK

Year 2010 Year 2020 Year 2030

Figure 4.13 Average share of built-up surface in FUAs per country

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Figure 4.14 Annual average land taken per inhabitant in the periods 2010–2030 (left) and 2030–2050 (right)
 80 C. Lavalle et al.
countries with a higher share of artificial areas will continue to consume more
land, and to increase such behaviour.
The annual land take per inhabitant provides a measure of the rate of
growth of artificial surfaces in each Functional Urban Area. Figure 4.14 gives
the rate for the periods 2010–2030 and 2030–2050. The two time spans pre-
sent fairly different behaviours – despite rather similar trends of demographic
changes, due to the capability of LUISA to include densification phenomena
(e.g. urban compactness) provoked by the various parameters used in the
simulation (accessibility, suitability, attraction/repulsion rules etc.) which
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dynamically vary along the time.

Urban areas with highest rate of growth are spread out in Europe without
a clear spatial pattern, including cities such as Le Mans, Martigues, Chartres,
Nimes and Rennes in France, Namur, Leuven and Charleroi in Belgium,
Tampere, Oulu and Helsinki in Finland. Large cities in Germany, namely
Berlin, Stuttgart, Dresden and Frankfurt, are amongst the ones with almost null
annual rate of growth.
The overall picture of urban growth in Europe can be gathered by analys-
ing the development of built-up areas in each FUA, in direct relation to the
demographic changes (Figure 4.15). The analysis reveals that 41 per cent of
the FUAs are depopulating while the surface covered by built-up areas grows
positively. In particular, in 23 per cent of the FUAs, artificial surfaces evidence
an increase faster than population. The reverse behaviour is, however, mani-
fested in 36 per cent of FUAs, where population grows faster than built-up
areas, hinting that these cities tend to use land more efficiently, at least for what
concerns the optimisation of the space employed for housing, industry, com-
merce and infrastructure.

Figure 4.15 Population growth vs. built-up growth, 2010–2050 (Lopes Barbosa, 2016)
 Modelling and projecting urban land cover 81
Conclusions
This chapter has illustrated an example of the application of advanced land use
modelling for the analysis of urban development in Europe. Urban develop-
ment and accessibility are important contributors to overall social and territorial
cohesion. Projecting future land use according to the EU Reference Scenario
2014 gives an indication of how these two dimensions can be foreseen to
evolve in the future. The modelling results show that in general, land use
intensities are foreseen to decline in the EU-28 (Figure 4.16). This implies
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an average increase of 6 per cent of the amount of land consumed per person
between 2010 and 2030. The impact on urban sprawl is much higher. On
average we foresee a relative increase in urban sprawl of 23 per cent (from 1.1
UPU/m2 in 2020 to 1.36 UPU/m2 in 2030). This trend is particularly strong
in the main capitals of the European Union.
As concluded in the study by Batista e Silva et al. (2013a) some of these
effects can, however, be offset if adequate urbanisation policies are put in place.
As such, economic growth and cohesion funds can, but do not necessarily have
to be detrimental to the environment as long as appropriate spatial planning
policies and recommendations are considered at different territorial scales, and
more efficient land use and investment in green infrastructure is encouraged.
The coming years will see much work to improve LUISA as a comprehensive
tool for evaluating the effects of various policies on land use and associated indi-
cators. The end goal of LUISA’s development should be a modelling framework
that closely approximates true economic land conversions, explicitly modelling
all costs and benefits that are internalised in the land use change process, while
broadly taking into account both the internal and external costs and benefits of

Trend of population and urban areas in EU-28

140
37
135
36
130
35
Index (1990 = 100)

Persons/hectare

125
34
120
33
115
32
110
31
105
30
100
29
95
1990 2000 2010 2020 2030 2040 2050

Urban areas Population LUI

Figure 4.16 Land use intensity in EU-28, 1990–2050

 82 C. Lavalle et al.
land use changes when evaluating model results. This end goal includes a better
grasp of the various economic activities that drive anthropogenic land use. Lastly,
a number of efforts need to be undertaken in order to better underpin the validity
of the model approach, variable selection and model reliability. In the following
paragraphs we discern short-term plans, for which necessary data is available, and
long-term plans, which will require data sources that are currently unavailable.
One of the most important planned improvements concerning the applica-
tion of LUISA in urban areas is the integration of air quality indicators. To do so,
assumptions on activity levels have to be extended further from the population
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allocation model already in place. By integrating air quality levels in the model,
the modelling platform gains a useful indicator necessary to understand the full
range of external costs of land use change and also opens up possibilities to evaluate
air quality improvement policies that aim at promoting behavioural changes and
structural measures. Another important improvement involves redesigning the
link between regional urban land use claims, the population allocation module
and the discrete allocation method. Other works that will be undertaken on the
short term aim to (1) underpin the conversion cost matrices currently used in
the model with either empirically obtained probabilities or costs derived from
an economic rationale, which serves to link more closely the model to real pro-
cesses and (2) include water scarcity levels as a suitability factor for particular land
uses, in order to better assess direct and indirect effects of water policies.
The frequent use of the LUISA framework in policy consultation presses the
need to validate the model’s output in terms of accuracy and reliability. In 2013
the JRC began a cross-validation exercise with other national and international
institutes that also employs a land use model. It is expected that this validation
exercise will yield useful insights into the importance of various model settings
and factors that differ between the various models. Furthermore, data to do
an empirical validation of the model using historical trends is finally becoming
available, in the form of a historical time series of municipal population counts
and historical time series land use data (EC, 2014; Barranco et al., 2014). These
historical data will be instrumental in empirical validation projects that are
planned in the short to medium term.
Lastly, one of the most substantial improvements planned in the long term is
to fully integrate an economic rationale into the land use model – based on true
utilities, true costs and true willingness-to-pay data. This would better underpin
the rationale of the model, and would allow inductive approaches in the model
to evaluate the effect of policies on land use behaviour (i.e. not starting from an
assumed overall effect, but from a clearly defined added cost or financial incentive
in the utilities of particular land use conversions). In this improvement, currently
unavailable data on the financial aspects of land use conversions will be critical.

Note
1 www.nordregio.se/Global/Events/Events%202014/Attraktiva%20och%20h%C3
%A5llbara%20stadsregioner/Report%20Review%20of%20Land-Use%20Models
%202014-01-10.pdf, accessed 7 June 2015.
 Modelling and projecting urban land cover 83
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 5 Drivers of urban expansion
Stefan Fina
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Introduction
This chapter deals with the most important drivers of urban expansion
and urban sprawl according to a list of influencing factors compiled by the
European Environment Agency in 2010. The structure helps to differenti-
ate between different sectors and spatial scales that allow for a more detailed
explanation of the complex aspects that drive urban expansion and urban
sprawl. Examples mainly from Europe are used to give an overview over the
different strands of research that are dedicated to the analysis of urbanization:
economics, architecture, energy, ecology, as well as spatial planning and envi-
ronmental disciplines. The emphasis is on drivers of urbanization that lead to
urban expansion, not just in a sprawling sense, but in any way that impacts on
the natural properties of the land. This usually leads to an increase in the two-
dimensional coverage of urban and transport land, and to an increase of soil
sealing and therefore loss of farmland and agricultural productive capacity. In
terms of spatial coverage, the chapter will offer an overview over global and
specific European drivers, and present case examples on countries, regions and
local authorities mainly from Europe.

Understanding the drivers of urban expansion

Urban expansion does not have the same immediate environmental effects
that pollution has. Land consumption does neither smell nor make any noise
(apart from construction activities that go along with it). It therefore does not
trigger the same public levels of attention as environmental effects from emis-
sions and other polluting activities. Public protests about urban expansion or
land consumption activists are not very common in any type of environmental
movement. The environmental stresses that it causes are more indirect but a
time bomb nevertheless: land consumption adds to a multitude of environ-
mental problems that are all too often irreversible. It fixes development paths
to urban structures that create more land demand, for example through auto-
mobile dependencies or a segregation of land uses. And once these structures
are in place, they are incredibly difficult to retrofit or change to something that
 86 S. Fina
could be called sustainable (Chin, 2002; Dielemann and Wegener, 2004). The
worst manifestations of urban expansion in terms of these impacts are referred
to as urban sprawl. Although there is no lexicographic definition for the term,
most researchers agree that urban sprawl refers to a state of urban land use con-
figurations that is very inefficient to service in terms of infrastructure, and at
the same time it refers to the process leading to such configurations. The mani-
festations are often a combination of low-density and uniform land-expansive
residential blocks detached from other land uses like industry and business,
leading to long travel distances and high levels of automobile dependency
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(Ewing et al., 2002; Wolman et al., 2005). In the context of this chapter, urban
expansion and urban sprawl are not being used as synonyms: urban expansion
can also be a positive amalgamation of urban areas that leads to benefits to the
resource efficiency of the urban compound. But in almost all instances urban
expansion leads to a conversion of land that was previously used or preserved
for natural or agricultural land uses.
Figure 5.1 shows a conceptual framework that illustrates how the pro-
cesses of land use change are understood in the research community. The
framework is closely related to the pressure–state–response monitoring frame-
works established by the European Community in the late 1990s (European
Environment Agency, 1999) and identifies the system interdependencies
between the process of land use change in general with its drivers and effects
and regulatory framework. Within this framework, assessment and aware-
ness aspects are crucial, because any type of regulation needs to be based on
policy strategies and possibly targets, which are ideally derived from a soci-
etal consensus and a clear political mandate. The controversies, however,

Figure 5.1 Conceptual framework for an analysis of land use change (source: Fina
et al., 2014a)
 Drivers of urban expansion 87
start with the inherent conflicts in objectives between economically and
socially desirable growth initiatives that lead to land use change, and envi-
ronmental impacts and regulatory initiatives that aim to contain that land
use change. Environmental impacts are often detached from the actual land
use change in terms of timing and sometimes also in its spatial manifesta-
tion, for example in terms of changing floodplain patterns downstream from
urbanization on a local scale, or climatic effects like heat islands when new
developments block fresh air corridors.
It is therefore timely and prudent to adopt a more comprehensive monitor-
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ing and tackle the interrelationships and flow-on effects that can be attributed
to land consumption. There is a large consensus amongst the research com-
munity that one needs to act on the driving forces of land consumption first,
and strengthen regulatory systems in parallel. All of this in a way that caps land
consumption and urban sprawl in an effective and accountable way without
neglecting social and economic needs (Anthony, 2004; Bengston et al., 2004;
Dielemann and Wegener, 2004; Frenkel, 2004; Song and Knaap, 2004).
Christiansen and Loftsgarden (2011) have further investigated these drivers
for Europe in general and Norway in particular. One of their main findings is
that the multitude of drivers is very difficult to analyse if one wants to identify
the most dominant one(s) in a certain geographic context. And they enhance
the driving forces for urban expansion (they also speak of urban sprawl in their
report) by a policy and regulatory framework group, which seems to be not a
driver but a response at first glance. But looking at the actual market mecha-
nisms in Europe, the revenue streams for territorial authorities from land sales
and development are such that there is indeed an economic interest in urban
expansion without other factors driving it. This is also the conclusion from a
large study undertaken by Siedentop et al. (2009), where they looked at the
land designations in urban regions exposed to demographic decline and found
that one of the policy strategies against these trends was to offer more land to
attract people willing to settle. The authors in this study utilize regression mod-
elling to find the importance of driving factors on a municipality level. The
results show that one needs to differentiate between demand- (population and
employment, economy, transport etc.) and supply-side (land availability and
pricing, infrastructure and accessibility etc.) driving factors and include devel-
opment and spatial aspects in any type of comprehensive assessment. A policy
climate and awareness about land consumption issues is also seen as influential:
regions with a history of development pressures and associated land conflicts
are likely to have developed planning processes that lead to a more efficient
use of land resources.
Other strands of literature, however, describe the effect of planning and
compact city policies on urban growth as minor. Angel et al. (2011a), for exam-
ple, argue that global urban expansion will lead to massive growth in urban land
cover anyway, with regional variations that are due either to population growth
or to a decrease of urban density. Where in developing countries in Africa
and Asia growth rates will be between 2 and 6 per cent until the year 2050,
 88 S. Fina
countries in Europe (and also Japan) will experience between 1 and 3 per cent
growth in the scenarios supporting the analysis. One of the key assumptions for
the higher scenarios is that urban densities will decrease by an average of 2 per
cent, due to reasons of demographic decline and a higher demand for urban
area per capita (including more living space per person and thus decreasing
household sizes, and more recreational and infrastructure land per person). One
of their key interpretations is that despite policy efforts to save land resources
this growth will occur within the predicted ranges. The quality of the urban
compound, however, is highly dependent on city planning with foresight and
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intelligent smart growth principles.

Other authors reflect upon the drivers of urban expansion with a view
towards development that will inevitably lead to a consumption of land, but
the question here is what types of land are being used (Angel et al., 2011a;
Hasse and Lathrop, 2003). In many ways the easiest and most frequently prac-
tised urbanization is at the cost of agricultural resources, to a very limited
degree at the cost of unprotected forests, and very rarely at the cost of water
dominated land uses (for example in the polder areas in the Netherlands).
In this context, case studies undertaken in German and Italian cities have
shown that over 95 per cent of urbanization takes place on land previously
used for agriculture (Fina et al., 2014a). The conclusion here is that growth-
oriented driving factors of urban expansion are not going to contain it in
terms of quantity but rather manage it in terms of impact on urban struc-
ture and availability of land. According to the OECD we are living in the
metropolitan century, and societies strive for urban amenities that have
made cities successful – in all parts of the world (Organisation for Economic
Cooperation and Development, 2015). These amenities require space and
land resources in any case, but the land configuration they result in can differ
significantly. Figure 5.2 shows different development paths in this respect,
using CORINE land cover data for selected metropolitan regions in Europe,
taking a 25 kilometre radius around the city centre for scale. They not only
show that the amount of urban land differs extremely, they also show that the
population base is not necessarily the reason for the difference. Some cities
manage to contain urban expansion within spatial designations (Amsterdam,
Munich, Manchester), designed to provide advantages in terms of service
and transport structures. The opposite of that compactness we refer to as
urban sprawl, where city structures are dispersed and disconnected across the
region, theoretically resulting in problematic configurations to service a city
efficiently with infrastructure.
It is fair to assume that these different manifestations of urban structure
are the result of numerous factors. The topographic restrictions of seaside
metropolitan regions like Porto or Amsterdam are an obvious one, but other
effects are also known to be the result of planning strategies or compact city
policies that allow for a more economic decision making with regard to land
resources (Organisation for Economic Cooperation and Development, 2012).
In this sense, researchers have often shown that population and economic
 Drivers of urban expansion 89
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Figure 5.2 Urban land use configurations in European metropolitan regions (source:

Siedentop and Fina, 2012b, p. 2766)

pressures do not necessarily have to be catered for with the same amount of
urban land, and that compact growth policies can be effective in terms of a
more resource-efficient city development. At the same time, the debate in
the scientific community and amongst decision-makers only recently started
to acknowledge the value of agricultural land as public goods in this respect.
Humankind across the globe has initially settled in the most fertile regions
for very obvious reasons, and if we now need to expand the urban foot-
prints around these initial settlements for more or less good reasons, it is
very likely that these fertile soils are being consumed and irreversibly being
lost for cultivation. Agricultural resources are all too easily being seen as
replaceable by goods from further away or from a global market, but they
are actually not. It has only recently been put to the forefront of the politi-
cal agenda that agricultural land actually plays a much more diverse role in
the land use mix of city regions: it acts as a buffer for flood events and other
forms of climate change stresses that are likely to play a role in the future
(e.g. heat stress), it compensates for a multitude of infrastructure projects in
environmental impact assessments, and it offers a range of other public goods
that are increasingly being valued as cultural assets to a region (farmland and
animal welfare, recreational and vegetation landscapes etc.; see, for example,
Cooper et al., 2009).
In summary it can be concluded from this brief literature analysis that there is
a range of studies that allows for an identification of the main drivers for urban
 90 S. Fina
expansion, but their actual influence is all too often a result of their combina-
tions. On one hand, an assessment of their importance needs to look into the
geographical settings on multiple scales and systematically analyse global, regional
and local trends for different sectors. On the other hand, there is an increasing
need to specifically identify drivers from the impact side, i.e. which drivers are
the crucial ones that lead to land consumption of agricultural resources, and
which ones can be managed in a more efficient way to protect agricultural
resources from urban expansion? These questions guide the structure of the
subsequent sections and pick up on Figure 5.3, where the main drivers of urban
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expansion are illustrated in a matrix design. It was developed for the State of the
Environment report by the European Union in 2010 and depicts the drivers from
left to right on different scales of observation (global, regional, local). From top
to bottom it lists the drivers that are seen as most influential for urban expansion
in sectors. Drivers in bold print are seen as root causes for urban expansion, oth-
ers can possibly contribute under certain circumstances. The report emphasizes
that these drivers can be mutually reinforcing in some cases, in some they would
level each other out in terms of the actual amount of land consumption they
cause (see European Environment Agency, 2010a, pp. 22f.).

Figure 5.3 Drivers of urban sprawl (source: European Environment Agency,

2010a, p. 23)
Note: Drivers have been organised in two dimensions: type (vertical) and spatial scale (horizontal).
Demand/supply has not been differentiated. In bold: factors that drive urban sprawl; the remaining
factors may become drivers under certain conditions.
 Drivers of urban expansion 91
Society
For societal drivers, the European Union names population growth and declin-
ing household size as global root causes of urban expansion, and ageing and
lifestyle as globally influencing variables. This assessment is very much in line
with research conducted by Angel et al. (2011b) that sees almost no natu-
ral growth in Europe and Japan. In their models, however, urban areas will
still grow between 1 and 5 per cent until 2050 (based on urban land cover
in 2000), because density decline drives urban expansion in societies with a
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shrinking population base. This interpretation can also be substantiated by the

European Union targets that aim to limit the net land take for urbanization
in the European Countries to zero. The base for setting this objective was
actually a net land take in the 2000s that was four times as high as population
growth, leading to an unprecedented density decline in the urban compound
(European Environment Agency, 2015).
The underlying dynamics of these density declines and their conse-
quences on urban expansion have been subject to a range of research efforts
in the past that explain to some degree the local root causes of urban expan-
sion due to societal changes. A clear indication that demographic change
and ageing play a role have been found in studies in Germany that looked
at the housing stock build between the 1950s and 1970s. In this period
the private family home became a realistic material asset to large parts of
the society and led to an unprecedented suburbanization in all parts of the
country. Energy prices were low and family sizes potentially high, so that
family homes were built in a rather generous fashion. Today the architec-
tural focus is on energy efficiency, healthy living and quality of life, aspects
that the post-war building stock can only deliver with considerable mod-
ernization efforts. The remaining building stock is therefore today a burden
to city planners especially in shrinking regions, with lack of investments,
underutilized capacities and empty houses in private hands that are just
too difficult to rejuvenate and throw whole neighbourhoods into a down-
ward spiral of decline (Fina et al., 2009; Fina et al., 2012). Especially in the
post-socialist countries there is massive density decline, due to population
migration into the liberalized labour markets of western European coun-
tries and a new wave of suburbanization with people leaving undesirable
prefabricated building blocks in the inner cities. These societal transfor-
mations and their effects on urban expansion have been documented for
example by Schmidt et al. (2014) and Schmidt (2011).
These trends of excess capacities are often being labelled as a form of rema-
nence, meaning that family homes only have a certain lifespan in which they
cater for the intended number of people, and after a period of underutiliza-
tion when the children move out (‘empty-nesters’) the attractiveness of these
homes for the new generation depends on individual decisions like locational
preferences, employment opportunities, but also on housing preferences and
building characteristics. With the latter, lifestyle preferences are such that large
family homes are generally not the most sought after properties anymore. Over
the last few decades Western societies have seen an erosion of the traditional
 92 S. Fina
family model and therefore a substantial loss of potential interested parties in
the existing stock of family homes. And it often proves to be more attractive
and sometimes even more economic for the new generation to meet their
requirements in new developments and new buildings, thus consuming new
land (see for example Häußermann et al., 2008).
However, these mechanisms where lifestyle and locational preferences lead
to new developments are only possible if regulatory regimes and market con-
ditions provide the settings. In that sense there are strong interdependencies
between the social and economic drivers when societal demand meets eco-
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nomic supply. The regulatory regime has a decisive role to play in this respect,
because inner city decline and its land use consequences can be managed on
the local level with foresight and innovative ideas to some degree. There are
numerous examples by now where even regions with an eroding population
and economic base have successfully managed to retain inner city vibrancy,
improve the environmental conditions and provide healthy environments, and
attract investors to rejuvenate the building stock with successful regional devel-
opment strategies. Very often these strategies rely on certain unique values, for
example for recreation, the health industry or tourism, or on key businesses
that have their traditional roots within the city. Despite these examples, the
mainstream development strategy in shrinking regions at this point is market
competition. In this respect, local communities are trying to attract families and
businesses to build in new designation areas with cheap land, and neglect the
long-term effects and costs for the whole community that they should be well
aware of (see for example Siedentop et al., 2009).
The effect that socioeconomic transformations lead to new urbaniza-
tion is also visible in the many demographically stable regions in western
Europe. In these cities, immigration from overseas and rural migration from
the shrinking hinterlands stabilize the net loss of population that would
result from natural birth rates, and offset any form of decline to the future
(Siedentop and Fina, 2012a; Fina et al., 2014b). The cumulative effect of
demographic change is still projected to lead to a form of decline within
the next decades, but for the time being there is an actual need for more
housing and infrastructure. This is often true for medium-sized cities in the
surrounds of the dynamic metropolitan regions, where employment oppor-
tunities are within commuting distance and a relatively stable population
base has an urgent need for more housing opportunities for a more and
more diverse social strata. Amongst these social classes, high-income earners
continue to drive land demand for single family homes. Investors pick up on
this demand for more affordable housing with block and terraced housing
projects in suburban locations, with the supporting infrastructure in terms of
social infrastructure (schools, kindergartens, hospitals) and shopping oppor-
tunities, leading to additional land demand.
Within the booming regions, inner cities are often exposed to gentrification
processes where global players find lucrative investments in attractive mar-
kets and (re-)develop brownfield land resources or invest in the modernization
 Drivers of urban expansion 93
and expansion of the existing building stock with a view towards profit. As
a consequence certain parts of the population are driven out of the resulting
overheated real estate markets, and expensive developments in the inner cities
and new waves of affluent in-migrants drive the demand for affordable housing
or a displacement of other inner city functions that have to relocate to sub-
urban settings, thus driving additional land demand. Examples are university
locations and research clusters, but also office parks and logistics enterprises, all
of which require expansive land resources but also need to be accessible from
the main city (Lüthi et al., 2012). These expressions of land demand are closely
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linked to the next section.

Economy
On the global and European level, the economic drivers of urban expansion
need further explanation since it may not be self-explaining why globaliza-
tion, cheap energy or the European integration would fuel land expansion
at first glance. The most evident of these root causes seems to be economic
growth, with all economic activities leading to a form of land demand per
se. However, this relationship does not work as a very strong predictor on
a national scale. Figure 5.4 shows the growth in Gross Domestic Product
(GDP) for the countries of the European Union on the x-axis, and the
growth of urban land cover on the y-axis. The symbols differentiate two
different time periods, from 1990 to 2000 (triangles) and from 2000 to 2006
(circles). The tendency shows that economic growth and urban growth lie in
the same quadrant, but in a bivariate comparison the correlation is certainly
not significant. Other variables will play a role and explain the difference
possibly further, but with some of the outliers visible here (Ireland, Spain,
Portugal) we now know that urban expansion in the years of the observa-
tion period have created a financially troublesome real estate crisis from 2008
onwards. One conclusion here could be that economic forces do not drive
urban growth on a national scale, at least not in the larger and heterogeneous
countries. In this context, Vogel et al. (2010) describe the role of global city
regions and their economic performance as the increasingly more important
drivers of economic growth than the national scale.
Taking Germany again as an example, urban growth patterns and economic
performance differ widely. A limited number of metropolitan regions (Munich,
Stuttgart, Frankfurt, Hamburg, to some degree also Berlin) drive the economy
with specific internationally visible strengths and an economically resilient
backbone of industry, production and service sectors. A second tier of larger,
well-connected cities is increasingly benefitting from these globally oriented
economic metropolitan regions, absorbing some of the development pressures.
In contrast, the remote rural areas are undergoing significant transformations
in terms of their economic setup. The PLUREL project conducted by the
European Union between 2007 and 2010 shows that across Europe most rural
areas are struggling to compete economically on a global market. Maintaining
 94 S. Fina
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Figure 5.4 Economic growth and urban expansion for EU countries (source:

Siedentop and Fina, 2012b, p. 2780)

a form of competitiveness in times of globalization often means to strengthen

the functional linkages between the rural hinterland and the market it services
(Nilsson, 2011). And as such, a hierarchical settlement structure requires rural
hubs to develop economic strategies fit to react to the requirements of a global
market. This could be agricultural processing facilities in modern business parks,
renewable energy power plants, or commercial centres servicing a more and
more automobile-oriented client base. All of this results in a demand for land
resources that becomes manifest in sprawling development patterns especially
in medium-sized towns of lower central place hierarchies (= rural townships)
and a form of competition for land that has worldwide effects: certain land use
functions are nowadays being outsourced to land markets with cheaper agri-
cultural resources, processing facilities or a more competitive labour market.
Global trade flows allow for a timely and cost-effective delivery of even heavy
products from many economic sectors, so that it is increasingly the locational
advantages and global investment patterns that decide about the actual land
use, rather than the local actors. In this context, Figure 5.5 shows the most
striking effect of this globalization, often labelled with the negative connota-
tion of ‘land grabbing’ by investors from developed countries that exploit land
resources in developing countries for their production needs.
In this sense, any assessment that strives to monitor development paths
towards a more sustainable land use for a certain monitoring area needs to have
these downstream effects in mind and try to comprehensively include the land
use that the consuming party is responsible for. This aspect has received wider
attention with the concept of ‘virtual water’ that was established by Allan and
Mallat (1995) in the mid 1990s and drew attention to the fact that imported
 Drivers of urban expansion 95
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Figure 5.5 Transnational land acquisitions, 2005–2009 (source: www.eea.europa.eu/

soer-2015/global/ecosystems, last accessed 2 June 2015, based on research
by Rulli et al., 2013)

products (in his studies from the Middle East) carried an amount of water
resources with them that could possibly be missing in production chains for
other products in the countries of origin. The example of a cup of coffee con-
sumed in developed countries bearing a production requirement of 11 litres
of water at the production site has become a famous metaphor for this effect.
Regardless of the debate about the accuracy of these numbers, the general out-
come is certainly also valid for land use and land resources.
Specific structural funds dedicated to the development of rural areas in
Europe may have had ambivalent effects in this respect as well: national gov-
ernments and the European integration initiatives like the Territorial Cohesion
Programme or the Common Agricultural Policy (CAP) aimed at balancing
out living standards across regions, with a view towards better economic and
social participation in disadvantaged areas. Evaluation of funding has shown
that investments for infrastructure projects and business opportunities in some
of these areas may have triggered disproportionate urban growth and sprawl
without a long-term sustaining need for it (see for example Schmidt et al.,
2014). Another effect was that the predominant development patterns boosted
automobile-oriented development and led to a form of car dependence. This
was further exacerbated by the absence of any efficient type of public trans-
port and the mutually reinforcing reliance of increasing car availability and
 96 S. Fina
car-oriented settlement structures. There is convincing evidence that, on
average, the growth rates in the peri-urban environment and in rural town-
ships has and will continue to have the highest growth rates when it comes
to urbanization. Key explanations of this trend are the decentralization of
certain types of industry, businesses and administrative functions into these
areas and the increased reach of commutersheds (see also ‘Transport’, below).
Cairncross (1997) has already pictured the ‘death of distance’ as a force of
socioeconomic transformation, enabling businesses to operate from wher-
ever they want without the need to be physically present in pricey locations.
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Now this has certainly not eventuated in general, for some industries prox-
imity advantages within global world regions may actually have become
more important in the time that has since passed. What can be taken from
Cairncross’ thoughts, however, is that some Silicon Valley-type new technol-
ogy start-ups or business innovations could be successful from anywhere and
connect via the internet to the rest of the world, with flow-on effects in terms
of revenue streams and economic downstream effects that would trigger addi-
tional development, transplant new lifestyle forms into rural areas and possibly
eventuate new land consumption. The predicament for such developments,
next to high-speed internet connectivity, is certainly also relatively cheap
energy, where the operation of businesses is possible without major invest-
ments into supporting infrastructure and proximity to energy facilities. Energy
production and consumption patterns will also play a key role in future land
use strategies and potentially transform the economic base in agricultural areas
further. This is especially true in countries with high subsidies for renewable
energies like Germany, where biomass production for energy has become
economically so attractive that farming communities change their land use
patterns in favour of such income options. This may potentially lead to a new
form of land consumption that does not exhibit the same problem dimensions
that we know from urbanization. There is no permanent soil sealing or loss
of agricultural productivity involved as such. Nevertheless, energy production
competes with food production for land resources, and the debate about food
security versus sustainable forms of energy supply is only in the making.
On the regional and local levels, the European Environment Agency depicts
rising living standards as a root driver of urban sprawl. This aspect is certainly
linked with the social trends of decreasing household sizes, because this is
most likely not only a question of preference and family structures, but also of
affordability. In the most overheated real estate markets people would still find
themselves living together in communal forms regardless of family structures
due to economic reasons, sharing flats and houses. Internet platforms provide
new possibilities in that respect, connecting people with no other relation than
the search for affordable housing in a certain location. With decreasing eco-
nomic limitations, however, large layouts of living space become a desirable
lifestyle, sometimes also a second home or in some cases even multiple liv-
ing locations for business or recreational reasons. At the same time, businesses
will locate around the most profitable client and customer base, providing
 Drivers of urban expansion 97
land-consuming services that are easily accessible and offer a comfortable envi-
ronment to use their services. This becomes manifest in automobile oriented
mall developments at the outskirts of cities or along major roads, with not only
large floor-space requirements but also land needed for parking. It is also evi-
dent in shopping centres that often house very similar businesses that compete
with each other, a form of redundancy that further exacerbates land needs
without an actual basic reason for it. The issue of second homes, for example,
has also been a particular driver of urban growth along the Mediterranean
coast in Spain and in Portugal in the late 1990s and in the early 2000s, also in
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Denmark and other European countries where affluent city dwellers invested
in houses and flats, either privately or using investors’ developments that put
large numbers of second homes on the market (see for example Couch et al.,
2007; Garcia, 2010; Christiansen and Loftsgarden, 2011).
The price of land is certainly a key driver in the satisfaction of demand and
supply of new urban areas, especially in comparison to the price of infill
development and investments into expansions (either vertically or through
densification), or modernizations of the existing building stock. In this respect,
some researchers observed over the last few decades that individual preferences
about location and amenities favour inner city locations over new developments
at the outskirts, and that many countries are entering a phase of reurbanization
(see for example Siedentop, 2008). Further analysis of these initial preferences
shows that the reurbanization movement can actually lead to gentrification
processes that are closely linked to real estate prices in the inner cities and
the options to buy land within the commutershed of the urban agglomera-
tion. The attractiveness of such options very much depends on age and family
structures and is not haphazard. It is rather a cumulative cohort effect that still
drives suburbanization, possibly to a lesser extent than in the previous decades,
but with a greater reach into the urban hinterland due to improved acces-
sibility by high-speed public transport connections or other mobility options
(Haag, 2002). The European Environment Agency finds in a technical report
(European Environment Agency, 2010b) that tax, tax relief and urban pressure
are the most influential aspects for land prices; to a lesser degree or in regional
variations subsidies play a role, as well as inflation, commodity prices, land pro-
ductivity and amenities. The interesting point here is that these drivers of land
prices actually drive land use change. This is especially true for urban pressures
where land previously used for agriculture can jump up scales of value if desig-
nated as development land in the urbanization process (e.g. in district plans or
local development plans), and lends itself to all forms of speculation.
An interesting aspect in this observation is the role of the local authorities
and the competition between municipalities. Their interest to put land on the market
for residential or business developments is only partly to stabilize the demo-
graphic future and provide employment opportunities. Authors like Gutsche
et al. (2007) have convincingly shown that in tax regimes where the local
authorities benefit from property sales, business taxes lead to fiscal interests that
drive urban expansions disproportionately. In some cases new developments
 98 S. Fina
are being marketed in a way that seems to be financially attractive in compari-
son to investments in the inner city, but only because maintenance costs are
neglected and local infrastructure costs are financed by the commune. If devel-
opment contributions and long-term maintenance would be properly included
in project costs, the cost–benefit ratios would look different (see also Siedentop
et al., 2009). A range of tools and initiatives have recently been disseminated by
the German government to provide more accurate calculation routines to the
local communities and establish stricter planning controls instruments for the
designation of new urban land.
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Lastly, the real estate market certainly has an economic interest to drive
urbanization and put homes on the market. In local communities real estate
agents and stakeholders in the property market can easily get involved in land
use decisions, be it through lobbyism or active participation and a political
mandate in the community. The same is true for the banking sector that has
an economic interest in people and businesses taking up mortgages and loans
to finance housing developments. Soule (2006) and his contributing authors
describe these processes for the United States in their remarkable book Urban
Sprawl: A Comprehensive Reference Guide as an actual form of subsidy, where
mortgage deductions, communal development costs and low local property
taxes are financed by state institutions to make the American Dream of housing
property come true for large parts of the population. There are similar incen-
tives for commercial developments, albeit with a more complex interaction of
landowners, developers and businesses that rent the land or the facilities. In all
cases, however, laissez-faire policies can be expected to result in land speculation
under free market conditions. Self-regulation in terms of land-saving develop-
ments are not on the top of the agenda of the real estate industry, although the
more serious players in that field would certainly strive for resource-efficient
and environmentally sustainable neighbourhood developments, either in reac-
tion to high levels of community awareness or as a best-practice selling point
generated by market demand.

Governance
It seems to be contradictory that the government sector is listed as a sepa-
rate driver of urban expansion or urban growth when part of its duties is to
care for a resource-efficient development of land use structures. The European
Environment Agency starts their explanations with EU policies in this respect,
not as a root driver, but as a factor that could be driving urban expansion under
certain conditions. The key policies named here that are of influence are the
transport and cohesion policies, where more than EUR 80 billion has been
dedicated to structural funds, mainly for road projects. The improved accessibil-
ity in these areas made them certainly more attractive to investors for additional
development, causing the following dilemma for land use planners: economic
incentives in the form of infrastructure development are an effective instrument
to trigger market investments, but the effects in terms of land conversion are
 Drivers of urban expansion 99
undesirable from an environmental and sometimes also social point of view. In
this respect, Figure 5.6 gives an assessment of the impact of transport policy on
economic growth, researched within the ESPON framework of the European
Union. It clearly shows the effects in the new member states in eastern Europe,
but also in structurally weaker regions in the Mediterranean states and in remote
areas of Scandinavia. It is fair to expect that these achievements have resulted in
flow-on effects on land demand, be it for business or industrial enterprise along
these routes or new residential developments or city expansions, although no
detailed studies exist on this correlation.
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International regulations do not seem to play that much of a role since there
are no binding international laws that would influence the governance of land
use-relevant policies directly. However, international agreements like the Kyoto
Protocol on the reduction of CO2 emissions are likely to be translated into
national and regional legislation and regulations further downstream and can then
have an effect on land use. An example is mitigation, where certain strategies aim
to prevent the production of carbon emissions, for example by replacing fossil
energy production chains by renewable options. This potentially transforms the

Figure 5.6 Transport policy and economic growth (source: ESPON Atlas, http://
atlas.espon.eu/, last accessed 2 June 2015)
 100 S. Fina
agricultural landscape significantly and possibly leads to changing land use pat-
terns that affect the availability of agricultural land negatively. Another example
is adaptation to climate change, not necessarily triggered by international regula-
tions, but certainly put on the forefront of the agenda by an increasing awareness
about climate change risks through the work of international organizations like
the Intergovernmental Panel on Climate Change (IPCC). One result of these
adaptation strategies could, for example, be that floodplains are being adjusted to
climate change scenarios, preventing and offsetting urbanization trends to other
areas. Another effect could be that city planners place more and more value on
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fresh air corridors and bio-connectivity within the urban compound to preserve
future options for air ventilation, water infiltration and biodiversity. Planning
instruments like green belts or green wedges in regional planning serve, amongst
others, such functions, but are also known to trigger increased development
beyond the boundaries of the green belts. The term ‘induced development’
is often used in this respect, because planning instruments that contain urban
development may not necessarily prevent the eventuation of new urban areas
altogether. If the development pressure is very high and infill options are lim-
ited, development may be displaced to disconnected urban sprawl-like locations
beyond the green belt that are highly undesirable from a compact city point of
view and negatively impact upon the agricultural assets (see for example Bae and
Jun, 2003; Kühn, 2003; Bengston and Youn, 2006).
In terms of weak land use planning as a driver of urban expansion one can only
reiterate that free markets in combination with laissez-faire policies cannot be
expected to deliver sustainable land use decisions. Within the PLUREL pro-
ject mentioned above, Tosics et al. (2010) have come up with a classification
of European countries in terms of their land use regulations, based on general
country profiles and more in-depth case studies (see Table 5.1).

Table 5.1 Classification of the public sector in relation to the level of control of urban
development
Control mechanism Most important supra- Local level 2 Countries
from supra-local levels local level (from land
of the planning system use change perspective1

C) strongly Large (>1) any

controlled spatial Medium (0.5–1) any Portugal
policies Small (<0.5) any Cyprus, Greece,
Lithuania
B) medium level of Large (>1) Large (>30) Denmark, Netherlands,
control United Kingdom
Medium (10–30) Belgium, France,
Germany
Small (<10) Italy, Spain
Medium (0.5–1) Large (>30) Ireland
Medium (10–30)
Small (<10) Austria
 Drivers of urban expansion 101
Small (<0.5) Large (>30) Sweden
Medium (10–30) Finland
Small (<10) Estonia, Latvia,
Luxembourg, Malta
A) Weak level of any Large (>30) Bulgaria
control Medium (10–30) Poland, Slovenia
Small (<10) Czech Republic,
Hungary, Romania,
Slovakia
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Source: Tosics et al. (2010, p. 60).

Notes
1 Numbers refer to millions of inhabitants.
2 Numbers refer to thousands of inhabitants.

After grouping countries into families of spatial planning systems, the

authors have looked into the different tiers of government, included hybrid
approaches, and combined their results into the ranking of land use controls
shown in Table 5.2, with stronger land use controls having higher values.
This classification itself does not necessarily correlate with the highest rates
of urban growth since the different countries are exposed to completely dif-
ferent growth pressures and starting points for urbanization, especially on a
regional level. Comparative studies have shown that national policies are rarely
decisive in land use controls, something that Tosics et al. (2010) pick up on as
well. They only work in combination with effective planning control systems
on the regional and local levels and a planning environment with a strong man-
date for compact growth strategies. In this respect, the European Environment
Agency in 2006 has already presented research by authors who argue that the
lack of planning coordination between the planning tiers is a key driver of
urban sprawl (European Environment Agency, 2006). And even if planning
controls are in place, there might still be sufficient leverage for market forces
to override legislative barriers. One such example is the urban encroachment

Table 5.2 Strength of land use controls in European countries

Value1 Countries

7
6 Denmark, Netherlands, Portugal, United Kingdom
5 Belgium, Cyprus, France, Germany, Greece, Ireland, Lithuania
4 Italy, Spain, Sweeden
3 Austria, Bulgaria, Finland
2 Estonia, Latvia, Luxembourg, Malta, Poland, Slovenia
1 Czech Republic, Hungary, Romania, Slovakia
Source: Tosics et al. (2010, p. 61).
Note
1 Higher values correspond to stronger control level.
 102 S. Fina
of single development into green wedges in Germany, where the planning
authorities had to allow some developments under special circumstances for
local communities in order to be able to establish green wedges as a planning
instrument in the first place. Over the years, however, the cumulative effect of
all special circumstances can lead to a devaluation of the green wedge in terms
of its original function. Because it is not fully natural anymore it may have lost
its effectiveness as a divider between settlement structures and its ecological
function as a fresh air corridor. Once this devaluation leads to lifting its status,
the door is open for further development if no other restrictions are being
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put in place instead. In actual planning practices – these processes are quite
commonplace – regulations can often not be dictated top-down to the local
authorities without allowing room for development at all. Scholars in the field
of urban sprawl often cite the term ‘tyranny of small decisions’ in this respect,
which was originally coined by the economist Alfred Kahn and describes the
market failures to control single decisions with a view towards their cumulative
effects (Kahn, 1966). This mechanism is very true for land use decision making
between different tiers of government, regardless of coordination and enforce-
ment. The conflicts are inherent and each decision has a valid base for it to go
forward. But there is a time lag difference between its initial assessment in the
light of the existing land use structure, and the potential impact it may have if
all other existing or yet unknown proposals to change the current land use are
going to go ahead.
Another governance issue that has already been touched upon is public
subsidies for home ownership as a driver of urban growth. It is somehow corre-
lated to the economic and social aspects of rising living standards and housing
preferences and the security that private property can provide in terms of
the individual’s living arrangements. Some societies have a stronger history
in this respect; property levels differ widely across the world. Andrews and
Sanchez (2011) have investigated home ownership rates and their driving
forces for selected countries of the Organisation for Economic Cooperation
and Development (OECD) in 2011.
Table 5.3 shows home ownership rates for these countries, and Figure
5.7 gives an indication of which countries offer the most public subsidies for
their residents to get onto the property ladder. The latter can either be direct
subsidies or tax reliefs for home ownership investments that governments use
in family politics. In Germany, for example, families with children qualify
for direct subsidies for residential home developments, with a view towards
demographic stabilization. Other subsidies are available for investments in
energy modernization in existing or new housing developments for home-
owners. Overall, home ownership is changing not only due to government
incentives, but also due to modified conditions for financing. Today, the
loan-to-value ratios for initial down payments on housing are substantially
lower than a few decades ago, albeit with some regional variations. The
financial crisis of the late 2000s has not stopped this trend, quite the contrary.
Because interest rates remain low and loans are easily available in the finance
 Drivers of urban expansion 103
Table 5.3 Aggregate homeownership rates in selected OECD countries
Country 19901 20042

Australia 71.4 69.5

Austria 46.3 51.6
Belgium 67.7 71.7
Canada 61.3 68.9
Denmark 51.0 51.6
Finland 65.4 66.0
France 55.3 54.8
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Germany 36.3 41.0

Italy 64.2 67.9
Luxembourg 71.6 69.3
Netherlands 47.5 55.4
Spain 77.8 83.2
Switzerland 33.1 38.4
United Kingdom 67.5 70.7
United States 66.2 68.7
Source: Andrews and Sanchez (2011, p. 212).
Notes
1 1987 for Austria, 1990 for Spain, 1991 for Italy, 1992 for Denmark and Switzerland, 1994 for
Canada, France, Germany and Netherlands, 1995 for Australia, Belgium and Finland, 1997 for
Luxembourg and United States.
2 1999 for Netherlands, 2000 for Belgium and France, 2003 for Australia, 2007 for Germany
and United States.

sector, investments into real estate, including new housing developments,

are more attractive than capital investments. The OECD study finds that
home ownership rates are essentially driven by three groups of influencing

Figure 5.7 Tax relief on debt financing cost of homeownership, 2009 (see Andrews
and Sanchez, 2011, p. 216 for details on methodology and data sources)
 104 S. Fina
factors: demographic factors, socioeconomic aspects and governance support
in combination with mortgage markets. Their conclusion is that home own-
ership has effectively been boosted in some countries by public policy and
financial support, which verifies the assumption that this form of governance
is likely to drive urban growth as well.
The last aspect in the governance sector is the role that the poor enforce-
ment of existing plans plays on the local level. This is certainly the case in
many countries with difficulties in policy implementation, where build-
ing codes are either non-existent or not being enforced, or where control
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instruments are unfit to regulate the land market. Apart from these obvious
failures, this area also covers the effective management of a cadastral system
fit to support land management. It is not only the less developed countries
that struggle with this task. More developed countries are also oftentimes
overburdened with policy objectives that require detailed and robust infor-
mation on development trends. The lack of (standardized) information in
this respect can all too easily be used by interested parties to push for new
development even if it goes against existing law. In cases of doubt the
courts or other judicial institutions in charge of final land use decisions will
often be legally bound to allow developments to go ahead. An example
is the German land consumption target that was established in the early
2000s, taking cadastral information from the 1990s as the basis for the for-
mulation of a reduction target. It states that for the whole nation, average
consumption rates of 130 hectares per day in the 1990s shall be reduced to
30 hectares by the year 2020. This target was strongly supported by envi-
ronmental stakeholders and interest groups as well as spatial planners, but
often criticized by economic interest groups not only for its effects on the
economy and possibly on the social side in terms of affordable housing, but
also for its methodological problems. The base data included cadastral land
use classes from inner city recreational areas and agricultural activities that
were subsequently reclassified in the cadastral base. This led to a reduc-
tion of land consumption purely due to changes in the cadastre, and it was
impossible to monitor the actual effect of spatial planning. Today there are
many proponents of the cause that say one should have gone for a zero land
consumption rate, and that due to the flawed information we now actually
have a target that allows too much urbanization to still go ahead (see for
example Siedentop and Fina, 2010).

Transport
Many scholars agree that the transport sector is one of the key drivers of urban
expansion and urban sprawl in modern times. Especially in the post-war period
of the twentieth century, transport innovations and the advent of the private
motor car have triggered suburbanization to a degree that would have been
impossible to think of without modern mobility options.
 Drivers of urban expansion 105
Regional commutersheds are commonplace, with an expansion of metro-
politan areas that allows workers to drive urbanization in locations far from
their places of work and benefit from lower land prices. Some researchers
have critically reflected upon transport infrastructure improvements that led to
increased urban sprawl along these routes (Haag, 2002; Muniz and Galindo,
2005), others emphasize that efficient transport routes between city centres
are actually quite resource efficient if serviced by different modes of transport
effectively (Calthorpe and Fulton, 2001).
Apart from the accessibility gains that led to a shift in urbanization patterns,
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the European Environment Agency names the low cost of fuel as a key driver for
urban expansion. This driver has to be seen in relation to household income,
since fuel prices actually increase in absolute terms, but not in comparison to
the growth rates in household income or gross domestic product. Tanguay and
Gingras (2011) find that for Canadian metropolitan areas changes in fuel price
do play a role in urban expansion and seem to be a promising policy instru-
ment to reduce urban sprawl. However, household income in their study has a
positive effect on urban sprawl, which suggests that the effects of a taxation of
fuel prices could be offset by income gains. Glaeser and Kohlhase (2004) show
that net transportation costs have actually decreased significantly and allowed
household expenditure to be dedicated to private property and real estate
investments. The same is true for freight transport and equally land-effective
due to business sprawl across metropolitan regions. The authors’ conclusion is
that urban theories that have explained the cities of the past need to be updated.
Today’s cities do not need to be near natural resources anymore and efficient
automobility across city environments would render city centres redundant,
being remnants of a time when physical proximity was crucial. Nowadays
locational aspects of quality of life are more important, not so much proximity.
This argumentation has obviously some weaknesses in respect to a sustainable
integration of city structures in a resource-efficient compact growth model
and the authors do acknowledge that commuting times have threatened their
view of cities due to increasing congestion and environmental problems that
unlimited automobility causes as well. What remains uncontested, however,
is that the low cost of fuel is still driving urbanization in a way that can easily
become a risk to the society at large if prices increase drastically. Such sce-
narios of skyrocketing fuel prices have been quite widespread in the late 2000s,
where the image of peak oil triggered substantial research efforts to test what
would happen in post-fossil fuel times to urban structures and land use. A large
research project in the metropolitan region of Hamburg, Germany, found that
the consequences could be dramatic and advocates for spatial planning strate-
gies that prioritize transit-oriented development patterns and compact growth
initiatives along the lines of city development patterns suggested by authors
like Newman (Newman and Kenworthy, 2006; Gertz et al., 2015). Other fis-
cal instruments lead to additional reductions in transport costs. One example in
transport policy that has been widely discussed in Germany is the regular tax
 106 S. Fina
breaks for commuters, where the costs of commuting can be deducted from
incomes so that commuting is actually a subsidized activity. Urban growth
scientists see this as a fatal misincentive and even environmental government
institutions label these kinds of tax reliefs as environmentally harmful subsidies
(Umweltbundesamt, 2014).
On the national/regional and local scale, high levels of car ownership have a
reinforcing effect on urban growth. On the one hand, authors like Glaeser and
Kahn (2003) argue that car ownership together with low transportation costs
have triggered suburbanization and urban sprawl. On the other hand, authors
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like Bento et al. (2005) emphasize that urban structure and transport options
are crucial, with suburban locations causing a form of car preference over other
modes of transport and thus causing car reliance over time. Figure 5.8 shows
an interesting projection of the amount of vehicle miles travelled and car own-
ership across world regions. One can clearly see the worrisome amount of
increase in vehicle miles travelled for all regions and the correlation between
the two variables. If extended to land consumption scenarios that these driv-
ers would cause if these projections hold true, urban growth is not likely to
enter any form of sustainable development path in the near future. Studies in
Stuttgart, Germany, have shown that the availability of cars for the majority
of the population can have flow-on effects on the urban structure in the sense
that it becomes highly car-dependant, other mobility options are being phased
out due to low demand, and the infrastructure to support more cars and more
efficient car travel becomes a structural need (Siedentop et al., 2013).
The availability of roads is certainly another aspect that drives the develop-
ment of car-oriented urban structures. The land take for roads is a significant
component of urban land use change in itself, and leads indirectly to the sup-
port of urban sprawl as described above. Figure 5.9 shows the combined effect
of land take from road and rail infrastructure with rates between 1 and just over
4 per cent. On top of these direct land requirements, roads and rail lines cut
through habitats and are major environmental stressors for landscape fragmen-
tation and risks to other qualities of the natural environment.
Finally, poor public transport is an indirect driver of urban sprawl when it forces
people to shift their mobility habits to the private motor car and therefore adds
to automobile-oriented sprawling cityscapes. In this respect one needs to look
at the economics of public transport in terms of their catchments and efficien-
cies for different urban structures. Bertraud (2004) compares the urban layout
of the city of Atlanta, United States, with the city of Barcelona, Spain. Within
a 800-metre radius 60 per cent of the Barcelona population live within the
public transport catchments, in Atlanta it is only 4 per cent. This is certainly
reflected by the number of trips made by public transport in both cities, but
also in the quality of services that can be offered economically to the users.
However, public transport provision is not just a question of economics.
Some countries do invest in and subsidize public transport with a view towards
a more sustainable transport future, because the overall benefits are likely to
outweigh profit losses when only looking at passenger ticket revenues. Road
transport, after all, is also heavily subsidized with communal funds, be it for
 Downloaded by [University of California, San Diego] at 23:51 15 May 2017

Figure 5.8 Passenger-km per year per capita in 2000 and projected for 2050, and projected car ownership rates in 2050 (source: www.
eea.europa.eu/data-and-maps/figures/passenger-km-per-year-per-capita-in-2000-and-projected-for-2050-and-projected-
car-ownership-rates-in-2050/transport-outlook-map-graph.eps/image_original, last accessed 4 April 2014)
 108 S. Fina
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Figure 5.9 Land take by road and rail infrastructure for the 2001 European
Union member states (source: www.eea.europa.eu/data-and-maps/
figures/eu-land-take-by-roads-and-railways-as-percentage-of-country-
surface-1998, last accessed 2 June 2015)

initial construction or maintenance costs. The whole issue of travel demand

management and road pricing systems to primarily alter transport behaviour
towards a more public transport-oriented sustainable mobility future is beyond
the scope of this contribution. What can be said, however, is that the quality
of public transport is a crucial key with a view towards future urban develop-
ment. Authors like Calthorpe and Fulton (2001) and Newman and Kenworthy
(2006) advocate for integrated regional cities with centres and sub-centres con-
nected by high-speed rail, ultimately leading to polycentric city environments
that retain quality of life options in terms of open space and medium densities,
but also capable of being serviced efficiently by public transport.

Land
The last sector that drives urban expansion and urban sprawl according to the
European Environment Agency is the very general theme labelled ‘Land’. On the
 Drivers of urban expansion 109
country/regional and local level, the local geography and environment can become
a driver under certain conditions. This can be interpreted in a number of
ways – the authors do not give clear directions here. Obvious drivers from the
land and geography side are enclosed locations for example along the coast,
where urban expansion is usually limited to the inland direction, if no land rec-
lamation takes place. Examples are Porto and Barcelona, where the hinterland
has long been a barrier to urbanization leading to particularly compact structures.
The same can be said for cities surrounded by water or in mountainous regions,
where building on the slopes or in floodplains would either be inconvenient
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and risky, or very costly. The resulting patterns are often settlement structures
that are unusually compact, which can for example be seen in the valleys of the
Carpathian Mountains in Romania. Apart from these topographic restrictions,
there are also cultural differences that are linked to geography and history. What
has been researched in a study by Siedentop and Fina (2012b) and illustrated by
Hartog (2005) in his book Europe’s Ageing Cities is that there are some countries
in Europe that have a rather compact urban structure for historic reasons. The
hypothesis is that, for example, climatic factors in the Mediterranean countries
have favoured dense inner city building blocks that provide shade and cooling to
protect from heat and allow fresh air streams to penetrate the city. In the United
Kingdom and also partly in Scandinavia there is a tradition of countryside living
going back to Ebenezar Howard’s garden city and the health awareness at the
turn of the nineteenth century, ultimately favouring a type of rural sprawl and
high demands for inner city plots with gardening possibilities. In eastern Europe,
the socialist regimes have long favoured dense housing units with prefabricated
building blocks. And in Germany and France, traditional rural communities have
developed a range of settlement structures that were adapted to the local environ-
mental conditions and available building materials, to transport routes going back
to the Roman Empire, or to fortified structures with enclosed inner cities. Very
often the settlement cores were centred around particularly valuable resources, be
it a river valley for water supply, agriculturally attractive areas or a combination of
both. It is a historic paradox that the settlements with the most locational advan-
tages had the best chances to prosper and expand, thus using up and destroying
much of the resources that initially led to their success.
Apart from the loss of environmental resources, many of the historic and
geographic predicaments mentioned above have been overrun by the massive
suburbanization and modernization activities in the twentieth century, with
little consideration about the underlying cultural values and environmental
intelligence that are inherent to traditional settlement structures. However, the
initial structures persist to some degree, and many architects and city planners
rediscover and create new ways to integrate old and new settlement structures
in a sustainable way. At the same time, the path that urban structures have
taken differs in terms of urban sprawl, and some development paths are harder
to control or to contain due to path dependencies that sprawling landscapes
create for themselves. Amongst these are car dependency and a lack of public
transport infrastructure. Especially the latter is incredibly difficult to retrofit if
 110 S. Fina
it hasn’t been initially planned for and land resources haven’t been set aside.
The most successful public transport systems in Germany, for example, have
actually benefitted from the land availability after the bombings of the Second
World War, allowing planners to obtain land resources for reconstruction that
would have otherwise been difficult to alter in use (see for example Diefendorf,
1993). In that sense, geographically and historically embedded development
paths very often limit future options and create a form of path dependence.
Any substantial changes need massive interventions and require a level of pub-
lic and financial support that is very difficult to obtain a mandate for.
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Figure 5.10 shows examples of cities that can be taken as representatives for
such development paths in terms of urban sprawl. The categorization is based
on a study conducted by Kasanko et al. (2006), where 15 European metro-
politan regions have been assessed with different sprawl indicators and then
aggregated to one index value.

Figure 5.10 Distribution of Europe’s sprawling and compact cities (sources: European

Environment Agency, 2006; Kasanko et al., 2006)
 Drivers of urban expansion 111
The result shows that the most compact cities are in southern Europe
and to some degree in eastern and central Europe. The most sprawling cit-
ies are located in eastern and central Europe and also in northern and western
European cities. The geographic distribution should not be overestimated
in any type of interpretation, since this is only a limited number of cities.
However, the geographic and topographic as well as the cultural settings have
certainly a level of influence as drivers of urban sprawl that cannot be ignored.
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Conclusion
This chapter used a compilation of drivers of urban expansion and urban sprawl
published by the European Environment Agency to reflect upon sector- and
scale-specific influencing factors. The complexities of interactions between
drivers are such that any type of overarching assessment on a national level is
bound to fail – the different physical manifestations of urban expansion and
urban sprawl are more characteristic on a regional observation scale.
The different driving factors have been explained based on the literature
available on the respective topics, illustrated with examples mainly from
across Europe. One key message is that population growth which has been
driving urbanization to a large degree in the past is not the key motiva-
tion anymore. In today’s stagnant or even shrinking population numbers
the demand for new urban structures is nourished by social transformations
and economic growth, by the preference of spacious living environments
and lifestyle changes, by large-scale business developments and by the
increasing importance of accessibility in metropolitan regions. Governance
structures and policies have very mixed effects at best. Structural develop-
ment funds and economic aid are poorly coordinated with environmental
policies and contradict each other in terms of effective urban containment
strategies. The planning apparatus struggles with the provision of long-
term planning strategies, with the establishment of meaningful targets and
appropriate monitoring systems. Information is crucial, but difficult to use
in the political arena if there is disagreement on normative interpretations of
development paths. Local authorities have a local mandate to do everything
they can to preserve economic opportunities and stabilize the population
base, often using methods that consume more and more land and will ulti-
mately be bound to backfire economically if demographic change proceeds
at the predicted rates. And economic growth on a national scale is driven by
global competition, with a limited view towards sustainable land manage-
ment within the own country, but also increasingly in the countries where
the goods for the national market are produced. Land grabbing in other
countries or remote areas occurs at scales where we only have limited insight
until now, but the trend to outsource land-consumptive activities to areas
that are outside the monitoring responsibilities of an institution is certainly
not suitable to qualify as a move towards sustainable land management.
 112 S. Fina
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Figure 5.11 Trends and outlook: land use and soil functions (source: European
Environment Agency, 2015, p. 59)

Figure 5.11 and the Annex give an overview over Europe-wide (Figure 5.11)
and country-specific (Annex) trends and an outlook on land use and soil functions
that the European Environment Agency published in their summary State of the
Environment report in 2015 and in 2010, respectively, with worrisome assessments.
Most projections and forecasts presented in this chapter on the specific drivers are
very much in line with these forecasts: urban expansion, be it in the form of urban
sprawl or just as a form of land take that leads to surface sealing, reduction of farm-
land or natural land, is a problem area with no realistic improvement potentials
under current conditions and trends.
It is the task of researchers, scholars and planners alike to disseminate this
information more clearly into the decision-making arena. We can help to close
the argumentation gaps where confusing information allows profiteers of urban
expansion to dazzle the general public about the facts. But on top of that we also
need not only to argue for new and more effective controls that influence the
driving factors of urban expansion substantially, but to push them through and
implement where possible, even at the cost of new economic paths to which the
global community has to adapt: there is no other choice but to effectively pro-
tect land resources if sustainable land management is to be more than just talk.

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 Annex

Annex Table Land cover change in EEA member and collaborating countries: total
changes for 1990–2000 and 2000–2006, and examples of specific trends
for 2000–2006
Country Annual land Characteristic land cover changes, 2000–2006
cover change, %
of total area
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1990– 2000– Artificial areas Agricultural areas Forest and nature

2000 2006

Albania – 0.18 Very high rate Loss of Forest: gains from

of residential agricultural agriculture,
sprawl land losses to
urbanization
Austria 0.03 0.08 Expansion of Agricultural land Accelerated
sport, leisure uptake by decrease of
and recreation artificial areas alpine glacier
sites area
Belgium 0.17 0.10 Slow-down of Slow-down Internal forest
land uptake of change conversions,
dynamics, formation of
land uptake by water bodies
artificial areas
Bosnia and – 0.12 Diffuse Loss of pasture/ Semi-natural land
Herzegovina residential mosaics, transitions, fires
sprawl vineyards and
orchards
Bulgaria 0.11 0.09 Urban sprawl Overall Forest management
accelerates stabilization, has replaced
loss of pasture/ forest expansion
mosaics,
vineyards and
orchards
Croatia 0.19 0.17 Accelerated Uptake of Forest
artificial pasture by management,
sprawl driven arable and loss of open
by highway complex spaces,
construction cultivation re-growth of
land burnt areas
Cyprus – 0.49 Diffuse sprawl Consumption Transitional
of residential of agricultural woodland
areas, sport land formation over
and leisure burnt areas
facilities
Czech 0.81 0.33 Urban sprawl Slow-down, Stabilization
Republic accelerates, continued in natural
driven by conversion landscapes, some
construction from arable loss of natural
land to pasture grasslands
 Denmark 0.13 0.13 Diffuse Consumption of Forest creation,
residential arable land changes in
sprawl wetlands and
accelerated water bodies
Estonia 0.44 0.38 Doubled Slow-down Exchange between
sprawl of of changes, mineral
artificial areas: conversion extraction sites
mines and from pasture and forested land
construction to arable land
Finland – 0.35 Sprawl of Conversion from Forest
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housing and forest and management,

recreation wetlands to net loss of forest
arable land and wetlands
France 0.20 0.11 Continued Reduced Slowed changes
urban agricultural of natural
expansion transitions, loss areas, forest
of different management,
farmland types fires
Former – 0.14 Residential Transitions of Forest
Yoguslav sprawl, different land management,
Republic of development types, loss of new water
Macedonia of mineral vineyards and bodies, loss of
extraction orchards natural grasslands
Germany 0.24 0.10 Land uptake Decreased Forest and water
slows down change bodies created
dynamics, on open spaces
conversion and former
of pasture to mining areas
arable land
Greece – – – – –
Hungary 0.56 0.48 Expansion of Withdrawal of Transitional
construction farming, some woodland
and mineral conversion creation over
extraction of pasture to former farmland
arable land and grasslands
Iceland – 0.10 Land take Loss of pastures Decrease of
driven by to artificial permanent snow
construction land uptake and glaciers,
new transitional
woodlands
Ireland 0.79 0.38 Continued Rapidly reduced Transitional
expansion of agriculture woodland over
artificial areas dynamics, open natural and
on agricultural withdrawal of farmed areas
land farming
Italy 0.13 0.10 Growth of Loss of farmland, Reduced expansion
economic less farming on to farmland,
sites and withdrawal and transitions of
recycling of arable/pasture natural land
urban land transition cover

(continued)
 Annex Table (continued)
Country Annual land Characteristic land cover changes, 2000–2006
cover change, %
of total area

1990– 2000– Artificial areas Agricultural areas Forest and nature

2000 2006
Kosovo under – 0.16 Dominance of Loss of farmland Forest transitions,
UNSCR residential and conversion re-vegetation of
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1244/99 sprawl from pasture burnt areas

to arable/crop
land
Latvia 0.78 0.38 Faster artificial Slowed Recent forest
sprawl in agricultural transitions, loss
surroundings transitions, of pastures/
of capital city accelerated loss mosaics to
of farmland transitional
woodland
Liechtenstein * – – Steady increase Continued Observed impacts
of artificial decrease of of natural
areas agricultural land disturbances
Lithuania 0.48 0.25 Faster sprawl, Rapid slowdown Natural land
driven by of internal transitions, loss
development agriculture of pastures/
of conversions mosaics to
construction transitional
sites woodland
Luxembourg 0.15 0.23 Slow-down Accelerated Transitional
of sprawl of consumption woodland
housing and of pasture, becoming forest,
recreation formation of some loss to
facilities arable land economic sites
Malta 0.07 0.00 No change in No change in Natural areas
urban areas agricultural almost without
land cover change
Montenegro 0.02 0.04 Extension of Loss of pastures Forest transitions,
construction and mosaics loss of natural
sites and to artificial areas to
residential surfaces economic sites,
areas fires
Netherlands 0.30 0.27 Increased Agricultural land Growth of
construction uptake by natural areas,
and urban development e.g. grasslands,
land of artificial withdrawal of
management areas farming
Norway – 0.10 Extension Low intensity Forest transitions,
of sport of agricultural some loss of
and leisure changes natural areas,
facilities, fires, decrease of
residential glaciers
sprawl
Poland 0.10 0.10 Increased sprawl Loss of Transitional
of economic agricultural woodland on
sites, highway land (mostly former farmland,
construction arable) new water bodies
 Portugal 0.78 1.43 Development Slow-down of Forest transitions,
driven by agricultural new forested
construction transitions, land and water
around key farmland bodies, fires
areas abandonment
Romania 0.16 0.05 Residential Slow-down of Recent felling and
sprawl agricultural land transition,
accelerates transitions, loss some loss of
around main of pastures natural open
cities areas.
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Serbia 0.11 0.07 Slower residential New formation Low forest

sprawl, of arable land, formation, loss
doubled loss of pasture/ of grasslands,
extension of mosaics, fruit new water
mines and berry bodies
Slovakia 0.51 0.25 Slow-down of Slow-down of Forest creation
residential changes, loss after withdrawal
land take of agricultural of farming
land
Slovenia 0.02 0.03 New Limited changes, Limited changes,
construction loss of forest felling and
sites drive agricultural loss of land, new
future land land water bodies
take
Spain 0.34 0.29 Urban extension, Loss of arable Forest transitions,
faster land to, afforestation of
sprawl of olive groves, dry semi‑natural
construction vineyards, land, fires
and transport orchards,
land construction
Sweden – 0.49 Dynamic Loss of arable Forest transitions,
development land some uptake of
of artificial forested areas by
land cover economic sites
Switzerland ** – – Slower Decline in arable Remote area
urban and land, increase reverting to
infrastructure in pasture, wild vegetation,
extension withdrawal of glacier retreat
farming
Turkey – 0.08 Development Increased arable Loss of natural
mostly land e.g. open land to
driven by irrigated lands, transitional
construction loss of pasture/ woodland/shrub
and mining mosaics
United – – – – –
Kingdom
Source: European Environment Agency (2010a), based on Corine land cover data.
Notes
* Land cover changes in Liechtenstein remained below the detection level of the Corine land
cover change methodology; land cover trends are assessed from the national contribution to
SOER 2010.
** Land cover trends for Switzerland are assessed from the national contribution to SOER 2010.
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 Part II

Impact of land take and

soil sealing on soil-related
ecosystem services
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 6 The effects of urban expansion on
soil health and ecosystem services
An overview
Mitchell Pavao-Zuckerman and Richard V. Pouyat
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Introduction
Cities are often thought of as open systems with large, extractive footprints
that are dependent on the productivity and ecosystem services of surround-
ing hinterlands (Rees, 1997). The ecosystem service concept was developed
in part to help describe ecosystems in ways that might help conserve them in
the face of land use change (Daily et al., 1997, Setälä et al., 2014). Despite this
perspective on cities, many ecologists now recognize that cities can be thought
of as ecosystems, that have internal structures and functions that generate eco-
system services (Pickett et al., 2001, Adler and Tanner, 2013, Davies et al.,
2011, Grimm et al., 2000). Moreover, the well-being of urban residents may
be improved by the provision of urban ecosystem services (McPhearson et al.,
2014, Andersson et al., 2014, Barthel et al., 2010). This recognition of cities as
urban ecosystems has led to a paradigm shift and focus on urban ecosystem ser-
vices in research and planning perspectives, with new attention now being paid
to the supply and demand for urban ecosystem services (Ernstson et al., 2010,
Gill et al., 2008, Gomez-Baggethun and Barton, 2013, Pataki et al., 2011).
As cities develop they have many potential environmental impacts that
alter soils and their ecosystem services. Notably, urban development can have
a significant impact on soil formation factors, altering the trajectories of soil
development (Pickett and Cadenasso, 2009). Urban soil “parent material” is
often partially comprised of building debris, trash, and imported fill materi-
als, also affecting soil formation (Effland and Pouyat, 1997). These impacts on
urban soil formation are critical for understanding urban ecosystem services,
yet there are a host of urban impacts on soils that can be characterized as
either direct or indirect impacts (Pavao-Zuckerman, 2012, Pavao-Zuckerman,
2008). Direct impacts result from the physical process of urbanization and the
process of development. Relative to the process of soil development, direct
impacts tend to be rapid and short duration. Indirect impacts result from the
presence of built surfaces, the functioning of the city, and the actions of people
in urban management (that are not directly impacting soil physical properties).
Indirect impacts tend to occur over longer periods of time and their effects may
be cumulative. Ecologists differentiate disturbances as “pulses” or “presses”
 124 M. Pavao-Zuckerman and R.V. Pouyat
by their temporal impacts. Pulse disturbances are short-term and temporary,
where the system may recover easily from the disturbance once the pulse event
ceases. On the other hand, press disturbances are longer-term disturbances
with long-term consequences for an ecosystem. Press disturbances often drive
the system into a new equilibrium state, resulting in changes in ecosystem
structure and processes. In theory an indirect effect is “reversible,” if that pres-
sure is altered (i.e., if you mediate the urban heat island), whereas a direct effect
is a structural change (a press).
Soils are a critical ecosystem component underlying or directly supporting
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the majority of terrestrial ecosystem services. Soil can be seen as a form of

“natural capital” that supports the provision of ecosystem services (Figure 6.1;
Dominati et al., 2010, Robinson et al., 2009). Soil health (the ability of soil
to function and provide desired services and maintain environmental qual-
ity) is a critical component of a soil’s role as natural capital and an important
property supporting the provision of ecosystem services (Doran and Parkin,
1994). It is the knowledge of the interaction of physical, chemical, and bio-
logical properties of soils that underlies management of soil health through
conservation, restoration, and design practice (Heneghan et al., 2008, Pavao-
Zuckerman, 2008). In this chapter, we explore the direct and indirect effects
of cities on soils (Pavao-Zuckerman 2008, 2012) to explore the provision,
degradation, and restoration of ecosystem services in cities. The goal of the
paper is to discuss the implications for soil health and ecosystem services fol-
lowing the process of urbanization. Our focus is primarily on what happens
to soils within cities through the process of urbanization, while other chapters
in this volume cover the implications for land outside cities as the process
of urbanization occurs. The direct/indirect framework facilitates a concrete
understanding of the drivers and mechanisms of urban influences on soils and
ultimately support the management of urban ecosystem services in a way a
more generalized discussion of “urbanization” would not. In this chapter,
we discuss (1) the direct and indirect effects of urban expansion on soils,
(2) the effects of urban expansion on soil health and ecosystem services, and
(3) approaches to enhance ecosystem services in cities through restoration and
mitigation approaches for urban soils.

Direct effects of urban expansion

The characteristics of urban soils vary widely and are dependent on both direct
and indirect effects resulting from urban land use change (Figure 6.1). Examples
of direct effects include soil disturbances such as grading (McGuire, 2004, Pitt
and Lantrip, 2000, Trammell et al., 2011), management inputs such as irri-
gation (Zhu et al., 2006, Tenenbaum et al., 2006) and compaction through
trampling (Godefroid and Koedam, 2004), while indirect effects include envi-
ronmental changes such as the urban heat island effect (Savva et al., 2010) and
atmospheric deposition (Lovett et al., 2000, Rao et al., 2014). Here we address
the direct effects of urban land use expansion on native soils.
 Soil health and ecosystem services 125
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Figure 6.1 Direct and indirect effects of urbanization influence on both the natural
capital of soils and the generation of ecosystem services (source: adapted
from Dominati et al., 2010)

Urban disturbance
When land is converted to urban uses, both initial and post-development
factors that physically disrupt soil or result from horticultural management,
e.g., fertilization and irrigation, can have profound effects on soil character-
istics (Pouyat et al., 2010). Nevertheless, for most urban land use conversions
human-caused disturbance is more pronounced during rather than after the
land-development process. The initial phase of urban development typically
includes the clearing of existing vegetation, grading of soil, and the building
of structures, which collectively result in a drastic alteration of the C, N, and
water cycles in the resultant landscape. In turn, the extent and magnitude
of these initial disturbances is dependent on infrastructure requirements
(e.g., stormwater retention areas), topography, and other site limiting factors.
As an example, a topographic change analysis of 30 development projects in
Baltimore County, USA showed that the total volumetric change of soil per
development was positively correlated with mean slope of the site (r = 0.54,
p = 0.002) (McGuire, 2004).
Very little is known about C and N losses from urban soils although we do
know that large-scale development projects can physically impact large vol-
umes of surface soil. Using the topographic change analysis in McGuire (2004),
Pouyat et al. (2007b) estimated that the potential amount of SOC that was
disturbed during a 2,600 m2 development project with an average depth of 3 m
was roughly 2.7 × 104 kg SOC. How much SOC that actually gets lost during
 126 M. Pavao-Zuckerman and R.V. Pouyat
the development process is unknown and depends partly on the type of soil
and the ultimate fate of the surface soil layers, which in the USA are typically
sold as “topsoil” for the development of lawns (Pouyat et al., 2007a). Soil losses
of N potentially tend to be greater directly after a site is developed and are
reduced as soil organic matter, thus C concentrations, increase through time
(Golubiewski, 2006). These post-development effects on soil organic matter
can also translate to improved soil infiltration rates, particularly where attention
is paid to the use of soil amendments in post-development management (Chen
et al., 2014, Pitt et al., 2008).
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Urban management
In addition to direct or physical effects to soil during urban land-use change,
humans supplement urban soils with various amendments including fertilizer,
compost, mulch, lime, and irrigated water. Ironically, these supplements are
required to make up for the loss of SOC and nutrients that were lost during the
initial disturbance of native soils in the development process.
Results in the literature suggest that turf-grass systems can accumulate SOC
to levels that are comparable to or exceed other grassland and forested systems.
In comparing results from studies of managed lawns in California, Maryland,
and Wisconsin, Falk (1980) estimated that the range for net primary produc-
tivity was about 1.0 to 1.7 kg ha yr-1 in temperate climates, most of which
was below-ground. Other studies have shown somewhat lower productivity
rates for lawns (0.6 to 0.7 kg ha yr-1) (Blancomontero et al., 1995, Jo and
McPherson, 1995). In measuring C sequestration rates in turf-grass soils using
C14 analysis, Qian et al. (2010) found rates of accumulation between 0.32 and
0.78 Mg ha-1 yr-1 during the first four years after turf establishment. These
rates are similar in range to 0.9 to 1.0 Mg ha-1 yr-1 during the first 25 years
(Bandaranayake et al., 2003).
To manage turf grasses typically associated with lawns, homeowners and
institutional land managers in the USA apply about 16 million kg of pesticides
each year (Aspelin, 1997) as well as fertilizers at rates similar to or exceed-
ing those of cropland systems (Talbot, 1990). Moreover, lawns are typically
clipped on a regular basis during the growing season and depending on the
practice, can result in a significant amount of N on an annual basis (Templer
et al., 2015). Depending on the state of recovery of the turf-grass system after
development and the prevailing climate, the effect of fertilizer, pesticides, and
irrigated water on lawn productivity will vary from region to region and due
to the age of the development (Selhorst and Lal, 2013).
Although managed turf-grass systems have shown a high capacity to sequester
C, flux rates of C from these systems appear to be higher than the native systems
replaced. For instance, measurements in permanent forest and lawn plots in the
Baltimore metropolitan area indicate that fluxes from turf-grass plots generally
were higher than at forested sites (Groffman and Pouyat, 2009, Groffman et al.,
2009). Other soil-atmosphere exchanges of greenhouse gases, especially nitrous
oxide and methane, also have been altered by turf management. For example,
 Soil health and ecosystem services 127
trace-gas measurements in the Baltimore metropolitan area showed that turf-
grass soils have a reduced rate of methane uptake and increased nitrous oxide
fluxes compared to rural forest soils (Groffman and Pouyat, 2009; Groffman
et al., 2009). Similarly, in the Colorado Front Range, turf-grass systems had
reduced methane uptake and increased nitrous oxide fluxes relative to native
short-grass steppe in that region (Kaye et al., 2005). The specific mechanism
for elevated CO2 and nitrous oxide fluxes and a reduced methane sink in turf-
grass systems has not been determined, though a possible explanation is that
higher atmospheric concentrations of CO2, N inputs from fertilization, and ele-
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vated atmospheric and soil temperatures play significant roles in these soil-flux
responses (Yesilonis and Pouyat, 2012).

Sealed surfaces
Sealed or impervious surfaces can partially constrain distributions of plant
species, trace gas fluxes, water infiltration as well as the movement of nutri-
ents and contaminants in urban ecosystems (Pouyat et al., 2007a). The
disconnection of the soil and atmosphere “short circuits” the below-ground
from the above-ground ecosystem, which diminishes an ecosystem’s overall
ability to buffer changes in water, nutrient, and contaminant inputs. As a
result, the ecosystem’s capacity to retain or process these materials is altered.
For totally sealed soil surfaces, soil C stocks can be half of vegetated soils;
more data are reported in Chapter 10 of this book. In addition, atmospheri-
cally derived contaminant and nutrient inputs can accumulate on impervious
surfaces and be washed off repeatedly by small rainfall events onto nearby
exposed soil or into surface waters (Gobel et al., 2007, Lee and Bang, 2000,
Lee et al., 2002). In addition, gaseous exchanges between the atmosphere
and the soil-plant continuum should be diminished, again short circuiting
the ability of the below-ground ecosystem to assimilate C or gas-phase con-
taminants. However, we are unaware of any such measurements of sealed
soils in the literature.
The tendency of the built environment and human activity to concentrate
flow paths and chemical inputs can result in the development of “hotspots”
in the landscape. Hotspots are areas or patches that show disproportionately
high reaction rates relative to the surrounding area or matrix (McClain et al.,
2003). The concept of hotspots developed from studies of N processing in soil
cores (Parkin, 1987) and riparian zones that showed that anoxic microsites with
high C content were zones of elevated denitrification rates. Generally, hotspots
are sites where reactants for specific biogeochemical reactions coincide in an
environment conducive for the reaction to take place (McClain et al., 2003).
Human activities and the introduction of built structures provide such condi-
tions in urban landscapes at various scales. Examples include septic systems,
horticultural beds, golf greens, stormwater retention basins, and compost piles.
In all these examples, the potential for N leaching or trace gas emissions is
higher than in other soil patches found in urban landscapes. Urban soil hotspots
also can be sinks for contaminants, nutrients, or C.
 128 M. Pavao-Zuckerman and R.V. Pouyat
Indirect effects of urban expansion

Physical effects
Urban ecosystems are characterized by an alteration of energy, water, and
material fluxes that stem from disturbance, management, and other physical
alterations to the environment (Kaye et al., 2006). Cities therefore can indi-
rectly impact soils through these direct processes associated with urbanization.
Here we address indirect influences of urbanization on soils through changes
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in surface temperature, hydrology, chemical inputs, and ecological structures.

Urban heat islands are a ubiquitous pattern of environmental impacts of
cities. Ambient air temperatures differ across urban landscapes and in com-
parison to rural land due to the presence and percentage of built surfaces,
with urban cores tending to be warmer than surrounding areas. Using analy-
sis of satellite imagery, Buyantuyev and Wu (2010) observed a strong heat
island in the Phoenix metropolitan area. In addition to the general warm-
ing pattern, they also observed strong variability in surface temperatures that
were driven by intra-urban socioeconomic drivers, particularly median family
income (Buyantuyev and Wu, 2010). The observed variability of surface tem-
peratures across the urban landscape is a reflection of direct urban influences
on physical space through local processes of management and development.
Urban heat islands in turn can affect soil moisture status and impact rates of
soil biogeochemical cycling (McDonnell et al., 1997). Elevated rates of litter
decomposition and nutrient cycling have been attributed to urban heat island
effects in several urban areas (McDonnell et al., 1997, Pavao-Zuckerman and
Coleman, 2005, Pouyat et al., 1997).
The process of urbanization alters elements of the hydrologic cycle and
resulting water balance, including evapotranspiration, infiltration, and surface
runoff, at local and broader spatial scales. The ultimate urban water balance at
various scales ultimately depends on the proportion of a catchment or water-
shed that is covered by impervious surfaces and the extent of surface sealing of
soils (Wessolek, 2008), but a general pattern is that increases in imperviousness
generate reduction in infiltration rates and increases in surface runoff (Paul and
Meyer, 2001). Even patches of the urban landscape that are not impervious
may have reduced infiltration rates due to soil compaction, exacerbating sur-
face runoff dynamics in cities (Gregory et al., 2006). Water repellency of soils
is found to increase with rates of urbanization (McDonnell, 1997), and this
increase in hydrophobicity has been shown to reduce rates of water infiltra-
tion into soils (Doerr and Ritsema, 2005). Recent studies have turned to the
interaction of soil condition and characteristics to hydrologic processes in cit-
ies. For example, Ossola et al. (2015) observed that different approaches to park
management in Melbourne that reflected habitat complexity (Byrne, 2007) led
to variation in litter and soil surface characteristics. These properties altered
infiltration rates and saturated hydraulic conductivity in a way that suggests
that management of parks with an eye to soil properties and habitat complexity
may support better practices for stormwater management (Ossola et al., 2015).
 Soil health and ecosystem services 129
Chemical effects
Cities are also characterized by elevated concentrations and fluxes of chemi-
cals than surrounding areas that derive from both point (power plants,
industrial combustion, heating) and non-point sources (vehicle traffic) (Bilos
et al., 2001, Schauer et al., 1996). Additionally, local gradients of land-use and
the specific management of parcels in cities may in fact drive broader urbani-
zation gradients of pollutant deposition patterns (Tanner and Fai, 2000). A
general trend is for concentration and fluxes of anions and cations to decrease
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with increasing distance from an urban core (Lovett et al., 2000). Indices
of vehicular usage (such as CO2 emissions) and the urban core are strongly
correlated with inorganic nitrogen inputs to forest soils (Rao et al., 2014). A
recent study in Gold Coast, Australia links atmospheric deposition of Zn, Cd,
Ni, and Cu with local vehicle traffic drivers, and importantly makes the con-
nection between atmospheric deposition and stormwater runoff pollution,
which may serve as another transport mechanism by which pollutants move
into soils (Gunawardena et al., 2013).
Elevated wet and dry deposition can result in high amounts of nitrogen,
sulfur, and heavy metal deposition on urban soils (McDonnell et al., 1997).
Elevated N deposition has been shown to indirectly alter soil carbon dynamics
by shifting extracellular enzyme activities due to alterations in litter chemistry
(Waldrop et al., 2004). Deposition dynamics in an urban region may be well
understood by examining the source fingerprint and spatio-temporal dynam-
ics of atmospheric pollutants (Azimi et al., 2005). Rao et al. (2014) observed
significant NO3 leaching from sites that correlated with inorganic N deposition
rates, and concluded that this leaching did not require saturation of above-
ground and below-ground N pools.

Ecological effects
Finally, urbanization can indirectly impact biodiversity and community
structures, playing a key role in driving urban ecosystem services. The influ-
ence of urbanization on biodiversity is complex, varying greatly by taxa,
climate, land use and management. Despite their perception as being biolog-
ically “inert” urban places can harbor high levels of soil biodiversity. Recent
community analysis of the soil microbiota in New York City’s Central Park
describes a high level of microbial diversity, with an interesting degree of
endemism and novelty that matches those found in non-urban ecosystems
(Ramirez et al., 2014). Urbanization gradient studies showed reductions in
microbial populations but some settings indicated that urbanization may
alter the functional composition of soil food webs, but not overall diversity
levels (Pavao-Zuckerman and Coleman, 2005, Pouyat et al., 1994). The
role of environmental drivers and habitat conditions are important for soil
microfaunal abundances, indicating that urban management can strongly
affect biodiversity in soils (Byrne, 2007, Pavao-Zuckerman and Byrne,
2009). Again, urbanization can lead to unexpected results with respect to
 130 M. Pavao-Zuckerman and R.V. Pouyat
these drivers. For example, Tuhackova et al. (2001) demonstrated gradients
of polycyclic aromatic hydrocarbons (PAHs) in soils that were driven by
proximity to highways. These gradients of PAHs served as energy sources
for microbes and resulted in strong increases in abundance of both bacteria
and fungi close to highways. Importantly, ecosystem services are driven by
the function of organisms across spaces, yet the impact of urbanization on
the functional and physiological ecology of organisms is a critical issue and
an emerging frontier of research (Hahs and Evans, 2015).
Invasive species that are typically introduced into urban areas can have a
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strong influence on soil health in urban or urban-rural interface areas (Pouyat

et al., 2010). For example, in the northeastern and mid-Atlantic United States
where native earthworm species are rare or absent, urban areas are important
foci of invasive earthworm introductions, especially Asian species from the
genus Amynthas, which are expanding their range to outlying forested areas
(Groffman and Bohlen, 1999, Steinberg et al., 1997, Szlavecz et al., 2006).
Invasions by earthworms into forests have resulted in altered C and N cycling
processes, sometimes this can lead to increased losses of N through trace gas
and leaching fluxes (Carreiro et al., 2009, Bohlen et al., 2004, Hale et al.,
2005). Invasive earthworms may have compounding indirect effects through
influences on soil properties that drive microbial function, as their casts have
higher moisture contents than the soils that house them. Similarly, invasions
by exotic plant species can impact C and N losses, which in some cases can
facilitate the colonization of additional invasive species, further exacerbating
the turnover of N in the soil (Pavao-Zuckerman, 2008). Examples of plant
invasions in urban metropolitan areas that have been shown to alter C and N
cycles include species of the shrub Berberis thunbergii, the tree Rhamnus cathar-
tica, and the grass Microstegium vimineum (Ehrenfeld et al., 2001, Heneghan
et al., 2002, Kourtev et al., 2002).

Urban expansion effects on soil health

and ecosystem services

Direct and indirect effects on soil health and quality

As mentioned previously, urban land use change can affect soils indirectly
through changes caused in environmental factors and directly through physi-
cal or management effects with the former having the potential to influence
soils beyond the boundaries of what is considered urban land use (Pouyat
et al., 2007b). For example, forest soils within or near urban areas have been
shown to receive high amounts of heavy metals, organic compounds, and
acidic compounds in atmospheric deposition. Lovett et al. (2000) quantified
atmospheric nitrogen inputs over two growing seasons in oak forest stands
along an urbanization gradient in the New York City metropolitan area.
They found that the urban remnant forests received up to a two-fold greater
deposition of nitrogen than in similar rural oak forests. Similar results were
 Soil health and ecosystem services 131
found in Louisville, KY, USA, the San Bernardino Mountains in the Los
Angeles metropolitan area, CA, USA, the city of Oulu, Finland, and the city
of Kaunas, Lithuania, where N deposition rates into urban and forest patches
were higher than in rural forest patches (Bytnerowicz et al., 1999, Carreiro
et al., 1999, Fenn and Bytnerowicz, 1993, Juknys et al., 2007, Ohtonen and
Markkola, 1991).
Evidence of a similar depositional pattern has been found for heavy metals
along urbanization gradients in the New York City, Baltimore, and Budapest,
Hungary, metropolitan areas. Pouyat et al. (2008) found up to a twofold to
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threefold increase in contents of lead, copper, and nickel in urban than in

rural forest remnants. A similar pattern but with greater differences was found
by Inman and Parker (1978) in the Chicago, IL, USA, metropolitan area,
where levels of heavy metals, particularly lead and copper, were more than
five times higher in urban than in rural forest patches. Other urbanization
gradient studies have shown a similar pattern (Sawicka-Kapusta et al., 2003,
Watmough et al., 1998), although cities having more compact development
patterns exhibited less of a difference between urban and rural remnant forests
(Pavao-Zuckerman, 2003, Pouyat et al., 2008, Carreiro et al., 2009). Besides
heavy metals, Wong et al. (2004) found more than a twofold higher gradient
of Polycyclic Aromatic Hydrocarbons (PAHs) concentrations in forest soils in
the Toronto, Canada, metropolitan area, with concentrations decreasing with
distance from the urban center to the surrounding rural area. Similarly, Jensen
et al. (2007) and Zhang et al. (2006) found significantly higher concentrations
of PAHs in surface soils of Oslo, Norway, and Hong Kong, China, respec-
tively, than in surrounding rural areas.
How these pollutants affect the health of soil is uncertain, but results thus
far suggest that the effects are variable and depend on other urban factors
(Lorenz and Lal, 2009, Pouyat et al., 2007a, Pouyat et al., 2007b, Carreiro
et al., 2009). For example, Inman and Parker (1978) found a negative effect
of soil contamination of Cu (76 mg kg-1) and Pb (400 mg kg-1) on leaf litter
decay rates in urban stands suggesting a negative effect from pollution in the
Chicago metropolitan area. Similarly, Pouyat et al. (1994) found an inverse
relationship between litter fungal biomass and fungivorous invertebrate abun-
dances with heavy metal concentrations along an urbanization gradient in the
New York City metropolitan area. However, responses of soil invertebrates
along urbanization gradients in Europe were not related to urban environ-
mental effects but rather local factors, such as habitat connectivity or patch
size (Niemela et al., 2002). In fact, where heavy metal contamination of soil
is moderate to low relative to other atmospherically deposited pollutants ele-
ments such as N, biological activity may actually be stimulated. Decay rates,
soil respiration, and soil N-transformation increased in forest patches near or
within major metropolitan areas of the USA in southern California (Fenn and
Dunn, 1989, Fenn, 1991), Ohio (Kuperman, 1999), southeastern New York
(McDonnell et al., 1997, Carreiro et al., 2009), and Maryland (Groffman et al.,
2006, Szlavecz et al., 2006).
 132 M. Pavao-Zuckerman and R.V. Pouyat
Expanded spatial scales and development patterns on ecosystem services
Urban soils or native soils that have been influenced by urban environmental
conditions are generally thought of as having lower quality than native soils
found in a particular region (Craul and Klein, 1980, Jim, 1993, Patterson et al.,
1980, Short et al., 1986). However, more recent studies show a greater vari-
ety of soil conditions that are often more favorable for plant growth than the
preexisting native soil (Davies and Hall, 2010, Edmondson et al., 2012, Hope
et al., 2005, Pouyat et al., 2007b). For example, most literature assumes that
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the conversion of native soil types to urban uses results in losses of C (Lorenz
and Lal, 2009, Pouyat et al., 2010, Scharenbroch et al., 2005). Yet, depending
on the climate and native soils, C has been shown to accumulate in soils of
urban landscapes to a level that is greater than that in the native soil replaced
(Pouyat et al., 2015). The assumed cause for increasing C storage in what were
once disturbed soils is the supplementation of water and nutrients, which in
native soil and climate conditions would otherwise have limiting conditions
for plant growth. Thus, an important characteristic of urban land use change
with respect to C and N cycles is the replacement of native cover types with
lawn cover, which often requires added nutrients and water (Kaye et al., 2005,
Milesi et al., 2005, Golubiewski, 2006, Pouyat et al., 2009). In North America,
the estimated amount of lawn cover for the conterminous USA is 163,800 km2
± 35,850 km2, or 73 percent of all irrigated cultivated lands (excluding lawn
cover) (Lubowski et al., 2006). Moreover, it is estimated that roughly half of all
residences apply fertilizers (Law et al., 2004, Osmond and Hardy, 2004), which
can approach or exceed rates applied in cropland systems, e.g., > 200 kg ha-1
yr-1 (e.g., Morton et al., 1988).

Restoration and mitigation of direct and indirect

effects of urban expansion

Planning, design, restoration approaches to enhance services in cities,

organized by how approaches address direct effects and indirect effects
Urbanization represents a complicated mix of scales with respect to ecosys-
tem service provision and sustainability. The growth of cities often comes at
the expense of agricultural and natural ecosystems and the services that they
provide (Setälä et al., 2014). At the same time, a greater proportion of the
Earth’s population resides in cities, and the provision and flow of ecosystem
services to these urban dwellers is an important consideration for their well-
being, health, and the overarching sustainability and resilience of urban places.
From a cost–benefit perspective, restoration of ecosystem services in cities may
meet environmental, social, and economic goals for sustainable development
(Elmqvist et al., 2015). The recognition that cities are capable of providing
urban ecosystem services may reduce the reliance of non-urban ecosystem ser-
vice flows. For example, cities may produce 15–20 percent of the world’s
 Soil health and ecosystem services 133
food supplies through urban agricultural practices (Smit et al., 1996), with
many local instances (especially in developing nations) contributing a great
proportion of local food needs (Beniston and Lal, 2012). However, as discussed
above, urbanization has many direct and indirect impacts on ecosystems and
soils that alter their ability to provide ecosystem services (Pavao-Zuckerman,
2012). Here we discuss approaches to mitigate these effects of urbanization
through soil management, restoration, planning, design, and policy in order to
enhance ecosystem service provision within cities by improving soil quality.
Urban ecosystem service provision can be enhanced through soil man-
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agement approaches that address direct and indirect impacts of urbanization.

However, prescribing a universal solution to urban soil issues is problematic
because urban soils are very heterogeneous in nature. Thus the ability to gen-
eralize an urban soil “condition” is limited. This has significant implications
for urban soil management and restoration of ecological processes in cities.
Assessment of local conditions that drives site-specific strategies to mitigate
urban impacts on soils is critical. Therefore, a key focus for soil remediation
in cities looks to soil management practices at local scales (i.e., lot, parcel).
Soil carbon management is another critical approach to improving urban soil
quality. Increasing the soil carbon pool has many direct and indirect ecosystem
benefits for improving soil structure, enhancing infiltration rates, and increasing
populations of soil biota (Lal, 2007). This can be achieved through composts
and mulches and biochar, where repeated application can improve soil physical
properties affected by urbanization, such as bulk density, infiltration rates, and
soil water-holding capacity (Cogger, 2005). Indirect benefits of compost appli-
cations on soil properties may help to also alleviate urbanization impacts on
plant productivity (Scharenbroch, 2009), which may have additional indirect
benefits for urban soil quality through root and litter production. Cities often
produce large quantities of organic waste materials that could be redirected for
soil amendment and management (Beniston and Lal, 2012), further enhancing
the localized production of ecosystem service benefits.
Urban ecosystem service provision can also be enhanced through approaches
that increase the effective unsealed soil surface in a city. These approaches
largely fall within the general scope of green infrastructure, and serve to
increase the urban surface area of soils that interact with hydrologic flows or
support enhanced biological activity in urban soils. Large-scale tree planting
efforts have been viewed as a strategy to restore ecosystem services in cities
due to the many benefits that trees provide (Nowak and Crane, 2002, Oldfield
et al., 2014). In the initial stages of an afforestation project in New York City,
Oldfield et al. (2014) report that site preparation and soil amendment improves
the health of urban soils. Specifically, they observed reductions in bulk den-
sity, increases water-holding capacities, increased microbially available carbon,
and enhanced carbon storage. However, it should be noted that site prepara-
tion itself (weeding, rototilling to ~15cm, and surface mulching) dominated
treatment effects (compost amendment) in the early stages of the afforestation
project (Oldfield et al., 2014). Low-impact development approaches (such as,
 134 M. Pavao-Zuckerman and R.V. Pouyat
biofilters, bioretention basins, bioswales, rain gardens) seek to manage and con-
trol urban stormwater by increasing retention times and the duration that water
interacts with soils in the urban landscape (Askarizadeh et al., 2015, Fletcher
et al., 2014). These systems can be designed to provide ecosystem services
related to hydrologic and water quality goals. For example, soil media depth,
composition, mulching, basin geometry, and vegetation composition can all be
adjusted to reduce peak flows, enhance infiltration, sequester pathogens, nutri-
ents, and metals, and support plant growth along streets, lots, and parking areas
in cities (Hunt et al., 2012). Some low-impact development projects that seek
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to restore or enhance ecosystem services in cities can be viewed as forms of

novel ecosystems (Kowarik, 2011). For example, green roofs are assemblages of
soil (often constructed) and plants that are uniquely designed ecological systems
that take advantage of vast amounts of novel rooftop spaces in urban landscape
(Oberndorfer et al., 2007). The added soil surface of green roofs may enhance
rainfall retention, reduce runoff rates, and reduce nutrient concentrations in
runoff, although there is a great deal of variability in green roof performance
that is driven by design parameters (slopes, age, soil depth, etc.) (Berndtsson,
2010). The promise of green infrastructure for affecting urban sustainability
and resilience through ecosystem service provision is high (Andersson et al.,
2014, Tzoulas et al., 2007), the specific performance of green infrastructure in
situ and its ability to affect ecosystem services at the scale of neighborhoods,
watersheds, cities, and regions remains an important research direction (Pataki
et al., 2011, Berndtsson, 2010)
Vacant lots hold great promise for providing many types of ecosystem ser-
vices in the urban fabric. In an assessment of hydrologic properties of vacant
residential lots in Cleveland, OH, Shuster et al. (2014, 2015b) suggest that
policies and procedures for vacant lot management may positively impact
soil properties such that these lots become part of a green infrastructure that
addresses stormwater management issues. Moreover, similar lot-scale manage-
ment and processes may allow lots to function as stormwater harvesting green
infrastructure in semi-arid cities to help relieve irrigation burdens for land-
scaping (Shuster et al., 2015a). A review by Beniston and Lal (2012) indicates
that agriculture on vacant urban land has the potential to significantly address
human health and economic issues centering on localized food production.
Kremer et al. (2013) conducted a social-ecological assessment of vacant lot
utilization in New York City and found a range of uses (including garden-
ing, park space, parking, and athletic activities). Importantly, they found that
whether and how residents used lots was a localized phenomenon, and was
influenced by socio-economic factors (Kremer et al., 2013). This suggests that
planning and management of vacant lots for ecosystem services that takes into
account local conditions and demand for services might better contribute to
urban sustainability (McPhearson et al., 2014).
Ecosystem services play an interesting role in soil policy and manage-
ment in that they can serve as a communication tool to move conservation
policies forward, while at the same time be a beneficiary of such policies
 Soil health and ecosystem services 135
(Breure et al., 2012). Ecosystem services can thus be a nexus in urban soil
management, functioning as both a driver of and response to policy and
planning initiatives (Hough et al., in review, Burkhard et al., 2014). A host of
programs and policies directly addressing soil from ecological, societal, and
economic perspectives has emerged (largely in Europe), but broader initia-
tives focused on ecosystem services also indirectly link to urban soil ecosystem
services (i.e., TEEB—The Economy of Ecosystems and Biodiversity, the
Millennium Ecosystem Assessment, IBPES—Intergovernmental Platform on
Biodiversity and Ecosystem Services). At more local scales policy quickly
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becomes complex when local policy actors and stakeholders may value ser-
vices and perceive disservices differently (Otte et al., 2012). The transition
away from policies that derive from soil degradation paradigms to those of
sustainable use (Figure 6.2, Breure et al., 2012) have greater application in
urban areas because of the nature of urban soils and their potential role
in ecosystem service provision (Pavao-Zuckerman, 2008). This represents a

Figure 6.2 Two paradigms for policy and management related to urbanization

influences on soils and ecosystem services. (a) Through the DPSIR
(Driver – Pressure – State – Impact – Response) Framework, urbanization
is viewed from a degradation perspective (source: modified from
Kristensen, 2004). (b) An alternative view of urban soil management that
explicitly focuses on supporting the generation of ecosystem services,
rather than only the mitigation of environmental degradation (source:
modified from Breure et al., 2012)
 136 M. Pavao-Zuckerman and R.V. Pouyat
pragmatic approach that recognizes that in cities soil function and ecosystem
service provision may be constrained by direct and indirect urbanization
effects. Multiple stakeholder perspectives on ecosystem service values (both
monetary and non-monetary) allow the setting of realistic goals for service
provision within the constraints of urban environments that would be sup-
ported by management and policy, and ideally, coupled with evaluation and
adaptive management programs (Breure et al., 2012). The interface of sci-
ence and policy through policy-effective and actionable research generated
through a cycle of adaptive policy development through phases of assess-
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ment, implementation, and evaluation is ideally suited for urban landscapes

(Otte et al., 2012). The necessity of local-scale assessment and site char-
acterization, guarantees that policies will be rooted in local settings. The
experimental nature of environmental planning and design in cities helps to
facilitate an iterative and adaptive policy process that uses the city as a living
laboratory for the provision of ecosystem services (Felson and Pickett, 2005,
Felson et al., 2013).

Conclusions
The process of urbanization is a dominant and significant transformation of
ecosystem structure and function. Urbanization is in strong conflict with
other land uses that provide the ecosystem services that society relies upon for
resources, environmental regulation, and overall well-being. At the same time,
the majority of the world’s population now lives in cities, and this popula-
tion also relies on urban ecosystems to provide ecosystem services in cities,
towns, and suburban areas to contribute to environmental, social, health, and
economic well-being. Soils are a critical form of natural capital for ecosystem
service provision, yet in cities, direct and indirect environmental impacts can
limit their ability to provide ecosystem services to urban residents. Management
choices, development patterns, and localized land-use approaches and patterns
ultimately determine these direct and indirect impacts on soils and their abil-
ity to provide ecosystem services in cities. Despite these impacts on soils and
ecosystem services, mitigation approaches, restoration, ecological design and
planning all show significant promise for enhancing urban soil for the purpose
of ecosystem service provision.

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 7 Impact of land take on global
food security
Ciro Gardi
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Introduction
Food security is defined by Maxwell (1996) as existing when ‘all people, at all
times, have physical and economic access to sufficient, safe and nutritious food
that meets their dietary needs and food preferences for an active and healthy
life’. This definition underlines that food security includes four dimensions:
availability, stability, safety and accessibility. Urban expansion and urbaniza-
tion affects all four dimensions of food security (Matuschke, 2009), but for the
scope of this book, we will concentrate on food availability.
Urban expansion occurs in most cases at the expense of agricultural land,
reducing the availability of soil resources for agriculture production, limiting
the availability of food. For example, in India, Saharanpur lost in 10 years
(1988–1998) more than 30 per cent of its agricultural land (Fazal, 2000), and
in Turkey, Kahramanmaras registered an expansion of 1100 per cent between
1950 and 2006, mainly at the expense of high quality agricultural lands (Doygun
and Gurun, 2008). In Santiago (Chile) 19,600 ha, 70 per cent of which were
prime agricultural land (land capability classes I, II, III and IV), were lost in
14 years (Romero and Ordenes, 2004). In China between 1990 and 2010
the potential agricultural production decreased by approximately 34.90 mil-
lion tons due to urban expansion, accounting for 6.52 per cent of China’s total
actual production (Liu et al., 2015).

Global food security

The food crises of 2008, 2010, and 2012, as well as the continuing food price
volatility, underscore the vulnerability of the world’s food system. Soil sustains
(directly or indirectly) more than 95 per cent of global food production (FAO,
2008). The estimated global increase of the world population from 6.8 billion
in 2009 to 9.2 billion in 2050 (Speidel et al., 2009) will lead to a significant
increase in both food demand and land take, as consequence of urban expan-
sion. It has been estimated that to feed 9 billion people, global agricultural
production should increase by 70 per cent. At the same time, the combination
of these conflicting processes has raised international concern for global food
 Impact on global food security 147
security. Global food security is affected by several factors, such as cropland
shrinkage, fisheries reduction, increased wealth in countries such as China and
India with the consequent increased demand for food (Godfray et al., 2010),
global warming (Millennium Ecosystem Assessment, 2005) and the overall
intensification of agriculture and greater pressures on soils.
A total of 1.5 billion hectares of land is currently used worldwide for crop
production. This represents 11 per cent of the planet’s land surface (FAO,
2003). The FAO (2003) also reports that 2.7 billion hectares of land with crop
production potential remain unexploited. However, these figures might over-
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estimate the land available for agricultural production (Bot et al., 2000). Some
of the land that could potentially be used for agriculture is subject to ecological
constraints and pollution, while other land is protected or occupied by other
land uses (e.g. forests and woodlands, human settlements). If we consider the
availability of soils suitable for agricultural production, which means soils with
an intrinsic fertility, we should not be surprised that only a limited portion of
the planet’s lands are covered by such soils (Figure 7.1).
But what exactly happened in 2008? There had been a series of events
(unfavourable climate conditions, increase investments in biofuels crops, etc.)
that contributed to a drastic reduction of cereals production at the global scale.
At the same time demand increased for cereals for food uses (driven especially
by the fast growing Asiatic economies) and for non-food use (biofuel produc-
tion). Cereal prices rose rapidly, to the satisfaction of farmers (the price of
wheat doubled), but with consequences for the final consumers, who expe-
rienced concomitant increases in the prices of food such as bread, pasta, etc.
Another effect, less evident but probably more serious, was the drastic reduc-
tion of global cereals stocks, which are estimated at 520 millions of tonnes of

Figure 7.1 Distribution of intrinsically fertile soils

 148 C. Gardi
cereals, equivalent to just months (25 per cent) of global consumption. In other
words, a reduction of just 30 per cent in world cereal production will be suf-
ficient to bring humanity to the edge of food crisis.
The magnitude and rate of the increase in Chinese demand for wheat and
soybean combined with the conversion of land use in Brazil is already indicative
of the pressures that are piling on cropping systems and on residual terrestrial
natural and semi-natural ecosystems laying over fertile soils.
Based on this evidence and theses perspectives, it remains unclear why,
at the local scale, agricultural product prices have increased so little or even
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decreased over the last decade, suggesting farmlands and good agricultural
soils are an abundant and infinitely replaceable resources. The main reason
lies precisely in the market prices of agricultural commodities that, until the
recent peaks, did not guarantee profitable margins for small-scale farms and
seemed to indicate the existence of agricultural systems more efficient and
competitive and, above all, an unlimited availability of both food commodi-
ties and arable land. This fact, in the presence of increasing production costs
for small farms and strong competitive pressure for more profitable use of
the land in the short term, has encouraged the gradual abandonment of agri-
cultural activity, for the benefit of speculative processes of urban expansion.
There was an implicit (and wrong) social assumption that the loss of land
and agricultural production at the local scale would be compensated in some
other part the world, where agriculture is still a profitable and competitive
economic activity.
The signal provided by market prices of food commodities, however, is not
a reliable indicator of the absolute availability of a particular ‘good’, or more
correctly a ‘commons’ such as soil, that is non-renewable, finite and degrada-
ble. Prices in reality can be an expression of the scarcity or the abundance of
a resource, within a trading market, but do not account for all those people
who for various reasons can not access the market and express a demand in
monetary terms. In the globalization era we would think that all humanity
participates in some way in the game of supply and demand that sets the prices
of food commodities. In reality more than a third of the world’s population is
virtually cut off by this game and from the market, for the simple reason that it
does not have the economic resources to express its demand or, more explic-
itly, because they are too poor. According to UN figures, at the beginning of
the third millennium, two and a half billion people were living on less than
$2 a day. Of these, 850 million were in a state of malnutrition, an increasing
proportion compared to the 1990s. In addition to the people that are excluded
by the market, for lack of income, we should consider those who can not
participate because they do not exist – future generations. We refer to the pref-
erence and willingness to sacrifice the future of these 2.5 billion people, that by
2050, will added to the current 6.5 billion. If the legitimate food needs of those
who will and those who currently are in a state of undernourishment could
be expressed in terms of monetary demand discounted, the price of primary
products would certainly be different, as would be the value of agricultural land
and the perception of its global availability.
 Impact on global food security 149
On the supply side there is the need to consider that the price system reflects
the current productive situation. It does not provide information on the
medium and long-term sustainability of current yields and, more generally, of
intensive farming systems. The occurrence of a sudden fertility crisis in specific
production areas of the globe, caused by soil salinization and the exhaustion
of overexploited aquifers for irrigation purposes, the spread of desertification
and soil erosion, such as that occurring in the Chinese loess plateau, or the
consequences of climate change, could in future further increase the demand
for agricultural land which may no longer be satisfied because of the irrevers-
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ible changes of land use occurring in the meantime because of misleading

market signals. But above all, current food commodities prices that encourage
and induce those irreversible land use changes, do not take into account the
expected future performance of a crucial variable for the entire system of global
food supply, that is the basis of those same prices and of the market structure
that makes them possible: the cost, or the availability, of fossil fuels.
The extraordinary increases in yield obtained in agriculture after the Second
World War were largely due to a massive and unprecedented use of subsidiary
fossil energy. We can consider that after this period modern agriculture became
more oil driven than photosynthetically driven. Fossil fuel energy is used for
the synthesis of chemical fertilizers and pesticides, as well as for the production
and operation of agricultural machinery and irrigation pumps. It is estimated,
for example, that only the production of nitrogen fertilizers covers 2 per cent
of world industrial energy consumption (Hawken et al., 1999). Large quantities
of fossil fuels are also consumed by the transport, processing, storage, packaging
and retailing of agricultural products, which is required to cover the distance
from the field to the (super) market shelf. Studies conducted on the American
food supply chain estimate that this consumption is four times higher than that
required for the agricultural production phase. The sum of this consumption
shows that, on average, every calorie contained in a food product purchased at
retail costs from 1 to 10 calories as fossil fuels. Such an energy ratio suggests that
with the food we are eating we are above all consuming oil.
Until a few years ago, the relatively low prices of fossil fuels meant that the
cost component of food related to transport, conservation and distribution was
almost irrelevant compared to other factors such as labour costs. This explains
why you could find in the local market vegetables and fruits grown several
thousand kilometres away with prices lower than or at least competitive with
similar products from local supply chains. The possibility of importing afford-
able products from countries in the southern hemisphere, like New Zealand,
Chile, South Africa also promotes a misleading perception of seasonality, lead-
ing to the belief that at the market it is always summer. Despite the relatively
low price of oil in the present day (2014–2015) there are reasonable grounds
for thinking that this sort of aberration in the food supply system, based on the
low cost of energy for transport and storage, cannot last much longer, at least
to this degree. Just a few years ago the steady increase in oil prices (the main
energy source for transport) that exceeded the threshold of $100 per barrel
(2008) revealed all the limits of a system based on cheap oil or energy prices.
 150 C. Gardi
The role of bioenergy crops
The increased use of agricultural lands for bioenergy production also represents
a major concern with regard to land availability for food crop production. The
International Energy Agency scenarios for land use (IEA, 2004) estimated that
approximately 5 per cent of the EU’s cropland area would have to be con-
verted to biofuel production in order to replace 5 per cent of its petrol supply,
while 15 per cent of its cropland area would have to be converted to substitute
5 per cent of the diesel supply (Escobar et al., 2009). Forecasts from the same
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Agency (IEA) indicate that by 2050, 27 per cent of the world transporta-
tion fuels could be provided by bioenergy crops (from the actual 2 per cent).
During the last decade biofuel production has almost tripled (from 45 to 130
billions litres) (IEA, 2011). Considering an average biofuel yield of 4,500 l/ha,
we can estimate that the area of agricultural land devoted to this use has risen
from 10 million ha to almost 29 million ha. If the IEA’s forecasts are respected,
considering also the increasing demand of transport worldwide, more than
300 million hectares (20 per cent of the actual cultivated land) will have to be
dedicated to biofuel production. A possible correction to this alarming situa-
tion could be provided by the third generation of biofuels, which allow the
production of fuels from lingo-cellulosic materials. In other words, instead of
using the main product of the crop (cereals, sugar, etc.) only the by-product of
the crop would be used.

Urban sprawl, urban expansion and land take

Sprawling cities tend to consume the best agricultural lands, forcing agri-
culture to move to less productive areas (Scalenghe and Marsan, 2009). The
extent of agricultural land and, to a smaller extent, woodlands and semi-natural
and natural areas, is decreasing due to conversion to residential, industrial
or commercial areas (EEA, 2011). Urban centres often expand on the most
productive land because cities are historically built mainly on fertile soils
(Satterthwaite et al., 2010).
Furthermore, land take causes environmental perturbations that affect
agricultural ecosystems (e.g. landscape fragmentation, changes in the water
cycle and reduced habitats). There is increasing evidence that European cit-
ies tend to become more dispersed, as a result of the spread of low-density
settlements (urban sprawl) (Kasanko et al., 2006), increasing similarities with
urban areas of the US. However, differences among the urban structures
and their dynamics between the US and Europe remain important, mainly
because different relationships exist between central and local governments
(Summers et al., 1999). Land-take processes are occurring in other parts of
the world at considerably higher rates than in Europe, especially in countries
with rapidly growing economies. For example, 5.1 per cent of the overall
territory in China was lost to manufacturing and municipal activities during
the period 1996 to 2003 (Chen, 2007), and in the Beijing–Tianjin–Hebei
 Impact on global food security 151
region urban area growth expanded by 71 per cent between 1990 and 2000
(Tan et al., 2005). Similar growth rates have been recorded also in India
(Fazal, 2000) and in other fast growing countries. Worldwide urban areas are
expanding at twice their population growth rates (Angel et al., 2011).

Land grabbing
In addition to these processes there is an increasing interest in farmland due
to rising food and fuel prices, biofuel mandates, food security concerns, cli-
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mate finance incentives and worries about climate change effects on scarce
resources. There is concern that the remaining areas of cultivable land that are
currently mostly used under customary rights are vulnerable to speculators or
unscrupulous investors, who exploit smallholder farmers, herders and other
local people who lack the power to stand up for their rights. This increasing
interest in land has resulted in land grabbing. Land grabbing refers to large-scale
land acquisitions, especially in developing countries, by domestic and transna-
tional companies, governments and individuals. This phenomenon was also
known and practised in the past, but nowadays with the term ‘land grabbing’
we primarily refer to large-scale land acquisitions following the 2007–2008
world food price crisis. Tens of millions of hectares have been subject to some
sort of negotiation with a foreign investor. Half of the total in African coun-
tries (Ambalam, 2014; Anseeuw, 2013; Lavers, 2012; Lisk, 2013: Manji, 2012;
Millar, 2015; Sulieman, 2015; Veldwisch, 2015), one-fourth in Asia (Feldman
and Geisler, 2012; Kenney-Lazar, 2012; Jiao et al., 2015; Semedi and Bakker,
2014; Siciliano, 2014), and not less than 10 per cent in Latin America (Borras
et al., 2012; Brent, 2015; Bulkan, 2014; Economist, 2011; Grajales, 2015;
Grandia, 2013; Holmes, 2014; Murmis and Murmis, 2012; Oliveira, 2013;
Perrone, 2013; Piñeiro, 2012; Rocheleau, 2015; Urioste, 2012). The issue suf-
fers from a lack of transparency as contracts are often kept secret and most of
these data remain undisclosed.

Impact of land take on potential agricultural

production in Europe
In 1985, the European Commission launched the CORINE (Coordination
of Information on the Environment) programme. The main objectives of the
CORINE Land Cover (CLC) project were to provide reliable quantitative
data on land cover across Europe, and to develop one complete spatial dataset
covering the EU member states (MS) plus several other European and North
African countries. The datasets contain homogeneous data on land cover
areas, which are represented as polygons (shapefiles), although unfortunately
not all the countries involved in the programme have the same temporal
coverage. Due to this limitation, CORINE Land Cover datasets from 1990,
2000 and 2006 were used to assess the extent of land take of agricultural lands
in 21 EU member states.
 152 C. Gardi
In order to estimate the impact of land take on potential agricultural produc-
tion, the regional average of winter wheat yields (NUTS2 level, 1992–2004
period – MARS, 2012), were used. These data were available for 19 of the 21
countries (Gardi et al., 2015). For each NUTS2 area, the potential agricultural
production losses were calculated on the basis of the following equation:

PAP_LOSSESNUTS2 = ALTNUTS2 × AWWYNUTS2

where:
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•• PAP = Potential Agricultural Production

•• PAP_LOSSESNUTS2 = Losses of PAPC at NUTS2 level (in tonnes of win-
ter wheat)
•• ALTNUTS2 = Land take of agricultural area at NUTS2 level (ha) for the
given period
•• AWWYNUTS2 = Average Winter Wheat Yields at NUTS2 level (t/ha) for
the given period

Land take was calculated using CORINE Land Cover maps of 1990, 2000 and
2006. For 21 of the 27 European Union member states, agricultural land take
was computed to be 752,973 ha for 1990–2000 and 436,095 ha for 2000–2006,
representing 70.8 per cent and 53.5 per cent, respectively, of the total EU land
take for these periods.
Table 7.1 shows the land take data, on a yearly base, expressed both in abso-
lute and relative terms. The small countries, characterized by high population
densities, present, in relative terms, the greatest loss of agricultural area due to
land take. The Netherlands, for instance, experienced the highest rate of land
take in relative terms, and one of the largest also when absolute values are con-
sidered (Table 7.1). This country lost almost 2.5 per cent of its agricultural land
during the period 1990–2000 and 1 per cent during the period 2000–2006.
The greatest land take in absolute terms, however, took place in the largest
EU countries: Germany, Spain and France (1990–2000) and Spain, France and
Germany (2000–2006).
The impact of this land take on the production capabilities of the agricul-
tural sector for the period 1990–2006 for 19 of the 21 states was estimated to
be equivalent to a loss of more than 6 million tonnes of wheat (0.81 per cent of
the total available potential agricultural production (PAP)). From this example
we can conclude that, taking a long-term perspective (e.g. 100 years), land take
could be an important threat to food security in the EU. In this assessment, it
was estimated that 19 EU countries lost approximately 0.81 per cent of their
PAP capacity between 1990 and 2006, with large variability between regions.
A more detailed analysis showed that certain regions, such as those around the
largest cities, in metropolitan areas and coastal zones, experienced the greatest
loss of their most fertile soils.
 Impact on global food security 153
Table 7.1 Absolute and relative yearly agricultural land take in 21 EU countries
Country Agricultural land take (ha y-1) Relative land take (% y-1)

1990–2000 2000–2006 1990–2000 2000–2006

Austria 1,034 870 0.01 0.01

Belgium 1,579 426 0.05 0.01
Bulgaria 281 570 0.00 0.01
Czech Republic 946 2,011 0.01 0.03
Germany 19,097 9,667 0.05 0.03
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Denmark 1,239 1,729 0.03 0.04

Estonia 148 366 0.00 0.01
Spain 11,872 17,638 0.02 0.04
France 11,570 12,697 0.02 0.02
Hungary 953 2,503 0.01 0.03
Ireland 3,120 3,275 0.04 0.05
Italy 7,931 7,735 0.03 0.03
Lithuania 52 551 0.00 0.01
Luxembourg 171 62 0.07 0.02
Malta 1 1 0.00 0.00
Netherlands 8,130 5,879 0.23 0.17
Poland 1,709 2,883 0.01 0.01
Portugal 4,244 1,838 0.05 0.02
Romania 743 1,396 0.00 0.01
Slovenia 12 70 0.00 0.00
Slovakia 1,034 870 0.01 0.01

The importance of land take as a threat to soil varies among EU countries.

In countries with high land-take rates and high PAP, such as the Netherlands,
land take is a particularly important issue. The same applies for most of the new
member states where the agricultural land-take trend has doubled in the past
few years, and for the countries affected by ‘real estate bubbles’, such as Spain
and Ireland.

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 8 Hydrological impact of soil sealing
and urban land take
Alberto Pistocchi
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Introduction
Land take by urban development is a major hydrologic threat: it entails soil
sealing and compaction, impervious surfaces may collect pollutants that are
periodically washed off, and artificial drainage generally transfers runoff away
much more quickly than in natural watersheds. Actually, the contemporary
city is mainly built on the concept that rainwater should be evacuated rather
than retained. A quick drainage of water is not an obvious and necessary need:
the birth of cities in the Neolithic has been put in relation with the feminine
wisdom and capacity to collect and retain (Mumford, 1961):

Under woman’s dominance, the Neolithic period is pre-eminently one

of containers: it is an age of stone and pottery utensils, of vases, jars, vats,
cisterns, bins, barns, granaries, houses, not least great collective contain-
ers, like irrigation ditches and villages. The uniqueness and significance
of this contribution has too often been overlooked by modern scholars
who gauge all technical advances in terms of the machine . . . . Wherever
a surplus must be preserved and stored, containers are important. . . . But
as soon as agriculture brought a surplus of food and permanent settlement,
storage utensils of all kinds were essential.

Retaining and recycling water is indeed an essential urban function in arid or

semi-arid environments, and the capacity to adapt landscapes to collect water
and make it available for human use has been key to the development of civi-
lizations across the world (Laureano, 2001).
Contrary to early agglomerations, however, modern cities have grown
largely inconsiderate of the need to manage landscapes to retain water, and
now urban drainage systems, made of underground pipes serving impervious
surfaces such as roads and roofs, are regarded as ‘traditional’. The mechanism
of hydrological alteration operated by soil sealing and ‘traditional’ urban drain-
age is essentially a reduction of the permanence of water in the landscape, by
avoiding infiltration in soils, hence shortening the hydrological pathways of
runoff, and ultimately accelerating its delivery to the receiving water bodies.
 158 A. Pistocchi
The effect is exacerbated by the connection of impervious areas to receiv-
ing streams through artificial drainage networks. Sometimes, the ‘connected’
impervious fraction of a catchment is considered as ‘effective’ and looked at as
a better predictor of the impacts of soil sealing (Walsh et al., 2005a, as summa-
rized by Hamel et al., 2013). This causes, on the one side, the drying-up of the
landscape, and on the other the reduction in duration, and increase in intensity
of stream water discharges. A dryer landscape, at the same time, evaporates less
water. It is customary to denote soil moisture and evapotranspiration flows
as ‘green water’ in contrast to ‘blue water’ in liquid form in streams, aquifers
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and other water bodies, following a concept initially proposed by Falkenmark

(1995). We may think of soil sealing as a process shifting water from ‘green’ to
‘blue’ (moreover available for shorter time). The consequences of this shift may
be significant on the water cycle not just within the affected watershed, but
also at regional scale, where reduced evapotranspiration may alter precipitation
feedbacks (e.g. Rockström et al., 2014).
Soil sealing should be avoided, limited, mitigated and compensated as much
as possible (e.g. European Commission, 2012a). The only effective way to
compensate the hydrological effects of soil sealing is through water storage,
with characteristics that may vary considerably depending on the compensa-
tion target. In this contribution we discuss the impacts of soil sealing and land
take from urban development in terms of water quality, water availability and
floods, and we summarize options for the mitigation of impacts on floods and
water availability.

The ecological consequences of soil sealing

In USEPA’s Causal Analysis/Diagnosis Decision Information System (CADDIS),
urbanization is considered a specific source of stress for the aquatic environment;
its impacts, due to morphological alteration of the water bodies, chemical emis-
sions and hydrological alterations, affect water quality (with increased nutrients
and toxic substances), flow regime (with flashier flows), physical habitat (with
simplification of channel morphology), water temperature, and energy flow
with less organic matter retention (USEPA, 2010). As a complex mixture of
stressors that can be hardly disentangled, urbanization induces systematically a
set of symptoms in water bodies, which have been called the ‘urban stream
syndrome’ (Walsh et al., 2005b). The CADDIS urbanization stressor module
outlines the main ecological consequences on water bodies of urbanization
(USEPA, 2010) as follows:

1 Urbanization can significantly modify the structure of aquatic biotic com-

munities. The impervious cover fraction of the catchment has been put in
inverse relationship with the probability of occurrence of certain fish spe-
cies, and generally with ecological stream conditions. The effect becomes
very strong beyond a threshold of share of the watershed or riparian area
affected by urbanization (somewhere between 20 and 30 per cent).
 Hydrological impact 159
2 The hydrological alteration induced by urbanization, with increased
runoff volumes and reduced flow duration, may create conditions for inci-
sion of the stream channels, in turn reducing the water storage capacity of
the alluvium. As a result, riparian vegetation may significantly change and
so may the capacity of riparian areas to retain nutrients.
3 Pollution associated to urbanization is not just due to wastewater treatment
effluents, but also to combined sewer overflows and wash-off of impervi-
ous surfaces where contaminants build up during inter-storm periods. The
wastewater-related enrichment of streams contributes to altering macro-
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invertebrate diversity, while chemicals bypassing wastewater treatment or

discharged with combined sewerage overflow may include endocrine dis-
ruptors or be toxic to aquatic organisms.
4 Wash-off of impervious surfaces typically carries sediments, nutrients,
household pesticides, metals, polycyclic aromatic hydrocarbons (PAHs),
oil and grease. The transport of contaminants is mainly associated with
impervious surfaces only when they are connected with an urban drainage
system. Anyway, chemical concentrations in the water bodies have been
correlated to the impervious cover fraction of the watershed. Pollutants
associated to urban runoff can be predicted on the basis of empirical mod-
els, among which the classic one by Heaney et al. (1976), although the
predictability of pollutant loads with statistical methods may be rather low
(e.g. Brezonik and Stadelman, 2002).
5 The alteration of riparian vegetation cover and the heat transferred from
warm sealed surfaces to rainwater during wash-off tend to increase water
temperature, which in turn affects the activity of microorganisms and the
chemical reactivity of substances. A net effect of warmer streams is the
decrease of fish and macro-invertebrate abundance and variety.
6 Sediments supply tends to decrease in the long term; this may trigger chan-
nel bank erosion and the enlargement of channels, in turn altering physical
habitat availability. Road crossings also affect physical habitat through
hydrodynamic alteration and scour around piles and piers. Substrates in
urbanized streams may tend to increase the fine fraction of sediments and
become less stable, or on the contrary be eroded and armoured.
7 Organic carbon input and metabolism are changed in urban streams: less
natural carbon, and more anthropogenic carbon, is conveyed to the stream.
The storage of carbon is generally reduced.

Walsh et al. (2012) present urban storm water runoff as a new class of environ-
mental flow problem: contrary to ‘traditional’ environmental flow problems
where a minimum water volume flowing in streams should be left unaffected
by abstractions in order to protect ecosystems, in this case avoiding excess
volumes to reach the streams generally improves ecological conditions and the
services provided by water ecosystems (Figure 8.1). Therefore, urban storm
water harvesting can be regarded as a win–win solution for water supply to
human activities.
 160 A. Pistocchi
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Figure 8.1 Conceptual graphs of ecological and human value of water (source: Walsh
et al., 2012, under the Creative Commons Attribution licence)

Impacts on water availability and their mitigation

Although the increase in runoff volumes due to soil sealing may facilitate storm
water harvesting, flows from urbanized catchments tend to be flashier, with
relatively high and short-lasting discharges. In other terms, the flow dura-
tion curve of an urbanized catchment tends to become steeper compared to
undeveloped conditions, with higher high-flows of shorter duration and lower
low-flows of longer duration. As a consequence, overall water availability may
be reduced. This response of flow duration curves to soil sealing has excep-
tions (e.g. Hawley and Bledsoe, 2011) that may be due to, for example, leaking
water supply infrastructure or reduced evapotranspiration. Burns et al. (2005)
find flow recession speed to increase with the degree of development in the
catchment in the Croton river basin, New York, USA, but they also find an
increment of base flow. Simultaneous increase of low-flows and high-flows
can be generally expected where rainfall is relatively uniform in time, and
subsurface flows contribute relatively little to total discharges. This makes a
generalization about the impacts on water availability difficult (Hamel et al.,
2013). Evidence of change of flow duration curves is reported in several cases
(e.g. Schoonover et al., 2006; Guo and Quader, 2009; Mejía et al., 2014), and
is predicted by hydrological modelling studies (e.g. Yang et al., 2014).
Sustainable urban drainage systems (SUDs) and low impact development
(LID) have long been advocated in order to mitigate the hydrological impacts
of soil sealing. According to the SUSDRAIN platform of the construction
industry research and information association (CIRIA).
 Hydrological impact 161
Sustainable drainage is a departure from the traditional approach to drain-
ing sites. There are some key principles that influence the planning and
design process enabling SuDS to mimic natural drainage by: storing run-
off and releasing it slowly (attenuation); allowing water to soak into the
ground (infiltration); slowly transporting (conveying) water on the surface;
filtering out pollutants; allowing sediments to settle out by controlling the
flow of the water.
(www.susdrain.org/delivering-suds/using-suds/suds-principles/
suds-principals.html, accessed 17 November 2016)
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Similarly, USEPA characterizes LID as follows:

LID is an approach to land development (or re-development) that works

with nature to manage stormwater as close to its source as possible. LID
employs principles such as preserving and recreating natural landscape
features, minimizing effective imperviousness to create functional and
appealing site drainage that treat stormwater as a resource rather than a
waste product. There are many practices that have been used to adhere
to these principles such as bioretention facilities, rain gardens, vegetated
rooftops, rain barrels, and permeable pavements. By implementing LID
principles and practices, water can be managed in a way that reduces the
impact of built areas and promotes the natural movement of water within
an ecosystem or watershed. Applied on a broad scale, LID can maintain or
restore a watershed’s hydrologic and ecological functions.
(http://water.epa.gov/polwaste/green/,
accessed 17 November 2016)

LID may be an effective solution for both water quality and quantity alterations
associated to soil sealing. For instance, Dietz and Clausen (2008) find that runoff
volume and loads of contaminants from LID areas do not depend on the per-
centage of impervious area as strongly as those from traditional developments.
More recently, the European Commission’s Blueprint to safeguard Europe’s
waters (European Commission, 2012b) has endorsed the use of natural water
retention measures (NWRMs) as a kind of approach to retain water in the
landscape, by this mitigating floods, ad improving water quality and availability.
NWRMs broaden the scope of LID and SUDs:

Natural Water Retention Measures (NWRM) are multi-functional

measures that aim to protect and manage water resources and address
water-related challenges by restoring or maintaining ecosystems as well
as natural features and characteristics of water bodies using natural means
and processes. Their main focus is to enhance, as well as preserve, the
water retention capacity of aquifers, soil, and ecosystems with a view to
improving their status. NWRM have the potential to provide multiple
benefits, including the reduction of risk of floods and droughts, water
 162 A. Pistocchi
quality improvement, groundwater recharge and habitat improvement.
The application of NWRM supports green infrastructure, improves or
preserves the quantitative status of surface water and groundwater bodies
and can positively affect the chemical and ecological status of water bodies
by restoring or enhancing natural functioning of ecosystems and the ser-
vices they provide. The preserved or restored ecosystems can contribute
both to climate change adaptation and mitigation.
(European Commission, 2014)
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SUDs, LID and NWRMs include a variety of solutions (green roofs, grassed
swales, constructed wetlands, detention and retention ponds, etc.) that need
to be appraised and evaluated on a case-by-case basis depending on the condi-
tions where they have to be applied, both for new developments and for the
retrofitting of existing ones. Both the SUSDRAIN platform and the European
Commission’s NWRM platform (nwrm.eu) aim at providing a clearinghouse of
examples by collecting case studies providing evidence for the cost-effectiveness
of these solutions on a continuous basis.
In order to mitigate the impact of urban development, these solutions
should in principle aim at restoring the ‘natural flow regime’ of streams (Poff
et al., 1997), which requires an in-depth understanding of the hydrological
behaviour of the catchments before land development, as well as hydrologi-
cally considerate design.

Impacts on floods and their mitigation

There is evidence that soil sealing and urban development not only increase
annual runoff volumes, but also flood hazards (e.g. Pitt, 2008), especially when
acting together with other mechanisms. Du et al. (2015), for instance, show
evidence of increased hazards from combined urban soil sealing and acceler-
ated erosion produced by the displacement of agriculture. Flood hazards increase
particularly in smaller catchments, where urban development tends to represent
larger shares of the contributing area. Flood peaks due to extreme rainfall tend
to increase because of two synergistic mechanisms: on the one side, the capacity
of natural soils to infiltrate and detain rainfall is reduced by soil sealing; on the
other, urban areas equipped with traditional drainage systems deliver runoff more
quickly and more efficiently, with reduced detention of rainfall on urban surfaces
compared to undeveloped lots. The relative importance of the two mechanisms
depends on local factors. For very high return period floods, often the initial soil
moisture conditions at the start of the flood event are such that soil water storage
and infiltration are already quite limited. However, an extreme rainfall event may
also occur after soils have had sufficient time to drain antecedent precipitation,
and their infiltration capacity is near optimal conditions. In engineering practice,
extreme rainfall infiltration capacity of unsealed soils is often evaluated in the
range 20–70 per cent of the event rainfall volume, depending on soil characteris-
tics and initial conditions (e.g. ASCE, 1960). On the contrary, sealed soil is often
assumed to deliver 80–100 per cent of the event volume.
 Hydrological impact 163
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Figure 8.2 Ante-operam runoff coefficient based on direct runoff from LISFLOOD

model simulations (see text for details). The red polygon is the perimeter
of Emilia Romagna

For what concerns the detention of runoff on the soil surface, typically non-
sealed agricultural or natural land provides at least 50 m3/ha of volume given
primarily by surface roughness and depressions. This amount is greatly reduced
in urban land, e.g. by a factor of three to four (Pistocchi, 2001; Sofia et al.,
2014). Smaller volumes mean shorter residence time, i.e. faster concentration
of runoff and consequently higher discharge peaks. Pistocchi et al. (2015) pre-
sent an example of an area significantly affected by urban expansion in northern
Italy, where soil sealing has particularly impacted the watersheds of the sec-
ondary and artificial drainage network of the plains in the region of Emilia
Romagna (shown in Figure 8.2). The channels, suitable for the drainage of
 164 A. Pistocchi
agricultural land as in pre-development conditions, require retrofitting in order
to keep flood risks under control.
In order to avoid the need to retrofit the drainage networks in consequence
of new urban developments, the local flood management plans have intro-
duced provisions for the mitigation of flood impacts, known as the principle of
hydraulic invariance of land use change (Pistocchi, 2001; Pistocchi and Zani,
2004): the hydrological impact of soil sealing should be offset by increasing the
detention volume at the soil surface. The detention volume required to offset
the alteration of flood peak discharges is not linearly related to the extent of
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urban development. For the urbanization of a share X of the catchment, the

required volume is computed as (see Pistocchi et al., 2015):

 α (1 − X ) + β X
1.92

W = WN  
 α 

where WN is the detention volume capacity of undeveloped land, respec-

tively, while α and β are the fraction of the rainfall volume during the flood
event that contributes to runoff from undeveloped and developed land
respectively. This volume should be made available in the catchments after
urban transformation in order to offset the effects of soil sealing. We may
have a first estimation of W by assuming that 90 per cent of rainfall on imper-
vious surfaces contributes to runoff during a flood event in a small catchment
(β = 0.9), and on unsealed soil this is reduced to 20 per cent (α=0.2), and by
setting WN = 50 m3/ha, as assumed by the Emilia Romagna regional flood
management plans (Pistocchi, 2001). The detention volume W allows in
principle to offset the increase in peak flood discharge, not the increase in
total flood volume. Moreover, this volume is not effective to harvest water,
but just to slow down the flow of water to the catchment outlet.
The detention volume computed above can be used as an indicator of urban
development impact on peak discharges. For the sake of illustration, we propose
hereafter a demonstrational calculation over Europe. A European map of the
fraction of direct runoff (Burek, 2014, personal communication) prepared at 5
km resolution as part of the input parameters of the European scale hydrological
model LISFLOOD (Burek et al., 2013) was transformed into a map of runoff
coefficients by assigning a value of 0.2 to 5 × 5 km2 cells having no direct runoff
contribution, a value of 0.7 to cells with 100 per cent direct runoff, and linearly
distributed values in between. This map, Φ, is shown in Figure 8.2 and may be
considered a first approximation of present European conditions, ‘ante operam’.
As such, it may be used for the evaluation of impacts due to recent land take
by urban expansion. For this purpose, we use the map of the changes in Urban
Morphological Zones (UMZs) provided by the European Environment Agency
(EEA: www.eea.europa.eu/data-and-maps/data/urban-morphological-zones-
changes-2000, last accessed January 2014), a binary map of 100 m resolution
indicating with 1 those grid cells turned from non-urban to urban between
 Hydrological impact 165
2000 and 2006, and with 0 those unchanged. This map was aggregated to a 1
km resolution by assigning 1 km × 1 km cells the sum of the values of the 100
m resolution map. This sum corresponds to the percentage of the 1 km2 cell
turned to urban land use. The resulting map was used as a weight to a flow
accumulation operation (e.g. Pistocchi, 2014, ch. 7) that yielded the contribut-
ing area upstream of each cell turned to urban land use between 2000 and 2006.
The ratio of this weighted flow accumulation to the standard flow accumulation
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Figure 8.3 Compensation volume (1 mm = 10 m3/ha) estimated for the compensation

of new urban areas built between 2000 and 2006 (see text for details). The
red polygon is the perimeter of Emilia Romagna
 166 A. Pistocchi
gives the fraction of the catchment turned urban in the period, X. A map of the
required offset volume (m3/ha) to offset soil sealing through ‘hydraulic invari-
ance’ was estimated as:

 ( Φ1 − X ) + 0.9X
1.92

W = 50  
 Φ 
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as we assume WN = 50 m3/ha. If we average the volume values within Europe.

If we average the computed values of W at the regional level within Europe,
the resulting map (Figure 8.3) highlights where recent urban expansion may
cause higher impacts on the flood peaks of the secondary and artificial drainage
networks. The map reflects the distribution of new UMZs and does not take
into account local conditions of precipitation and soil/land management other
than reflected by the abovementioned direct runoff map. As such, it must be
regarded as a purely illustrative example of the assessment of land take impacts
on the local drainage network.

Conclusions
We have provided an overview of the hydrological impacts of soil sealing and
urban expansion. These affect the water cycle, hence ecology, as well as water
availability and flood hazards in catchments. The effects of soil sealing have
been identified as a significant threat to water bodies and should be appropri-
ately addressed first of all by limiting impervious surfaces in a catchment and by
avoiding their direct drainage to streams. Impervious surfaces that cannot be
avoided should be accompanied by appropriate mitigation measures based on
the paradigms of SUDs, LID and NWRMs, and (limited to the issue of flood
peak discharges) the principle of hydraulic invariance.

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 9 Impact of land take and soil
sealing on biodiversity
Geertrui Louwagie, Mirko Gregor, Manuel
Löhnertz, Ece Aksoy, Christoph Schröder
and Erika Orlitova
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Introduction
Biodiversity can cover a lot of different things and refers in its simplest form
to the diversity of habitats, species and genes. Thus, ‘the concept covers not
only overall richness of species present in a particular area but also the diversity
of genotypes, functional groups, communities, habitats and ecosystems there’
(Haines-Young, 2009).
Soils host many soil-dwelling species – from the very small (like fungi and bacteria)
to the very large (like earthworms and moles) – and provide valued habitats for
them. Soil organisms break down organic matter and transform nutrients, making
them available to other plants and organisms. Soil biodiversity also controls the
degradation and release of many pollutants. Soil is thus at the heart of many envi-
ronmental processes and the benefits humans derive from ecosystems in general.
Many of our valued habitats and rare plant species are dependent on very specific
soil conditions. Soil thus has an important role in sustaining biodiversity.
Land take by the expansion of artificial areas for urban settlements and related
infrastructure has been the main cause of net land cover change in Europe
since at least the 1990s (when land cover monitoring in Europe started).1 Land
take differs from increase in soil sealing or imperviousness, as not all of the
area mapped as artificial may actually be covered with impervious material.
Nevertheless, land take is often used as a proxy for soil sealing.
In this chapter we focus on the effects of land take and soil sealing on the dif-
ferent aspects of biodiversity. In describing the effects, we distinguish between
two types: micro- to meso-scale effects happen at or close to the concentration
of soil sealing, whereas macro-scale effects occur at a distance from the soil seal-
ing concentration. Assuming that the biggest concentration of sealing occurs in
core urban areas, micro- to meso-scale effects occur in the core urban space,
whereas macro-scale effects extend into the peri-urban and rural space.

Impact

Micro- to meso-scale effects

Sealing interrupts the contact between pedosphere and atmosphere and thus
changes the gas, water and material (including nutrients) fluxes (Burghardt
et al., 2004), and thus directly influences biogeochemical cycling in soils.
 170 G. Louwagie et al.
Experiments from Beijing (China) have shown that an impervious cover
(in comparison to forest and bare land) significantly affects microbial biomass,
enzyme activity and nitrogen transformation processes (mineralisation and
nitrification) (Zhao et al., 2012). Sealing particularly affects soil characteristics
in the upper 10 cm of the soil, with decreasing effects, including on soil micro-
bial activity, with depth.
Ultimately, owing to the hampered exchanges between soil fauna and
external inputs, sealing can lead to severely depleted soil biodiversity, and a
slow death of most soil organisms (European Commission, 2010). Soil biota
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can initially survive on the moisture and organic matter that was present in
the soil before sealing, until these resources are exhausted. Then, the bacteria-
dominated microorganisms may enter an inactive state (dormancy) or simply
die off, while small (dominantly micro-arthropods) and larger (e.g. earth-
worms) invertebrates may either move away from the sealed area or, when
sealing covers vast areas, die off.
Soil sealing is also a key contributor to the urban heat island effect,
which is generated by the differences in heat storage of construction mate-
rials along urban–rural gradients. Soil sealing in particular influences the
albedo (or reflection coefficient) of surfaces, which may lead to increased
temperatures above sealed areas (micro-scale) and within urbanised areas
at large (meso-scale) (Burghardt et al., 2004). The urban heat island effect
and the related increase in air and soil temperatures have exerted an evolu-
tionary pressure on soil organisms (Scalenghe and Ajmone Marsan, 2009).
The effect can be observed in soil fungi, organisms that cannot regulate
their own temperature.
Soil sealing can eliminate a natural habitat for plants. Temperature increases
owing to the heat island effect have also caused changes in plant phenology,
with spring-blooming plants blooming earlier in the city than in the sur-
rounding habitats in a variety of ecosystems in North America, Europe and
China (Neil and Wu, 2006). Over time, such change (along with other fac-
tors, such as climate change or introduced species) can lead to differences in
species composition.

Macro-scale effects

Freshwater and related terrestrial habitats

The soil sealing pattern heavily influences water infiltration and preferential
flows: rainwater cannot directly infiltrate an impervious area; thus, it either
drains beside the sealed area (e.g. along a small road) or is discharged and enters
the sewer system (e.g. around big buildings or parking lots). Sediment and dust
particles follow the same pathway. Soil sealing also decreases plant and soil
evapotranspiration. If water infiltration is not facilitated at the edges of com-
pletely sealed, larger areas, groundwater recharge may be decreased; whereas
 Impact on biodiversity 171
when facilitated, surface water may break through to the groundwater, with
an increased risk of groundwater pollution as a consequence (Burghardt et al.,
2004). This so-called barrier effect at the edges of impervious areas may equally
lead to erosion in adjacent areas.
In connection to these hydrologic changes and interruptions, Arnold
and Gibbons (1996) reviewed literature on the effects of impervious surface
coverage on stream health and connected terrestrial habitats. Research con-
sistently shows a strong correlation between the imperviousness of a river
basin and the health of the receiving stream. Stream health is among others
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defined by pollutant loads, habitat quality and aquatic species diversity and
abundance. Stream degradation reportedly already occurs at relatively low
levels of imperviousness (from 10 per cent onwards) in the watershed. The
same threshold is deemed crucial for maintaining wetlands in good ecologi-
cal condition. Referring to the earlier-mentioned increased erosion risk,
Arnold and Gibbons (1996) also point at substantial losses of both stream-
side or riparian (where erosion occurs) and in-stream (where sedimentation
happens) habitats.

Ecosystem loss and fragmentation

Development of built-up areas and transport infrastructure also leads to land-
scape and ecosystem loss and fragmentation, worldwide considered the main
threat to biodiversity conservation.
When ecosystems are completely lost, the effects on biodiversity can be
assessed by mapping the different ecosystem types affected, including their rar-
ity (Geneletti, 2003). Rarity is a measure of how frequently an ecosystem type
is found within a given area; protection of rare ecosystems is often considered
the single most important function of biodiversity conservation.
Fragmentation has a number of ecological effects, such as the decline and
loss of wildlife populations, an increasing endangerment of species, changed
water regimes and a change in recreational quality of landscapes (EEA-FOEN,
2011) (Table 9.1). Habitat fragmentation can clearly cause remaining habitats
to become too small for some organisms to persist, or too fragmented so that
remaining patches may be too far apart for organisms to move between.
The fragmentation effects of land take and soil sealing have particu-
lar bearing on the liveability of cities. When cities are dense (i.e. have an
increasing amount of built-up area), they are to a large extent dependent
on ecosystem services from outside; the size and variety of urban green
areas as well as their connection with the ecosystems surrounding the city
will largely determine the biodiversity potential (Bolund and Hunhammar,
1999). However, the transport network and patches of commercial and
industrial service areas around cities often lower the stabilising effect of
outer core areas due to their barrier effect.
 172 G. Louwagie et al.
Table 9.1 Effects of landscape fragmentation on flora and fauna
Death of animals caused by road mortality (partially due to attraction of animals by roads
or railways: ‘trap effect’
Higher levels of disturbance and stress, loss of refuges
Reduction or loss of habitat; sometimes creation of new habitat
Modifications of food availability and diet composition (e.g. reduced food availability for
bats due to cold air build-ups along road embankments at night)
Barrier effect, filter effect to animal movement (reduced connectivity)
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Disruption of seasonal migration pathways, impediment of dispersal, restriction of

recolonization
Subdivision and isolation of habitats and resources, breaking up of populations
Disruption of metapopulation dynamics, genetic isolation, inbreeding effects and
increased genetic drift, interruption of the processes of evolutionary development
Reduction of habitat below required minimal areas, loss of species, reduction of
biodiversity
Increased intrusion and distribution of invasive species, pathways facilitating infection
with diseases
Reduced effectiveness of natural predators of pests in agriculture and forestry (i.e.
biological control of pest more difficult)
Source: EEA-FOEN (2011).

Box 9.1 Mapping the impact of land take on soil biodiversity

in Europe
Soil sealing has already been identified as one of the major threats to
soils in the 2002 European Commission’s Communication ‘Towards a
Thematic Strategy on Soil Protection’ (COM(2002) 179 final2). Through
the disruption of cycles, soil sealing contributes to the loss of valuable soil
functions, such as hosting the biodiversity pool, and thus to the loss of
soil-based ecosystem services.
Pan-European maps of a number of soil functions3 have recently
become available and were used to analyse the impact of land cover
changes on the potential of soils to provide those soil functions. In this
specific case the impact of land take, i.e. the expansion of areas with
artificial cover over areas that previously had (semi-)natural cover, on the
capacity of soils to act as biodiversity pool was assessed.
Soil biodiversity throughout Europe was estimated by using critical
thresholds for specific indicators that may regulate and affect the conditions
of soils for biodiversity: physical and chemical soil parameters, climate, soil
biomass production potential and land use/land cover. In general, the map
and the underpinning model rather express the quantity (abundance) than
the diversity (species richness) of soil organisms. However, as those two
 Impact on biodiversity 173
aspects (abundance and diversity) are often positively related, locations
with a high abundance of soil organisms are expected to possess a higher
species richness as well.
Figure 9.1 shows that there exist areas in the United Kingdom, Ireland
and parts of central Europe (e.g. France or the Netherlands) where soils
have a high potential to host the biodiversity pool. Those high levels are
mainly caused by the relatively high weight that is given to the land use/
land cover parameter. In addition, grassland soils with a high biomass
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Figure 9.1 Capacity of soils to serve as a biodiversity pool (source: ETC ULS

based on texture (European Soil Database version 2.0); topsoil
pH water, topsoil organic matter, total mean annual precipitation,
mean annual temperature (EFSA Spatial Data version 1.1);
evapotranspiration (JRC MARS); soil biomass productivity potential
(JRC SoilProd model) and Corine Land Cover (version 16) data sets)
Note: the classes represent the biodiversity potential of soils, from 1 (low potential) to
10 (high potential).

(continued)
 174 G. Louwagie et al.
(continued)
production potential generally appear with higher soil biodiversity values
than soils with high production potential under other land uses.
The impact of land take (between 2000 and 2006) is expressed per
NUTS 3 area,4 as the percentage of lost area with good soil biodiversity
potential in relation to the area with good soil biodiversity potential in
that NUTS 3 area (Figure 9.2). Several clusters of NUTS 3 regions with
high impacts (in relative terms) can be detected: the Netherlands (along
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with border regions in Germany), eastern Ireland, central UK, the coastal
Pays de la Loire (France), northern Portugal and the metropolitan area
of Lisbon, northern Spanish coastal regions and central Spain, and the
Budapest region.

Figure 9.2 Percentage decline (per NUTS 3 area) of land with good soil
biodiversity potential due to urban residential sprawl (LCF2), and
sprawl of economic sites (commercial, industrial) and infrastructure
(LCF3) between 2000 and 2006 (source: ETC ULS based on soil
biodiversity potential (ETC ULS, Figure 9.1) and Corine Land
Cover (version 17) data sets)
 Impact on biodiversity 175
In some regions these high percentage values correspond to big areas
with good soil biodiversity potential: in eastern Ireland, three regions
account for nearly 6,000 ha. The large expansion of commercial sites
and infrastructure in the metropolitan area of Dublin (EEA, 2006) and
surrounding regions particularly affected grassland areas with good soil
biodiversity potential. Land take between 2000 and 2006 had a major
impact in nearly all Dutch NUTS 3 regions, affecting the whole terri-
tory. The region of Utrecht (NL310) stands out with more than 1,200 ha
affected by land take. Also here, large shares of grasslands can be found in
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the regions with the highest soil biodiversity potential. As a final exam-
ple, the construction of EU-funded infrastructure (mainly motorways) in
the northern Spanish regions of Asturias (ES130) and Cantabria (ES120)
affected a total of 1,974 ha of soils with good potential for soil biodiversity.
There are also regions with high relative impact where the total
affected area is relatively small (< 200 ha, e.g. Portuguese and central
Spanish regions). These regions are also known as hotspots of land take
for the period 2000–2006 (EEA Land take indicator5), regardless of their
high potential to host soil biodiversity.

Responses to soil sealing

A role for planning

Landscape and habitat fragmentation can be seen as ‘the negative’ or opposite
of connectivity. ‘Green infrastructure’ (GI) is a concept that is closely related
to connectivity in an urban context, as it ‘can be considered to comprise of all
natural, semi-natural and artificial networks of multifunctional ecological sys-
tems within, around and between urban areas, at all spatial scales’, emphasising
the importance of quality, quantity, multi-functionality and interconnected-
ness of urban green spaces (Tzoulas et al., 2007). The GI concept originates
from spatial planning practice and highlights the role of green space in urban
systems (Sandström, 2002).
Land use planning is indeed recognised as an essential instrument to curtail
the negative effects of land take and soil sealing on the capacity of soil to sustain
biodiversity. Local planning authorities may take account of the biodiver-
sity effects that development projects may engender under the legislation for
environmental impact assessment (as for example the Environmental Impact
Assessment Directive in the European Union6).
In peri-urban areas (i.e. the transition zone from the core urban to the
surrounding rural area), the location of new developments – implying land
take and possibly soil sealing – in connection to protected nature areas is par-
ticularly interesting. In some countries or regions the planning system and
designation of sites of high conservation interest may be sufficiently strong
to constrain development on land with such value. In Scotland conservation
status is adequate to protect land with valuable and/or rare habitats and sites of
 176 G. Louwagie et al.
high biodiversity against development, and most of the extensive areas of val-
ued and/or rare habitats in Scotland are not adjacent to potential development
sites either (Dobbie et al., 2011). However, conservation status is not always
enough to prevent land take (Box 9.2). Also in Scotland’s 2011 state-of-the-
soil report it is recognised that conflicts between development and conservation
may arise in specific cases, such as golf course developments (often including
considerable built elements) or land-based renewables (e.g. hydro schemes and
windfarms, often requiring significant road infrastructure).
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Box 9.2 Spatial patterns of land take in relation to

protected areas in the European Union (EU)
Natura 2000 is an EU-wide network of protected areas designated under
the Habitats Directive7 (‘Special Areas of Conservation’) and the Birds
Directive8 (‘Special Protection Areas’), created with the aim of ensuring
the conservation of Europe’s most valuable and threatened habitats and
species. The Birds and Habitats Directives restrict land use changes in
Natura 2000 areas and limit the range of activities that can take place in

Figure 9.3 Land take in and near nature areas protected by Natura 2000 status
(source: ETC ULS based on Natura 2000 (version 2014, May 2015)
and Corine Land Cover (version 18.3) data sets)
Legend: N2000 = Natura 2000; LT = land take.
Note: while the analysis only focuses on terrestrial sites, the map presents both terrestrial
and marine Natura 2000 sites.
 Impact on biodiversity 177
these areas. The Habitats Directive also foresees the implementation of
compensating measures in case potential implications of a development
are assessed as negative; nevertheless, even if evaluated negatively, pro-
jects or plans can still be carried out for ‘imperative reasons of overriding
public interest’.
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Figure 9.4 Land take in and near Natura 2000 areas for selected NUTS 3 areas
(mean annual change in hectares per year) (source: ETC ULS based
on Natura 2000 (version 2014, May 2015), Corine Land Cover
(version 18.3) and NUTS (2013 scale 1:1 million) data sets)
Legend: N2000 = Natura 2000; LT = land take.

(continued)
 178 G. Louwagie et al.
(continued)
Consisting of over 26,000 sites, the Natura 2000 network covers
approximately 18.4 per cent of the EU territory,9 and constitutes the largest
protected area system worldwide (Figure 9.3). With the aim of protecting
wild fauna, flora and habitats, and maintaining ecosystem services, these
directives highly restrict land use changes and place certain limits on the
range of activities that can take place in these areas.10 Natura 2000 sites over-
lap with many nationally protected areas. However, close to half of them do
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not have a national designation, and thus the network provides an impor-
tant expansion of protected areas. In total, about 25 per cent of land in the
EU-27 is protected either by Natura 2000 sites or nationally designated
areas.11 Species listed in Annex I of the Birds Directive have been evaluated
as having benefited from the nature legislation (Sanderson et al., 2015).
Based on their protection status, it is expected that land take is greatly
reduced or halted, potentially even reversed, in and close to Natura 2000
areas. However, this is not always the case (Figure 9.3), as also illustrated
for selected NUTS 3 areas (Figures 9.4 and 9.5).

Figure 9.5 Spatial pattern of land take in and near a Natura 2000 area in a
NUTS 3 region defined as a hotspot (BE236 – Arrondissement of
Sint-Niklaas) (source: ETC ULS based on Natura 2000 (version
2014, May 2015), Corine Land Cover (version 18.3) and NUTS
(2013 scale 1:1 million) data sets)
Legend: N2000 = Natura 2000; LT = land take.
Note: hotspots are based on the criterion of highest mean annual increase of land take
(per NUTS 3 area).
 Impact on biodiversity 179
Notes
1 EEA land take indicator: www.eea.europa.eu/data-and-maps/indicators/land-take-2/
assessment-2, accessed 11 November 2016.
2 http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52002DC0
179&rid=1, accessed 11 November 2016.
3 As described in the European Commission’s ‘Thematic Strategy for Soil Protection’
(COM(2006)231 final): http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?
uri=CELEX:52006DC0231&from=EN, accessed 11 November 2016.
4 NUTS: nomenclature for territorial units for statistics in the EU – for an overview
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see: http://ec.europa.eu/eurostat/web/nuts/overview, accessed 11 November 2016.

5 www.eea.europa.eu/data-and-maps/indicators/land-take-2/assessment-2, accessed
11 November 2016.
6 http://ec.europa.eu/environment/eia/review.htm, accessed 11 November 2016.
7 Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats
and of wild fauna and flora.
8 Directive 2009/147/EC of the European Parliament and of the Council of 30
November 2009 on the conservation of wild birds.
9 Natura 2000 Barometer December 2013.
10 Collectively, ecosystem services are estimated to be worth EUR 200 to EUR 300
billion a year, significantly more than the annual cost of some EUR 6 billion to
manage the network (http://europa.eu/pol/env/flipbook/en/files/environment.
pdf, accessed 11 November 2016).
11 www.oee.hu/upload/html/2014-02/YPEF_Educational_material_2014.pdf,
accessed 11 November 2016.

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 10 Impacts of land take and soil
sealing on soil carbon
Klaus Lorenz and Rattan Lal
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Introduction
Land use and land cover change (LULCC) by urbanization and, in particular,
the expansion of urban areas is increasingly affecting the terrestrial carbon (C)
stock as the global urban land cover is projected to increase by an area the size
of South Africa until 2030 (Seto et al., 2012). Currently, larger than previously
thought areas in Europe are already covered by settlement structures includ-
ing cities, villages, and groups of houses along rivers, roads, and rail tracks or
spread into the arable countryside (Figure 10.1). The processes of land take or
land consumption interconnected with urban expansion can be defined as an
increase of settlement areas over time (European Commission Staff Working
Document, 2012). Land take includes the development of scattered settle-
ments in rural areas, the expansion of urban areas around an urban nucleus
(including urban sprawl), and the conversion of land within an urban area
(densification). By conversion of open into built-up areas, some part of the
land take will result in soil sealing by buildings, roads, and parking lots because
gardens, urban parks and other green spaces are not covered by an impervious
surface (European Commission Staff Working Document, 2012). Otherwise,
land take can also be defined as the increase of artificial surfaces (e.g., housing
areas; green urban areas; industrial, commercial and transport units; road and
rail networks) over time (European Commission, DG Environment, 2011).
Soil sealing means the permanent covering of an area of land and its soil by
completely or partly impermeable artificial material (e.g., asphalt, concrete), for
example, through buildings and roads (European Commission Staff Working
Document, 2012). Soil sealing causes the loss of soil and some of its biological
functions including C sequestration and loss of biodiversity, either directly or
indirectly, due to fragmentation of the landscape (European Commission, DG
Environment, 2011).
Both land take and soil sealing seem to be inevitable as most social and
economic activities depend on the construction, maintenance and existence
of sealed areas and developed land. New housing, business locations and
road infrastructure, in particular, are mostly realized on undeveloped land
outside or at the border of existing settlements, usually resulting in new
 182 K. Lorenz and R. Lal
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Figure 10.1 Map of urban structures in Europe in unparalleled precision based on

data acquired by radar satellites (source: DLR, www.dlr.de/eoc/en/
desktopdefault.aspx/tabid-9630/#gallery/24123, accessed 24 March 2016)

soil sealing (European Commission, DG Environment, 2011). The global

extent of soil sealing is further increasing as globalization increasingly influ-
ences processes of land change. Land-use changes, in particular, are strongly
influenced by globalized flows of commodities, information, capital and
people, and are increasingly driven by factors in distant markets, often asso-
ciated with the growing urban consumer class in emerging markets (Lambin
and Meyfroidt, 2011).
Land take and soil sealing for settlements can directly alter biomass C,
and soil C that is comprised of soil inorganic carbon (SIC) and soil organic
carbon (SOC) (Figure 10.2). Land use changes for urbanization occur com-
monly at the expense of agricultural land as many cities and settlements were
founded in agricultural areas on coastal plains and in river valleys (Hooke
et al., 2012). However, there is little direct quantitative evidence of how,
for example, sealing affects soil C storage (Scalenghe and Marsan, 2009).
Thus, data on urban soil C are urgently needed for an integrated understand-
ing of the processes of urbanization and the impacts of urban areas on C
flows (Romero-Lankao et al., 2014). Human activities associated with land
take such as additions of natural and technogenic materials, and physical soil
disturbance by excavation, export and mixing, and soil sealing alter directly
the soil C balance (Lorenz and Lal, 2009). Indirectly, urban soil C may be
affected by changes in environmental conditions of urban compared to those
of pre-urban environment such as the heat island effect. In the following sec-
tion are discussed some examples of the effects of land take and soil sealing on
soil C for urban areas (Table 10.1).
 Impact on soil carbon 183
Table 10.1 Maximum relative changes (%) in soil organic and inorganic carbon
stocks (Mg C ha-1) by land take compared to natural soils, and by soil
sealing compared to unsealed soils
Soil depth Process Soil organic carbon stock Change Soil inorganic carbon stock Change
(cm) (Mg C ha-1) ( %) (Mg C ha-1) ( %)

0–10 Land take 11.0 → 4.5 -59 0.18 → 0.62 +344

9.7 → 24.5 +253
Soil sealing 8.8 → 3.6 -59
0–20 Land take 31.3 → 39.3 +126 13.0 → 11.2 -14
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4.2 → 6.4 +152

Soil sealing 45.2 → 23.5 -52
0–100 Land take 172 → 59 -66 0 → 79
320 → 650 +203 0 → 348
Soil sealing 47.5 → 46.0 -3
127.3 → 234.0 +184
Sources: see text.

Effects of land take on soil organic carbon stock

The effects of land take on the SOC stock depends on the balance between the ini-
tial SOC stock of the land converted for settlement area, and the C input into urban
soils after land take derived directly from plant photosynthesis, organic amend-
ments, and additions during construction of man-made soils (Figure 10.2; Lorenz
and Lal, 2015). Thus, depending on the pre-urban soil replaced or disturbed, urban
soils may have higher, similar or lower SOC stocks (Lorenz and Lal, 2009).

Figure 10.2 Soil carbon losses (bold arrows) by land take and soil sealing processes in
urban areas (source: modified from Lorenz and Lal, 2009)
 184 K. Lorenz and R. Lal
Hao et al. (2013) modeled that the conversion of grassland to urban green
land resulted in an increase in SOC stocks to 30 cm depth from 49.0 to 91.7
Mg C ha-1 in Tianjin Binhai New Area, China. While the low grassland
SOC stock was explained by high soil salinity resulting in low plant C inputs,
continuous fertilizer and (human) manure applications at urban green land
contributed directly to higher SOC stocks due to higher organic matter (OM)
inputs and indirectly due to more productive plant growth (Hao et al., 2013).
In Kaifeng city, China, the normal sequence of urban soils was disturbed by
construction activities whereas suburban soils were characterized by natural
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soil sequence (Sun et al., 2010). Frequent fertilizing, watering and scarifica-
tion enhanced plant growth in urban green spaces and this, together with OM
inputs, contributed to SOC accumulation in urban soils. Specifically, urban
soils had 2.53-fold more SOC than suburban soils at 0–10 cm depth (24.5 Mg
C ha-1 vs. 9.7 Mg C ha-1). To 100 cm depth, urban soils had 1.56 times more
SOC (99.7 Mg C ha-1) than suburban soils (63.9 Mg C ha-1). The intense
human activities altered also the vertical distribution of SOC with 60 per-
cent of the SOC stocks to 100 cm depth in industrial, recreational and traffic
districts stored in 0–30 cm depth. In comparison, suburban soils stored only
40 percent of the 100 cm SOC stocks to 30 cm depth (Sun et al., 2010). The
SOC stocks to 20 cm depth of urban soils in Shanghai, China, were 1.26 times
higher than those of soils in the countryside (39.3 Mg C ha-1 vs. 31.3 Mg C
ha-1) (Xu et al., 2012). However, SOC stocks at 160–180 cm depth were com-
parable among urban and countryside soils due to limited human influence
(15.5 and 15.8 Mg C ha-1, respectively). The SOC stocks to 30 cm depth
across disturbed village land uses in China, i.e., constructed (mostly housing
and roads) and disturbed (mostly unused land around buildings and roads) were
23.6 and 25.5 Mg C ha-1, respectively (Jiao et al., 2010). In contrast, orna-
mental and paddy (Oryza sativa L.) land uses had SOC stocks of 33.6 and 33.8
Mg C ha-1, respectively, to 30 cm depth. Thus, human residence and not just
agricultural practice was an important control on SOC stocks across village
landscapes in China (Jiao et al., 2010).
Greenspace soils in Chuncheon, Korea, stored less SOC to 60 cm depth
at urban compared to natural lands (24.8 vs. 31.6 Mg C ha-1) (Jo, 2002). The
lower SOC storage for urban lands may have been the result of sparse tree
plantings compared to natural lands, and less composting.
The SOC stocks in urban soils of Moscow and Serebryanye Prudy,
Russia, were comparable with or exceeded the SOC stocks in the natural
background soils (Vasenev et al., 2013). Specifically, the SOC stock in the
topsoil horizons and cultural layer of Moscow was 50 percent higher than
that in the zonal soddy-podzolic soil (70–90 Mg C ha-1). Further, the SOC
stocks to 150 cm depth were 810 Mg C ha-1 for Serebryanye Prudy and
610 Mg C ha-1 for natural soils in the forest-steppe zone. Thus, the regional
C budgets calculated without due account for urban soils may be under-
estimated (Vasenev et al., 2013). For example, mean SOC stocks at 0–10
cm and 10–150 cm depths for non-urban, conventional and urban-specific
 Impact on soil carbon 185
maps in Moscow region were 30 and 147 Mg C ha-1, 39 and 335 Mg C ha-1,
and 31 and 156 Mg C ha-1, respectively (Vasenev et al., 2014). Total
SOC stocks based on the map including urban areas were considerably
larger than for those excluding them, with 90 percent of all SOC stored
at 10–150 cm depth.
Urban soils in Leicester, UK, stored on average significantly more SOC to
100 cm depth than their counterparts in regional arable lands, i.e., 202 vs. 143
Mg C ha-1 (Edmondson et al., 2012). Specifically, SOC storage to 21 cm depth
in green spaces was 99 Mg C ha-1 compared with 86 Mg C ha-1 in pasture and
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73 Mg C ha-1 in arable lands. The addition of peat, composts, and mulches,

and cultivation of trees and shrubs contributed to greater SOC stocks in urban
greenspaces compared to those of agricultural soils in the region (Edmondson
et al., 2014). Very large SOC stocks were monitored in 55 urban soil profiles in
the north east of England, in a region with a history of coal burning and heavy
industry (Edmondson et al., 2015). To 100 cm depth, urban SOC stocks ranged
between 320 and 650 Mg C ha-1. In comparison, soil under semi-natural veg-
etation and peat (to 100 cm depth) in the UK stored 320 and 520 Mg SOC
ha-1, respectively. Further, the urban soils also captured a large proportion of
black carbon (BC) particulates emitted within urban areas. Thus, UK urban
soils may be highly enriched in both BC and SOC (Edmondson et al., 2015).
Pouyat et al. (2002) estimated SOC stocks to 100 cm depth for some cities
in the northeast and mid-Atlantic region of the US. Residential lawn areas had
nearly the same C density as northeastern forests, and higher density than mid-
Atlantic forests (155 Mg C ha-1 vs. 162 and 112 Mg C ha-1). The high SOC
stocks of residential lawns may be explained by high rates of nutrient inputs and
water, resulting in increases in below-ground productivity, and also a much
longer growing season than forests. On a preliminary basis, Pouyat et al. (2002)
estimated that urban soils in the US stored on average 82 Mg SOC ha-1 to
100 cm depth but the net changes in SOC stocks by land take and soil sealing
compared to non-urban soils were uncertain.
The SOC stocks to 15 cm depth of well-maintained lawns in Fort Collins,
CO, USA, were higher than those of native shortgrass steppe soils (4.8 vs. 2.9
Mg C ha-1) (Kaye et al., 2005). However, at 15–30 cm depth, SOC stocks
were not significantly different with 2.2 Mg SOC ha-1 stored in urban soils and
1.9 Mg SOC ha-1 stored in native soils, respectively. Thus, urbanization of arid
and semiarid ecosystems may increase SOC stocks only at shallow soil depths.
Estimates for urban SOC stocks to 100 cm depth for Boston, MA, and
Syracuse, NY, USA, were 59 and 71 Mg C ha-1 in comparison with 172 Mg C
ha-1 for both cities before urban development (Pouyat et al., 2006). This large
difference may result from the high SOC stocks in soils under native forest in
the settlement area of both cities, and may have even be higher as wetlands
were not considered. In contrast, cities in warmer and/or drier climates had
slightly higher SOC stocks for post- than pre-urban measurements. For exam-
ple, SOC stocks to 100 cm depth for Chicago, IL and Oakland, CA, USA,
were estimated at 55 and 59 Mg C ha-1, respectively (Pouyat et al., 2006). In
 186 K. Lorenz and R. Lal
contrast, native soils in the settlement areas of Chicago and Oakland had SOC
stocks to 100 cm depth of 52 and 57 Mg C ha-1, respectively. Pouyat et al.
(2006) concluded that there is potential for substantial losses of SOC by urban
land take in temperate regions. However, in more arid climates, urban conver-
sions have the potential to increase SOC storage. Nonetheless, only a limited
number of data on urban SOC stocks are available for US cities to support this
conclusion.
Mean surface (0–10 cm) SOC stocks in Phoenix, AZ, USA, were lower
in desert and xeric yards (4.5–5.0 Mg C ha-1) than in mesic yards or agroeco-
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systems (7.5–11.0 Mg C ha-1) (Kaye et al., 2008). At 10–30 cm depth, mean

SOC stock was higher in agricultural soils (10.2 Mg C ha-1) than in urban
soils (5.3–7.3 Mg C ha-1). Thus, SOC stocks to 30 cm depth were not always
higher in urban compared to desert areas (Kaye et al., 2008).
The SOC stocks to 90 cm depth of natural forests near Apalachicola, FL,
USA, were similar to those under urban land use (Nagy et al., 2014). Specifically,
natural forests, urban forests and urban lawns stored (Mg C ha-1) on average 73,
107, and 159, respectively, at 0–90 cm depth. However, soils under urban
lawns had significantly higher SOC stocks at 0–7.5 cm and 7.5–30 cm depths
compared to those under natural forests (14 and 35 vs. 10 and 14 Mg C ha-1,
respectively). Thus, increases in urban SOC storage are possible with continued
urbanization if lawns are incorporated into built-up areas (Nagy et al., 2014).
Despite differences in water and N inputs, and vegetation shifts, SOC stocks
to 10 cm in Boston, MA, USA, did not differ among urban and nonurban
areas, and ranged from 34 to 44 Mg C ha-1 (Raciti et al., 2012b). Otherwise,
SOC stocks to 100 cm depth under residential lawns in Baltimore, MD, USA,
were higher than those of forested reference sites (69.5 vs. 54.4 Mg C ha-1)
(Raciti et al., 2011). Lawns on former agricultural land had higher SOC stocks
than those on former forest land. In particular, about 0.8 Mg C ha-1 accumu-
lated annually in lawn soils built on former agricultural land. Thus, lawn soils
in residential areas on former agricultural lands in Baltimore have a large C sink
capacity to sequester SOC to 100 cm depth (Raciti et al., 2011).
The SOC stocks to 15 cm depth in undisturbed urban forest soils adjacent
to urban interstates in Louisville, KY, USA, were lower than those filled with
local or imported material (46.0 Mg C ha-1 vs. 54.7 and 79.0 Mg C ha-1,
respectively) (Trammell et al., 2011). However, SOC stocks were the lowest
where A and B horizons had been removed and sub-soils exposed (38.1 Mg
C ha-1). Trammell et al. (2011) concluded that alterations of soil profiles dur-
ing highway construction, influx of unknown material post-construction and
vegetation management contributed to differences in SOC stocks between the
undisturbed and disturbed forest soils adjacent to urban interstates.
Urban soils in Baltimore stored 71.1 Mg SOC ha-1 to 100 cm depth
or about 35 percent less than native soils but 24 percent more SOC than
cultivated soils, respectively (Pouyat et al., 2009). Further, residential turf
grass soils stored more SOC to 100 cm depth than rural forest soils (110 vs.
67 Mg C ha-1). Otherwise, SOC stocks of two urban forest remnants were
 Impact on soil carbon 187
not different from those of turf grass soils. However, in turf grass soils only 50
percent of SOC to 100 cm depth accumulated in 0–20 cm depth compared
to 70 percent accumulating to 20 cm depth in urban forest remnants. In con-
clusion, residential turf grass soils in Baltimore have the capacity to sequester
large amounts of SOC (Pouyat et al., 2009).
In conclusion, land take has highly variable effects on SOC stocks depending
on the pre-urban conditions, and the soil and land-use management practices
after conversion to settlement area (Table 10.1). Compared to natural soils,
soils affected by land take may have 66 percent lower SOC stocks to 100 cm
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depth. Otherwise, SOC stocks in 0–10 cm depth may be 253 percent of those
in natural soils. However, data for many cities and urban regions are missing
(Lorenz and Lal, 2015).

Sealing and soil organic carbon stock

The sealing of soils by impervious materials is detrimental to SOC storage as
exchanges of C, energy, water, and gases between urban soils and the sur-
rounding environment are restricted. The negative effects on SOC originate,
in particular, from partial or total loss of soil and its SOC stock, and the par-
tial or total loss of plant cover and its soil C input. However, little specific
research is available that describes the effects of sealing on soil properties
(Scalenghe and Marsan, 2009). Nevertheless, soils beneath sealed surfaces are
part of the urban ecosystems and their properties must be studied (Kida and
Kahawigashi, 2015). Some examples of studies on SOC stocks under sealed
surfaces are given in the following section.
Artificial sealing of soils in Nanjing City, China, resulted in 40.9 percent
and 45.5 percent lower water-soluble organic C and SOC contents, respec-
tively, to 20 cm depth (Wei et al., 2014). Further, the SOC density to 20
cm depth for impervious soils was lower than those for open soils (23.5 vs.
45.2 Mg C ha-1). Thus, sealing of urban soils in Nanjing City resulted in a
decrease of the SOC sink and degradation of soil fertility (Wei et al., 2014).
The SOC stocks to 100 cm depth under impervious cover in China’s urban
areas were estimated to be highly variable, ranging between 46.0 Mg C ha-1
in Xinjiang, northwest China, and 234.0 Mg C ha-1 in Heilongjiang, north-
east China (Zhao et al., 2013). In comparison, soils under green space to
100 cm depth contained 47.5 Mg C ha-1 in Xinjiang and 127.3 Mg C ha-1
in Heilongjiang. However, data were uncertain and no statistical analyses
were performed. Thus, the effects of sealing on SOC stocks in China’s urban
areas are unclear (Zhao et al., 2013). Sealed areas across village landscapes in
China had SOC stocks of 22.9 Mg C ha-1 to 30 cm depth (Jiao et al., 2010).
In contrast, soils under vegetation cover tended to have higher SOC stocks
with values ranging between 24.4 and 28.8 Mg C ha-1. However, differences
were statistically significant only in the subtropical hilly region with 32.1 Mg
SOC ha-1 stored to 30 cm depth under annual land cover and 16.7 Mg SOC
ha-1 in sealed soils (Jiao et al., 2010).
 188 K. Lorenz and R. Lal
Kida and Kahawigashi (2015) studied the effects of asphalt sealing on min-
eral soils beneath the roads in Tokyo, Japan, but without comparing differences
by statistical methods. The average TOC concentration in top mineral soils
(34.1 g C kg-1) was lower than those of surface soils of urban parks covered
with turf grass (55.2 g C kg-1) or tree plantations (52.9 g C kg-1). Otherwise,
the TOC in subsoils (~35 g C kg-1) did not differ among three land uses (i.e.,
road, urban park, and tree plantations). However, TOC content in mineral
soils beneath asphalt pavement was lower than that in an A-horizon of soil
under natural ecosystems. The decline of TOC concentration reflected trunca-
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tion of surface soil rich in OM by land deformation and pavement construction

(Kida and Kahawigashi, 2015).
Piotrowska-Długosz and Charzyński (2015) studied the impact of the
degree of sealing on soil properties in Toruń, Poland. Samples from partially
sealed and impervious completely covered soils were collected from topmost
horizons that survived the process of pavement construction from a depth of
15–25 or 10–20 cm, depending on the thickness of the technic hard rock.
Unsealed, vegetated reference sites were sampled to the same depth. The SOC
stocks calculated from organic C content and bulk density for 10 cm incre-
ments at partially sealed and impervious covered soils were 3.6 and 4.4 Mg
C ha-1, respectively, in comparison with 8.8 Mg C ha-1 at the reference sites.
Thus, soil sealing reduced SOC storage at the soil depths studied (Piotrowska-
Długosz and Charzyński, 2015).
Soils beneath impervious surfaces in Leicester, UK, stored considerable
amounts of SOC to 100 cm depth at a city-wide scale (Edmondson et al.,
2012). However, SOC storage beneath impervious surfaces was limited by
the depth of excavation for the capping surface. Specifically, SOC stocks in
40–100 cm depth beneath roads and other load bearing surfaces were 67 Mg C
ha-1 compared with 24 and 135 Mg C ha-1 in 45–60 cm and 15–100 cm depth
beneath pavements and footpaths, respectively. Soil processes probably remain
active potentially accumulating SOC beneath the patches of impervious sur-
face (Edmondson et al., 2012).
The average SOC stock to 15 cm depth under impervious cover for some
neighborhoods in New York City, NY, USA, was 22.9 Mg C ha-1, and it
was significantly lower than those for urban open areas (56.7 Mg C ha-1)
(Raciti et al., 2012a). Thus, SOC stocks under impervious surfaces cannot be
neglected in the assessment of urban SOC storage. However, the fate of SOC
lost or depleted from areas now covered by impervious surfaces must also be
understood (Raciti et al., 2012a).
In conclusion, in topsoil horizons soil sealing causes losses of SOC stocks
relative to those of natural soils, i.e., up to 59 percent of the SOC stock of natu-
ral soil may be lost to 10 cm depth (Table 10.1). More uncertain are the effects
on SOC at deeper depths as quantitative evidence is scanty. Nevertheless, the
SOC stocks of sealed soils cannot be neglected in studies dealing with stock
and flux of urban soils.
 Impact on soil carbon 189
Effects of land take on soil inorganic carbon
Soils affected by land take may contain SIC originating from carbonate-bearing
soil parent material (Lehmann and Stahr, 2007). Especially in arid and semi-
arid regions, the SIC stock may be up to 10 times larger than the SOC stock
(Eswaran et al., 2000). In addition, demolition waste (particularly cement and
concrete) may contribute to urban SIC storage (Washbourne et al., 2012). The
coarse fraction (> 2 mm) of urban soils may substantially contain SIC in the
form of demolition waste, limestone, and chalk fragments (Rawlins et al., 2011).
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Thus, land take for settlements may affect the stocks of SIC aside those of SOC.
However, the assessment of changes in SIC stocks by land take is hampered
as national and regional databases seldom include the SIC data (Rawlins et al.,
2011). Thus, only a few examples are given in the following section.
The SIC stock to 20 cm depth for urban areas in the Jiangsu Province,
China, was 1.52 times those of soils in countryside under agricultural ecosys-
tems (6.4 vs. 4.2 Mg C ha-1) (Xu and Liu, 2013). The increase in urban SIC
stocks may be caused by soil deposition of groundwater containing carbonates
and bicarbonates. In contrast, irrigation with alkaline groundwater may not
result in increased SIC storage as was the case in Shanghai, China (Xu et al.,
2012). Specifically, SIC stocks at 0–20 cm and 160–180 cm depths were 11.2
and 12.4 Mg C ha-1, respectively, for urban soils of Shanghai. In countryside
soils, 13.0 and 15.8 Mg SIC ha-1 were stored at 0–20 and 160–180 cm depths,
respectively (Xu et al., 2012).
Input of calcareous building rubble and limestone gravel together with car-
bonate-rich soil parent material may be the reasons for higher SIC stocks in
some urban soils of Stuttgart, Germany, compared to those for adjacent agri-
cultural and forest soils (Stahr et al., 2003). Specifically, SIC stocks to 30 cm
depth within Stuttgart ranged between 12 and 82 Mg C ha-1. In comparison,
rural forest soils contained no carbonates to 30 cm depth while rural agricul-
tural soils stored 7 Mg SIC ha-1 to the same depth. At 30–100 cm depth, urban
SIC stocks ranged between 67 and 266 Mg C ha-1. Again, rural forest soils
were carbonate-free while rural agricultural soils contained 2 Mg SIC ha-1 at
30–100 cm depth (Stahr et al., 2003).
The SIC stocks of lawns in Fort Collins, CO, USA did not differ from
those in soils under adjacent native ecosystems with values ranging between
0.19 and 1.26 Mg C ha-1 in 0–15 cm, and between 1.99 and 12.14 Mg C ha-1
in 15–30 cm depth (Kaye et al., 2005). In contrast, irrigation water saturated
with CaCO3 contributed to higher SIC stocks at 0–10 cm and 10–30 cm
depths in urban (i.e., 0.45–0.62 and 0.98–1.04 Mg C ha-1, respectively) com-
pared to desert soils in Phoenix, AZ, USA (Kaye et al., 2008). Specifically,
desert soils contained 0.18 and 0.64 Mg C ha-1 at 0–10 cm and 10–30 cm
depths, respectively.
In conclusion, land take has highly variable effects on SIC stocks (Table 10.1).
However, strong increases in SIC stocks were observed in urban areas where
calcareous demolition waste was buried in urban soils.
 190 K. Lorenz and R. Lal
Effects of soil sealing on soil inorganic carbon
Research data are scanty regarding the effects of soil sealing on urban SIC stocks
compared to those of unsealed non-urban soils. The pH values and soil Ca
contents are often but not always (e.g., Piotrowska-Długosz and Charzyński,
2015) higher in sealed urban soils compared with those in urban open soils
(Morgenroth et al., 2013; Wei et al., 2014; Kida and Kahawigashi, 2015). The
dissolution of calcareous materials in cement and concrete used for construc-
tion of soil sealing may elevate Ca concentration of urban soil, and increase
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soil pH due to a strong carbonate reaction (Burghardt, 1994). Thus, soil seal-
ing may increase SIC stocks. For example, correlation between inorganic C
and CaO content under asphalt in Tokyo indicated that inorganic C exists as
CaCO3 in pavement materials and top mineral soils (Kida and Kahawigashi,
2015). In conclusion, soils beneath sealed surfaces may potentially have higher
SIC stocks than those in unsealed, non-urban soils, but data are rather scanty.

Conclusions
Data on urban soil C are urgently needed for an enhanced and integrated
understanding on the processes of land take and soil sealing on C flows. While
land take has variable effects on SOC stocks, covering soil with impervious lay-
ers by sealing generally results in a loss of topsoil SOC stocks, probably because
C-rich surface soil is removed during sealing construction and C inputs are
altered subsequently. In contrast, SIC stocks may strongly increase by land
take as this process is often associated with additions of calcareous materials.
However, many more cities and urban areas must be studied for a reliable
global assessment on the effects of land take and soil sealing on soil C.

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 11 Urban sprawl, soil sealing and
impacts on local climate
Luigi Perini, Andrea Colantoni, Gianluca Renzi
and Luca Salvati
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Introduction
Urban dispersion has a considerable impact on ecosystems and ecological
resources, which provide social and environmental benefits simply by exist-
ing and functioning (Angel et al., 2005). The environmental impact of urban
sprawl and the consequent increase in the soil imperviousness rate spans all
the geographical scales. An unintended consequence of soil sealing driven by
low-density suburban growth is a high resource consumption rate leading
to greater environmental damage compared to a compact development pat-
tern (Couch et al., 2007). While an immediate consequence of growing rates
of combustion processes of fossil fuels (due to higher consumption rates of
low-density urban centers) is air pollution, the carbon dioxide in vehicular
emissions and power stations is a major greenhouse gas linked to global warm-
ing. Long-term effects of fossil fuel combustion are subjected to a certain
degree of uncertainties. Nevertheless, according to the Intergovernmental
Panel on Climate Change (IPCC), there is a general agreement that human
activities are significantly contributing to the rise in greenhouse gases (GHG)
in the atmosphere, which are believed to be responsible for climate changes.
If the rationale that urban sprawl leads to higher energy consumption and
land use per capita is accepted, then its role in contributing to climate changes
must be considered. The present contribution is intended to explore some of
the potential effects that urban expansion has on heat balance and climate at
the urban scale. We initially show some basic concepts for the study of urban
climate. Subsequently, we describe the potential effects of the urban growth
on the rise in temperature and precipitation extremes along the urban–rural
gradient. Finally, we discusses the need to use methods for analyzing weather
and climate specific to the urban climate and to prepare adaptation strategies
to the urban climate change.

The urban heat island

The climatic conditions of the city are significantly different from other popu-
lated areas, in particular due to the so-called effect “urban heat island” (UHI),
 194 L. Perini et al.
which configures the urban environment as “bioclimatic island” in which
specific weather events occur (Oke, 1982). The UHI is related to the increase
in temperature of urban areas compared to their rural surroundings. The tem-
perature difference is usually larger at night than during the day, and it is
particularly evident when winds are weak, although it is observed during both
summer and winter. The UHI is caused mainly by two factors. First, dark
surfaces such as roadways and rooftops efficiently absorb heat from sunlight
and reradiate it as thermal infrared radiation. Second, urban areas are relatively
devoid of vegetation, especially trees, that would provide shade and reduce air
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temperature through the process of evapotranspiration. As cities sprawl out-

ward, the heat island effect expands, in both geographic extent and intensity.
This is especially true if the pattern of development features extensive tree cut-
ting and road construction.
UHI structural factors include the percentage of albedo expressed by urban
surfaces, the thermal capacity of the coating materials of soil and surfaces, shape,
orientation and ventilation of buildings and, finally, the reduction of evapo-
rating surfaces. These factors create a sort of heat dome of 150–200 meters
that—in particular during the winter and in the night—determines a thermal
inversion at higher elevations (Figure 11.1). Additional factors include the pro-
duction of heat from air conditioning systems, vehicle traffic, industry and even
the metabolic activities of the inhabitants (Oke, 1982; Arnfield, 2003; Salvati
and Forino, 2014).

Figure 11.1 Urban heat island profile

 Impacts on local climate 195
The heat balance in urban areas
The radiant energy emitted by the sun that reaches the surface of the Earth con-
sists of short wave electromagnetic radiation. Part of this energy is absorbed and
then re-emitted as long-wave radiation (infrared or thermal) in the atmosphere.
The air is then heated mainly by the emissions of the soil and not directly from
the sun. In the case of a natural surface the heat balance is given by:

Q+H+E+G=0
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where Q is the global net radiation, H and E are respectively the sensible heat
and the latent heat absorbed or transmitted by air and soil, G is the heat trans-
ferred by thermal soil conductivity. However, in urban areas the heat balance
is more complex due to the presence of urban structures. The formula cited
above should be integrated as follows by introducing two factors: Qp, indicat-
ing the exchange of heat with the road surfaces and buildings, and Qf, the heat
generated by the anthropogenic burning of fossil fuels. The thermal balance in
a urban area is:

Q + Qp + H + E + Qp = 0

Under field conditions the equation should be even more complex if consider-
ing the tribute of eventual thermal advection.
Industrial areas near urban cities can significantly modify the thermal
balance: hot emissions can cause heat accumulation up to four times greater
than in non-industrial areas; while the particulate in the polluted air can hinder
the incoming solar radiation (10–20 percent less than rural areas) and pro-
duce a cooling effect (Bonan, 2008). Domestic heating in the winter and air
conditioning in the summer contribute to heating. Moreover, some of the
construction materials have high thermal conductivity. Temperature differ-
entials between the exterior and the interior of urban buildings create a heat
flow that runs through the thickness of walls from a surface to another (from
the outside towards the inside and/or vice versa). Urban areas therefore cool
slowly during the night in respect to non-urban areas. Combustion processes
in, for example, transport, conditioning devices and industrial machines, pro-
duce greenhouse gas emissions released into the atmosphere possibly altering
the radiation thermal exchange with the earth’s surface by changing the final
heat balance. Another factor affecting urban climate is the high concentration
of aerosols, tiny particles suspended in the atmosphere, resulting mainly from
industrial and car emissions. In addition to damages on human health, they
impact both the propagation and absorption of solar radiation, affecting the
“transparency” of the air. In other words, they influence the physical processes
of condensation of atmospheric moisture, as potential condensation nuclei
promoting the formation of smog and mists.
 196 L. Perini et al.
The growing demand for mobility implies growing emissions. For example,
in 2005 transport emissions accounted for 20 percent of greenhouse gas emis-
sions in the European Union (EU-25), while road transport was responsible
for 93 percent of total emissions in the transport sector with about 900 mil-
lion tons of CO2. In the period 1990–2002, the number of kilometers of
road passenger in the EU-25 increased by 26 percent, while the number of
cars increased by 35 percent, with about 40 cars per 100 inhabitants in the
EU-15. In the same period the number of tonnes of goods per kilometer also
increased by 36 percent, while CO2 emissions from road transport increased
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by 18 percent (István, 2010).

Profiling urban climate

Urban areas have a similar structure to the natural canyons, in terms of absorp-
tion of solar radiation, surface temperature, evaporation rates, storage/heat
radiation and direction and intensity of the wind. The amount of solar radia-
tion received by an urban canyon depends on the height of the buildings and
the orientation of the road. In an urban canyon, as in the natural one, the
so-called phenomenon of “trapping” of solar energy is quite common. Due to
the wall-to-wall reflection within the canyon, this phenomenon contributes to
the increase of the fraction of energy absorbed by land surfaces. As a general
rule, about 60 percent of the net radiation is released in the atmosphere in the
form of sensible heat, 30 percent is stored in the surface of roads and buildings
and 10 percent is used for the evaporation of green areas, streams or wetlands
(Spronken Smith et al., 2006). The temperature ranges are closely connected to
the surface and the shape of the buildings, land cover, the presence of vegeta-
tion and man-made radiation sources (Giridharan et al., 2004; Jonsson, 2004;
Unger, 2004; Johnson and Wilson, 2009).
Profiling urban climate regimes may benefit from the comparative analy-
sis of weather variables in strictly urban and neighboring rural sites (Hawkins
et al., 2004; Sakakibara and Owa, 2005). A survey carried out on the basis of
the criteria recommended by the World Meteorological Organization (WMO)
on gauging stations located along the urban gradient in Rome and Milan (Italy)
shows different patterns between maximum and minimum temperatures. In
Rome, the differences between the values recorded inside and outside the city
are reduced slightly in the warmer months, persisting throughout the year. In
Milan, the values are instead more correlated with a similar pattern and sig-
nificant differences only in a deficiency months (Figure 11.2). The fact is that
the minimum temperatures are the result of thermal conditions expressed by
the atmospheric layer close to the ground, while the maximum temperatures,
depending generally on convection heating and consequent mixing of the air
mass above the soil, are representative of the thermal conditions of the tropo-
sphere (Beltrano and Perini, 1997). These outcomes can therefore confirm that
the difference between urban and rural areas during the day is low, increasing
gradually during the evening and night.
 Impacts on local climate 197
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Figure 11.2 Correlation coefficient of mean monthly temperatures (Tmin and Tmax)

between urban and rural environmental contexts in Milan (a) and Rome
(b), Italy

Climate and urban form

Urban climate regimes can be seen as the product of a “cultural mediation”
between the diversity of approaches in designing and planning cities in
terms of materials and structures. Traditional and compact urban forms may
improve specific micro-climate conditions. The Physiological Equivalent
Temperature (PET) index, which combines temperature, humidity and
wind conditions, was proposed to assess the variations of the thermal con-
ditions (and the related “thermal comfort”) in the cities, according to
selected architectural parameters and considering the season and hour of
the day (Matzarakis et al., 2007). In a neighborhood with compact set-
tlements and east–west orientation in Fez (Morocco), the threshold of
well-being (PET = 33°C) is abundantly exceeded at street level for most
of the day. Under porches, in particular those to the north side of the
road, the thermal comfort is always instead at better levels (Ahmed, 2003;
Johansson, 2006). Urban elements may thus be effective in the mitigation
 198 L. Perini et al.
of environmental conditions. Conversely, the geometry of the buildings
can result in extreme weather, including storms of considerable intensity
(Ntelekos et al., 2007).
A detailed analysis of the urban climate should benefit from a classification
of different types of settlement along the urban–rural gradient. A stand-
ard classification was proposed based on the Local Climatic Zones (LCZ)
(Bechtel et al., 2015). The LCZ are intended as homogeneous areas whose
characteristics influence the thermal properties as the fraction of built-up area,
the aspect ratio of buildings, the sky view factor, the height of the elements
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that constitute the “roughness” of the surface, the heat flux of anthropo-
genic origin and the surface of heat radiation (Stewart and Oke, 2009). The
scaling factor is also important as the representativeness of meteorological
stations varies based on the instrument adopted, the conditions around the
station and the surface geometry (Oke, 2004). The standard measurement of
temperature in gauging stations is generally less reliable in built-up areas in
respect to the open field. The spatial dimension of a LCZ varies depending
on the measurement conditions imposed by the site. Table 11.1 shows an
example of LCZ classification.

Table 11.1 An example of Local Climatic Zones for the analysis of urban contexts
Built types Description Land Description
cover types

Compact Context of tall buildings (tens Dense Rich landscape of

high-rise of floors). Land cover mainly trees vegetation. Forest
paved. Presence of concrete, area, plant cultivation
steel, stone and glass building or urban park. Mostly
materials. Deficiency or permeable land cover.
absence of trees.
Compact Context of midrise buildings (3–9 Sparse Landscape vegetation
midrise floors). Land cover mainly trees dispersed. Forest area,
paved. Presence of concrete, plant cultivation or
tile, brick and stone building urban park. Mostly
materials. Deficiency of trees. permeable land cover.
Compact Context of low-rise buildings Bush and Presence of bush and
low-rise (1–3 floors). Land cover scrub scrub. Agricultural
mainly paved. Presence of or natural scrubland
concrete, tile, brick and stone areas. Mostly
building materials. Deficiency permeable land cover.
of trees.
Open Open collocation of tall buildings Low Shapeless landscape of
high-rise (tens of floors). Presence of plant grass or herbaceous
concrete, steel, stone and glass crops with deficiency
building materials. Plenty of or absence of trees.
permeable land cover (low Natural grassland,
plants and sparse trees). agricultural area or
urban park.
 Impacts on local climate 199
Open Open collocation of midrise Bare Shapeless landscape of
midrise buildings (3–9 floors). Presence rock rock or paved cover
of concrete, steel, stone and or with deficiency or
glass building materials. Plenty paves absence of trees.
of permeable land cover (low Natural desert or
plants and sparse trees). urban transportation.
Open Open collocation of low-rise Bare soil Shapeless landscape of
low-rise buildings (1–3 floors). Presence or soil or sand cover
of concrete, brick, wood, stone sand with deficiency or
and tile building materials. absence of trees.
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Plenty of permeable land cover Natural desert or

(low plants and sparse trees). agricultural area.
Lightweight Dense mix of buildings with Water Water bodies.
low-rise one floor. Land cover mainly
hard-packed. Lightweight
construction materials.
Deficiency of trees.
Large Open collocation of low-rise
low-rise buildings (1–3 floors). Land
cover mainly hard-packed.
Presence of concrete, steel,
stone and metal building
materials. Deficiency of trees.
Sparsely Dispersed collocation of small-
built medium buildings in natural
context. Abundance of
permeable land cover.
Heavy Low-rise and midrise industrial
industry buildings. Land cover mainly
paved. Presence of concrete,
metal and steel building
materials. Deficiency of trees.
Variable land cover proprieties

Land cover variables that considerably change with synoptic weather conditions,
agricultural practices and/or seasonal cycles.
Bare trees Bare deciduous trees. Increased factor of the sky view and reduced albedo.
Snow cover Snow cover ( > 10 cm in depth), low admittance and high albedo
Dry ground Parched land, low admittance, large Bowen ratio and high albedo.
Wet ground Waterlogged ground, high admittance, small Bowen ratio and reduced
albedo.

Monitoring climate and planning sprawl in urban areas

Urban climatic regimes have characteristics that justify a specific approach for
permanent monitoring and adaptation strategies. For example, it is not pos-
sible to assess climate variables strictly according to the measurement criteria
recommended by WMO. New technologies are applied to the analysis of the
effects of urban areas on the formation of clouds, precipitation and the storms.
Satellite remote sensing (Schumacher and Houze, 2000), LIDAR (Zhou et al.,
 200 L. Perini et al.
2004) and Doppler radar (Russo et al., 2005) allow a detailed analysis of the
rainfall spatial variability at a disaggregated geographical scale. Using such
methodologies, Souch and Grimmond (2006) confirm that urbanization has
effects on precipitation by increasing the hygroscopic nuclei of condensation
of atmospheric moisture due to the air turbulence caused by the increased
“roughness” of the land surface and to convection caused by the proper-
ties and different thermal states of the materials (see Lowry, 1998). Average
air temperatures are 1–2°C higher in urban areas than in the surrounding
rural areas, particularly at night and during summer. Vehicular traffic, the air
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conditioning of buildings and the quality of material for the covering of land
surfaces contribute to heating, while the scarcity of green areas, associated to
the lower ventilation, reduces the efficiency of the natural forms of mitigation
during extreme events. This implies that the negative effects of climate change
can be exacerbated in strictly urban areas and reduced along the urban–rural
gradient (Szymanowski, 2005).
Multi-scalar adaptation strategies at both national and local level are also
necessary to cope with meso-scale climate changes in metropolitan regions.
Specific measures to adapt to climate change at the urban scale are thus neces-
sary (Hallegatte et al., 2011; Hunt and Watkiss, 2011).
One of the major objectives of urban planning is to promote efficient
settlement forms that rely less on the consumption of fossil fuels and agri-
cultural/forest land reducing the local-scale impact of climate variations. For
example, the European Commission proposed specific policies coping with
climate changes in metropolitan regions with the aim to balance the bio-
climatic regimes and to affect positively local communities, the activities of
policy-making and the dissemination of good behavior in the daily life of the
inhabitants (Castan Broto and Bulkeley, 2013). Several European countries
have adopted national strategies for adaptation to climate change (Westerhoff
et al., 2011). The issue seems to be pressing national authorities in the aftermath
of the exceptional 2003 when more than 3,000 deaths were directly related to
the repeated heat waves affecting large urban areas only in Italy (Conti et al.,
2005). Adaptation strategies at the local level were proposed to include specific
measures which adapt the urban structure to the risk of heat waves (MATTM,
2013). Additional actions are targeted (1) to stimulate the use of weather-alert
systems, (2) to promote the reduction of energy consumption and the thermal
efficiency of public and private structures, (3) to restore green spaces and to
promote the re-naturalization of riparian areas and the proper management of
urban waterways.

Conclusions
Cities are complex integrated systems interconnected by infrastructures of
transport, energy, water and services. With urban dispersion, peri-urban areas
became progressively more vulnerable, especially to the impacts of climate
change, such as floods, drought or heat waves, depending on local characteristics
 Impacts on local climate 201
such as urban topography, economic structure and socio-spatial organization
(Hallegatte and Corfee Morlot, 2011). The present contribution outlines that
the main features of the urban climate are not represented by simple biophysi-
cal factors, while being dependent on the shape and spatial organization of
each city. Urban planning and socioeconomic policies may contain the weak-
ness caused by climate change when addressing place-specific and multifaceted
factors integrating the biophysical and socioeconomic dimension. At the same
time, monitoring urban climate cannot be reduced to schematic interpreta-
tions, while opening up an in-depth discussion on how urban life styles may
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affect weather conditions at the local scale. Improving the quality of urban
life, for example by enlarging and better designing green urban areas, promot-
ing sustainable architecture and renewable energy policies, developing public
transport networks and “soft mobility,” are actions mitigating land and popula-
tion vulnerability to climate change and reducing the socioeconomic loss due
to extreme weather events.

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 12 Impacts of urban sprawl on
landscapes
Marie Cugny-Seguin
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Introduction
Landscapes can be seen from many views depending on the phenomenon
under consideration. For landscape ecology, focused on the understanding
of the interactions between spatial heterogeneity and ecological processes,
‘a landscape is an area that is spatially heterogeneous in at least one fac-
tor of interest’ (Turner et al., 2001; Turner, 2005). Other authors insist
on anthropogenic aspects: ‘A heterogeneous area comprising interacting
ecosystems that are repeated in similar form throughout, including both
natural and anthropogenic land cover, across which humans interact with
their environment’ (Forman and Godron, 1981). For social science, land-
scape is understood ‘as an arena where conflicting interests meet, but also
as sites of importance for people’s individual and collective memories and
identifications’ (Tengberg et al., 2012). According the European Landscape
Convention, ‘landscape means an area, as perceived by people, whose char-
acter is the result of the action and interaction of natural and/or human
factors’ (Committee of Ministers of the Council of Europe, 2000). The con-
vention promotes the integration of landscapes in any policies with possible
direct or indirect impacts on landscapes such as cultural, environmental,
agricultural, social and economic policies, using a participatory approach.
That means to integrate landscape issues into spatial and urban planning
policies and to develop strategies and guidelines to create, enhance, protect,
restore and manage landscapes. For this contribution we have adopted the
definition of the European Landscape Convention.
Therefore, the notion of landscapes comprises not only physical and
spatial parameters but also cultural, social, historical, aesthetic and even reli-
gious connotations. Landscapes are crucial for the quality of life of people
everywhere (in urban areas and in the countryside), the formation of local
cultures and the consolidation of the identity of a place. The Millennium
Ecosystem Assessment (MA) related to cultural and amenity services stresses
that human cultures, knowledge systems, religions, heritage values and
social interactions have always been influenced and shaped by the nature
of the ecosystems and the ecosystem conditions in which a culture is based
(MA, 2005a).
 Impacts on landscapes 205
Landscapes are dynamic systems. They are continuously affected by human
activities and natural processes and these continual land use changes have a
significant effect on ecosystem services supply (Maes et al., 2011). Landscapes
evolve because of individual and unrelated actions upon the environment
(e.g. actions of inhabitants), local decisions (e.g. urban planning), external fac-
tors (e.g. change in the hierarchy of cities due to the globalisation of economy),
changes in technology (e.g. mobility by car), change in lifestyle (e.g. the pref-
erence for a detached house with private garden), and the action of natural
forces (e.g. floods, cyclones).
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Since the second half of the twentieth century, with the rise of mobility
and commuting, cities have physically expanded around a major urban centre,
mainly into the surrounding agricultural areas. This expansion of built-up areas
has generated urban sprawl characterised by areas of low density, patchy and
scattered development (EEA, 2006). This growth of artificial surfaces associated
with the development of linear transportation infrastructure has fragmented the
landscapes and generated adverse ecological effects. The fragmentation contri­
butes significantly to the decline and loss of wildlife populations, the increasing
endangerment of species in Europe (e.g. through the isolation of populations)
and the spread of invasive species (EEA, 2011); it also affects the water regime
and the aesthetic and recreational quality of landscapes.
Landscapes are made by humans and reflect changes in society (e.g. culture,
values, behaviour, lifestyle) and its relationship to the natural environment
(Antrop, 2000a). The speed, frequency and magnitude of changes has varied
according to historical periods. During many centuries changes were local and
slow (Antrop, 2005). The use of land resulted in a traditional landscape with a
recognisable structure and significant aesthetic values that give clear identity to
a place. With the economic rationalisation of agriculture and the rapid urbani-
sation, landscapes of large areas have lost and continue to lose their diversity
and territorial identity.
The major challenge of landscape planning and management is to mini-
mise the disturbance effects of human interventions while satisfying the human
needs for activities. Land ownership is the main difficulty of landscape manage-
ment; land is owned by many people who all have their own particular interest.
However, landscape is a common good that provides habitats for flora and
fauna and is the base for human activities. The detrimental effects on a land-
scape are seen not only by the citizen, generally the owner, who has decided
the change of a landscape element (e.g. to build a house, to cut a hedgerow)
but shared by all society (inhabitants, tourists, visitors) and for a long time. In
the same way, ecosystems services provided by landscapes benefit the well-
being of all people living or visiting the place and not only the citizen who has
decided on the transformation.
The following key questions will be analysed in this chapter:

•• What are the ecological impacts of landscape fragmentation?

•• What are the impacts of urban sprawl on cultural services of landscape
degradation?
 206 M. Cugny-Seguin
The ecological impacts of landscape fragmentation
Landscape fragmentation is the product of the linkage of built-up areas via
linear infrastructure, such as roads and railroads. It is the result of transform-
ing large habitat patches into smaller and more isolated fragments of habitat
(EEA, 2011). These transformations generate an increase in the amount of
patches, and therefore in the amount of patch edges, changes of their shape and
their spatial arrangements and the interspersion of anthropogenic and natural
land. Landscape fragmentation has an impact on landscape structure, including
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changes in landscape configuration and heterogeneity1 (Matthew et al., 2015).

Patches and corridors are the key spatial elements for increasing connectiv-
ity and therefore preventing fragmentation (Forman, 2008). The size and the
shape of a patch affect how a patch functions on its own or in relation with
the other patches; larger patches provide larger habitats and are more effective.
The corridors (e.g. linear landscape elements such as hedgerows) between patches
determine the opportunities of movement across the landscape.
There is a trade-off between the level of fragmentation and the supply of
services. Road density eases the accessibility to specific services, their supply
and their exploitation. However, at the same time, the supply of different
services are affected by landscape fragmentation and the scale at which frag-
mentation occurs, in particular when their flows depend on the movement of
organisms, matter, energy or people across landscapes (e.g. fresh water provi-
sion, water quality, natural hazards). ‘Regulations and maintenance services’
such as species movement, water-related services (e.g. with the increase of
imperviousness, less water infiltrates and run-offs increase) or erosion preven-
tion are particularly impacted. ‘Provisional services’ such as food or timber
production (e.g. due to small land parcels or reduced quality of agricultural
products along roads) are also affected as well as ‘cultural services’ (e.g. aesthetic
value of a landscape).

Box 12.1 Landscape fragmentation in Europe

By using the method of ‘effective mesh density’, it is possible to quantify
the degree to which the possibilities for movement of wildlife in the
landscape are interrupted by barriers. The effective mesh density values
across the 28 investigated European countries cover a large range, from
low values in large parts of Scandinavia to very high values in west-
ern and central Europe. Many highly fragmented regions are located
in Belgium, the Netherlands, Denmark, Germany, France, Poland and
the Czech Republic. High fragmentation values are mostly found in the
vicinity of large urban areas and along major transportation corridors.
The lowest levels of fragmentation are usually associated with mountain
ranges or remoteness.
 Impacts on landscapes 207
The density of the transportation network and the extent of landscape
fragmentation is largely a function of interacting socioeconomic drivers
such as population density and geophysical factors such as topography.
According the report Landscape Fragmentation in Europe, the most relevant
variables affecting landscape fragmentation are population density, gross
domestic product per capita, volume passenger density and the quantity
of goods loaded and unloaded per capita (EEA, 2011).
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Figure 12.1 Landscape fragmentation per 1 km² grid in 2009 (source:

EEA, 2011)
Note: Landscape fragmentation was calculated using fragmentation geometry FG-B2.

Landscape fragmentation is a major cause of the decline of many wildlife

populations. It creates smaller habitat patches that support fewer species, and
contain smaller and more vulnerable populations with a reduced genetic
variability (Forman and Alexander, 1998; IUCN, 2001). It increases the
edge effect that negatively affects the persistence of native species (Dobson
et al., 2006; Matthew et al., 2015). It contributes to the destruction of estab-
lished ecological connections between areas of the landscape (Jaeger et al.,
2005) and reduces the ability of plant and animal species to move across
landscapes. Roads and traffic reduce their access to the different types of
habitat they need during their life cycle (e.g. foraging and breeding habitats),
enhance mortality due to collisions with vehicles and generate disturbance
and dispersal events. Several examples of the detrimental effect of land-
scape fragmentation, combined with intensive agricultural practices, exist
 208 M. Cugny-Seguin
(e.g. the continuous decline of the brown hare (Lepus europaeus) populations in
Switzerland (EEA, 2011)).
In addition, physical processes such as radiation, flows of water and wind
speed can be changed by removing large pieces of native vegetation. These
physical changes affect biological processes such as litter decomposition, nutri-
ent cycling, composition and structure of vegetation, and hydrological regime.
The greatest impacts occur mainly at the edge of patches but changes occurring
in one patch are accumulated across the landscape and finally have an impact
on the entire landscape. For example, in the Western Australian wheat belt
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massive loss of native vegetation has resulted in a rise of groundwater, bringing

stored salt to the surface and finally reducing agriculture productivity (Bennett
and Saunders, 2010). The effect of fragmentation of habitats on the biota take
many years to be expressed, in particular for long-lived organisms (e.g. trees)
that can persist decades before disappearing without replacement.

The impacts on cultural services

Cultural services are defined as ‘the non-material benefits people obtain from
ecosystems through spiritual enrichment, cognitive development, reflection,
recreation, and aesthetic experiences’ (MA, 2005b). Landscapes contain not
only physical information but also exhibit the social history and the identity of
the place. They show the beliefs, values, shared habits and preferences of the
different cultures that have formed the landscapes. From a social point of view,
they are the result of the past representations and future expectations of society
(Black, 2003).
Cultural aspects (e.g. heritage and aesthetic values) of landscapes are
threatened and can be irreversibly lost by uncontrolled urbanisation and the
development of transportation networks. Europe and North America experi-
enced a first wave of urbanisation in the course of two centuries (1750–1950)
and poorer and emerging countries are currently experiencing a ‘second wave’
of demographic, economic and urban transitions, much bigger and much faster
than the first (UNFPA, 2007). After the 1960s, urban sprawl became a world-
wide problem, not only in North America, Western Europe (EEA, 2006)
and Japan, but also in some large cities in developing countries. The causes
of sprawl can be different according to country and period; for example, in
Western countries, urban sprawl is the result of suburbanisation2 (Mills, 2003)
whilst in China, it is mainly due to low-density urbanisation and industrial
development at the urban fringe (Zhao, 2010, 2011).
In Europe, cities experienced three major transformations until the twentieth
century. First, with the increase of the urban population, at a level incompa-
rable to the previous periods in history, cities became denser and covered a
larger space. Second, during the industrial period, urban landscapes were trans-
formed by the need of places for mass production, easy access to raw materials
and energy, transportation infrastructure and workers’ settlements close to the
manufactories. Third, the introduction of new means of transportation (train,
electric subway, metro and cars) changed the structure of European cities
 Impacts on landscapes 209
that became more dispersed, overcrowded and distributed over larger spaces.
During the second half of the twentieth century, urban sprawl occurred with
the development of road networks, the rise of income, the increase in private
car ownership, the preference for single-family houses, the land market attract-
ing people in the periphery and insufficient or non-relevant urban policies.
Then, with the decline of industrial production and the emergence of
a new kind of knowledge-based and service-oriented urban economy,
European cities entered a new phase of development (Cremaschi and
Eckardt, 2011). Post-industrial landscapes, without any cultural background
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and looking all alike, are being created. Nowadays changes in landscape are
more driven by external decisions (e.g. from multinational firms) or global
tendency (e.g. economic crisis) rather than local or regional decisions. The
growth of the service sector is extremely visible in today’s urban landscape.
Since the 1980s, large public and private investments have been made in
and near the city centres or at the edge of the cities, changing the physical
appearance of the cities. Huge office buildings, with an architecture focused
on status and prestige (impressive architecture is designed to suggest eco-
nomic power), have generated new urban landscapes that express a new
economic reality. In the post-industrial city, wastelands – remains of the pre-
vious industrial transformation – have become strategic places for new urban
development (e.g. harbours, industrial brownfields, abandoned rail tracks
etc.) and for changing the place (e.g. new landscape of Marseille waterfront,
culture-led regeneration of Psiri in Athens).
The different phases of urbanisation have transformed landscapes. According
to the country and regional area, different phases of urbanisation have been
identified and described to explain the concentric zones of influence around
urban centres. For example, five concentric zones of influence have been
described for the cities of Western Europe (Antrop, 2000b): the urban core
(completely built up area with different periods – Middle Ages, nineteenth
century etc.), the inner urban fringe (post-Second World War garden cities
with a dense housing pattern), the outer urban fringe (urban landscape char-
acterised by a complex mosaic of land use), the rural commuting zone, with
important functional changes due to demographic transition (emergence of
exurbs), the depopulating countryside with relicts of old landscapes.
With the rise of car-mobility, landscapes have become fragmented by
highways and roads, even into the urban fabric. Transport networks con-
nect to destinations (e.g. commercial malls, airports, zones of activities, office
parks, allotments) rather than to ‘places’ that refer to identity. During the
last few decades, single-use zoning has produced landscapes characterised by
a functional homogeneity. In the same way, the intensification of agricul-
ture and the removal of small landscape elements (e.g. hedgerows, isolated
trees) reduce spatial variation. Territories, isolated from the others and with
a unique function (e.g. commerce, housing, gated communities), and often
with similar architecture, have been created. Between these new developed
areas, open spaces can be left for potential urbanisation over years. In this
world of ‘hypermobility’, space and distance are measured in time.
 210 M. Cugny-Seguin
Urban sprawl has major impacts not only on the environment (surface seal-
ing, ecosystem fragmentation, emissions from transport, run-off etc.) but also on
the social structure of an area (by spatial segregation, lifestyle changes etc.) and
on the economy (via distributed production, land and housing prices, scale issues
etc.). Residential segregation that is often combined with fewer services for the
population (e.g. poor transportation, health, deprived housing) is happening in
most big metropolises everywhere in the world and in different manners (racial
groups, ethnicity, religion or income status) according to the cultural and his-
torical context. In some counties, spatial segregation of the poor often occurs
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within informal settlements characterised by chaotic landscape without formal

streets. With the proliferation of gated communities, voluntary segregation has
become a new force in both the Northern and Southern hemispheres (e.g. India,
USA) because of the demand (e.g. perception of security, new lifestyle) and the
supply (better profitability with large-scale internalisation of externalities).

Box 12.2
Urban sprawl results in discontinuous, scattered urban and low density
growth. It creates interstitial open spaces, generates interwoven agricul-
tural enclaves in urbanised areas, and wastes valued productive agricultural
land (UN-HABITAT, 2012). The transition between urban and rural

Figure 12.2 Urban profile in Europe (source: Corilis 2006 (2000 for Greek
cities) based on Corine Land Cover version 16)
Source: EEA, 2015.
Note: Above, graph showing the urbanisation pattern from the city centre to a maximum
distance of 50 km for the Urban Audit’s selection of cities over 50,000 inhabitants. Each
line represents the share of urbanised area (per cent) in a 1 km buffer ring from the city cen-
tre (centroid of city boundaries as defined by Urban Audit) for selected cities (London, Paris
and Brussels) and the mean value at European level (EU28 + Norway and Switzerland).
 Impacts on landscapes 211
areas is a continuum. The distinction between urban and rural patterns are
more diffuse and fuzzy. In 2010, according the new urban–rural typology,
40.4 per cent of EU-27 population were living in urban areas and 35.4
per cent in intermediate areas.3 This diffusion of artificial areas into rural
landscapes, that have a distinct and recognisable structure, contributes to
rapidly changing traditional rural landscapes that have been formed rather
slowly by rural lifestyles and therefore have harmoniously integrated natu-
ral conditions and cultural patterns (Antrop, 2000a). On the contrary,
suburban landscapes are highly dynamic and new landscapes, that a bit
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chaotic, are created.

Urban landscape is the physical environment, where people live their daily life,
work, move, do shopping, have their social interactions etc. The quality of
urban life depends on the quality of public spaces (including green open areas)
that can be considered as the ‘living room’ of the city (Burgers, 2000; EEA,
2009); they range from grand central plazas and squares, streets and their pave-
ments to small, local neighbourhood parks. In a context of urban adaptation to
climate change, accessible green open spaces, as well as green walls and green
roofs, are also crucial in providing health and wellbeing benefits for a city’s
residents (de Vries et al., 2003; EEA, 2012) and might become an important
part of urban landscape in the future. Architecture is also a key component
contributing to the quality of the urban environment and the transformation of
the urban landscape; new architectural design produces buildings with innova-
tive forms that are radically changing the physical landscape of the cities.
Finally, to ensure their long-term viability, some cities are trying to develop
an urban sustainable model that lead cities to manage urban development in
a way that minimises the environmental impacts and the land use per capita
as well as promotes a mix of land use and proximity. With increasing con-
cerns about climate change, the reign of cars is finishing; walking, cycling and
using public transport are becoming ideal models of mobility in urban areas.
Compactness and integrated urban development, such as eco-district or eco-
city, are becoming mainstream in new urbanised areas where urbanism can be
strongly integrated. In Europe, urban sustainability is mainly based on the ret-
rofitting of existing urban infrastructure and building stocks, the conversion of
underused or abandoned industrial areas, the conversion of low-density subur-
ban environments into high-density areas and the upgrading of non-sustainable
settlements. These changes in urban systems should produce a new cycle of
urban landscape.

Conclusion
The main challenges for landscape management are to integrate landscape eco-
logical needs and to supply ecosystems services. Management landscape has to be
focused, at the same time, on the preservation of the quality of landscape for nat-
ural resources (such as biodiversity, habitats, connectivity, water cycle) and the
 212 M. Cugny-Seguin
delivery of ecosystems services that are crucial for the well-being of society, in
particular the delivery of cultural services (e.g. scenic beauty, heritage landscape)
that are crucial for the identity of the place and the sense of the community.
Understanding the different aspects of services provision, and what features of
landscape structure, fragmentation and heterogeneity control those services, can
significantly improve the ability to manage landscapes for ecosystem services.
More balanced relationships between humans and nature as well as between
rural hinterland and urban areas are needed. The permanent and dynamic
changes of urban and peri-urban landscapes provide an opportunity for plan-
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ning authorities to consider not only the quality of a landscape itself but also
the level of existing development and the ability of the landscape to absorb
further development without altering its character. That means to understand
the supply of ecosystem services at landscape scale and, at the same time, to
analyse the potential impacts on the close environment of people, even in the
most common landscapes, where a transformation can easily be perceived (e.g.
changes in scenic beauty, quality of open public spaces or noise).
Landscape planning and landscape management face major challenges. First,
the perception of landscape is subjective and depends on the person who looks
at it. Second, elements of a landscape have no absolute value; the change of
one significant element can contribute to change the whole landscape and
the same element in another geographical context may have a different value
(Antrop, 2000a). Third, the changes usually occur in a gradual manner and are
not immediately perceived as dramatic and the cumulative impact therefore
underestimated.
Participation of stakeholders at the decision-making process is a way not
only to know the expectations of people with regard to the landscape features
of their surroundings, but also to raise the landscape awareness of the entire
society. The perception of the urban landscape depends of how people move
through the city. Each city resident develops their own experience of a city
according the place they live, work or socialise. The personal understanding of
a city by its residents cannot match with the ‘real’ city because it does not take
into account the ‘real’ scale (e.g. metropolitan area), the degree of complexity
(e.g. interactions between activities), the interrelationship between nature and
human, the past and the future etc. To contribute effectively to urban sustain-
ability and prosperity, landscape planning has to be based on a shared vision.
Finally, urban sprawl is recognised as a major issue in several countries
and strategies limiting land take in order to mitigate the negative effects of
market-led development have been developed (e.g. the Federal Sustainable
Development Strategy of the German federal government, and strategies for
urban containment in China) (EEA, 2016). Many cities have also developed
their own objectives in order to achieve compactness (e.g. the Master Plan
project for the Paris metropolitan areas) (OECD, 2012) and to limit land take.
The implementation of these policies needs strong leadership by local authori-
ties, monitoring progress and proposing regular and transparent reporting. All
these land policies can contribute to preserve urban and peri-urban landscapes,
but we need to underline that land policies are different from landscape policies.
 Impacts on landscapes 213
Notes
1 The number of habitats is generally higher in a heterogeneous landscape than in
simpler landscapes and this affects species richness positively.
2 Suburban: generally of lower density contiguous built-up areas that are attached to
inner urban areas and where houses are typically not more than 200 metres apart
(Peri-Urbanisation in Europe: Synthesis Report, Plurel FP7 project www.plurel.net/
images/Peri_Urbanisation_in_Europe_printversion.pdf, accessed 15 August 2015).
It is a patchwork of residential, commercial, municipal, and industrial land uses and
related transportation and utility corridors often adjacent to urban centres. See also
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Australian Government, Department of Infrastructure and Transport (2011).

3 http://ec.europa.eu/eurostat/statistics-explained/index.php/File:Share_of_population_
according_to_the_original_OECD_classification_and_the_new_urban-rural_typology_
new.png, (Nuts3) accessed 18 August 2015.

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Part III

Case studies
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 13 Soil consumption monitoring
in Italy
Michele Munafò and Luca Congedo
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Introduction
In Italy, ISPRA is undertaking several activities related to land cover monitor-
ing to assess soil consumption evolution over the last few decades. In particular,
ISPRA developed a soil consumption monitoring network based on the sam-
pling approach and the photo interpretation of very high resolution images;
also, in the frame of the Copernicus initiative, ISPRA validated and enhanced
the High Resolution Layers (HRLs) of 2012, which are land cover rasters. In
addition, a Very High Resolution Layer (VHRL) of built-up was produced for
2012 with a spatial resolution of 5 m.

Italian Soil Consumption Monitoring Network by ISPRA

In 2005, ISPRA and the National System for Environmental Protection
(ARPA/APPA) developed a Soil Consumption Monitoring Network in order
to overcome the lack of updated and homogenous data about soil consump-
tion. This system allows for the assessment of soil consumption trends in Italy
from the 1950s to today with a stratified sampling that implements the photo-
interpretation of very high resolution images and topographical maps. The
monitoring network is the official benchmark for national soil consumption in
the National Statistic Program 2014–2016.
The survey is integrated with cartographic data required for the valida-
tion and to ensure coherence with spatial data, in particular with the activities
undertaken in the Copernicus framework. This network based on the inter-
pretation of very high resolution data is not affected by the constraints of the
minimum mapping unit, allowing for more accurate and reliable estimation. In
particular, it is possible to include sparse built-up that individually covers small
impervious surfaces for the assessment of small land cover changes. These data
allow for the calculation of soil consumption indicators, accuracy assessment
and error estimation.
The network has about 180,000 samples – 12,000 belong to the national
monitoring network, about 28,000 to the regional network and the remainder
to the municipal network. The result is a two-level classification of land cover
and soil sealing.
 218 M. Munafò and L. Congedo
Table 13.1 Soil consumption in Italy (km2 and percentage over the national surface)
1950s 1989 1996 1998 2006 2008 2013 2014

Soil 8,100 15,300 17,100 17,600 19,400 19,800 20,800 21,000

consumption (2.7%) (5.1%) (5.7%) (5.8%) (6.4%) (6.6%) (6.9%) (7.0%)
Source: Munafò et al. (2015).

Estimates show that soil consumption in Italy is significantly increas-

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ing, although the pace of growth is slowing: between 2008 and 2013 soil
consumption involved about 55 ha per day, with a speed of 6–7 m2/s. In
particular, the temporal analysis shows that soil consumption increased from
2.7 per cent in the 1950s to 7.0 per cent in 2014, with a difference of 4.3
percentage points. Globally, about 21,000 km2 of soil have been occupied by
built-up (see Table 13.1).
Soil consumption is continuously occupying natural and agricultural areas,
where impervious surfaces like asphalt and concrete are growing with build-
ings, roads and infrastructures, often in low-density urban areas.
In 2013, 15 regions reached 5 per cent of soil consumption, with higher
values in Lombardia and Veneto (northern Italy), and Campania and
Puglia (southern Italy), as illustrated in Table 13.2. It is worth noticing that

Table 13.2 Percentage of soil consumption (range) in Italian regions

1950s 1989 1996 1998 2006 2008 2013

Piemonte 2.2–3.9 4.4–6.3 4.7–6.7 4.8–6.8 5.0–7.0 5.1–7.1 5.9–8.2

Valle d’Aosta 1.1–2.3 1.7–3.0 1.8–3.1 1.8–3.1 2.0–3.4 2.0–3.4 2.2–3.7
Lombardia 3.9–5.8 6.8–9.0 7.5–9.9 7.7–10.1 8.5–11.0 8.8–11.3 9.6–12.2
Trentino-Alto Adige 0.9–2.0 1.5–2.7 1.6–2.8 1.6–2.9 1.8–3.1 1.8–3.1 1.8–3.2
Veneto 3.0–4.8 5.0–7.1 6.2–8.3 6.5–8.7 7.7–10.1 8.3–10.8 8.6–11.1
Friuli-Venezia Giulia 2.2–3.8 4.4–6.3 5.0–7.0 5.1–7.1 5.5–7.5 5.6–7.7 5.8–7.9
Liguria 2.0–3.5 4.2–6.1 5.0–7.0 5.2–7.2 5.6–7.7 5.6–7.7 5.9–8.0
Emilia-Romagna 1.8–3.0 5.7–7.7 6.4–8.4 6.6–8.7 6.7–8.8 6.8–8.8 6.9–8.9
Toscana 1.6–3.0 3.7–5.5 4.5–6.4 4.5–6.5 5.1–7.2 5.2–7.2 5.3–7.4
Umbria 1.1–2.3 2.6–4.2 3.1–4.8 3.2–4.9 4.2–6.2 4.2–6.2 4.3–6.3
Marche 1.9–3.5 3.9–5.8 4.6–6.6 4.8–6.8 5.1–7.3 5.3–7.4 5.7–7.9
Lazio 1.3–2.4 4.5–6.3 5.5–7.4 5.9–7.9 6.1–8.0 6.1–8.1 6.4–8.4
Abruzzo 1.0–2.2 2.7–4.3 3.2–4.9 3.3–5.0 3.6–5.5 4.0–5.8 4.2–6.1
Molise 1.3–2.7 2.2–3.7 2.4–4.0 2.5–4.1 2.7–4.3 2.8–4.5 3.0–4.7
Campania 3.5–5.4 6.0–8.2 6.5–8.7 6.6–8.8 7.2–9.5 7.5–9.8 7.8–10.2
Puglia 2.6–4.3 5.3–7.2 6.0–8.0 6.3–8.4 7.1–9.3 7.3–9.6 7.4–9.7
Basilicata 1.5–3.0 2.2–3.7 2.6–4.1 2.7–4.3 3.3–5.1 3.4–5.2 3.6–5.3
Calabria 1.6–3.1 3.1–4.8 3.4–5.2 3.4–5.2 3.9–5.7 4.3–6.1 4.5–6.4
Sicilia 1.4–2.8 4.5–6.5 4.9–6.9 5.0–7.0 5.5–7.7 5.5–7.7 5.8–7.9
Sardegna 1.1–2.3 2.0–3.3 2.3–3.7 2.4–3.8 3.2–4.8 3.3–5.0 3.4–5.0
Source: Munafò et al. (2015).
 Italy 219
estimation is provided with a confidence interval of 95 per cent, depending
on the regions, the characteristics of the monitoring network and the
estimation error.

Copernicus High Resolution Layers

Copernicus is a European initiative aimed at monitoring the environment
through several products and services. The High Resolution Layers (HRLs)
are rasters from 2012 that monitor the land cover of European countries with
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a high level of detail (i.e. 20 m – higher resolution than Corine Land Cover).
In particular the following issues are monitored: soil imperviousness, forest,
grassland, wetland and surface water.
The production of HRLs was multi-step: the first phase was performed by
different service providers that processed the IMAGE2012 dataset, which is
composed of remote sensing images such as RapidEye; subsequently, interme-
diate HRLs were validated and enhanced, using regional and local cartography
and ancillary data.
It is worth noting that Copernicus services and data are provided free of
charge to users. The intermediate HRLs were produced using a semi-automatic
approach, and in Italy were validated and enhanced by ISPRA.
The Degree of Imperviousness is specifically designed to monitor soil con-
sumption, in particular providing a percentage of soil sealing per pixel. The
spatial resolution of HRLs is particularly useful at the regional level for assessing
urban sprawl, defined as unplanned, low-density urban expansion, charac-
terized by a mix of land uses on the urban fringe (European Environmental
Agency, 2006).

High Resolution Land Cover Map of Italy

ISPRA developed and distributed a High Resolution Land Cover Map of Italy
(20 m spatial resolution), which is the result of the integration of HRLs 2012.
The Degree of Imperviousness was reclassified in order to obtain a binary map,
where imperviousness values greater than 29 per cent were considered built-up
(Maucha et al., 2011).
Figure 13.1 shows the map with the following land cover classes:

•• Built-up
•• Broadleaved forest
•• Coniferous forest
•• Grassland
•• Wetland
•• Permanent Water Bodies
•• Other
•• Unclassified.
 220 M. Munafò and L. Congedo
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Figure 13.1 High resolution land cover map of Italy

Very High Resolution Layer of built-up

In Italy, ISPRA has developed a Very High Resolution Layer (VHRL) that
identifies built-up areas with a spatial resolution of 5 m (see Figure 13.2).
 Italy 221
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Figure 13.2 Very high resolution layer of built-up area

Similar to Copernicus HRL, VHRL production was based on the semi-

automatic classification of satellite images (i.e. RapidEye acquired in 2012) and
the integration of local ancillary data such as OpenStreetMap, in order to iden-
tify the built-up.
It is worth pointing out that this VHRL and the HRL Degree of
Imperviousness are different in terms of spatial resolution (5 m and 20 m),
 222 M. Munafò and L. Congedo
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Figure 13.3 Comparison of resolutions for orthoimagery (left), HRL (centre) and

VHRL (right)

classification system (binary map and percentage of imperviousness) and

class definition: railway lines, dump sites and mines are excluded from the
Copernicus HRL, but included in the ISPRA VHRL; in fact, the inclusion of
these features in HRLs is still debated, which for ISPRA should be considered
soil consumption. Figure 13.3 compares VHRL and HRL, showing that the
VHRL outperforms the HRL especially for the identification of streets and
small buildings.

Urbanization pattern
Understanding urbanization structures and patterns is a requirement for defin-
ing effective policies for limiting soil consumption and fostering sustainability
governance. HRLs allowed for the assessment of urban dynamics through
the calculation of landscape metrics that are measures describing the charac-
teristics of landscape patches regarding the structure, function and changes
thereof, initially developed for ecological studies (McGarigal and Marks,
1995). Spatial metrics are useful for assessing the physical characteristics and
patterns of landscape, in particular for studying land cover change in urban
areas (Huang et al., 2009).
For the Italian case study, the following landscape metrics were calculated
using the HRL Degree of Imperviousness (Munafò et al., 2015):

•• Largest Class Patch Index (LCPI): percentage of landscape occupied by the

largest patch, indicating landscape compactness;
•• Residual Mean Patch Size (RMPS): the mean patch area excluding the
largest patch, providing the dimension of urban sprawl around the city
centre;
•• Edge Density (ED): the perimeter of urban areas dividing the area thereof,
describing urban fragmentation;
•• Urban Sprawl Index: ratio of high-density and low-density areas, describing
the variation of urban density related to urban sprawl.
 Italy 223
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Figure 13.4 LCPI at the provincial level (source: Munafò et al., 2015)

LCPI, a compactness indicator, has higher values in cities with a large urban
centre, lower values where urban sprawl is predominant. Results at the pro-
vincial level are shown in Figure 13.4, where Napoli, Milano and Trieste have
the highest value.
The RMPS is highly influenced by the study scale, and it provides the
dimension (in hectares) for sprawl around cities; high RMPS values imply
polycentric cities while low values mean fragmentation of the periphery not
connected to the city centre. The results at the provincial level are shown in
Figure 13.5, where Milano has the highest value due to the presence of compact
areas around the city. In order to understand this phenomenon it is necessary to
combine this with evaluation of the other metrics, especially LCPI.

Figure 13.5 RMPS at the provincial level (source: Munafò et al., 2015)
 224 M. Munafò and L. Congedo
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Figure 13.6 ED at the provincial level (source: Munafò et al., 2015)

In order to assess urban sprawl, the ED was calculated as it is related to the

morphological characteristics of urban boundaries, which are influenced also
by altitude and slope. In particular, higher values mean irregularity of urban
boundary, while low values are related to compact shapes with regular bounda-
ries. The ED results are shown in Figure 13.6, where large urban areas such as
Milano and Napoli have lower values.
The Urban Sprawl Index describes urban dispersion and fragmentation, as
it is discontinuous areas divided by the total area; low values mean compact-
ness while higher values represent sprawling cities (European Environmental
Agency, 2006; ESPON, 2011). Milano and Napoli again have lower values,
because of their characteristic compactness (see Figure 13.7).

Figure 13.7 Urban Sprawl Index at the provincial level (source: Munafò et al., 2015)
 Italy 225

Figure 13.8 Classes of urban development

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The above results refer to the provincial level, but it is worth noting
that analysis was performed also at the municipal level and described in
ISPRA (2013).
Consequently, urban areas were grouped into five classes (Figure 13.8):

•• ‘Full Monocentric’: municipalities with compact urban development, also

beyond the municipal boundaries;
•• ‘Monocentric’: municipalities with compact urban development, only
within the municipal boundaries;
•• ‘Mainly Monocentric’: municipalities with a centre tending to sprawl in
the periphery;
•• ‘Urban Sprawl’: municipalities affected by fragmentation without a city
centre;
•• ‘Policentric’: municipalities with several small centres.

The results of the landscape metrics calculated for provincial capitals are shown
in Figure 13.9, where classes of urban development are defined.
Municipalities that have sprawling features such as Mainly Monocentric
and Urban Sprawl classes are affected by the worst risk caused by the
negative effects of urban fragmentation. Also, greater attention is required
for Full Monocentric cities, such as Milano and Torino, which exceed
municipal boundaries.
Most Italian cities are Mainly Monocentric, such as Campobasso and Reggio
Emilia, although several cities are Monocentric, such as Firenze, Genova and
Bologna. Polycentric cities (e.g. Venezia, Bari, Taranto) are less numerous, and
the shape thereof is influenced by the morphology of the ground, coast line
and growth of industrial areas or infrastructures.
There are also several Urban Sprawl cities, characterized by the intersper-
sion of urban features in natural and agricultural areas, such as Trapani, Latina
and Ferrara.
The results of this analysis provide an important step forward in understand-
ing landscape dynamics and urban shapes that are crucial for environmental
sustainability.
 226 M. Munafò and L. Congedo
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Figure 13.9 Distribution of provincial capitals according to the landscape metrics and

classes of urban development (source: Munafò et al., 2015)

Urban fragmentation
Urban fragmentation and configuration was analysed through landscape met-
rics calculated using the High Resolution Land Cover Map, in order to assess
spatial configuration and heterogeneity.
Table 13.3 shows the landscape metrics calculated for the Italian landscape
at the provincial level. Metrics with a high degree of correlation have been
excluded from the calculation, in order to avoid redundant information in the
analysis (Bogaert, 2005).

Table 13.3 Landscape metrics calculated at the provincial level

Indicator Description

MPA Average of the area of individual patches for each

(Mean Patch Area) class, tends to increase with the increasing
homogeneity of the landscape
PD Number of patches dividing the landscape area,
(Patch Density) high values mean landscape fragmentation
PLADJ Percentage of adjacencies between pixels belonging
(Percentage of Like Adjacencies) to different classes, high values mean landscape
heterogeneity
SHDI Indicator combines class abundance and landscape
(Shannon Diversity Index) homogeneity, describing the landscape diversity
MSI Indicator describing patch shape: 1 indicates regular
(Mean Shape Index) shapes (e.g. circle) and the number tends to
increase with shape irregularity and complexity
 Italy 227
These indicators are designed to characterize the degree of homogeneity
(MPA) and complexity (MSI) of the landscape, the heterogeneity and diver-
sity of forms present (PLADJ, SHDI) and the fragmentation of landscape
units (PD). The combination of these indicators enables the assessment of the
Italian landscape according to the classification system provided by the High
Resolution Land Cover Map. Figures 13.10, 13.11, 13.12, 13.13 and 13.14
show the calculated metrics.
Results show that Italian landscape is generally not homogenous with a high
level of fragmentation (i.e. MPA < 15 per cent and PLADJ > 90 per cent),
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also with irregular shapes (i.e. MSI between 1.2 and 1.5). The high variability
of MPA and SHDI describe the diversity of landscape, where some provinces

Figure 13.10 Mean Patch Area calculated at the provincial level (source: Munafò
et al., 2015)

Figure 13.11 Patch Density calculated at the provincial level (source: Munafò

et al., 2015)
Downloaded by [University of California, San Diego] at 23:51 15 May 2017

Figure 13.12 Percentage of Like Adjacencies calculated at the provincial level (source:

Munafò et al., 2015)

Figure 13.13 Shannon Diversity Index calculated at the provincial level (source:

Munafò et al., 2015)

Figure 13.14 Mean Shape Index calculated at the provincial level (source: Munafò
et al., 2015)
 Italy 229
such as Ancona and Cagliari are highly homogenous (as shown by PD and
SHDI values). Other provinces are more fragmented as shown by higher PD
values and lower MPA values; in particular, the high SHDI of Napoli is char-
acteristic of landscape variability.
It is worth noting the influence of local morphology on these metrics – for
example, Potenza and Campobasso have high MSI values due to the mountain
areas, and present homogenous characteristics (as shown by low MPA and
SHDI values).
An overall analysis of metropolitan areas reveals a trend of landscape met-
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rics around average values, which can be explained by the predominance of

the built-up class (having more regular shape and less fragmentation) over the
natural and semi-natural areas.

Soil consumption
The VHRL of 2012 allows for the accurate estimation of soil consumption at
the local level, and thus ISPRA (Munafò et al., 2015) calculated soil consump-
tion for all Italian municipalities. Nevertheless, soil consumption estimates are
generally lower (i.e. about 1 per cent) than actual soil consumption (i.e. sam-
pling method) due to the cartographic method that tends to omit very small or
narrow surfaces (e.g. small roads).
At the municipal level, Rome has the highest soil consumption surface
(about 30,000 ha) while the provincial capitals have very high values (Milano,
Torino and Napoli with values higher than 4,000 ha). However, several non-
capital cities have high values of soil consumption (e.g. Marsala in Sicily).

Table 13.4 Soil consumption (%) at the municipal level for the top 20 municipalities,
2012
Municipality Province Soil consumption [ %]

1 Casavatore Napoli 85.4

2 Arzano Napoli 78.9
3 Melito di Napoli Napoli 76.0
4 Cardito Napoli 67.9
5 Frattaminore Napoli 66.9
6 Torre Annunziata Napoli 65.2
7 Lissone Monza e Brianza 64.0
8 Casoria Napoli 63.1
9 Portici Napoli 62.3
10 San Giorgio a Cremano Napoli 60.1
11 Aversa Caserta 60.0
12 Mugnano di Napoli Napoli 59.1
13 Lallio Bergamo 59.1
14 Frattamaggiore Napoli 59.1
15 Curti Caserta 59.0
16 Sant’Antimo Napoli 58.1
17 Fiera di Primiero Trento 57.9
18 Torino Torino 57.6
19 Napoli Napoli 57.0
20 Sesto San Giovanni Milano 56.8
 230 M. Munafò and L. Congedo
Table 13.4 contains the highest percentages of soil consumption at the
municipal level; it is worth noting that several municipalities belonging to the
Province of Napoli and Caserta, Milano and Torino have values higher than
50 per cent. Urban development in these municipalities appears to be strongly
influenced by the economic power of capital cities.

References
Bogaert J. (2005) ‘Metriche del paesaggio: definizioni e utilizzo’, Estimo e Territorio 9:
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8–14.
ESPON (2011) ESPON Climate: Climate Change and Territorial Effects on Regions and
Local Economies. Final Report Annex 4: Case Study Mediterranean Coast of Spain,
Technical report. Dortmund: ESPON and IRPUD ESPON.
European Environmental Agency (2006) Urban Sprawl in Europe: The Ignored Challenge.
Copenhagen: EEA/OPOCE.
Huang, S.-L., Wang, S.-H. and Budd, W.W. (2009) ‘Sprawl in Taipei’s peri-urban
zone: responses to spatial planning and implications for adapting global environ-
mental change’, Landscape and Urban Planning 90(1–2): 20–32.
ISPRA (2013) ‘Il monitoraggio del consumo di suolo in Italia’, Ideambiente 62: 20–31.
www.isprambiente.gov.it/files/ideambiente/ideambiente_62.pdf, accessed 21 June
2016.
McGarigal, K. and Marks, M. (1995) ‘FRAGSTATS: spatial pattern analysis program
for quantifying landscape structure’, General Technical Report PNW-GTR-351.
Portland, OR: Pacific Northwest Research Station.
Maucha, G., Büttner, G. and Kosztra, B. (2011) ‘European validation of GMES
FTS soil sealing enhancement’, Data 31st EARSeL Symposium and 35th General
Assembly 2011, EARSeL, 223–238.
Munafò, M., Assennato, F., Congedo, L., Luti, T., Marinosci, I., Monti, G., Riitano, N.,
Sallustio, L., Strollo, A., Tombolini, I. and Marchetti, M. (2015) Il consumo di suolo
in Italia, Edizione 2015. Rapporti 218/2015. Rome: ISPRA.
 14 Urban land expansion and its
impacts on cultivated land in
the Pearl River Delta, China
Xiaoqing Song and Zhifeng Wu
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Introduction
Urban land expansion has been a key driving factor for multifunctional rural
land loss. Moreover, cultivated land loss in the process of urban–rural inter-
action has posed major challenges for food security worldwide (Foley et al.,
2005). Since the reform and opening up in 1978, intensive land use change
involving urban land expansion and cultivated land use conversion have been
witnessed in China (Deng et al., 2015; Liu et al., 2014; Wang et al., 2012).
For example, small towns grew into megacities, especially in the coastal
regions such as the Pearl River Delta, which led to the loss of a large amount
of highly productive cultivated land (Seto et al., 2002; Altrock and Schnoon,
2014). In the meantime, a vast amount of additional cultivated land with
lower productivity was converted from forest and grassland in northeastern
and western China (Yan et al., 2009). To ensure food and ecological securi-
ties, the central government has implemented a series of land use policies for
guiding the smart growth of urban land. The proportion of urban popula-
tion, however, amounted to 54.77 percent of the total population in 2014,
according to the China Statistical Yearbook (National Bureau of Statistics of
China, 2015). It is projected that urban land expansion will continue, which
will inevitably lead to massive cultivated land use change in the future. Thus
it is urgent to seek solutions for coordinating sustainable urbanization, food
security, and ecosystem services.
The Pearl River Delta, located in the subtropical monsoon zone of south
Asia, is one of the greatest urban agglomerations in China. Cultivated land in
this delta is diverse with high productivity. After the reform and opening up,
urbanization in this delta proceeded rapidly, forced by an export-oriented eco-
nomic model, which successfully promoted conversion from manufacturing
to the service industry (Altrock and Schnoon, 2014). It is acknowledged that
urbanization in this delta has transformed into the post-urbanization stage with
the most developed economy and the highest proportion of urban population
in China. Thus, analysis, from the perspective of urbanization transition, of
urban land expansion and its impacts on cultivated land use change in this delta
is of significance for policy making with regard to coordinating sustainable
urbanization, food security, and ecosystem services.
 232 Xiaoqing Song and Zhifeng Wu
Data and methodology
The land use dataset used in this study was produced using the ERDAS
IMAGINE software based on Landsat TM images, with a ground resolution
of 30 m. The time dimension covers the years 1980, 1990, 2000, 2005, and
2010. Urban land and cultivated land were categorized according to land use
classification system in China employing both unsupervised classification and
supervised classification methods.
Urbanization transition refers to any change in urbanization from one state
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to another. From the perspective of urbanization transition, land area (Area) and
number of land parcels (NP) were used as key indicators for analyzing change
in urban land expansion and cultivated land use change. Specifically, urban land
expansion changed from experiencing increase to undergoing a decrease while
the number of urban land parcels went from increasing to decreasing, indicating
a transition in urban land use. Cultivated land loss also changed from experienc-
ing increase to undergoing decrease while the number of cultivated land parcels
went from increasing to decreasing, indicating a transition in cultivated land use,
too. Urban land parcels were categorized into five levels using 50 hm2, 100 hm2,
500 hm2, and 1,000 hm2, considering the average size of urban landscape in the
Pearl River Delta. Cultivated land parcels were categorized into six levels using
1 hm2, 5 hm2, 10 hm2, 50 hm2, and 100 hm2, considering the average size of cul-
tivated landscape in the Pearl River Delta. Then, changes of Area and NP among
the different size levels were analyzed. Additionally, cultivated land use structure
changes were analyzed using the ratio of dry farmland area to paddy fields area.

Results

Change in urban land expansion in 1980–2010

Table 14.1 shows that the total area of urban land increased by 81.22 percent
from 1980 to 2010. Urban land expansion, however, slowed down after 2005.
Specifically, the annual expansion of urban land increased from 5,050.33 hm2
to 15,925.70 hm2 from 1980–1990 to 2000–2005. In 2005–2010, the annual
expansion of urban land decreased to 11,776.40 hm2. Although the total num-
ber of urban land parcels increased by 3.64 percent before 2000, it decreased by
8.15 percent in 1980–2010. Annual growth of the average area of urban land
parcels increased from 0.14 hm2 to 0.74 hm2 from 1980–1990 to 2000–2005.
However, it decreased to 0.54 hm2 in 2005–2010.
Among the different size levels, urban land parcels of more than 1,000 hm2
had the largest expansion with the expansion area of 276,540.42 hm2 in 1980–
2010. Meanwhile, urban land parcels smaller than 50 hm2 and between 50 hm2
and 100 hm2, however, had much less expansion with the expansion area of
1,778.66 hm2 and 2,106 hm2, respectively. The number of urban land parcels
smaller than 50 hm2 decreased by 2,909. The number of urban land parcels
between 100 and 500 hm2 had the largest increase (60), followed by the num-
ber of urban land parcels bigger than 1,000 hm2 (30).
 Pearl River Delta, China 233
Table 14.1 Change in urban land parcels with different sizes in 1980–2010, Pearl
River Delta (unit: hm2)
1980 1990 2000 2005 2010

Total Area 376,377.44 426,880.78 543,569.51 623,198.03 682,080.05

NP 33,982 34,222 35,221 32,548 31,211
<50 hm2 Area 136,141.10 139,325.57 149,593.63 140,415.80 137,919.77
NP 33,333 33,547 34,426 31,797 30,424
50–100 hm2 Area 23,589.88 24,681.32 27,515.44 23,508.53 25,695.88
NP 337 351 394 338 367
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100–500 hm2 Area 52,366.78 52,439.37 65,470.93 63,738.72 65,766.10

NP 263 265 324 316 323
500–1,000 hm2 Area 17,111.86 19,855.68 19,281.29 29,914.53 28,990.07
NP 24 28 30 44 42
≥1,000 hm2 Area 147,167.81 190,578.84 281,708.22 365,620.45 423,708.24
NP 25 31 47 53 55

Change in cultivated land loss in 1980–2010

Table 14.2 shows that the total area of cultivated land decreased by 29.08
percent from 1980 to 2010. Cultivated land loss, however, slowed down after
2005. Specifically, the annual loss of cultivated land increased from 8,190.20
hm2 to 18,203.25 hm2 from 1980–1990 to 1990–2000. From 2000–2005
to 2005–2010, however, the annual loss of cultivated land decreased from
13,586.85 hm2 to 8,842.94 hm2. The annual loss of total number of cultivated
land parcels increased from 494 to 715 from 1980–1990 to 1990–2000. In
2000–2005 and 2005–2010, the annual loss of total number of cultivated land
parcels was 1,196 and 1,823 respectively. The average area of cultivated land
decreased from 12.97 hm2 to 11.75 hm2. From 2000 to 2010, however, it
increased from 11.78 hm2 to 12.65 hm2.
Among the different size levels, the annual loss of area of cultivated land par-
cels less than 1 hm2, between 1 hm2 and 5 hm2, and between 5 hm2 and 10 hm2
increased by 310.61 hm2, 1,900.98 hm2, and 698.18 hm2 from 1980–1990
to 2005–2010, respectively. The annual loss of area of cultivated land par-
cels between 10 hm2 and 50 hm2, between 50 hm2 and 100 hm2, and bigger
than 100 hm2, increased by 2,559.83 hm2, 1,828.93 hm2, and 5,447.14 hm2
from 1980–1990 to 1990–2000. From 1990–2000 to 2005–2010, however,
the three annual losses above decreased by 2,511.92 hm2, 2,358.16 hm2, and
7,222.85 hm2, respectively. The annual loss of number of cultivated land par-
cels smaller than 1 hm2 and between 1 hm2 and 5 hm2 increased by 1,155 and
3,747 from 1980–1990 to 2005–2010, respectively. The annual loss of number
of cultivated land parcels between 5 hm2 and 10 hm2, between 10 hm2 and
50 hm2, between 50 hm2 and 100 hm2, and bigger than 100 hm2, increased
by 26, 509, 106, and 83 from 1980–1990 to 1990–2000, respectively. From
1990–2000 to 2005–2010, however, the four annual losses above decreased by
382, 1622, 363, and 266, respectively.
 234 Xiaoqing Song and Zhifeng Wu
Table 14.2 Change in cultivated land with different sizes in 1980–2010, Pearl River
Delta (unit: hm2)
1980 1990 2000 2005 2010

Total Area 129,3486.66 1,211,584.63 1,029,552.08 961,617.62 917,402.93

NP 99,712 94,774 87,622 81,642 72,526
0–1 hm2 Area 14,038.73 13,761.37 13,365.79 12,145.50 10,453.78
NP 20,502 19,426 17,246 15,622 13,391
1–5 hm2 Area 112,356.66 108,341.44 105,544.91 99,460.37 87,947.88
NP 46,739 45,476 45,012 42,245 37,235
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5–10 hm2 Area 95,851.98 88,313.72 77,903.49 73,514.15 66,254.14

NP 13,575 12,515 11,099 10,471 9,437
10–50 hm2 Area 313,369.30 286,680.83 234,394.06 219,772.85 206,189.08
NP 14,926 13,665 11,291 10,582 9,830
50–100 hm2 Area 151,268.38 140,879.14 112,200.54 101,868.95 99,320.44
NP 2,189 2,036 1,626 1,476 1,429
≥100 hm2 Area 606,601.60 573,608.12 486,143.29 454,855.80 447,237.61
NP 1,781 1,656 1,348 1,246 1,204

Change in cultivated land use structure in 1980–2010

Table 14.3 shows that the ratio of total area of dry farmland to total area of
paddy fields increased by 0.69 from 1980 to 2005, without the slightest decrease
in 2000–2005. In 1980–1990 and 1990–2000, growth of the ratio was 0.09 and
0.24 respectively. Growth of the ratio increased by 0.35 in 2000–2010.
Among the different size levels, the ratios of area of dry farmland to area
of paddy fields between 10 and 50 hm2 and bigger than 100 hm2 increased
by 0.73 and 0.70 in 1980–2010, respectively. Meanwhile, the ratios of area
of dry farmland to area of paddy fields smaller than 1 hm2 and between
1 and 5 hm2 increased by only 0.17 and 0.40, respectively. Moreover, in
2000–2010, the ratio of area of dry farmland to area of paddy fields bigger
than 100 hm2 increased by 0.40, which was the greatest increment among
the six size levels.

Table 14.3 Change in the ratio of area of dry farmland to area of paddy fields with
different sizes in 1980–2010, Pearl River Delta (unit: hm2)
1980 1990 2000 2005 2010

Total 0.79 0.88 1.12 1.09 1.48

0–1 hm2 1.08 1.10 1.11 1.11 1.25
1–5 hm2 1.12 1.20 1.28 1.27 1.52
5–10 hm2 0.93 1.07 1.28 1.29 1.60
10–50 hm2 0.86 0.98 1.28 1.33 1.59
50–100 hm2 0.81 0.92 1.27 1.25 1.31
≥100 hm2 0.67 0.75 0.97 0.90 1.37
 Pearl River Delta, China 235
Discussion

Urban land use transition in the Pearl River Delta

In the process of urban land expansion in 1980–2010, urban land use transi-
tion occurred as an annual expansion of urban land, and the annual growth
of the average area of urban land parcels turned from increase to decrease
after 2005. This transition in essence mainly resulted from the increasing land
value. Specifically, as urban expansion and economic growth proceeded, land
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scarcity became the key factor in rising land value, which in turn restrained
the expansion of urban land especially smaller urban land parcels. Meanwhile,
more expansion was allocated to the larger urban land parcels to achieve
economies of scale and to raise land use efficiency, such as urban land parcels
bigger than 1,000 hm2.

Cultivated land use transition in the Pearl River Delta

Results of change in cultivated land loss show that annual loss of cultivated
land turned from increase to decrease after 2000. Meanwhile, the average area
of cultivated land turned from decrease to increase. This implies that cultivated
land use transition occurred in 2000. This transition mainly resulted from the
growing value of cultivated land and the strict land use control policies for
cultivated land protection (Lichtenberg and Ding, 2008), e.g., the dynamic
balance policy in which the total amount of cultivated land converted was
compensated for by reclaimed land with equivalent qualities (Li et al., 2009).
Moreover, cultivated land parcels with the largest size had the most remarkable
transition, and vice versa. Thus large-scale land management with the advan-
tage of economies of scale contributes to hindering cultivated land conversion.

Implications for coordinating sustainable urbanization,

food security, and ecosystem services
Urban land use transition mainly resulting from the increasing land value in the
Pearl River Delta hints at a sustainable urbanization model for other regions
in China. Moreover, cultivated land use transition presents an opportunity for
alleviating the contradiction between urbanization and food security in China.
However, urban land use transition and cultivated land use transition are not
determinative and not a certainty. Both of these transitions interact with and
are forced by economic and institutional development (Figure 14.1).
Additionally, the growing ratio of dry farmland area to paddy fields area was
forced by the farmers’ desire for more income. Specifically, more and more
paddy fields with rice farming were converted to dry farmland for the large-
scale management of cash crops such as vegetables, fruits, and flowers, especially
at the level of large cultivated land parcels in 1980–2010. This conversion,
 236 Xiaoqing Song and Zhifeng Wu
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Figure 14.1 Mechanisms of urban land use transition and cultivated land use transition
in the Pearl River Delta, China

however, has posed major challenges for sustaining ecosystem services. First,
paddy fields could provide more climate regulating services and soil formation
services than dry farmland. Second, chemical input intensity on dry farmland
was much higher than in paddy fields, which contributed to massive biodi-
versity loss in farmland. For example, according to the Data Compilation of the
National Agricultural Costs and Returns, nitrogen fertilizer used for vegetables
in dry farmland is 330.15 kg/hm2, which is 189.90 kg/hm2 more than that in
paddy fields for rice farming (Department of Price of National Development
and Reform Commission, 2013). Thus, more attention should be paid to eco-
system services maintenance to guide the smart conversion from paddy fields to
dry farmland in the process of cultivated land use transition.

References
Altrock U. and Schnoon S. (2014) Maturing Megacities: The Pearl River Delta in Progressive
Transformation. Dordrecht: Springer.
Deng, X., Huang, J., Rozelle, S., Zhang, J. and Li, Z. (2015) ‘Impact of urbanization
on cultivated land changes in China’, Land Use Policy, 45, 1–7.
Department of Price of National Development and Reform Commission (2013) Data
Compilation of the National Agricultural Costs and Returns. Beijing: China Statistics
Press (in Chinese).
 Pearl River Delta, China 237
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R.,
Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T.,
Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C.,
Ramankutty, N. and Snyder, P. K. (2005) ‘Global consequences of land use’,
Science, 309(5734), 570–574.
Li, W., Feng, T. T. and Hao, J. M. (2009) ‘The evolving concepts of land administra-
tion in China: cultivated land protection perspective’, Land Use Policy, 26, 262–272.
Lichtenberg, E. and Ding, C. R. (2008) ‘Assessing farmland protection policy in
China’, Land Use Policy, 2008, 25, 59–68.
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Liu, J., Kuang, W., Zhang, Z., Xu, X., Qin, Y., Ning, J., Zhou, W., Zhang, S.,
Li, R., Yan, C., Wu, S., Shi, X., Jiang, N., Yu, D., Pan, X. and Chi, W. (2014)
‘Spatiotemporal characteristics, patterns, and causes of land-use changes in China
since the late 1980s’, Journal of Geographical Sciences, 24(2), 195–210.
National Bureau of Statistics of China (2015) China Statistical Yearbook 2015. Beijing:
China Statistical Press (in Chinese).
Seto K. C., Kaufmann R. K. and Woodcock C. E. (2002) ‘Monitoring land use change
in the Pearl River Delta, China’, in Linking People, Place, and Policy, ed. Walsh, S. J.
and Crews-Meyer, K. A. Dordrecht: Springer, 69–90.
Wang, L., Li, C., Ying, Q., Cheng, X., Wang, X., Li, X., Hu, L., Liang, L., Yu, L.,
Huang, H. and Gong, P. (2012) ‘China’s urban expansion from 1990 to 2010 deter-
mined with satellite remote sensing’, Chinese Science Bulletin, 57(22), 2802–2812.
Yan, H., Liu, J., Huang, H. Q., Tao, B. and Cao, M. (2009) ‘Assessing the con-
sequence of land use change on agricultural productivity in China’, Global and
Planetary Change, 67(1), 13–19.
 15 Urbanization in Latin America
with a particular emphasis
on Mexico
René R. Colditz, María Isabel Cruz López,
Adrian Guillermo Aguilar Martínez, José Manuel
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Dávila Rosas and Rainer A. Ressl

Introduction
Most Latin American cities were established by the sixteenth century. These
urbanized core areas were symbols of territorial possession and centers from
which the surrounding countryside could be administered and exploited. In
Central American and Andean countries they are often located away from
the coast and build upon pre-colonial settlements. The most important towns
in colonial Latin America were political and cultural centers, for example,
Mexico City, Lima and Buenos Aires, which were also capitals of viceroyal-
ties. Other economically important cities were mining centers (Taxco, Potosi)
and major ports along the coast of the Caribbean sea and Atlantic ocean such
as Cartagena, Santo Domingo, Rio de Janeiro and Montevideo. The political
and administrative centers of the past remain the major urban centers in Latin
America today.
By the mid-twentieth century industrialized countries such as Argentina,
Brazil and Mexico achieved rapid growth with manufacturing activities
concentrated in the capitals and chief ports. This pattern of concentra-
tion became most prominent after World War II and it had an impact on
urban growth, migration and regional development strategies. Away from
these manufacturing cores, export-processing industries created economic
enclaves in intermediate cities and peripheral zones. Urban primacy became
a distinctive geographic feature in most of Latin America (Aguilar and
Vieyra 2008).
In the early 1980s, Latin America adopted a free-market economic model.
Opening-up national economies led to increasing deindustrialization, dete-
riorated labor conditions, growth of the informal sector and an increase in
urban poverty. This, in turn, shifted growth from large metropolitan areas
towards middle-sized urban centers that became more competitive in the
global economy, such as border towns like Tijuana in Mexico, export-
oriented manufacturing poles such as Medellin in Colombia or Ciudad Juarez
in Mexico, and tourist centers like Cancun, Panama City or Rio de Janeiro
(Aguilar and Vieyra 2008).
 Latin America with an emphasis on Mexico 239
Data sets
In the following sections regional definitions from the United Nations (UN
2014a) were adapted in the following way: Latin America was defined as all
land from Mexico to Tierra del Fuego including all Caribbean islands. This
area was subdivided into three regions: Central America (Mexico to Panama),
Caribbean (islands of Greater and Lesser Antilles) and South America (the
remainder). For population analysis statistical data of the World Urbanization
Prospects 2014 were employed with population data from 1950 to 2050 and
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urban agglomerations (more than 300,000 inhabitants) from 1950 to 2030

(UN 2014b), excluding countries with an area smaller than 5,000 km2. The
National Institute for Statistics and Geography (INEGI) provides population
census data for Mexico for 1950, 1960, 1970 and 1990 to 2010 at five-year
intervals (INEGI 2014). Urban areas were defined as localities with more than
2,500 inhabitants, but city analysis only focused on agglomerations with more
than 15,000 people.
Defense Meteorological Satellite Program – Operational Linescan System
(DMSP-OLS, Elvidge et al. 1997) images of annual average stable lights
from 1992 to 2009 were cross-calibrated (Elvidge et al. 2009) and employed
for defining urban areas uniformly in space and time using threshold value
DN>=55 or 87 percent of the data range (Imhoff et al. 1997, Small et al. 2005).
For countries and states with more than 200 urban pixels linear least-square
regression was used for trends estimation and F-test for statistical significance
analysis of the regression model.
Land take was analyzed using a land cover map of Latin America derived
from 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) images
for the year 2008 (Blanco et al. 2013). For the country of Mexico and local
analysis of Cancun and Merida a 250 m MODIS-based land cover time series
(2005–2011) was employed (Colditz et al. 2012, Colditz et al. 2014a, Colditz
et al. 2014b). INEGI vegetation data (1970, 2012) were used for studying the
urban expansion of Mexico City (INEGI-INE 1999, INEGI 2013).

Urbanization in Latin America, its regions and countries

In 2015, the estimated population of Latin America was 630 million or 8.6
percent of the world’s population (Table 15.1; UN 2014b). Over the course
of time from 1950 to 2050 the total population growth rate declined more
rapidly than global numbers. The reason for slower population growth in
Latin America is the stable low rate of mortality and decreasing rate of fertility
which puts most countries in stage 3 out of 4 of the demographic transi-
tion model (Pacione 2009). Latin America comprises a total area of 2,055
million ha or 15.1 percent of the global land surface excluding Antarctica
(Table 15.1). In 2015, population density in Latin America was only 30.7
people/km2 (53.8 people/km2 for the world) with notable regional disparities.
 240 R. Colditz et al.
Table 15.1 Area, total population and urban population (selected years) for the
World, Latin America and its regions and Mexico which were used to
calculate population density and urban proportions
Area Total population [millions] Urban population [millions]
[million ha]
1950 2000 2015 2050 1950 2000 2015 2050

World 13,616 2,525.8 6,127.7 7,324.8 9,550.9 746.5 2,856.1 3,957.3 6,338.6
Latin 2,055 167.9 526.3 630.1 781.6 69.3 396.3 502.8 673.6
America
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Caribbean 23 17.1 38.4 43.1 47.6 6.2 23.5 30.3 38.4

Central 248 38.3 139.6 171.9 228.8 15.0 96.1 126.9 187.2
America
South 1,783 112.5 348.2 415.1 505.1 48.1 276.6 345.6 448.0
America
Mexico 196 28.3 103.9 125.2 156.1 12.1 77.6 99.2 134.8
Source: UN (2014b).
Note: Area for the world excludes Antarctica.

The Caribbean, by far the smallest region (1.1 percent), also hosts the small-
est population proportion of 6.8 percent, but the population density of
184.2 people/km2 is the highest among global regions (UN 2014b). Central
America, with 12.1 percent of the land surface and 27.3 percent of the popu-
lation, shows an intermediate density of 69.3 people/km2. South America
is the largest region but population density is low (23.3 people/km2), also
because of large, nearly uninhabited areas like the Amazon, which puts it
among the sparsely populated regions of the world.
In 2015, in Latin America 502.8 million people, that is 79.8 percent, live
in urban areas (Table 15.1). This puts it in second place with a slightly lower
urban population proportion than North America (81.6 percent) and well
above the global average (54.0 percent, UN 2014b). While there is a relatively
linear increase in global urban population by approximately 0.37 percent per
year, Figure 15.1A shows for Latin America an increase of, on average, 0.69
percent until 2000 and since then 0.21 percent. It should be noted that the
growth of urban population was above the growth of total population; hence
there is a steady decline in rural population proportion and for most countries
also a decrease in absolute numbers due to rural-to-urban migration. Regional
disparities can be noted in Figure 15.1B, e.g. Guatemala, Guyana, Honduras,
Nicaragua and Paraguay show lower than average Latin American percentages
of urban population in 2015, while Argentina and Uruguay are well above
average. Most countries show increasing trends in urban population propor-
tion (Figure 15.1C) with Brazil, Costa Rica, the Dominican Republic, Haiti,
Honduras and Puerto Rico clearly above Latin American and global trends.
The percent urban area, estimated from DMSP between 1992 and 2009
(Elvidge et al. 1997, 2009), replicates the above-described pattern of popu-
lation density (Figure 15.1D). While the world shows a nearly zero trend
over 18 years, Latin America and regional tendencies are all positive but not
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Figure 15.1 Urban population proportion and urban area for the World, Latin America and its regions and countries (sources: UN 2014b and DMSP)
Note: s. . .linear trend, p. . .significance of F-test for linear regression model. Lines in scale bars indicate global and Latin American average. Grey indicates countries
not analyzed due to too few urban area pixels.
 242 R. Colditz et al.
always significant (p>5 percent). Percent urban area in Bolivia, Nicaragua and
Peru is clearly below the Latin American average, while Costa Rica,
Dominican Republic, El Salvador, Jamaica, Mexico and Puerto Rico are
above (Figure 15.1E). Trends also vary widely (Figure 15.1F) with Guatemala,
Mexico and Trinidad and Tobago showing significant, above-average trends
of urban area growth. All negative trends, e.g. for Colombia, Costa Rica,
El Salvador, Jamaica, Puerto Rico, Uruguay and Venezuela, were not sig-
nificant (p>0.05). These countries indicate a particular tendency to urban
densification, e.g. constructing higher buildings or reducing individual space
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with smaller apartments to accommodate the growing urban population in

nearly the same area. This development may be fostered in countries with
a small national territory or large cities in mountainous areas which, due to
construction in floodplains or steep slopes increases susceptibility to natural
hazards such as mudslides and inundations.
Land transformation due to urban growth is difficult to analyze due to
lacking long time series of spatially-explicit land cover information. A sim-
ple attempt was undertaken using a MODIS-based land cover map of Latin
America for the year 2008 (Blanco et al. 2013) and assuming uniform urban
growth of 2 km around each urban agglomeration. The proportion of land
cover classes potentially transformed to urban was summarized for each coun-
try in pie charts (Figure 15.2A). Notable is the high proportion of cropland
loss in many countries. Large reductions of forested land are shown for Brazil,
Colombia, Ecuador, Paraguay, Trinidad and Tobago and Venezuela. Bolivia,
Chile and Peru also depict a transformation of high elevation barren land to
urban areas. However, land transformation is a local process and depends on
local actors and the dominating land cover in this region.
The majority of the world’s urban population lives in centers smaller
than 300,000 inhabitants, but this proportion is declining as more people
agglomerate in large cities (above 1 million) and megacities (above 10 mil-
lion) (UN 2014b). Latin America is no exception; the urban population
proportion of bigger settlement categories of Argentina, Chile, Colombia,
Mexico and Peru have already or will soon surpass the group with fewer
than 300,000 inhabitants.
In 2015, there are 205 cities with more than 300,000 people in Latin
America: 83 with 300,000–500,000, 55 with 500,000–1 million, and 59 with
1–5 million (UN 2014b). There are 4 cities with 5–10 million habitants: Lima
(9.8 m), Bogota (9.7 m), Santiago de Chile (6.5 m) and Belo Horizonte (5.7
m). Out of the 29 global megacities with more than 10 million people Latin
America hosts four: Sao Paulo (21.0 m), Mexico City (20.9 m), Buenos Aires
(15.1 m) and Rio de Janeiro (12.9 m); by 2030 Bogota and Lima are expected
to join this group. Figure 15.2B shows the location of all 205 cities with the
diameter indicating the proportion of urban population residing in those cent-
ers relative to the total urban population of each country. In the Caribbean
and small Central American countries as well as Paraguay and Uruguay most
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Figure 15.2 A: Potential land cover change in 2 km buffer zone around urban areas from a MODIS-based land cover map of
2008 (source: Blanco et al. 2013). B: Urban population proportion for cities with more than 300,000 inhabitants in
2015 (urban population of city in relation to urban population of each country) (source: UN 2014b)
 244 R. Colditz et al.
of the urban population concentrates in one city, usually the capital. In other
counties, despite a higher number of urban centers, one agglomeration clearly
dominates with 20–40 percent of the total urban population, e.g. Argentina,
Chile, Colombia, Cuba, Ecuador, Mexico and Peru. This concentration
reflects the above-mentioned centralized political and economic develop-
ment of most Latin American countries. Brazil and Venezuela form a group
in which, despite large cities, urban population proportion is not concentrated
in only one major center, e.g. only 12.1 percent of the Brazilian urban popu-
lation resides in Sao Paolo and 10.5 percent in Caracas. A singular case is
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Bolivia with three major centers: Cochabamba, La Paz and Santa Cruz. In
both, Brazil and Bolivia the government moved to another city which led to
notable proportional decreasing trends in Rio de Janeiro and La Paz (colors in
Figure 15.2B indicate trends in urban population proportion). Other notable
decreasing trends of urban population proportion are noted for Buenos Aires,
Caracas, Montevideo and Quito, nevertheless, all cites have gained population
in absolute numbers. However, there are also large urban centers with relative
increases, e.g. Bogota, Lima, Santiago de Chile, San Juan (Puerto Rico), Santa
Cruz (Bolivia) and Ciudad de Este (Panama).

Urbanization in Mexico at the national and state level

In 2015, Mexico was home to 1.7 percent of the global and almost
20 percent of Latin Americas population (Table 15.1). The country multi-
plied its population almost five times between 1950 and 2015 but population
growth is slowing down. In 2015, the urban population is almost 80 percent
and is expected to reach 86 percent in 2050. In terms of area the country
makes up almost 1.5 percent of the global land surface and nearly 10 percent
of Latin America. The population density of 63.8 people/km2 in 2015 is
above Latin American and global numbers. With respect to urban popula-
tion, urban area and city development in general, Mexico is a representative
example for Latin America.
Figure 15.3 indicates urban population proportion (INEGI 2014) and urban
area from DMSP (Elvidge et al. 1997, 2009) at the state level (for names see
Figure 15.3B). While high urban population proportion in the center of the
country is due to the highly centralized system around Mexico City, con-
centration in the northern states (Figure 15.3A) is due to water scarcity.
Touristic development is the reason for above-average urban population in
Quintana Roo. Chiapas and Oaxaca are the only states with a higher rural than
urban population. Figure 15.3B shows a positive trend for all states, except
the Federal District with a nearly zero growth of urban population (at a level
of 99.5 percent of urban population in 2010). In fact, the capital has spread
into the surrounding State of Mexico and larger export-oriented industries
have settled in a wider realm in the states of Queretaro, Puebla and Tlaxcala.
Migration due to employment in tourism and relocating elderly, partly for-
eign residents has caused urban population growth in Baja California Sur and
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Figure 15.3 Urban population proportion and urban area for the states of Mexico (source: INEGI 2014 and DMSP)
Note: For state names see 15.3B. Lines in scale bars indicate the average for Mexico, Latin America and the world.
 246 R. Colditz et al.
Quintana Roo. Also, there is a national migration pattern towards the northern
states and in particular border towns to the United States due to employ-
ment in local export-oriented manufacturing industries and the eventual goal
of working in the United States. Over time regional disparities at the state-level
have increased as rural states such as Campeche, Veracruz, Oaxaca and Chiapas
depict lower than average trends.
In contrast to the average of 0.62 percent urban area on the national level
(Figure 15.3C), 60.5 percent of the Federal District is urban, 9.7 percent in
State of Mexico, 5.9 percent in Morelos, 5.4 percent in Tlaxcala, 1.9 per-
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cent in Queretaro and 1.5 percent in Puebla (data from DMSP). The urban
area proportion below the national average in the north and south indicates
for the former that few people live in larger agglomerations due to limiting
environmental factors and for the latter a generally higher population in small
settlements dispersed over the state territory. The trends in Figure 15.3D indi-
cate the expected pattern with substantial urban growth around the Federal
District, while the district itself shows non-significant (p>5 percent) nega-
tive tendencies. Notable are growing urban areas in Baja California, Jalisco,
Nuevo Leon, Quintana Roo, Tamaulipas and Yucatan. A particular case is
Aguascalientes, a small but highly industrial state with significant urban growth.
A spatially-explicit change product based on 250 m MODIS data from
2005–2011 was employed for estimating land take due to growth of urban
areas (Colditz et al. 2014a, Colditz et al. 2014b). The total annual change varies
between 0.08 and 0.11 percent of which 2 to 4 percent were urban changes.
The bar totals in Figure 15.4 depict gain and loss of class urban for each
bi-annual comparison and the colors indicate class-specific from-to change.
The smallest urban expansion occurred between 2005 and 2006 and high-
est between 2008 and 2009. Even though almost 30 percent of the national
territory is forested land, 37 percent shrubland and nearly 9 percent grassland
(Colditz et al. 2012), few of these semi-natural areas were transformed to urban.
In all years the majority of area transformed to urban was managed cropland (20
percent of the total national territory). Transformations from water to urban
is a result of spatially unconstrained change detection and unlikely in reality.
In the period 1990–2010 the number of urban centers with at least 15,000
inhabitants increased from 312 to 384 (Figure 15.5). In 2010, there were 11
cities with more than 1 million inhabitants, which can be distinguished in two
groups, cities with 2.5 million or more (Guadalajara, Mexico City, Monterrey
and Puebla), with growth rates below the national average of 2 percent and the
remainder (Ciudad Juarez, Leon, Queretaro, San Luis Potosi, Tijuana, Toluca,
Torreon), which are more dynamic and with a growth rate mostly above 3
percent. Notable is the historically low growth rate of Mexico City with 0.9
percent between 2000 and 2010 (Aguilar and Graizbord 2014).
A second important aspect is the metropolization process that is affecting
mostly the bigger cities. Whereas in 1990 there were 37 metropolitan zones in
the country with 31.5 million people living in them, by 2010 there were 50
of these zones with 63.8 million inhabitants. These metropolitan centers have
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Figure 15.4 Gain and loss of urban area in hectares for bi-annual comparisons
(2005–2011) of MODIS-based land cover maps (source: Colditz et al.,
2014a)
Note: Colors indicate class-specific from-to changes.

Figure 15.5 Location and population of urban areas of Mexico in 2010

 248 R. Colditz et al.
emerged as the nodes of higher hierarchy in the urban system because they
concentrate 56.8 percent of total population and generate approximately 75
percent of the national gross domestic product. Although they have a favorable
influence in their respective regions to impulse socioeconomic development,
they also demand a high quantity of land for the excessive expansion of built-
up areas often with a notable peri-urbanization process accompanied by lower
densities and disperse urbanization (Aguilar 2014). This corresponds to the
above-noted loss of managed agricultural areas and indirectly moves the fron-
tiers reducing also natural land and its supporting, provisioning and regulating
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ecosystem services such as forests, water retention and purification, biodiversity

etc. In addition, a great proportion of population living in peripheral areas
constitute informal settlements in precarious conditions that contribute to
environmental damage. In general terms, in the last 30 years, during which
urban population in all urban centers has doubled, the expansion of their built-
up areas has, on average, multiplied by a factor of seven (SEDESOL 2011).
Mid-sized cities with a population between 100,000 and 1 million have mul-
tiplied in different regions and are now the nodes of the urban deconcentration
process. In the last 20 years (1990–2010) this number increased from 55 to 84
and its inhabitants almost doubled passing from 17.6 to 30.30 million people.
They are now important centers for productive activities such as oil exploitation
(Ciudad del Carmen, Coatzacoalcos, Villahermosa) or new export-oriented
manufacturing centers (Aguascalientes, Leon, Queretaro, San Luis Potosi),
thriving border towns with significant service for the US market (Matamoros,
Mexicali, Nuevo Laredo, Tijuana) or touristic centers which are also the pre-
ferred destiny of foreign migrants (Cancun, Los Cabos, Puerto Vallarta).

Moving the frontiers—the expansion of Mexico City

Administratively, Mexico City consists of 16 boroughs (delegations) which
form the Ciudad de México (before January 29, 2016, Federal District), but
has grown beyond those borders into the surrounding State of Mexico. In
2005/2006 the Metropolitan Area of the Valley of Mexico (MAVM) was
established which today consists of all 16 boroughs, 59 municipalities of the
State of Mexico and one of the state of Hidalgo.
In 1325, the settlement was founded on islands in the Texcoco lake as
the capital of the empire of the Mexica from which also originates its name.
The early urban growth of Mexico City is related to historic processes such as
the arrival of the Spanish conquerors in 1521 and establishment of the vice-
royalty of New Spain in 1535, the independence from Spain in 1810 and the
Mexican revolution in 1910. The concentration of political and economic
power in one place has shaped Mexico City over the centuries and is still rel-
evant for business decisions today.
Figure 15.6A shows all municipalities of the MAVM from its historic core
to the most recent expansions. Between the end of the Mexican revolution
in 1929 and the first decade of the twenty-first century several authors found
 Latin America with an emphasis on Mexico 249
seven phases of urban growth and associated them to models of concentric
rings (Negrete et al. 1993, Delgado 1988, SEDESOL CONAPO INEGI
2012). Starting with the expansion from its core in Cuauhtémoc into the
boroughs of Miguel Hidalgo, Venustiano Carranza and Benito Juarez from
1930 to 1950, the second phase (1950–1970) followed due to significant
industrial development, extending in all cardinal directions and for the first
time including four municipalities from the State of Mexico. The third phase
(1970–1986) incorporated four boroughs in the south, six municipalities to
the north and two to the east. By 1990, among others, the last borough of the
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Figure 15.6 Urban expansion of Mexico City. A: Seven phases of urban growth

between 1930 and 2010. B: Land take due to urban expansion between
1970 and 2012. C: Stable lights (2009) in the larger Mexico City area
and light contamination beyond the urban area extent. D: Megalopolis
Mexico City and growth rates in percent between 2000 and 2010
Note: The red box indicates the subset for Figures 15.6A and 15.6B.
 250 R. Colditz et al.
Federal District (Milpa Alta) was included. During this time the growth of
Mexico City was fostered by the growing cities outside the Mexican Valley
(Toluca, Cuernavaca, Puebla and Pachuca). The fifth phase (1990–1995)
includes four large municipalities in the north including one from Hidalgo
and during the sixth (1995–2000) there is an expansion to the northeast and
division of previously included municipalities in the east and north. During
the last phase (2000–2010) 24 municipalities were added. Today, more than
20 million people (18 percent of the total population) live in the MAVM
with an area of only 7,800 km2 (0.4 percent of the national territory). This
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corresponds to 2,564 people per square kilometer, the highest population

concentration in the country (INEGI 2010).
Figure 15.6B shows the state of the urban area in 1970 in red, in red cross-
hairs the built-up area in 2012 and in different colors the land use and land
cover, such as grassland, agriculture, forest and shrubland (INEGI-INE 1999,
INEGI 2013). Over the four decades the city grew by 235 percent from 610
km2 in 1970 to 1,440 km2 in 2012. Over 90 percent of the land take affected
agricultural area and grassland in relatively flat terrain to the north and east.
Approximately 8 percent of the expansion has affected forested areas at the
western and southern edge of the valley, mainly during the last two decades. In
those areas steep slopes limit the growth by increasing construction costs and
loss of valuable environments and related ecosystem services.
In particular water is a scarce resource as the city requires a supply of
35.2 m3/s with a current deficit of 3 m3/s of potable water (FCEA 2015).
Statistics of the National Water Commission (CONAGUA) indicate that the
natural water supply of the Mexican Valley for all land uses (urban, agricul-
ture, industry) is far from being self-sustainable. 54 percent of the water is
therefore supplied by other hydrologic regions several hundred kilometers
away and 46 percent by internal sources such as the Rio Magdalena and
ground water (GDF 2008). All forms of water supply cause serious and large-
scale secondary effects on the environment. Energy is needed to pump water
from lower hydrologic regions into the city. In addition there are conflicts in
water use and amount of water extraction with local municipalities and other
large cities. The over-exploration of the local aquifer causes significant sub-
sidence as large parts of the city were constructed on sediments of the former
Texcoco lake. For instance, the city center has subsided by approximately
10 m over the last 60 years (SACMEX 2012). Water infiltration is achieved in
conservation areas but expansion of the impervious cover has reduced its sur-
face area and percolation to the subsiding areas is slow. Wastewater removal
is another issue, for which drainage systems and retention bodies were con-
structed to limit ground water contamination. Wastewater treatment is just
in its initial stages.
Air contamination is another pressing environmental problem in Mexico
City, in particular due to its topographic location in a valley and frequent
formation of atmospheric inversions. High aerosol ozone concentrations affect
many citizens by respiratory and skin sickness and cause high societal costs
 Latin America with an emphasis on Mexico 251
for health care. The main cause for emissions is transportation (45 percent),
followed by industry (21 percent), housing (20 percent) and 14 percent by
others (GDF 2015). The list of environmental issues caused by urban agglom-
erations can be continued, e.g. soil reduction, contamination and erosion with
increasing risks during earthquakes, biodiversity loss and changes in species
behaviors in surrounding areas, nutrient loss, fuel consumption including fos-
sil fuel, high water and energy use, waste dumping, treatment and recycling,
increased velocity of contagious diseases, etc.
A less studied issue is nighttime light contamination and its effect on sur-
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rounding areas. Figure 15.6C shows data of the DMSP sensor for the year
2009 in tones from brown to yellow and black cross hairs marking the urban
area of Mexico City in 2012. Considering values of 55 and higher in yellow
the area is 17 percent larger than urban mapped from official sources (INEGI
2013) and includes several natural areas in the southern mountainous region.
Disturbance by artificial light not only affects the behavior of nocturnal species
but the functioning of ecosystems in general (López Acosta et al. 2009, Meyer
and Sullivan 2013, Gaston et al. 2013).
The continuing expansion of Mexico City today needs to be seen in a larger
context. Although there is a physical separation by high mountain ranges,
the pass elevation to the south, east and west entrance is above 3,000 m,
the city is highly interconnected with small and large surrounding cities
(Figure 15.6C), which by themselves often form metropolitan areas. Altogether
they form the megalopolis Mexico City, and colors in Figure 15.6D indicate
their growth between 2000 and 2010. The higher positive trend of surround-
ing cities in comparison to the core area, also known as polarization reversal
(Aguilar and Rodríguez 1995), is in line with our previous analysis at state
level using DMSP. The attractive location in a large megalopolis with short
connections to business partners and political stakeholders, space for larger
industrial plants and proximity to recreational facilities, but still with the
option to take advantage of the cultural urban life in the core area and not
suffering from all the negative issues, is the main reason for increased growth
in the periphery, either by new businesses or relocation. It needs to be seen
if at some point urban areas completely connect even across physical barriers.
First tendencies can be noted, e.g. between Mexico City and Pachuca, Toluca
and Tula (Figure 15.6C).

Selected drivers for urbanization

Tourism—Cancun
The state of Quintana Roo on the Yucatan peninsula is internationally rec-
ognized for its beautiful white beaches with excellent offshore reef snorkeling
and diving opportunities along the Mexican Caribbean coast, known as
the Riviera Maya and Costa Maya. The tourist development started in the
late 1960s, primarily as a result of a government-initiated study to develop
 252 R. Colditz et al.
a counterbalance to the Mexican Riviera on the Pacific coast and also to
compete with resort destinations on several Caribbean islands (Collins 1979).
In 1968, the Tourism Infrastructure Promotion Fund (INFRATUR, later
FONATUR) was created and together with the Bank of Mexico they pro-
moted six sites for major tourist developments, among those Cancun. At that
time the fisherman’s village of Cancun accounted for 120 inhabitants and
increased rapidly during the different development stages in the 1970s, 1980s
and 1990s to a population of 628,306 in 2010 (INEGI 2010). Cancun has
undergone a radical transformation, becoming the most important tourist
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resort of the country. Already in 1990, Cancun accounted for 110 hotels,
with more than 17,000 rooms and received approximately 1.5 million visitors
annually (FONATUR 2001). Successively the entire Caribbean coast south
of Cancun to the border of Belize has been developed with significant impact
on coastal ecosystems and the environment.
The urbanization trends in Cancun and surrounding areas are dramatic
with increased dynamics since 1990. The spatial changes of these trends can
be easily detected with satellite remote sensing. For instance, analysis of 250
m MODIS land cover maps between 2005 and 2010 (Colditz et al. 2014b)
revealed an increase of 2,500 ha of urban area around Cancun, that is 500 ha
per year, which corresponds to public statistics reporting an annual growth rate
of 616 ha or 3.1 percent for the Benito Juarez municipality to which Cancun
belongs (Veloz Avilés 2011). Urban growth spatially occurs around the air-
port and along the western part of the city with new commercial and housing
sections to accommodate the increasing population, but fewer hotel develop-
ments were detected along the coastal strip during this period in comparison
to previous times.

Commerce—Merida
The rise of Merida, capital of the state of Yucatan, started in the late nineteenth
century as one of the centers of sisal (henequén) cultivation, a fibrous plant for
twine and rope production, also known as the “green gold” (Duch Colell 1998).
Until the beginning of the twentieth century agriculture was the primary
activity in the area around Merida. Over the course of the twentieth century
Merida gained importance as the peninsula’s center of commerce, in par-
ticular based on many assembly plants that were established since 1980 and
tourism. All these factors progressively attracted the rural population on the
Yucatan peninsula migrating to and working in Merida, which resulted also
in a strong increase of accompanying service industries. In 2010, 78.7 percent
of the economically active population of Merida was occupied in the tertiary
sector, 20.3 percent in the secondary and only 1 percent in the primary sector
(SEDESOL 2013).
According to the 2010 census, the population of Merida reached 777,615
inhabitants (INEGI 2010). High birth rates and a continuous rural-to-urban
 Latin America with an emphasis on Mexico 253
migration result in constant urban sprawl, which is also expressed by the high
population density of 938 people/km2 in the municipality (SEDESOL 2013).
These demographic and socioeconomic changes in the region have caused
pressure on the city of Merida. The resulting spatial consequences of regional
migration can be documented with satellite imagery. For instance, there are
significant expansions and new developments of urban area between 2005 and
2010 in Merida, which can even be noted in coarse resolution MODIS satellite
data (Colditz et al. 2014a). The “Fraccionamiento Las Américas” was con-
structed for an expected population of 20,000 habitants, which was mapped as
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a newly constructed urban area of 225 ha in the land cover map of 2010. The
largest new city section, Ciudad Caucel west of Merida, encompasses an area
of 875 ha and is designed for 30,000 new houses and an expected population
of 100,000 new residents. In fact, Ciudad Caucel was found to be the largest
single patch of newly constructed urban surface between 2005 and 2010 in
entire Mexico. Annual images of this site indicate that most of the construction
occurred in the years 2007 and 2009.

International migration—Tijuana
Over the last 110 years Tijuana has transformed from a ranch with 224
inhabitants in 1900 to one of the 10 most important cities in Mexico. In
2010 the population was more than 1.5 million (INEGI 2010), and with
San Diego it forms the largest binational conurbation in the world. Located
directly along the border to the state of California the city attracts large
groups of legal and illegal emigrants. Most migrants are from the Federal
District, Jalisco, Michoacán, Oaxaca and Sinaloa of which most work in fac-
tories, often awaiting permission of entry to the United States. There is also a
significant number of immigrants, mainly from China, Central American and
Andean countries and ultimately from the United States due to lower living
costs. Daily or weekly commuters as well as visitors from all over the world
make Tijuana–San Diego the busiest land-border passage in the world with
more than 300,000 daily crossings.
The growth of Tijuana was always linked to the political and economic
situation of the United States. For instance, during the years of prohibition
(1919–1933) in the United States, Tijuana offered the respective services
of alcohol retail and consumption in bars and night clubs, which caused a
population increase by 1,000 percent. During the Great Depression, for-
eigners working in the United States were forced to return to their country.
Approximately 400,000 Mexicans returned, of which many stayed in
Tijuana, mostly in precarious conditions hoping for permission of reentry.
During World War II the United States required manual laborers in the
agricultural areas and implemented programs such as Braceros (1942–1964).
Thousands of migrants arrived in Tijuana and those who were not allowed
to enter the United States frequently stayed in this city. Another boost
 254 R. Colditz et al.
occurred upon the end of Braceros when returning workers often remained
close to the border.
This rapid population growth is also reflected in the growth of the urban
area. By 1950 the urban area was approximately 1,450 ha (Padilla 1985), by
1973 it had expanded to 6,620 ha, which coincides with the major popula-
tion growth during the 1960s. In 1993 the urban area had grown to 16,830
ha (Bocco and Sánchez 1996) and reached 24,240 ha in 2010 (INEGI 2010).
It is estimated that by 2030 the city of Tijuana will have 2.8 million inhabit-
ants (IMPLAN 2010) which poses substantial challenges to urban planners to
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provide the urgently needed public services.

Perspectives
The urban population of Latin America is expected to reach 673.6 million in
2050, which is 86.1 percent of its total population (UN 2014b). The further
growth of already overpopulated cities will have additional consequences on
the environment and requires innovative solutions for already existing social
issues. Successful sustainable urbanization requires competent, responsive and
accountable governments charged with the management of cities and urban
expansion. Attention not only has to go to big cities, but also to new urban
forms that have acquired importance in recent years like peri-urbanization,
rural–urban transition zones, and intermediate and small cities, that suffer simi-
lar problems to those of a big metropolis.
Water availability and wastewater treatment, increasing needs of energy,
higher demands on agricultural lands also in distant regions to feed the rural and
urban population, transportation of commodities and people, and air and water
contamination are worrisome environmental concerns as many large cities and
megacities already seem on the “verge of collapse.” In addition the existing social
conflicts will intensify, such as the gap between the rich and poor living together
on very limited space, a still too-small middle class, lack of education, violence
and crime, corruption and lack of law enforcement or even impunity. Therefore,
a change in policies is needed, away from the highly centralized political and
economic power present in most Latin American countries and towards a more
balanced distribution across several cities in various regions. These policies can
help responding to the challenges of providing urban infrastructure and basic
social services for the urban poor, and mitigating the negative environmental
impacts associated with large and rapidly growing urban agglomerations.
There are indicators that the growth of megacities larger than 10 million
people attracts fewer businesses and population growth is slower in comparison
to cities with 1–5 million inhabitants. However, there is a risk that those cities
undergo unplanned and uncontrolled growth which soon will expose them
to similar negative environmental issues and social conflicts. Urbanization will
continue to be the distinctive geographical feature for Latin America at an even
faster pace affecting all levels of society and environment.
 Latin America with an emphasis on Mexico 255
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 16 Monitoring built-up areas in
Dar es Salaam using free images
Michele Munafò and Luca Congedo
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This case study presents the activity of land cover monitoring of Dar es Salaam
(Tanzania), in the frame of the ACC Dar Project (Adapting to Climate Change
in Coastal Dar es Salaam, www.planning4adaptation.eu).
Dar es Salaam is located in eastern Tanzania on the Indian Ocean coast,
covering an area of about 1,800 km2. The ACC Dar project aimed at improv-
ing the effectiveness of municipal initiatives in Dar es Salaam to support
coastal peri-urban dwellers, who are partially or totally dependent on natural
resources, in adapting to climate change impacts. In particular, one of the
ACC Dar activities was the development of a methodology for land cover
monitoring in order to understand land cover change drivers in the Dar
es Salaam region, with special attention to peri-urban development within
the coastal plain.
During the last few decades, Dar es Salaam has grown rapidly because of
unplanned settlement development and a regulatory framework characterized
by long administrative procedures to make land available (Kironde, 2006).
The ACC Dar project had the specific goals of: developing a methodology
for the monitoring of urban sprawl, analysing urban development and land
cover change, investigating the relationships between urban sprawl and popu-
lation growth. The methodology used was designed especially to fulfil these
requirements and involved: the use of free or very low-cost remote sensing
images; the availability of images for past years; the use of semi-automatic clas-
sification to reduce the time and cost of land cover mapping; preprocessing and
processing phases that were achievable with open-source software.
Landsat images were used because of their medium spectral resolution
(i.e. 7 bands), although images have coarse spatial resolution (i.e. 30 m), and a
large image archive for the past few decades is available; images are provided
for free by the USGS, therefore allowing for the affordable classification of land
cover, especially impervious surfaces (Fan et al., 2007).
The use of a supervised, semi-automatic, maximum likelihood algorithm
allowed for the classification of each image pixel based on spectral similarity
with spectral signatures, assuming a multivariate normal distribution of the
classes’ probability (Richards and Jia, 2006; Song et al., 2001). Furthermore,
the use of vegetation indices (i.e. Normalized Difference Vegetation Index and
 Dar es Salaam 259
Enhanced Vegetation Index) and knowledge-based classification improved the
identification of urban areas (Congedo and Munafò, 2012).
During the data processing, the following land cover classes were identified:

•• Continuously Built-up: a high-density urbanized class

•• Discontinuously Built-up: a low-density urbanized class, characterized by
a mix of urban and vegetation or soil pixels
•• Full Vegetation: a high density vegetation class
•• Mostly Vegetation: a vegetation class with medium density
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•• Soil: bare soil surfaces

•• Water: surface water.

The image processing was performed with commercial software; however, a

free open-source program was produced within the ACC Dar project – Semi-
Automatic Classification Plugin for QGIS – which allows for the semi-automatic
classification of multispectral images and can satisfactorily replace commercial
software for land cover classifications (Congedo and Munafò, 2014).
The land cover classification results for years 2002, 2004, 2007, 2009 and
2011 are listed in Table 16.1.
The Continuously Built-up and Discontinuously Built-up classes have
increased during the past few years; in particular, the fast rate of increase from
2007 on is noticeable. The growth of the Discontinuously Built-up class is
related to urban sprawl, which has occurred along Dar es Salaam’s main roads.
Over the years, some of the low-density areas have changed to Continuously
Built-up areas.
It is worth noting that the 30 m Landsat spatial resolution is a constraint in
land cover monitoring, because a pixel in the image could contain a mixture of
cover types, causing a mixed spectral signature (i.e. mixed pixel) depending on
composition and kinds of materials on the ground (Small, 2006).
The validation of land cover classifications was performed through the cal-
culation of error matrices and accuracy statistics for every land cover class. For
this purpose, about 500 sample units were selected randomly, and photo inter-
preted using high-resolution images (i.e. images freely available from Google
Earth software, developed by Google). A field survey was performed for 100 of
these samples in order to improve the photo interpretation process.

Table 16.1 Land cover classification results (in hectares)

2002 2004 2007 2009 2011

Continuously Built-up 8,415 10,025 10,447 12,370 14,808

Discontinuously Built-up 8,098 9,134 12,509 17,318 23,678
Soil 102,079 95,732 76,011 57,385 66,791
Water 193 276 304 7 199
Full Vegetation 14,887 13,172 14,905 26,751 18,195
Mostly Vegetation 35,164 40,631 54,798 55,144 45,313
 260 M. Munafò and L. Congedo
Furthermore, fuzzy error matrices were calculated (i.e. considering the
presence of secondary classes) in order to improve the assessment of mixed classes;
mixed pixels frequent appear in Landsat images because of pixel size. The fuzzy
error matrix for the land cover classification of 2011 is shown in Table 16.2.
Table 16.3 shows the accuracy statistics calculated for each land cover class.
Considering the secondary class of the fuzzy error matrix, the accuracy sta-
tistics improve significantly for the built-up classes, as shown in Table 16.4.
The statistics of the fuzzy error matrix for the urban Continuous Built-up
class are considerably improved; the user’s accuracy of the Discontinuous
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Built-up class has also particularly improved, while the producer’s accuracy of
the same class is slightly better (66.9 per cent considering only the primary class,

Table 16.2 Fuzzy error matrix calculated for land cover classification based on Landsat
images of 2011
Reference data
Continuous Discontinuous Full Most Soil Total
Built-up Built-up Vegetation Vegetation

Continuous Built-up 93 (4)(2) (0)(0) (0)(0) (0)(0) 99

Discontinuous Built-up (1)(2) 81 (0)(0) (3)(1) (3)(0) 91
Full Vegetation (0)(0) (0)(0) 29 (5)(7) (0)(3) 44
Most Vegetation (0)(0) (0)(0) (3)(4) 56 (6)(16) 85
Soil (0)(5) (2)(32) (0)(3) (2)(55) 47 146
Total 101 121 39 129 75 465

Table 16.3 The accuracies of user and producer

Class User’s accuracy Producer’s accuracy
[ %] [ %]

Continuous Built-up 93.9 92.1

Discontinuous Built-up 89.0 66.9
Full Vegetation 65.9 74.4
Most Vegetation 65.9 43.4
Soil 32.2 62.7

Table 16.4 The accuracies of user and producer

Class User’s accuracy Producer’s accuracy
[ %] [ %]

Continuous Built-up 98.0 93.1

Discontinuous Built-up 96.7 71.9
Full Vegetation 77.3 82.1
Most Vegetation 76.5 51.2
Soil 34.9 74.7
 Dar es Salaam 261
71.9 per cent considering the secondary class). The field survey has confirmed
the reliability of the photo interpretation, and allowed for the creation of a
photographic database.
The accuracy results show that land cover monitoring can be affordable and
reliable; however, the class Discontinuous Built-up is more affected by errors
due to the coarse pixel size, which is larger than small buildings and caused
mixed spectral signatures. In particular, the class Discontinuous Built-up is
related to urban sprawl areas where a single pixel is covered by impervious and
pervious surfaces, therefore the definition of a spectrally distinct land cover
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class is difficult.

References
Congedo, L. and Munafò, M. (2012) Development of a Methodology for Land Cover
Classification in Dar es Salaam using Landsat Imagery. Technical report. Rome:
Sapienza University, ACC Dar Project Sapienza University.
Congedo, L. and Munafò, M. (2014) ‘Urban Sprawl as a Factor of Vulnerability to
Climate Change: Monitoring Land Cover Change in Dar es Salaam’, in Climate
Change Vulnerability in Southern African Cities, edited by S. Macchi and M. Tiepolo.
Cham, Switzerland: Springer, 73–88.
Fan, F., Weng, Q. and Wang, Y. (2007) ‘Land Use and Land Cover Change in
Guangzhou, China, from 1998 to 2003, Based on Landsat TM /ETM+ Imagery’,
Sensors, 7, 1323–1342.
Kironde, J.M.L. (2006) ‘The Regulatory Framework, Unplanned Development and
Urban Poverty: Findings from Dar es Salaam, Tanzania’, Land Use Policy, 23(4),
460–472.
Richards, J.A. and Jia, X. (2006) Remote Sensing Digital Image Analysis: An Introduction.
Berlin: Springer.
Small C. (2006) ‘Comparative Analysis of Urban Reflectance and Surface Temperature’,
Remote Sensing of Environment, 104(2), 168–189.
Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P. and Macomber, S.A. (2001)
‘Classification and Change Detection Using Landsat TM Data: When and How to
Correct Atmospheric Effects?’, Remote Sensing of Environment, 75(2), 230–244.
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Part IV

Policy and good practices

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 17 The European approach
Limitation, mitigation and compensation
Gundula Prokop and Stefano Salata
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Introduction
In the mid-2000s, growing awareness of the risk caused by an uncontrolled
growth of urbanization forced the European Commission to introduce the
Thematic Strategy for Soil Protection (COM(2006) 231) that was a binding
measure for European Union (EU) member states to limit the process of soil
consumption due to land take caused by urbanization.
Even if at first the proposal (2006) was intended in a few years to become a
binding measure (Soil Framework Directive), the binding aspect wasn’t passed
for political reasons. In March 2010, in the Environment Council, a minority
of member states blocked the progress of the proposal for reasons of subsidi-
arity, excessive cost and administrative burden (COM(2012) 46 final). This
first, fundamental proposal was blocked at the Council’s table, and a clear polit-
ical message about the competence on land use management was launched:
member states don’t want to be forced by the EU to adhere to communitarian
legislation regarding land use constraints or quantitative thresholds for the con-
tainment of urban expansion.
In the absence of communitarian legislation on soil, the European
Commission approved a number of measures focused on soil-related issues,
demonstrating that the Soil Framework Directive was not a unique and
systematic approach to protecting and monitoring soil from the risk of deg-
radation. Independent of the legal aspect, knowledge about the status and
the quality of soils remains fragmented between member states, and soil pro-
tection is not undertaken in an effective and coherent way in all of them
(COM(2012) 46 final).
Furthermore, the political orientation of the European Commission seems
to be focused on the application of a guidelines document, which is not as
binding as a Soil Framework Directive, but which focuses on common targets
for limiting, mitigating and compensating soil sealing. Even if the applica-
tion of the guidelines document at the national scale is not compulsory, the
general impression is that land use management has drawn greater attention at
the European level, and the enforcement of land use monitoring requires the
major use of technical instruments to present, assess and control the trends of
land use change in Europe.
 266 G. Prokop and S. Salata
Nevertheless, the absence of European legislation on soil still demonstrates
the lack of a common agreement on a field that has many related aspects (e.g.
the real estate market). Overcoming this position is now fundamental: some
recent initiatives aim to introduce new strategies to define the risk of uncon-
trolled growth and to clarify that land take is a matter not only of ‘quantity’ of
soil that is no longer available for other uses (e.g. food production), but also of
the ‘quality’ of citizens’ lives in the urban environment.
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Soil Thematic Strategy: an important

key driver for awareness
Even if it has been argued how the originally planned way to achieve common
soil legislation has failed, it has to be considered that the proposal has acted
as a key driver for the development of a huge number of studies, indicators
and databases on land use variation and soil related issues, including confer-
ences, debates, scientific and non-scientific publications on soil, climate change
and biodiversity. Consequently a working group on Awareness Raising and
Education in the context of the European Soil Bureau Network (ESBN) and
a European Network for Soil Awareness (ENSA) (COM(2012) 46 final) have
been established. Thus the proposal at least acted to promote studies and tech-
nical reports: around 25 research projects have been started, including those
listed below (for a deeper understanding of the mission and final results of each
project see the specific websites):

•• RAMSOIL, www.ramsoil.eu/UK/
•• ENVASSO, http://eusoils.jrc.ec.europa.eu/projects/envasso/
•• SOILSERVICE, www.lu.se/soil-ecology-group/research/soilservice
•• LUCAS, http://eusoils.jrc.ec.europa.eu/projects/Lucas/
•• BIOSOIL, http://forest.jrc.ec.europa.eu/contracts/biosoil.

All projects are integrated by specific policies focused on the sustainable use of
the soil:

•• Common Agriculture Policy (CAP): focused on promoting good agricultural

and environmental conditions, limiting erosion, improving organic matter
and avoiding compaction.
•• Industrial Installation: focused on saving soil quality from potential negative
impacts of future industrial plants.
•• Cohesion Policy: focused on the rehabilitation of industrial sites and
contaminated land.
•• State Aids for the remediation of soil contamination.

In this context, land degradation resulting from soil sealing by urbanization, but
also other threats such as soil erosion, desertification, salinization, contamination
 Limitation, mitigation and compensation 267
and acidification, is monitored by ongoing activities of the technical organism
of the European Commission, the Directorate-General, Joint Research Centre
(DG-JRC), which observes land use change at the European scale to make
more efficient use of resources.
Despite further attention being given to soil related issues, at the moment
soil is not subject to a commonly defined and systematic set of rules in the
EU. Thus, existing EU policies in other areas are not sufficient to ensure an
adequate level of soil protection. Furthermore, having been blocked at the
Council’s table in 2010, the proposal for a Soil Framework Directive was
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finally withdrawn in May 2014.

Despite the lack of success in the top-down approach, the right to recom-
mend EU legislation via the European Citizens’ Initiative (ECI) is always
possible with bottom-up approaches such as petitions. One of the emergent
proposals, ‘People 4 Soil’, is a free and open network of European NGOs,
research institutes, farmers’ associations and environmental groups that
aims to relaunch the Soil Framework initiative. The petition was prepared
during the International Year of Soils (2015), launched in 2016, and will last
for 12 months.

Soil sealing: drivers and impacts

Soil sealing is the most impacting effect of land take, because when the top-
soil (which is the upper layer of soil) is covered by impermeable materials, all
biological functions are compromised. This is why the equation ‘soil sealed’
is equal to ‘soil lost’ is true: even if practices of soil recovery are applied for
desealing, neither the quantity nor the quality of biological functions can be
restored. Hence the permanent covering of land by impermeable artificial
material (e.g. asphalt and concrete) directly affects essential ecosystem services
(e.g. food production, water absorption and the filtering and buffering capaci-
ties of soil) and biodiversity (European Commission, 2012).
We therefore here detail the drivers of soil sealing by urbanization in order
to reveal how ‘wide’ the phenomenon is, and how economic, social, legisla-
tive and specific planning programmes combine as causes of the current trend
of land consumption.

1 The need for housing, industry, business locations and infrastructure in

response to the growth of population and the demand for a better quality
of life and living standards. The EEA points out that urban expansion is
more a reflection of changing lifestyles and consumption patterns rather
than an increase of population (EEA, 2006).
2 The preference to live in a place far from the compact city, even if the
commute between home and work is long. This model presents a spatial
consumption of different resources (soil but also energy) and generates a
long-term social cost in terms of services and health.
 268 G. Prokop and S. Salata
3 The dependency of some local authorities on incomes provided by urban-
ization feeds, which stimulates competition between municipalities for
offering cheap land for development.
4 The idea that there is no need to worry about additional soil sealing
because of the relative abundance of open space in rural areas.
5 The general lack of appreciation of soil in terms of environmental val-
ues (soil sealing is an irreversible process of transformation of the topsoil),
social values (soil sealing reflects a private use of land pushed by real estate
market dynamics) and economic values (soil sealing results in high public
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costs in the long term).

Even if drivers of soil sealing are diverse (e.g. economic, lifestyle, social and
fiscal, mobility, planning legislation and so on) the impacts mostly relate to (the
loss of) soil biodiversity, which implies a reduction in ecosystem service sup-
ply. Indeed, the action of covering topsoil with asphalt or concrete has a direct
impact on soil-related ecosystem services. The most important effects:

•• Soil sealing affects the normal flow of water drainage.

•• Soil sealing has an impact on ground biodiversity and fragments ecosys-
tems and the landscape.
•• Soil sealing normally occurs in the most fertile areas, and impacts food security.
•• Soil sealing has a direct impact on the carbon cycle.
•• Soil sealing reduces evapotranspiration.
•• Soil sealing reduces air quality.
•• Soil sealing directly affects the quality of life in urban and rural areas.

Enforcing a political agreement on the problematic of soil sealing also requires

that technical aspects in limiting soil sealing and land take in general be taken
into account. Spatial planning has a specific role to play in this regard. At first,
an integrated approach is required, which means mixing different planning
theories that aim to apply an ecological approach to the salvage of green areas.
An integrated approach requires also the full commitment of all relevant public
authorities, in particular those levels of government that normally are directly
responsible for the management of land use.
Local planning requires exploiting fully the possibilities offered by the
Strategic Impact Assessment (SEA) Directive and, when relevant, the
Environmental Impact Assessment (EIA) Directive, collecting detailed soil data
and establishing suitable indicators, regular monitoring, critical assessments, as
well as providing information, training and capacity-building for local decision
makers (European Commission, 2012).
At all scales, the starting point has to be to assume that dealing with land take
is a necessity. The process of urbanization affects all European Countries with
varying intensity, and still affects states experiencing combined demographic/
economic recession. The first requirement is to limit the phenomenon, acting
at a political level but also using spatial planning measures.
 Limitation, mitigation and compensation 269
Reducing soil sealing needs a tiered approach
The efficient protection of soils from further sealing can only be achieved by
following an integrated approach, requiring the full commitment of all govern-
mental units (and not only those dealing with spatial planning and environment),
by improving awareness and competence within all concerned stakeholders,
by freezing counterproductive policies (i.e. funding of single family houses at
urban fringes, commuter bonus, etc.), by establishing clear financial incentives,
and by introducing binding legal requirements. In this context the European
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Commission in 2012 published Guidelines on best practice to limit, mitigate

or compensate soil sealing. The guidelines demand a three-tiered approach,
similar to the logic used in waste materials streams. The priority solution must
be to limit soil sealing, as sealing is an almost irreversible process. Where it is
not possible to avoid sealing, the second best option is to mitigate its impacts,
reducing the worst effects where possible. The third option, a last resort, is to
compensate for sealing soil in one location by soil-related remediation activity
in another. Different options for translating these measures into practice are
explored below.

Successful examples for limiting soil sealing

There are two ways to limit soil sealing: by reducing land take, the rate at
which natural areas are converted into developed areas; or by continuing to
seal soil, but only on land that has been previously developed. To ‘pave the
way’ for successful prevention of soil loss the following basic principles need to
be implemented at the policy level:

•• Establish the principle of sustainable development in spatial planning by

following an integrated approach, requiring the full commitment of all
governmental sectors (and not only spatial planning and environment).
An example of best practice would be that the majority of EU member
states establish the principle of sustainable development in their key spa-
tial planning regulations, making reference to the economic use of soil
resources and avoidance of unnecessary urban sprawl. However, without
binding measures, regular monitoring and critical assessment soil functions
this cannot be protected adequately.
•• Define realistic land take targets at the national and the regional level.
One of the best practices is placing quantitative limits on annual land
take. Such limits exist only in six member states – Austria, Belgium
(Flanders), Germany, Luxembourg, the Netherlands and the United
Kingdom. In all cases the limits are indicative and are used as moni-
toring tools. In the United Kingdom and Germany the national
targets are taken most seriously and their progress is regularly assessed.
Only in the United Kingdom are development targets also defined at
the regional level.
 270 G. Prokop and S. Salata
•• Streamline existing funding policies accordingly by freezing subsidies
that encourage land take and soil sealing (i.e. public subsidies for private
housing on undeveloped land, subsidies for developments on green field
sites, commuter bonuses, etc.). No best practices for this point have been
identified.
•• Develop specific regional approaches according to the actual land use pres-
sures and, in particular,
1 Steer new developments to already developed land and provide
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financial incentives for the development of brownfield sites. One

of the best practices is initial or supportive funding to encourage
new infrastructure developments on brownfield sites, which exists
in several member states and which is usually co-ordinated by desig-
nated brownfield organizations. Brownfield redevelopment projects
are mostly realized in the form of public–private partnerships:
(1) English Partnerships is probably the most experienced public
land developer in the European Union and provides funding for
social housing developments on derelict areas; (2) France disposes
of a network of more than 20 public land development agencies,
which among other activities develop brownfield land for social
housing; (3) the land development agencies Czech Invest and Invest
in Silesia are in charge of developing major industrial brownfields for
new industrial investors; (4) in Flanders specific contracts (brown-
field covenants) are negotiated between the government and private
investors to promote brownfield redevelopment.
2 Improve the quality of life in large urban centres. Best practices to
mention are several urban renewal programmes recently launched
with the objective of attracting new residents and creating new jobs
in central urban areas in decline. Best practice examples in this respect
are (1) the urban renewal programmes of Porto and Lisbon and the
neighbourhood renewal programme in Catalonia, all of which are
supported by European Regional Development Funds, (2) the Västra
hamnen project in Malmö, which is built on derelict harbour premises
providing 1,000 new dwellings with the lowest possible environmental
impact, (3) the Erdberger Mais development in Vienna, which is built
on five inner urban brownfield areas, providing housing for 6,000 new
inhabitants and 40,000 work places, (4) the Randstad programme in
the Netherlands, which puts special emphasis on improving the attrac-
tiveness of inner urban areas in the metropolitan agglomeration of
Amsterdam, Rotterdam and Den Haag.
3 Make small city centres more attractive in order to counteract dispersed
settlement structures in rural regions with shrinking population. One
of the best practices to mention is the Danish Spatial Planning Act,
which puts clear restrictions on the construction of large shops and
shopping centres on green fields outside the largest cities and promotes
small retailers in small and medium-sized towns.
 Limitation, mitigation and compensation 271
4 Impose development restrictions on top agricultural soils and valuable
landscapes. Best practices are established where member states have
promoted specific policies to avoid further land take and sealing on
their best agricultural soils and most valuable landscapes, as is the case
(1) in Spain where building activities within the first 500 metres from
the sea are strictly controlled, (2) in France and the Netherlands where
designated ‘green and blue’ landscapes are protected from infrastruc-
ture developments, (3) in the Czech Republic and Slovakia where the
conversion of top agricultural soils requires a fee.
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Mitigating soil sealing (examples of technical measures)

Permeable surfaces can help to conserve soil functions and mitigate the effects
of soil sealing to a certain extent. They contribute to the local water drainage
capacity and can in some cases also fulfil biological or landscaping functions.
Another advantage is their positive contribution to the micro-climate thereby
trapping the heat and moderating temperatures in the area. Unsealed, green
shaded surfaces have lower surface temperatures than sealed surfaces, the differ-
ence can amount to up to 20°C. In the case of storm water a parking area built
with permeable surfaces discharges to the local sewage system by at least 50 per
cent compared to a conventional asphalt surface. It can even be designed as an
independent system without discharges to the local sewage system.
A broad range of materials and concepts is available for permeable surfaces.
In addition to their clear ecological advantages most types of surfaces have
lower lifespan costs compared to conventional impermeable surfaces. With
regard to sustainability most permeable surfaces are made of materials that are
locally available and reusable. Key barriers to implementation are currently
the fact that site-specific know-how and building competence is required to
construct them correctly. Furthermore, regular maintenance is needed to make
sure that they function properly. Parking areas have the greatest potential for
permeable surface application, in particular large parking areas in urban fringes.
Most advanced in this respect is the United Kingdom, where permeable

Figure 17.1 Overview of most common surfaces: (1) lawn, (2) gravel turf, (3) plastic
grass grids, (4) concrete grass grids, (5) water bound macadam,
(6) permeable pavers, (7) porous asphalt, (8) conventional asphalt
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Table 17.1 Comparison of benefits and limitations of most common permeable surfaces (in relation to asphalt)

Pedestrians
Parking, small vehicles
Parking, medium vehicles
Road traffic
Visual appearance
Vegetation possible
High drainage capacity
Regional materials
Improves micro-climate
High maintenance
Bad walking comfort
No disabled parking
Sludge accumulation
Dust formation
Unsealed surface (%)
Run-off coefficient
Cost: Asphalt = 100%

Application range Benefits Limitations

Lawn, sandy soil +++ +++ +++ +++ +++ +++ +++ 100% <0.1 < 2%
Gravel turf Y Y Y ++ ++ ++ +++ ++ + + + 100% 0.1-0.3 50-60%
Grass grids (plastic) Y Y ++ ++ ++ + ++ ++ ++ ++ + 90% 0.3-0.5 75%
Grass grids (concrete) Y Y Y Y ++ ++ + +++ ++ ++ ++ ++ + 40% 0.6-0.7 75-100%
Water bound surfaces Y Y Y + + +++ + ++ + + ++ ++ 50% 0.5 50%
Permeable pavers Y Y Y ++ + 20% 0.5-0.6 100-125%
Porous asphalt Y Y Y Y 0% 0.5-0.7 100-125%
Asphalt Y Y Y Y 0% 1.0 100%
Source: Prokop and Jobstmann (2011).
Note: * Indicative costs in relation to asphalt are provided; in 2010 average costs for conventional asphalt layers amounted to approximately 40 €/m² (without VAT),
including construction costs. For each surface type, material costs and labour costs were considered.
 Limitation, mitigation and compensation 273
surfaces are broadly used – even in big cities – and where research is continuously
developed and many guidelines exist.
Figure 17.1 shows the most common surfaces for ‘artificial’ open areas.
The surfaces are presented according to their permeability; i.e. the first picture
shows a conventional lawn which can be considered 100 per cent unsealed,
pictures 2 to 7 refer to various permeable surfaces, and the last shows asphalt,
being 100 per cent sealed. Table 17.1 compares the benefits and limitations of
most common permeable surfaces.
Parking areas have the greatest potential for permeable surface applica-
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tion. In Europe there are definitely more parking lots than cars. The number
of cars is increasing from year to year and together with this trend also the
number of parking lots; hence the application of reinforced grass systems
with gravel or grass grids is ideal for use in large short-term parking areas
such as in:

•• Recreational sites: e.g. ski resorts, football stadiums, golf courses, touristic
sites and trade fairs. Such surfaces improve the local drainage capacity and
contribute positively to the landscape.
•• Households: private driveways have great potential for the application
of permeable surfaces. For this type of use almost all surface types are
applicable.
•• Supermarkets: the use of permeable concrete pavers in combination with
drainage ditches is a long-lasting solution that allows heavy traffic. This
type of surface is increasingly being applied in supermarket parking areas.

The use of such surfaces has some limitations: areas with sensitive groundwater
resources or shallow groundwater (below 1 metre) are in general not suitable
for surface drainage. Moreover the costs have to accounted because apart from
natural stone pavements, it can be said that permeable surfaces do not bear
higher costs than conventional asphalt and are not dependant on the crude oil
price (unlike asphalt).
Moreover, gravel turf and concrete bricks are made of sustainable materials,
which are readily available in most European regions. As these materials can
easily be reused their life span is almost unlimited. Conventional asphalt on the
contrary has to be recycled for re-application with more energy input.
The above-mentioned reasons explain why many planning authorities in
Europe are currently revising their technical regulations towards surface seal-
ing. Increased drainage capacity has many advantages, in particular in areas
with flood risk or overloaded sewage systems. The fact that permeable sur-
faces can reduce or even avoid costs related to flood prevention, flood damage
repair or enlargement of existing sewage systems is attractive for local planning
authorities. For example, planning authorities in England, in the Alto Adige
region (Italy) and in selected cities in Germany and Austria already restrict sur-
face sealing for new building activities.
 274 G. Prokop and S. Salata
Compensating soil sealing
The idea behind compensating for soil sealing is to make up for sealing in one
place by restoring soil functions elsewhere in the same area. As a rule, compen-
sation measures should be equivalent to the ecosystem functions lost.
Environmental impact assessments of large projects and for planning pur-
poses can be used to identify the most appropriate compensation measure.
Examples of compensation schemes include:
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•• Reuse of topsoil: topsoil can be removed from a construction site and

used, for example, to upgrade agricultural sites, or to regenerate contami-
nated land and encourage seed germination, for example on a golf course,
or to improve soil quality in gardens.
•• Desealing (soil recovery): removing asphalt or concrete and replacing
them with topsoil on subsoil can help renew the soil functions of a previ-
ously sealed site, as well as restoring the beauty of the landscape. Desealing
is mainly used in urban regeneration projects, following the removal of
derelict buildings to create green spaces, for example. Sadly, this option is
not taken up often enough because the costs are perceived to be too high.
•• Sealing fee: authorities can impose fees for land take and soil sealing. This
could be used as a tool to limit soil sealing, but in practice fees are rarely
high enough to discourage land take. Instead, the money collected is used
to support soil-protection projects. Some countries in Europe use sealing
fees to protect the best farmland.
•• Eco-accounts and trading development certificates: in an eco-accounts
system, the ecological cost of soil sealing is determined and developers
have to ensure that compensation measures of equal value to sealing are
carried out elsewhere. Official compensation agencies oversee the system.

Conclusions
Despite a constant demand for urgent intervention and regulation that will
tackle the incessant consumption of open space calculated at an aggregated
scale, it seems that the problems of improvement of particular land-use devel-
opment patterns have not yet been properly addressed. Even if analysis on
land take is becoming much more significant, less successful cases of land take
reduction are registered. The application of the Guidelines approach demand
the greater advancement of research on land use management practices.
A simple contextualization of the analysis on land use trends gives sim-
ple but clear indications: traditional tools for land use/cover analysis are not
adequate for the evaluation of impacts on ecosystem services and insufficient to
steer local policies for land conservation. New approaches at the regional scale
are required to introduce more detailed evaluation of the impact of land take
on ecosystem services, with particular attention to the major effect of sealing
on soil, air and water.
 Limitation, mitigation and compensation 275
In order to implement soil sealing guidelines and activate a sustainable soil
and land governance, a multidisciplinary approach is needed to bridge the gap
between general, theoretical targets (e.g. land-take limitation) and the develop-
ment of specific patterns of land-use management at the local scale.
The need to go beyond the simplistic approach of land use change analysis
and to provide better information and more comprehensive data will enable
policy and decision makers to activate the right prescriptions, limitations or
regulations for land use management.
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References
Prokop, G. and Jobstmann, H. (2011) Report on Best Practices for Limiting Soil Sealing and
Mitigating Its Effects. Technical Report – 2011 – 050, European Commission, Brussels.
http://ec.europa.eu/environment/soil/pdf/sealing/Soil%20sealing%20-%20Final%20
Report.pdf, accessed 18 January 2016.
European Commission (2012) Guidelines on Best Practice to Limit, Mitigate or Compensate
Soil Sealing. SWD(2012) 101 final/2, European Commission, Brussels.
EEA (2006) Urban Sprawl in Europe: The Ignored Challenge. EEA Report 10/2006.
Luxembourg: Office for Official Publications of the European Communities.
 18 Policy, strategy and technical
solutions for land take
limitations
Stefano Salata
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Introduction: from knowledge to policy

Approximately 75 per cent of Europe’s population lives in urban environments
and a quarter of the EU’s land surface has been directly affected by urbaniza-
tion. This type of land use change affects environmental resources, and thus
the quality of human life is dependent on the capacity to govern the process
of urbanization.
If sustainable urban development is focused on ‘quality of life’, it is necessary
to assess and evaluate the effects of land take on soil-related ecosystems. But the
development of strategies against land take require the quantitative/qualitative
assessment of the environmental effects of urbanization (e.g. the impact on
specific ecosystem service (ES) degradation).
The recent ‘Scoping Study for DG ENV’ (PRACSIS, 2014) defines soil
resource as less attractive than other natural resources, and accordingly the cre-
ation of a common consciousness on soil related questions is far from being set.
Moreover, soil scientists and ecologists attribute land take to planning weakness
and then planners turn accusations to politics. Since no one bears responsibil-
ity, land take happens. The reasons are many:

•• the urban development mode is still based on expansion (with low popula-
tion density);
•• the land take by urbanization is mostly concentrated on prime quality land;
•• the urban development pattern promotes the private use of vehicles;
•• the urban development model is less concentrated on a re-use approach;
•• the lack of appreciation of soil as a finite, non-renewable resource.

Nevertheless, even if the causes are in the main addressed, the general
impression is that the gap between analysis (quantification and cause–effect
qualification of the land take phenomena) and regulation (improvement of
particular land use development patterns) is still unfilled (Nuissl et al., 2009).
This problem occurs due to a deep epistemological issue: while ‘land cover’
refers to the ecological state and physical appearance of the land surface based
 Policy, strategy and technical solutions 277
on a classification system, the ‘land use’ refers to human purposes in relation
to different things (e.g. the morphological characteristics of the soil, the prox-
imity to a centre/service, the landscape value etc.) (Turner et al., 1994; Dale
and Kline, 2013).
Furthermore, academic positions are problematic: although many disci-
plines recognize land take as a central environmental issue, a large part of the
research is still descriptive, rather than focused on supporting local policies for
land use management. The knowledge of soil quality is too poor for planning
disciplines, but even though an environmental phenomenon can be slightly
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shaded or undervalued, nowadays new economic paradigms emerge as cru-

cial issues for further discussion: are the long-term social costs of urbanization
higher than short-term incomes for operators over land use transformations?
The economic quantification of ES demonstrates that the value of natural capi-
tal is higher than any single income of real estate operation. However, as long
as it is not possible to demonstrate, and fully assess, the real economic side of
land take, the objective to achieve quality of life and public health by land use
control is far from being set.
Hence deeper analysis on the environmental effect of land take on ES
provided by natural soils is required (Helian et al., 2011), especially because
such analysis requires integrative assessment across different disciplines (Breure
et al., 2012). The lack of a more systemic and holistic agreement on common
considerations that land use/cover require a higher integration of knowledge
between ecological, social and economic studies is weakening the possibility of
achieving real ‘sustainable development’.
Moreover, the construction of an analytical framework for territorial
planning using ES as a real proxy for land use management can be pursued
using different steps: (1) framing a key policy issue related to ES preservation
or restoration, (2) identifying ES and users (e.g. the definition); (3) mapping
and assessing status; (4) valuation; (5) assessing policy options including dis-
tributional impacts.
Among the different approaches, explained below, the use of ES as a central
element for the re-definition of land use regulation seems to be the way to
overcome national/regional quantitative policies of reduction with ‘in-depth’
qualitative support for decision-making processes. Innovations in using ES are
mainly two: on one hand ES introduces qualitative elements of trade-off among
alternative uses of soil (acting against the flat dichotomy between urban/rural
uses), which allows secondary considerations; on the other hand ES helps not
only in understanding how much soil will move into the urban category of
land use (quantification) rather than define which kinds of soil are affected by
urbanization, but also in assessing what are the environmental effects of such
transformation (qualification). Finally, ES provides the possibility to associate
both biophysical and economic values, allowing the economic quantification
of losses or gains due to the uses of natural capital.
 278 S. Salata
How to limit land take
It is impossible to outline a unique approach to land take limitation. Cultural
context, legislative frameworks, but also planning tools and territorial con-
textualization require a mix of strategies rather than a single approach. While
the standardization of a methodological assessment is necessary for defining a
common knowledge system (e.g. definition, indicators), strategies for limita-
tions are many, and advanced experiences show that a single approach is not
strong enough to achieve great limitations targets – rather a mixed framework
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of policy, strategies and planning tools is required.

First, it is necessary to take into account that land take limitation is, at least,
directly controlled by land use regulations: excluding informal or illegal uses, all
other land use changes happen according to the local land regulation. Thus, it
is at the local scale that the power of land take limitation, mitigation and com-
pensation has to be enforced. If it is accepted that land use zoning is the tool
that allows land use transformations, then strategies pursued consequently have
to be adaptive and progressive, and communitarian as national/regional/local
authorities have to work together using dispositive approaches (e.g. guidelines,
or communitarian directives at the EU level), incentives (e.g. economics, since
economic and fiscal measures are mainly addressed to nation states), limitations
(e.g. thresholds on land take based on regional land use monitoring inventories)
and regulations (e.g. definitions of Urban Growth Boundaries, greenbelts or
land use prescriptions to stop land take on greenfields).
Above all, it has to be considered that land use regulation will only bring
about effective results through long-term application, and the construction of a
legislative context settled by an agreement between a communitarian approach
and national/regional legal frameworks is still laborious. For such reasons,
among others, a great deal of attention has to be dedicated to take, as soon as
possible, the right decisions.
To date no single measure appears to have achieved great success in limit-
ing land take. Data on urbanization trends demonstrate that the phenomenon
is affecting, with varying intensity, all countries. Among the numerous sets of
measures, the ‘market-based’ approach seems to be the most transversal way
to reduce the amount of land turned to urban uses. Fiscal measures designed
to address extra feeds for land take disincentives are gaining in efficacy, and
this seems to be the only sovra-local ‘strategy’ that offers a real possibility of
reducing land take.
When national fiscal measures are integrated with regional instruments
for monitoring land use change and local regulations to mitigate or compen-
sate land take based on ES assessment, the possibility of effective reduction
increases. The regulation of land use through fiscal measures at the national
level gives much greater power to local administration to act with additional
regulative planning measures. Taxation is also necessary for capturing urban
rent and provides the possibility of reducing speculation through the urban
transformation of greenfields.
 Policy, strategy and technical solutions 279
Some experiences are demonstrating how, over land use regulation by local
planning, the introduction of additional fees on free land is able to decrease
requests for transformation made by real estate operators. In Italy, the rein-
troduction of IMU (the Municipal Immobiliar Tax), which is far from being
settled as an environmental tax for land take limitation, has changed behav-
iour in relation to speculative plans by land owners. If previously requests
were commonly based on the extension of building rights on free land, now-
adays the introduction of IMU has turned requests upside down: land owners
ask to reconvert urban uses to agricultural ones where properties are located.
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Even if such requests reduce the possibility of building, and thus local plans
reduce the amount of land designated to transformation, this measure allows
them to avoid taxation.
The above-mentioned experience shows that market-based policy, based
on fiscal measures such as land taxation, should achieve better results than other
regulative prescriptions (e.g. land take thresholds).
The following sections detail experiences of taxation application as the main
paradigm for land take regulation.

For a theory of land taxation

Not all the costs of open, agricultural space are internalized in market transac-
tions involving agricultural land. This may cause an excessive penetration of
urban land into open spaces. In other words, the phenomenon of urban sprawl
may occur (Korthal Altes, 2009). These few words essentially explain why land
take happened with varying degrees of density or morphologies (dispersed,
fragmented, ribbon, leapfrog) mainly in agricultural land.
By such definition, urban sprawl is the effect of an economic cause: the
price of agricultural land is generally lower then urban land. If the difference
between the two values is not equalized, urban rent still persists as a parasitic
income for private operators who ask for free agricultural land to transform.
If this fundamental point is not solved, even between different alternatives of
localization for land use transformation, investment will be in greenfields rather
than in the existent stock of urbanized soils.
Thus sprawl is not a consequence of a citizen’s living preference, but the
effect of the real estate market economy. The fact that urban areas are still
expanding, even in the context of population decrease, is mainly dependent
on the old theory of urban rent. Indeed sprawl affects all countries, and even if
the requests for new housing are not coming from the upper classes but from
new immigrants and young populations, the real estate offer is still too unbal-
anced to provide market-price homes in the suburbs, with high dependencies
on private mobility.
One of the studies on urban rent done in the 1990s tried to introduce
economic values on open spaces, opening the way to ES economic quan-
tification (Costanza et al., 1997). At the base of the theory is the value of
soil as a natural, finite resource that, playing crucial functions (both tangible
 280 S. Salata
and intangible), guarantees human life on earth (Daily, 1997). In 1997 the
study, entitled ‘The Value of the World’s Ecosystem Services and Natural
Capital’, presented the ‘cost’ of such services, and the environmental econ-
omy became a discipline focused on assessing the balances of environmental
damages done by human transformation.
At the end of the 1990s, the first systematic studies were launched on the
environmental taxation of free land in order to avoid the decrease in the value
of ES, and debates for and against using fiscal measures to dissuade land use
changes on open fields arose.
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If local planning prescriptions aimed to control urban growth were his-

torically rooted in the planning discipline, for the first time the economic side
of regulation was considered a key driver in land use control. Immediately a
question arose: the creation of a hard limitation to the offer of urban land does
not directly imply that transformation should be concentrated in the already
urbanized land. The condition of a monopoly on the few urban areas suitable
for transformation gives rise to an increase in the value of brownfields and thus
a ‘block’ on marked real estate. Consequently, if taxation on greenfields is not
accompanied by additional measures to stimulate transformation on brown-
fields, the risk is to create only a block on the real estate market rather than to
limit the land take.
Despite all the technical questions that are slowing down the possibility of
applying a common theory to land taxation against land take, it is important to
recognize that, since the physical control of the city dominated the welfare state
of the twentieth century, today the attitude is to consider the market approach,
even for environmental good, as the only way to impact the real economy.
Taxing land take seems to be a necessity for an environmental policy
directed at sustainable land use planning. It is quite evident that individual
costs paid for land use transformation are not equal to the collective costs
paid by society for greenfield urbanization (Nuissl et al., 2009). The effects
on habitat quality (Price et al., 2006) soil and water buffering capacity (Haase
and Nuissl, 2007), atmospheric particulate concentration (Yang and Lo, 2002),
public health (Wells et al., 2007) and social segregation (Power, 2001) are quite
evident, even if not systematically addressed together as a consequence of land
take. Nonetheless an integrated approach is requested.
There are three main paradigms with regard to theories of land use taxation:

•• the application of fiscal extra feeds on the urban transformation of green-

fields (Korthal Altes, 2009);
•• economic incentives for urban reuse (Ring, 2008);
•• the introduction of a controlled market of land transformation (Nuissl
et al., 2009).

A paradigmatic experience of taxation is given by the case of the Netherlands,

where some prerequisites have facilitated the introduction of taxation as the
base of a national strategy for land use transformation:
 Policy, strategy and technical solutions 281
•• the value of agricultural soil is high (because both agricultural rent and
productivity are high);
•• agricultural conservation has high costs;
•• housing development has been mainly steered by national policies since
the 1980s;
•• even the planning system is regulated and it provides sufficient land for
transformation (Faludi and Van der Valk, 1994).

In this context a framework for a methodology of land take taxation was

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devised, taking into consideration that:

•• it is necessary to assess the welfare value of the free land for aesthetic
purposes;
•• it is required to capture the urban rent generated by land use plans changes;
•• it is important to steer public/private economic resources on re-development
towards existent urban areas, rather than towards greenfields;
•• extra funds for public administrations from land taxation can be guaranteed.

Obviously, the proposal opened a debate on the economic evaluation of envi-

ronmental goods such as the soil, and the debate turns back to the point stated
above: it is crucial to estimate what are the economic values of soil services to
achieve sustainability on land use. As long as economic values of ES are not
considered during the decision-making processes, the target of sustainability
will not be achieved.
As introduced, a great deal of research is dedicated to estimate the environ-
mental effects of land take processes, especially using ES as a proxy (Breure
et al., 2012; Jansson, 2013; Artmann, 2014; Li et al., 2014). From systematic
studies on surface and covers, to the complete assessment of urban transforma-
tion effects in hydrologic systems, a huge amount of research is focused on
the definition of ‘what happened on topsoil, and under it, when a process of
urbanization occurs’ (Gardi et al., 2014).
In general, as an ES approach has emerged as the main paradigm to esti-
mate quantitative and qualitative land transformation (Costanza et al., 1997;
Daily, 1997), there is a lack of technical assessment to introduce indicators that
hold different multidimensional aspects of soil transformation (e.g. the altera-
tion of productive capacity – land capability, impermeabilization, biodiversity
decrease, landscape and cultural values). Composite indicators on land take
are far from being rooted in scientific literature (even if they are well defined)
(Giovannini et al., 2008), and there is an impression that despite a great number
of words written claiming an interdisciplinary approach on land management,
no systematic results seem to be achieved. The demand for profound soil
knowledge is high (Havlin et al., 2010), and the teaching of soil related dis-
ciplines is mainly housed in geology, geography, environmental science and
agriculture programmes (Hopmans, 2007), but a major interaction of scientists
from other disciplines is required in order to achieve a broad holistic role in
 282 S. Salata
society, and the context of ‘fusion’ between different backgrounds needs to be
enforced (McBratney et al., 2014).
Land Use Change (LUC) allows us to quantify the loss of ecosystem func-
tions as an effect of change in cover or uses of land (Shuying et al., 2011).
Nowadays a weak assessment of indicators for specific ES functions demands a
high account in research for ES identification and mapping, especially for local
planning (Rutgers et al., 2011; Dominati et al., 2014).
For example, literature recognizes that the total ES value of each land use
category can be obtained through multiplying the area of each land category
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by the value coefficient: ESV = ∑ (Ai • VCi) – where ESV is the estimated
ecosystem service value (Euro•a-1), Ai is the area (ha) and VCi is the value
coefficient (Euro•ha-1•a-1) for land use category ‘I’ (Helian et al., 2011). Such
definition introduces the possibility of finding an economic overall evalua-
tion of ES. Even oversimplified (Pimm, 1997; Toman, 1998), such possibility
gives to public administration and planners an estimation of a variation of
values for non-commodities (soil) through land use planning. Rather than
absolute value, such methodology should be normally introduced as an eco-
nomic computation of ES variation between present (net present value) and
future (Bateman et al., 2013; Baral et al., 2014). Additional exploration of ES
values for specific land use/cover categories is reported in the study ‘Impact
of Urbanization on Natural Ecosystem Service Values: A Comparative Study’
(Shuying et al., 2011).
The critical ways in which ecosystems support and enable human well-
being are rarely captured in cost–benefit analysis for policy formulation and
land use decision-making (Laurans et al., 2013). Results showed that, although
a conventional, market-dominated approach to decision-making chooses to
maximize agricultural values, these monofunctional policies will reduce over-
all values (including those from other ES) from the landscape in many parts
of the territory – notably in upland areas (where agricultural intensification
results in substantial net emissions of GHG) and around major cities (where
losses of greenbelt land lower recreation values). In comparison, an approach
that considers all other ES for which robust economic values can be estimated
yields net benefits in almost all areas, with the largest gains in areas of high
population. Some analyses suggest that a targeted approach to land-use plan-
ning that recognizes both market goods and non-market ES would increase the
net value of land to society by 20 per cent on average, with considerably higher
increases arising in certain locations (Bateman et al., 2013).
Even at the theoretical stage, the ES approach raises the possibility of
estimating the net cost of an environmental service supporting the definition
of a theory for land take taxation. Nevertheless, legislative and economic
reasons seem to create obstacles: how can a theory of taxation influence the
real-estate market?
The fear of lowering the few private resources dedicated to real estate domi-
nates the position against the introduction of a land taxation system. Such a
position is based on the fact that urban rent is, at least, the core of real estate
 Policy, strategy and technical solutions 283
investment. The taxation model cannot recover all the urban rent generated by
land use change, otherwise the marginal incomes for operators would not be
sufficient for transforming the land. Thus the risk, in such a case, is to stop all
private real estate market operations (Korthal Altes, 2009).
Another kind of risk associated with the introduction of a purely fiscal
approach to land use regulation based on taxation is the potential that it invites
public administration to ‘use’ the tool to create extra income which would
generate distortions: a rush to capture private resources, resulting in a huge
number of transformations on greenfields. This would have the opposite effect
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to land take limitation.

Among others, the above-mentioned issues are sufficient to demonstrate
that a land take taxation model is far from being regularly adopted and,
moreover, that it is contextual and not a substitute for the traditional land
use regulation system. If a method of ‘low’ taxation is introduced by the
state, then local tools and instruments of land use regulation such as green-
belts or Urban Growth Boundaries are enforced. Such integration between
different instruments and strategies works better in a context where the
strategic dispositions of land use transformations and prescriptions are fixed
at a wider scale (sub-regional rather than at municipal level). It is widely
recognized that green infrastructure design requires an intermediate scale
of planning, detailed enough to be prescriptive for environmental issues,
but synthetic enough to fix common rules for homogeneous territories
(Crompton, 2007; EEA, 2014). Economic instruments of land use policy,
therefore, will considerably mitigate the negative impacts of land devel-
opment only when backed by traditional land use planning tools such as
zoning, which allow for the spatial guidance of land development in the first
place (Nuissl et al., 2009).
Another option would be to offer buildable land only with a land use change
permit. In this case building permits would be awarded to private operators
through negotiation on the basis of a planning regulation system. Even with
a market-based approach, this system does not introduce a system of taxation
based on land use definition; rather it invites discussion of the single initiative
of transformation and involves:

•• fixing a ‘price’ for building permits based on the value of soil quality;
•• creating market control of the offer of building rights.

In any case, at the base of the taxation theory, the debate for or against the
possibility to attaching economic values to a purely environmental resource
generates opposition. A branch of ecology states that it is impossible to fix the
overall value of non-commodities, others claim there is a ‘need to evaluate
ecosystems economically’, starting from the ‘real’ market value of some goods.
For example, the economic evaluation of biodiversity should be derived from
the market price of the ‘reproduction’ of specific land uses that provide such
ES: the cost of planting a forest, or the cost of a public garden for urban green
 284 S. Salata
areas. This means using a market price of ‘substitution’ (How much does it cost
to reproduce the goods?), using a biophysical environmental index as a proxy
of distribution of the service.

Local regulation
Tools focusing on the containment of land take mainly aim to define:

•• a target of reduction for the amount of land development (quantitative

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thresholds);
•• the improvement of specific land use patterns (qualitative control of set-
tlement distribution).

While the first approach is rooted in environmental discipline and mainly based
on the quantitative assessment of land take (Helming et al., 2011), the second
is less covered by scientific studies even if it seems to be evident that limitation
measures require full integration between quantitative and qualitative methods
of assessment (Haberl and Wackernagel, 2004).
The process of land take generally implies a reduction of the ES delivered
by soils on the basis of land use variation over different years. With the basi-
lar knowledge of urban land use changes (quantitative), comes the evaluation
(qualitative) of land take impact on ES (Shuying et al., 2011) that give support
to local practices of land conservation.
There are two consolidated approaches for land take limitation over local
land regulation:

•• the introduction of green border areas for the containment of urban

expansion (greenbelts);
•• the definition of regulative borders by planning constraints between build-
able and non-buildable areas (Urban Growth Boundaries – UGB).

The two approaches are traditionally used by local planning regulation to

achieve a compact settlement system. Since the legal planning system has been
theorized, the definition of city borders requires a project of green areas around
the compact city. Garden cities first experimented with such an approach,
which was exported over numerous European cases. It is a typical projectual
measure aimed to design a green corridor between dense urban built-up areas
and the countryside.
Nowadays international literature clearly talks about an explosion of the
urban (Brenner, 2013; Ove Arup & Partners International Limited, 2014) in
which the relationship between the central city and the surrounding regional
space cannot be described anymore in terms of an ‘inside’ and ‘outside’, of a
centre and a periphery, at least in a traditional way. Within this perspective
the metropolis is gone. In its place, there seems to be a post-metropolis, a
space without limits and with extremely diversified social and spatial models
 Policy, strategy and technical solutions 285
subject to continuous assembly and disassembly processes, leading to a pro-
gressive loss of meaning for terms such as city, countryside, suburbs. A fractal
city, extremely heterogeneous, with constantly changing centres and periph-
eries. Furthermore, the typical regulative approach based on greenbelts seems
to be obsolete.
New environmental issues have emerged and the approach to the city is
largely dominated by new spatial paradigms: for example, urban vs rural is
the matter of defining the efficiency of an urban fabric pattern, for example
compact vs sprawl (Antrop, 2004; Millward, 2006). The land-take concept
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dominates the physical approach to city development and nowadays it is

impossible to allow city expansion on new greenfields with the same intensity
of previous years.
Some of the aims of local plans include sustainable economic strategies for
cities; the preservation and development of favourable settlement structures;
mixed land uses and social integration; higher development densities and
the protection of open space; strengthened inner cities and local centres; the
protection of urban heritage; sustainable urban infrastructure and urban and
regional transportation systems (Hale and Sadler, 2012). A common key aim
is to limit the rate of urbanization of previously undeveloped land to defined
parameters (quantitative or qualitative) (Couch et al., 2011).
Trying to recapture its capacity to regulate space and society, sustainable
urban planning is today capturing the attention of politicians and opinion lead-
ers. The academic debate is gradually returning to old categories (for example
the opposition of urban vs rural), thus misinterpreting the original social nature
of these categories rather than acknowledging their spatial value. Debate on
‘post-metropolis’ and ‘regionalization’ is pointing to how physical boundaries,
administrative fragmentations and spatial policies can be fitted to the urban
dimension which is based on a continuity and homogeneity of spaces and
landscape, based on common lifestyles and flows of long distance mobility.
After a period in which a de-regulative approach has been applied (Mazza,
1997, 2004), debate is reconsidering how the physical dimension of regulation
is capable of governing territorial transformations.
Crucial to the further development of a policy of regeneration and re-use is
to define borders between the compact and sprawled city rather than to define
a perimeter where building rights are allowed or not. Nevertheless UGB are
good tools to support monitoring strategies to control local planning decisions:
when sovra-local authorities use UGB as a comparative border between the
existent built-up system and the one planned by the land use scenario, it is pos-
sible to easily control if, and where, land take occurs.
One of the most representative uses of UGB is that applied by the
Swiss Planning Policy, where local administrations are forced to present to
sovra-local authorities a regular border of the land use plan settled for the
built-up system definition. Every 10–15 years the border between dense and
dispersed settlement is verified and GIS monitoring is constantly applied
(Gennaio et al., 2009).
 286 S. Salata
This approach is based on the assumption that sovra-local monitoring
systems of land use change are constructed with a bottom-up approach: the
municipal level has to work with a high degree of precision for constructing a
land use database, in accordance with sovra-local and national inventories. In
this way local accountability should fit with regional or national tools of land
use monitoring.
The success of the Swiss case is supported by analysis made between 1960–1970
and 1990–2000. Positive results where demonstrated by:
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•• the low number of buildings erected in non-dense zones;

•• the rise of the density index in urban dense areas;
•• the concentration of land use changes inside the existent stock of urban
areas;
•• the application of ‘compact development’ (almost 70 per cent of new
buildings were built inside the UGB).

As noted above, a single measure cannot create a real reduction in the amount
of land take; rather a mix of options need to be considered. For example, some
regional authorities have tried to implement UGB using land use repertories
with a low degree of precision and not shared with the land use maps adopted
for local planning regulations. It has to be considered that the Swiss case rep-
resents an example where land use control is facilitated by the dimension of
administrative boundaries rather than the geomorphological reasons that facili-
tate the ‘compact’ development of a city.
In any case, one of the factors limiting land take control is the fact that a
large majority of soil indicators for land take assessment are consistent only
as descriptive tools for soil scientists, but less consistent as tools to steer local
policies for preserving soil degradation due to urbanization (Geneletti, 2013).
A national agenda of environmental policies would need to be supported by
aggregated data concerning the levels of urbanization: all nations engaged in
the discussion of an instrument that will limit the further growth of urban areas
(Germany, Netherlands, UK, etc.) are supported by national databases of land
cover/use. However, a theoretical model for land use management at the local
scale, specifically created for limiting land take, is still lacking where advanced
policies are designed (Dale and Kline, 2013; Calzolari et al., 2016).
For these reasons, among others, it is impossible to adopt a single approach
to limit land take. Thus it is still impossible to define a common methodo-
logical framework for adopting policies against land take, even based on deep
knowledge of cause–effect dynamics.
A great deal of research is dedicated to estimate the environmental effects of
land take processes, especially using ES as a proxy (Breure et al., 2012; Jansson,
2013; Artmann, 2014; Li et al., 2014).
Although the most common application of ES mapping is done at the macro-
scale using national inventories of land use rather than European ones (Corine
Land Cover), the challenge is to propose an evaluation at the micro-scale
 Policy, strategy and technical solutions 287
(here intended as the urban scale). Thus the potential role played by open
areas of vegetation with ecological characterization is to facilitate the planning
choices made during the screening phase of local plan construction. The appli-
cation of new operative methods of soil classification is also useful to provide
relevant information to urban planners during the decision-making process
(Dale and Kline, 2013).
Nowadays, collected data on the urbanization trend (land cover classifica-
tion, rate of change, urbanization per capita) is being well analysed (Benini
et al., 2010; Bhatta et al., 2010; Pileri and Salata, 2011; Munafò, 2013) and
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the proposed European guidelines for land-take reduction are supported by

national databases of land cover/use.
The goal of reducing land take with an integrative approach between analy-
sis and policies of local land regulation, for example, has to better consider the
role of Strategic Environmental Assessment (SEA Directive, 200142/EC). SEA
is aimed at monitoring the land take phenomenon, using environmental data,
and assessing impacts of land use change due to urbanization (Treville, 2011).
But SEA is not sufficiently qualified to perform a complete land take assessment
when used only for quantitative purposes (Geneletti, 2011).
Integrated into the urban planning discipline, the knowledge of the soil
functions in urban ecosystems – assumed by a scientifically recognized model
with a soil evaluation method – can orient the urban planner to make deci-
sions and choices for the rational use of land (Rametsteiner et al., 2011; Clerici
et al., 2014). However, future research has to be dedicated to understanding
the effects of land cover changes on ES, especially using both econometric and
biophysical evaluation as a proxy for sustainable urban planning.

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 19 Soil sealing and land take as global
soil threat
The policy perspective
Luca Montanarella
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Soil sealing and land take are a global threat to food security and social stability
as well as to biodiversity and ecosystem services. The exponentially increas-
ing consumption of the most fertile soils for urbanization and infrastructure
is affecting the availability of cropland for feeding the global population.
Expansion of infrastructure in pristine natural areas, like in the Amazon, is
rapidly affecting biodiversity and ecosystems. The general perception is that
urbanization and infrastructure are a sign of economic growth and increased
well-being of the population. Unfortunately the facts and figures in front of
us clearly demonstrate the contrary. The quality of life of the urbanized popu-
lation has been hardly increasing and the number of undernourished is not
significantly decreasing.
Taking stock of these facts, at the Conference for Sustainable Development
in Rio de Janeiro in 2012 (Rio+20 Conference, as 20 years had passed since the
first conference in 1992) the countries of the world adopted a new document
(the ‘Future We Want’) and asked for defining necessary sustainable develop-
ment goals in order to reverse the on-going unsustainable development trend.
In 2015 the proposed Sustainable Development Goals (SDGs) were defined
and adopted by the UN General Assembly. Soil and land are addressed specifi-
cally in three goals (Montanarella and Alva, 2015):

1 Goal 2. End hunger, achieve food security and improved nutrition and
promote sustainable agriculture.
2 Goal 3: Ensure healthy lives and promote well-being at all ages.
3 Goal 15: Protect, restore and promote sustainable use of terrestrial eco-
systems, sustainably manage forests, combat desertification, and halt and
reverse land degradation and halt biodiversity loss.

These goals have, then, specific targets that address soils explicitly:

Target 2.4 By 2030, ensure sustainable food production systems and

implement resilient agricultural practices that increase productivity and
production, that help maintain ecosystems, that strengthen capacity for
adaptation to climate change, extreme weather, drought, flooding and
other disasters and that progressively improve land and soil quality.
 292 L. Montanarella
Target 3.9 By 2030, substantially reduce the number of deaths and illnesses
from hazardous chemicals and air, water and soil pollution and contamination.
Target 15.3 By 2020, combat desertification, restore degraded land and soil,
including land affected by desertification, drought and floods, and strive to
achieve a land-degradation-neutral world.

Specific indicators are still in discussion in order to consistently monitor pro-

gress towards achieving these ambitious targets by 2030.
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Particular attention by policy makers as well as by scientists and other

stakeholders was triggered by the new concept of land degradation neu-
trality (Chasek et al., 2015). Originally introduced by the United Nations
Convention to Combat Desertification (UNCCD), if applied consistently also
in countries not affected by desertification it could eventually also reverse the
growing trend towards soil sealing and land take. Assuming that soil sealing
is a form of land degradation (a concept still to be accepted by most policy
makers), then achieving land degradation neutrality may also imply a reduc-
tion of land take and sealing of pristine natural areas and of productive fertile
land as well as an increase in recycling and re-use of already sealed and urban-
ized areas. Indeed recycling of brownfield and abandoned industrial areas has
been advocated as one of the possible good practices for achieving a substantial
reduction of the consumption of fertile agricultural land for housing and infra-
structure (European Commission, 2012). Unfortunately, neither at the EU
level nor at the global level has a binding legal obligation emerged for national
governments to limit the dramatically increasing consumption of fertile land.
The newly adopted Sustainable Development Goals and the related target for
achieving a land-degradation-neutral world by 2030 may help in initiating
some positive developments at the national level.
As a first step there needs to be a consistent definition of land degradation
neutrality to be adopted by all countries in the world. Recent debates within
the UNCCD have resulted in a still on-going controversy about the precise
definition of land degradation. From a scientific point of view the most
current definition refers to the loss of ecosystem services that a degraded
land can deliver. Indeed the Intergovernmental Science-Policy Platform on
Biodiversity and Ecosystem Services (IPBES) has defined, for the purpose
of its thematic land degradation and restoration assessment, land degrada-
tion as ‘the many processes that drive the decline or loss in biodiversity,
ecosystem functions or services, and includes the degradation of all terrestrial
ecosystems’ (IPBES, 2015). This definition implies that soil sealing and land
take are to be recognized as major land degradation processes. This paves
the way towards establishing appropriate policy measures in order to limit
this degradation and to restore already degraded areas. Compensating the
continuing trend of urbanization and soil sealing with equivalent areas being
restored and un-sealed would allow the achievement of the goal of a land-
degradation-neutral world.
 The policy perspective 293
Policies addressing soil sealing and land take need to limit the expansion of
urbanization on pristine agricultural land. A good start has been recently made
in Italy, with the proposal, still pending in the Italian parliament, for a law that
would limit soil sealing in agricultural areas (Russo, 2013). The interesting
approach developed by the Italian legislator is the limitation of soil sealing of
agricultural land on the basis of the previous subsidies that the owner of the
agricultural area has received in the framework of the Common Agricultural
Policy (CAP). The principle that if land has been receiving public support
from the taxpayers for a certain type of land use, specifically for agricultural
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production, the land owner cannot proceed, for a number of years after the last
payment of subsidies, in any changes of land use. The original proposal for the
period of restriction of land use change was of 10 years after the last payment,
but recent debates in the parliament have already watered down the proposal
to five years or less. Still the approach of restricting land use change if public
funding has been made available for certain land uses can be the basis for future
legislation at the national and also the EU level. For the moment only a few
countries have implemented some national policies in order to limit land take
and soil sealing (Prokop, 2011). Quantitative limits for annual land take exist in
six EU member states: in Austria and Germany a limit of soil sealing is defined
as hectares per day for a target year; in Belgium (Flanders), Luxembourg and
the Netherlands there are limits based on inner urban development, for exam-
ple 60 per cent of new developments within defined urban circles; in the
United Kingdom (England) limits are based on brownfield redevelopment, for
example housing on already developed land. Overall, policies for limiting soil
sealing are rather scarce and are usually not very effective. Land take and soil
sealing is continuing in Europe and worldwide at an increasing rate.
Effective limitation of soil sealing is actually only happening in protected
areas, like the NATURA 2000 sites in the EU, national protected areas and
national parks. Unfortunately the recent economic crisis in Europe and the
need to stimulate economic growth and job creation has put the existing EU
nature protection legislation under pressure. There is still a widespread opinion
that protected areas are preventing economic growth by restricting economic
activities like construction of houses, infrastructure and industrial installations.
A very old-fashioned model of economic development is still considered the
only way forward while alternative development models are a priori not taken
into consideration. Dismantling the system of protected areas in Europe is pro-
posed as the solution for reversing the negative economic trend of the EU area.
Extensive literature exists proving the contrary (Schoukens, 2015), but never-
theless the dominating ideology is not taking these options into consideration.
Decoupling economic growth from soil sealing is the only way forward for a
sustainable future for urbanization and infrastructure in the world. Developing
alternative city models, incorporating to a large extent green infrastructure,
urban gardening and more compact city designs can substantially contribute
to better living and a smaller ecological footprint. Reverting from consump-
tion patterns implying high soil sealing rates, like large commercial areas on
 294 L. Montanarella
city outskirts linked by extensive infrastructure for transport towards a more
sustainable polycentric city model with short transport distances and more dis-
tributed commercial and productive areas could as well substantially improve
the ratio between economic growth and soil sealing, with less sealing per GDP
unit. This goes hand in hand with a change of the agricultural production
model, moving away from a highly mechanized, energy intensive agricultural
model towards a more distributed and labour intensive model attracting part of
the urban population back to the rural environment. Smaller farms producing
high added value food products mostly for local consumption are the alterna-
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tive to the current system. Creating jobs in the agricultural sector is the way
forward to reverse urban expansion. A large amount of scientific literature
and theoretical work has already been done proving the advantages of such an
alternative system (Schell, 2011; Kuyper and Struik, 2014; Loos et al., 2014;
Petersen and Snapp, 2015). Terms like sustainable intensification, agroecology,
organic farming etc. have been debated in many conferences and scientific
seminars. Translating these scientific findings into effective policy measures is
still the missing step. There is the need for an efficient science–policy interface
addressing sustainable soil management. The recent establishment by the Global
Soil Partnership (Montanarella and Vargas, 2012) of the Intergovernmental
Technical Panel on Soils (ITPS) is a good step forward. A panel of soil scien-
tists nominated by governments and providing policy relevant scientific advice
may initiate the necessary steps for a coherent soil protection policy at the
national, regional and global scale. As a first deliverable, the ITPS has already
revised the World Soil Charter and the new version has been adopted by FAO
members (most countries in the world) (FAO, 2015). National governments
have now a legal basis for initiating the process towards national legislation for
sustainable soil management. The World Soil Charter recommends to national
governments to ‘incorporate the principles and practices of sustainable soil
management into policy guidance and legislation at all levels of government,
ideally leading to the development of a national soil policy’.
Not only action at global and national levels is needed, but also aware-
ness and action at the local level is mandatory for actual implementation of
sustainable soil management guidelines. Soil sealing is a direct consequence of
urbanization and therefore spatial planning authorities have to play a key role
in limiting soil sealing and land take. Spatial planning is usually a strictly local
competence of municipalities and local administrations. It is at that level that
effective measures could be taken, if sufficient political will is exercised by the
local administrators. Involving the local population in the decision-making
process for spatial planning is necessary, but will yield positive effects only if
associated with extensive awareness raising and education campaigns.
Cultural ecosystem services, urban soils form an integral part of urban envi-
ronmental education – bridging the gap between people and nature. The
incorporation of urban soils into education and outreach programmes and link-
ing urban soils to participatory urban restoration and gardening experiences are
a key way to ground urban residents in their local ecology.
 The policy perspective 295
Ultimately, effective policies for limiting soil sealing need to address the
underlying economic model of our society. As long as there will be a close
coupling between economic growth and increased soil sealing there will be
little hope to reverse the negative trend.

References
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zero net land degradation: the next stage in international efforts to combat deser-
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tification? Journal of Arid Environments, 112, 5–13. http://doi.org/10.1016/j.

jaridenv.2014.05.020, accessed 23 January 2016.
European Commission (2012). Guidelines on best practice to limit, mitigate or
compensate soil sealing. Commission Staff Working Document. http://doi.
org/10.2779/75498, accessed 5 December 2015.
FAO (2015). Thirty-ninth Session Rome, 6–13 June 2015 Global Soil Partnership –
World Soil Charter, April. www.fao.org/3/a-mn442e.pdf, accessed 23 January 2016.
IPBES (2015). Scoping for a thematic assessment of land degradation and restoration,
January.
Kuyper, T. W. and Struik, P. C. (2014). Epilogue: global food security, rhetoric, and
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Loos, J., Abson, D. J., Chappell, M. J., Hanspach, J., Mikulcak, F., Tichit, M. and
Fischer, J. (2014). Putting meaning back into ‘sustainable intensification’. Frontiers
in Ecology and the Environment, 12(6), 356–361. http://doi.org/10.1890/130157,
accessed 23 January 2016.
Montanarella, L. and Alva, I. L. (2015). Putting soils on the agenda: the three Rio
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January 2016.
Montanarella, L. and Vargas, R. (2012). Global governance of soil resources as a nec-
essary condition for sustainable development. Current Opinion in Environmental
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Prokop, G. (2011). Report on Best Practices for Limiting Soil Sealing and Mitigating Its
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Russo, L. (2013). Il consumo di suolo agricolo all’attenzione del legislatore. Aestimum,
63 (December), 163–174.
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 20 Conclusions
Ciro Gardi
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The percentage of urban population, as well as the total population of the

planet, will continue to grow in the future, and this will drive a further
increase in urban areas worldwide. The intensity of this process will be dif-
ferentiated, reaching a maximum in Africa and Asia, and the role of urban
expansion, as a process of irreversible soil degradation, will be of crucial
importance. It is essential to govern these processes, in order to limit, as
much as possible, the areas affected and to mitigate and compensate the
impact on ecosystem services.
Food production, in the absence of a second ‘green revolution’, could rep-
resent a critical issue in the future. Even if globally the demand for food could
be satisfied, the agricultural areas lost in some parts of the globe will determine
imbalances in the food supply chain. Areas that have traditionally been self-
sufficient for food production will become importers, causing an off-set of
agricultural area demand (indirect land use change).
Impacts of urban expansion will also be relevant for other ecosystem services.
The effects on water regulation are already evident in some part of the world.
In many cases the causes of flooding events are changes in the amount, intensity
and distribution of precipitation, changes in land use and physical properties of
soil (i.e. soil compaction), and also the increase of sealed surfaces.
The impact of urban expansion on climate, local and global, is another
relevant issue. In addition to the Urban Heat Island effect, caused by the altera-
tion of the radiative energy budget within urban areas, we have the indirect
effect of the loss of important carbon sinks, which could contribute to climate
change mitigation.
Other examples of the impacts of urban expansion on the capacity of soil to
deliver ecosystem services have been widely discussed in Part II of the book.
Considering that urban areas are unavoidably going to grow in the future,
the challenge is to define how much they should grow and which type of
urban environment is desirable. There is of course a trade-off between popula-
tion density in urban areas and quality of life. Modern architecture and urban
planning (with very few exceptions) has exacerbated the trade-off: dense settle-
ments are usually unliveable, while low-density residential areas are generally
 Conclusions 297
more attractive. This is where the challenge is, and it should involve not only
architects and urban planners, but also ecologists, agronomists, economists and
social scientists.
It is important, when we are envisioning the future, to have an honest
retrospective view on the excellent lessons from the past. If we consider some
shining examples, like the central Italian landscape with its beautiful small cit-
ies, it appears very clear that we have to learn from the past. These examples,
like many other traditional landscapes in the world, often represent an optimal
use of resources, ensuring a high quality of life that doesn’t neglect the social
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dimension. Furthermore, we should consider the fast changes that our ‘liquid
society’ is facing. Thanks to the digital revolution, we are moving towards
a decentralised society. In contraposition to globalisation, there is a growing
community of people who consider regionalism an added value. The need,
or the possibility, to move everything from everywhere to everywhere will
hopefully decrease in the future, halting the absurd alteration to the global
biogeochemical cycle that humankind is imposing on the Earth.
 Index
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accuracy 21, 26–8, 43, 46, 61, 82, 95, Cerrado 16–17, 37, 43, 48, 58–60
217, 260 climate: change 20, 28, 60, 65, 89, 100,
adaptation strategies 100, 193, 199, 200 149, 151, 162, 170, 193, 200–1, 211,
aesthetic values 2, 35, 238 258, 260, 291, 296; justice 4
agricultural commodities 63, 65, 148 combustion processes 193, 195
agroecosystems 186 commerce 80, 209, 252
air pollution 3, 193 common agriculture policy (CAP)
Amazon basin 17 95, 256
amenities 88, 97 commuting 76, 92, 105–6, 205, 209, 267
atmospheric: deposition 124, 129–30; compact: city 87–8, 100, 284, 293;
moisture 195, 200; pollutants 129; settlements 197
authorities 85, 87, 97, 102, 175, 200, 212, compaction 15, 124
268, 273, 278 competition for land 14, 94
compost 126–7, 133, 184
bioclimatic island 194 Copernicus 21–8, 35, 48, 217–19
biodiversity 4, 15, 19, 100, 129, 169–78, cultivated land use transition 235–6
181, 211, 236, 248, 251, 266–83, cultural: mediation 197; services 208, 212
291–92;
loss/losses 16, 236, 251, 291; Dar es Salaam 258–9
bioenergy crops 150 deconcentration process 248
biofuel 4, 147, 150–1 Defense Meteorological Satellite
Braceros 253–4 Program–Operational Linescan System
Brownfield 209, 270, 280, 292–3 (DMSP-OLS) 239
built-up 33–54, 69–72, 77, 171, 217–21; demolition waste 189
area 4, 33, 80, 171, 181, 186, 198, desealing 267, 274
205–6, 248, 250, 258–61, 284; change desertification 15, 149, 266, 291–2
33, 39, 40, 51; environment 33–4, 38, direct effects 124
44; expansion 4, 19, 33; presence index doppler radar 200
(BUPI) 39; map/mapping 38–40, 52; DPSIR (Driver-Pressure-State-Impact-
structure 38 Response) 135
drivers of urban expansion 85–112
calcareous material 190 dynamic systems 205
Cancun 238–9, 251–2
CAPRI 63, 65 Earth Observing System (EOS) 35
car ownership 106, 209 ecological: connections 202; constraints
carbonates 189 147; impacts 205–6
Causal Analysis/Diagnosis Decision economic growth 93, 99, 111, 235,
Information System CADDIS 158 291–5
cellular automata 60 economy 93–8
 Index 299
ecosystems: fragmentation 210; services hydraulic invariance 164, 166
4, 13, 15–16, 19–21, 33, 121, 123–36, hydrology 61–2, 128
171–2, 205, 212, 231, 235–6, 248–52,
267–8, 274, 280, 291–4, 296 impervious: areas 158, 161, 170; cover
emission 60, 85, 92, 127, 129, 158, 193, 158–9, 170, 250; layers 190; surfaces 9,
195–6, 210 20, 25, 43, 52–3, 127–8, 157, 159, 164,
energy: consumption 140, 193, 200; 166, 181, 188, 217–18
efficiency 91 imperviousness 161, 169–71, 193, 206,
environmental: damage 193, 248, 280; 219–22
degradation 16, 135; impacts 16, 87, 89, indirect effects 82, 124, 128, 130, 132
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123, 128, 136, 175, 193, 211, 268, 270, indirect land use change 17, 296
274; stresses 85; stressors 106 induced development 100
error matrix 27, 260 industrial development 208, 249
EU policies 98 infrastructure 86–9, 92, 95–6, 98,
European Environment Agency (EEA) 28, 105–6, 109
45, 85, 96–8, 101, 105, 111–12, 164
European Settlement Map (ESM) 46, Joint Research Center (JRC) 46, 59, 267
48–9, 52
European Space Agency (ESA) 35 laissez-faire policies 98, 100
externalities 3, 210 land: consumption 20, 85, 87, 90, 96–7,
104, 106, 181, 267; cover 4, 12, 19–29,
food: commodities 13, 148–9; 33–5, 42–5, 47, 49, 196,
consumption 13; crises 146, 148; 198–9, 219–20, 258–9; cover
security 14, 16, 96, 146–7, 151–2; classification 25–6, 259–61, 287; cover
supply chain 149 monitoring 19–21, 28–9, 169, 217, 258–
fossil fuel 105, 149, 193, 195, 200, 251 9, 261; degradation 266, 291–2; demand
fuel 3, 93, 105, 149–51 63, 65, 85, 92–3, 99; functions 62;
Functional Urban Areas (FUA) 76–7, 80 grabbing 94, 111, 151; ownership 205;
planning 9, 60, 100, 175, 280, 282–3;
GDP 5, 64, 93, 294 price 97, 105; regulation 278, 284, 287;
global cereals stocks 47 system science 20; take 9–10, 12–15, 20,
Global Earth Observation 37 70, 80, 91, 106, 121, 146, 150–3, 157–8,
Global Human Settlement Layer (GHSL) 169–78, 181–90, 212, 239, 246, 265–74,
46–8, 51, 53 276–87, 291–4; use change 17, 62–3, 65,
global land cover 4, 28, 35, 43, 45 81, 86–7, 97, 106, 123–4, 126, 130, 149,
globalization 205, 297 164, 176, 178, 182, 231–2, 265, 267,
governance 98–9, 102, 104, 111 275, 276–87, 293, 296; use intensity 64,
green areas 20, 171, 196, 200 70, 71, 81; use management 187, 265–6,
green belt 100 274–5, 277, 286; use models 59–62
green infrastructure 81, 133–4 Landsat 22–4, 258–9
green revolution 13–15, 296 landscape: fragmentation 106, 150, 172,
green wedges 100, 102 175, 205, 206–7; management 205,
greenfields 278–81, 283, 285 211–12; metrics 222–6; planning
greenhouse gases (GHG) 19, 126, 193, 205, 212
195–6 Latin America 151, 238–42, 244–5, 254
Lidar 199
habitats 4, 15, 19, 105, 128–9, 131, 150, linear infrastructure 206
158–9, 162, 169–72, 175, 205–7, liquid society 297
211, 280 living standard 95–6, 102, 267
heat balance 193, 195 local climatic zones (lcz) 198
home ownership 102–4 local scale 25, 59–60, 87, 106, 133, 135–6,
human activities 4, 127, 159, 182, 184 148, 200–1, 275, 278, 280
human settlements 33–4, 47, 49, 147 low impact development 133–4, 160
 300 Index
LUISA (Land Use based Integrated polarization reversal 251
Sustainability Assessment): 59, 61–3, 65, population growth 4–5, 9, 14, 16, 42, 50,
67, 71, 74, 77, 80–3 60, 73–7, 87, 91, 111, 151, 239, 244,
254, 258
marginal lands 4, 15 potential accessibility 63–5, 67, 73–6
medium-sized cities 92 precipitation 158, 162, 166, 193, 199, 200
megacities 5, 231, 241, 254 pressure-state-response 86
megalopolis 9, 251 protected areas 64, 176–8, 293
megatowns 94, 270
Merida 239, 252–3 QGIS 259
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metropolitan regions 88–9, 92–3, 105,

110–11, 200 relative network efficiency 73
Mexico City 238–51 remote sensing 19–21, 25–6, 28, 34, 37,
migration 4, 5, 12, 50, 69, 72, 74, 42–3, 46, 152, 199, 219, 252, 258
91–2, 238, 240, 244, 246 remotely sensed data 34, 51
Millennium Ecosystem Assessment 13, 16, resilient soils 13
135, 147, 204 restoration 124, 132–3, 136, 277,
minimum temperatures 196 292, 294
Moderate Resolution Imaging road density 206
Spectroradiometer (MODIS) 4, 35–7, rural areas 10, 50, 77, 93, 95–6, 131, 181,
43–4, 239 195–6, 200, 268
modeling platform 16–17, 21, 89, 91–2,
112–14 salinization 15, 149, 266
multi-temporal analysis 34 satellite imagery 34, 45, 53
multifunctional rural land 231 scale: effects 169–172; global 33–5, 43,
municipalities 73, 97, 225, 229–30, 46–7, 51, 53; local 33, 51
248–50, 268, 294 scenario: baseline 62, 65, 83; reference 62,
65, 67, 70, 77, 81, 83, 84
National Institute for Statistics and Sentinel-2 24
Geography (INEGI) census data 239 service industry 231
natural: capital 13, 15, 124, 136, 277, 280; settlement forms 200
process 205 shapeless landscape 198–9
natural water retention measures smart growth 88, 231
(NWRM) 161 soft mobility 201
nighttime light contamination 251 soil: biodiversity 129, 169–70, 172–5, 268;
nodes of higher hierarchy 248 carbon 129, 133, 181, 183; consumption
NUTS 65, 67–8, 72, 152, 174 19–22, 26, 28–9, 217–19, 222, 229–30,
265; erosion 15, 149, 266; degradation
OECD 70, 88, 103 15–16, 135, 286, 296; inorganic carbon
organic matter decline 15 182, 189–90; organic carbon 182–3,
187; organic matter 126, 173; quality
patches 39, 127–8, 131, 171, 188, 133, 266, 274, 277, 283, 291; sealing 20,
206–8, 222 29, 43, 45, 85, 96, 121, 157–8, 160–4,
Pearl River Delta 231–2, 235 166, 169–72, 175, 181–3, 185, 188, 190,
peri-urban 96, 169, 212, 258; areas 193, 217, 219, 265–71, 274–5, 291–5;
175, 200 security 4
peri-urbanization 248, 254 solar radiation 195–6
periphery 72, 209, 223, 225, 251 soybean 16–17, 148
permeable land cover 198–9 spatial: patterns 80, 176–8; planning 64, 71,
permeable surfaces 271–3 175–6, 268–70; scales 60, 85, 90, 128,
Physiological Equivalent Temperature 132, 169–72, 175
(PET) index 197 stormwater 125, 127–9, 134, 161
PLUREL 93, 100 subsidence 250
 Index 301
subsidies 96–7, 102, 106 130, 132, 146, 148, 156, 153–5, 163–4,
suburban growth 193 166, 181, 192–3, 201, 214, 219, 235,
suburbanization 91, 97, 104, 106, 109 237, 239, 246, 249, 254, 265, 267,
supervised classification 26 294, 296; fragmentation 222, 225–6;
sustainable: architecture 201; urban growth 4–5, 11–12, 14, 29, 31, 34, 60,
drainage systems 160 80, 83–4, 87, 93, 95, 97–8, 101–2, 104,
106, 113, 151, 193, 214, 238, 242, 246,
tax 97–8, 102, 105–6, 279 248–9, 252, 278, 280, 283–4, 288; Heat
technogenic material 182 Island (UHI) 193–4; land expansion
TEEB (The Economy of Ecosystems and 156, 231–2, 235; land use transition 64,
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Biodiversity) 19, 135 235–6, 281; planning 9, 11, 29, 60, 84,
temperature 23, 127–8, 158–9, 170, 193–8, 113–14, 139–40, 144, 155, 180, 192,
200, 271 200–1, 204–5, 213, 230, 285, 287–8,
terrain factors 64 296; policy 83, 213; renewal 270; sprawl
territorial cohesion program 95 3, 7, 45, 67, 71–3, 81, 85–8, 90, 96, 98,
Texcoco lake 248, 250 100–2, 104–12, 150, 181, 193, 204–5,
thermal balance 195 208–12, 209, 222–5, 253, 258–9, 261,
Tijuana 238, 246, 248, 253–4 269, 276; transformation 164, 278,
topsoil 126, 173, 184, 188, 190, 207–8, 280–1; rural gradient 137, 140–1, 144–5,
274, 281 170, 193, 198, 200; rural interaction
tourism 64–5, 92, 244, 251–2 261; remote sensing 42, 55–6
tyranny of small decision 102 urbanisation 33, 50, 85, 87–8, 91–2, 96–8,
100–1, 104–6, 109, 111
urban: climate 193, 195–8, 201; climate
regulation 199; climatic regimes 109, vacant lots 134
196–7; density 87, 222; development virtual water 94
20, 59–61, 67, 81, 102, 108, 125,
157–8, 162, 164, 211, 225–6, 293; waste water 10
dispersion 193, 200, 224; disturbance water contamination 250, 254
125; ecosystems 7, 9, 123–4, 127–9, water scarcity 82, 244
132, 287; expansion 3–5, 7, 9, 11–15, World Meteorological Organization
17, 54, 59, 85–91, 93–5, 98–101, 103–5, (WMO) 196, 199
108–9, 111–13, 115, 117, 123–4, 128, Weighted Urban Proliferation (WUP) 71
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