FOREST COVER CHANGE AND IT’S DRIVING FORCES IN LUME DISTRICT,

CENTRAL ETHIOPIA

M .Sc THESIS

HAILU WONDU JUFARE

HAWASSA UNIVERSITY WONDO GENET COLLEGE OF FORESTRY AND

NATURAL RESOURCE

MAY, 2019
WONDO GENET, ETHIOPIA
 FOREST COVER CHANGE AND IT’S DRIVING FORCES IN LUME DISTRICT,
CENRAL ETHIOPIA

HAILU WONDU

THESIS SUBMITTED TO
HAWASSA UNIVERSITY
DEPARTMENT OF GENERAL FORESTRY
COLLEGE OF WONDO GENET,
SCHOOL OF GRADUATE STUDIES,
HAWASSA UNIVERSITY,
WONDO GENET, ETHIOPIA

IN PARTILAL FULFILLMENT OF THE

REQUIREMENTS FOR THE
DEGREE OF

MASTER OF SCIENCE IN FOREST RESOURCE ASSESSMENT AND MONITORING

MAY, 2019
WONDO GENET, ETHIOPIA
Declaration
I, the undersigned declared that this thesis is my original work, has not presented at any other
university for a degree and all sources of material used for the thesis have been orderly
acknowledged.

Name: Hailu Wondu

Signature: _____________
Date: __________________

This thesis have been submitted for evaluation for my university advisor
Name: Mersha Gebrehiwot (PhD)
Signature: ____________________
Date: ______________________
APPROVA1 SHEET I
This to certify that the thesis entitled “Forest Cover Change and Its Driving Forces in Lume

District, Central Ethiopia” submitted in partial fulfilment of the requirement for the degree of

master’s with specialization in Forest Resource Assessment and Monitoring, the graduate

program of school of General Forestry and has been carried out by Hailu Wondu Jufare .Id.No

MSc/FRAM/011/10, under my supervision. Therefore, I recommend that the student has

fulfilled the requirements and hence hereby can submit the thesis to the department.

Mersha Gebrehiwot (PhD) _____________ ___________________

Name of advisor Signature Date

Zerihun Girma (PhD) _______________ ___________________

Name of Graduate coordinator Signature Date

i
APPROVAL SHEET II

We the undersigned members of the board of examiners of the final open defence by Hailu

Wondu have read and evaluated his thesis entitled “Forest Cover Change and Its Driving Forces

in Lume District, Central Ethiopia” and examined the candidate. Accordingly, this is to certify

that the thesis has been accepted in partial fulfilment of the requirement for the degree of Masters

of Science.

__________________________ ____________________ ____________________

Name of Chairperson Signature Date

________________________ ___________________ ___________________

Name of Advisor Signature Date

________________________ ____________________ ___________________

Name of Internal Examiner Signature Date

________________________ ____________________ __________________

Name of External Examiner Signature Date

_________________________ ___________________ __________________

SGS approval Signature Date

ii
Acknowledgment
First of all my interminable thanks consent for the Omnipotent God for his never-ending love
and kindness.

This thesis has been possible by guidance of many people. My gratitude thanks go to my Major

advisor Dr. Mersha Gebrehiwot for her indispensable support in all stages of my thesis. I

heartedly thanks her for devoting her valuable time in reading, commenting and correcting my

thesis.

I am also thankful for Lume woreda agricultural office for their cooperation in through provision

of secondary data and organizational support for data collection. My special thanks also go to

Development Agents of the study kebeles for their intensively helping me during data collection.

I thank my beloved friends Sangi Olani, Tadesse Leta, Daniel Belay for their endless support,

encourage, and suggestion for my research work.

At last but not the least, I thank all my family for their moral support, endurance and finance

that enabled me to complete my education and this thesis work comfortably. Finally, MRV

project, for providing me financial support for academics and this research work.

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Table of Contents

Acknowledgment .................................................................................................................................... iii

Acronyms ............................................................................................................................................... vii

List of figures ............................................................................................................................................x

List of plates ............................................................................................................................................ xi

Abstracts................................................................................................................................................. xii

1. INTRODUCTION ................................................................................................................................1

1.1 Background .....................................................................................................................................1

1.2. Statement of the problem ...............................................................................................................2

1.3. Objectives:- ....................................................................................................................................4

1.3.1. General objective ....................................................................................................................4

1.3.2. Specific Objectives..................................................................................................................4

1.4. Research Questions ........................................................................................................................4

1.5. Significance of the study ................................................................................................................5

2. LITRATURE RIVIEW ........................................................................................................................6

2.1. Forest cover change in Ethiopia .....................................................................................................6

2.2. Change Detection of forest area .....................................................................................................7

2.3. Accuracy Assessment.....................................................................................................................7

2.3. Drivers of forest cover change .......................................................................................................8

2.4. Application of GIS and RS in Ethiopia ........................................................................................11

3. RESEARCH METHODOLOGY ........................................................................................................13

iv
 3.1. Description of the study area........................................................................................................13

3.1.1. Location ................................................................................................................................13

3.1.2. Climate ..................................................................................................................................15

3.1.3. Demographic Characteristics ................................................................................................15

3.1.4. Agricultural Practice .............................................................................................................15

3.1.4. Vegetation cover ...................................................................................................................16

3.2.1. Material and Method .............................................................................................................16

I. Method for GIS and Remote Sensing ..............................................................................................16

3.2.1. Satellite Data Acquisition .....................................................................................................16

3.2.2. Image Pre-processing and Classification ..............................................................................18

2.4. Forest cover change detection ......................................................................................................19

3.2.3. Change detection analysis .....................................................................................................19

3.2.4. Determination of magnitude of change .................................................................................20

3.2. 5. Accuracy Assessment...........................................................................................................21

II. Method for Social Survey ...............................................................................................................22

3.2.6. Sampling design and data collection techniques ...................................................................22

3.2.7. Sample size determination ....................................................................................................24

3.2.8. Data Analysis ........................................................................................................................27

4. RESULTS AND DISCUSSION .........................................................................................................29

4.1. Investigating the magnitude and rate of forest cover change (1985-2018) ..................................29

4.2. Examining forest Cover trends and management system in the district ......................................46

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 (1985-2108).........................................................................................................................................46

4.3. Accuracy assessment for classified images of 1985-2018 ...........................................................50

4.4. Major Driving Forces of Forest cover change in the study district ..............................................54

4.4.1. Socio-economic characteristics of study population .............................................................54

4.4.2. Proximate Drivers .................................................................................................................55

4.4.3. Underlying Drivers................................................................................................................59

5. CONCLUSION AND RECOMMENDATION ..................................................................................63

5.1. CONCLUSION ............................................................................................................................63

5.2. RECOMMENDATION ...............................................................................................................64

REFERENCES........................................................................................................................................67

APPENDICES ........................................................................................................................................76

Annex 1: Questionaries’ on drivers of forest cover change and local community’s perception ........76

A. Personal information and questions for House Holds ................................................................76

B. Checklists for Key Informants....................................................................................................78

C. Checklist for Focus Group Discussion (FGDS) .........................................................................79

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Acronyms

AOI Area Of Interest

CSA Central Statistical Agency
DAs Development Agents
DN Digital Numbers
EMA Ethiopian Map Agency
ENVI Environment visualizing Image
FAO Food and Agriculture Organization
FDRE Federal Democratic Republic of Ethiopia
FGDs Focus Group discussions
GDP Gross Domestic Production
GFRA Global Forest Resource Assessment
GIS Geographic Information Science
GLS Global Land Survey
GPS Global Positioning System
HHS House Hold Survey
IHDP/IGBP International Human Dimension Programme/ International
Geosphere-Biosphere Programme
IPCC Intergovernmental Panel on Climate Change
KIIS Key Informant Interviews
L1TP The Standard Terrain Correction Landsat Level 1 Product
LULC Land Use/Land Cover
MOFED Ministry Of Finance and Economic Development
MSS Multi-Spectral Scanner
NASA National Aeronautics and Space Administration
OFWE Oromia Forest and Wildlife Enterprise
OLI Operational Land Imager
PFM Participatory Forest Management
PRA Participatory Rural Appraisal

vii
QGIS Quantum Geographic Information Science
ROI Region Of Interest
RS Remote Sensing
SCP Semi-automatic Classification Plugin
TIRS Thermal Infrared Scanner
TM Thematic Mapper
U.N United Nation
USGS United State Global Survey
WAO Woreda Agricultural Office
WFEDO Woreda Finance and Economic Development

viii
List of tables

Table 1: Type and Characteristics of Satellite images--------------------------------------------17

Table 2: Data source and data type for GIS and Remote Sensing-------------------------------18

Table 3: Description of land use/land cover types-------------------------------------------------19

Table 4: Social survey sampling method-----------------------------------------------------------27

Table 5: Categories and patterns of Land Use/Land Cover in the study area-----------------30

Table 6: Magnitude of Land Use /Land Cover change-------------------------------------------33

Table 7: LULC conversion matrix of 1985-19999------------------------------------------------36

Table 8: LULC conversion matrix of 1999-2013-------------------------------------------------36

Table 9: LULC conversion matrix of 2013-2018------------------------------------------------37

Table 10: Rate of change for Forest and Non Forest (1985-2018)------------------------------38

Table 11: Patterns of Forest and Non Forest cover in the district (1985-2018----------------40

Table 12: Magnitude of Forest and Non-Forest cover change (1985-2018)-------------------42

Table 13: Rate of change for Forest and Non-Forest (1985-2018)------------------------------45

Table 14:Investigated forest management system in the district----------------------------------50

Table 15: Confusion matrix for LULC of 1985-----------------------------------------------------51

Table 16: Confusion matrix for LULC of 1999----------------------------------------------------52

Table 17: Confusion matrix for LULC of 2013-----------------------------------------------------52

Table 18: Confusion matrix for LULC of 2018------------------------------------------------------53

Table 19: Socio-economic characteristics of sampled population---------------------------------55

ix
List of figures
Figure 1:This diagram shows the direct and underlying causes of forest decline------------10
Figure 2:Map of Lume District----------------------------------------------------------------------13

Figure 3:Flow-chart for the general methodology------------------------------------------------25

Figure 4: LULC map of Lume district in 1985----------------------------------------------------30

Figure 5: LULC map of Lume district in 1999----------------------------------------------------30

Figure 6: LULC map of Lume district in 2013----------------------------------------------------31

Figure 7: LULC map of Lume district in 2018----------------------------------------------------31

Figure 8: LULC change of Lume district (1985-2018)-------------------------------------------32

Figure 9: forest and Non forest cover map of Lume district in 1985---------------------------36

Figure 10: forest and Non forest cover map of Lume district in 1999-------------------------36

Figure 11: forest and Non forest cover map of Lume district in 2013-------------------------36

Figure 12: forest and Non forest cover map of Lume district in 2018-------------------------37

Figure 13: Changed map of Lume district between 1985 and 1999-----------------------------38

Figure 14: Changed map of Lume district between 1999 and 2013-----------------------------39

Figure 15: Changed map of Lume district between 2013 and 2018-----------------------------39

Figure 16: : Magnitude of Forest cover change in Lume district (1985-2018).---------------40

Figure 17: Rate of Forest cover change in Lume district (1985-2018)--------------------------41

Figure 18 LULC trends in Lume district (1985-2018)--------------------------------------------43

Figure 19: Forest and Non Forest cover trends in Lume district (1985-2018)-----------------44

Figure 20: Major direct drivers of LULC in Lume district----------------------------------------50

Figure 21: Population growth trends of Lume district 1998-2013--------------------------------52

Figure 22: Major indirect drivers of LULC in Lume district---------------------------------------53

Figure 23: Summary of proximate and underlying drivers-----------------------------------------54

x
List of plates

Plate 1: Field Works. ---------------------------------------------------------------------------------24

Plate 2: The spatial interrelation of urban, agriculture land, shrub land and Forests in Lume
district----------------------------------------------------------------------------------------------------51

xi
Abstracts
Forest cover change and forest degradation is a serious global problem that affects the socio-
economic and ecological function of forest landscapes in the Globe. Lume district in Ethiopia
is one of the forested area that has important socio economic and ecological function. Currently,
despite their contribution to both economic and ecological service forests of Lume district are
under serious streak both from anthropogenic and natural calamities. Hence, the fundamental
aim of this study is to investigate the magnitude and rate of forest cover change, identifying the
respective driving forces for the last 33 years (1985-2018). Quantitative data was collected
using Landsat5 TM and Landsat8 OLI_TIRS satellite image; these data were used to define the
spatial and temporal change-using quantum GIS (QGIS). Qualitative data were collected using
key informant interviews, household surveys and focus group discussion for determining the
driving forces of the change. SCP, QGIS 2.18.2, MOLUSCE, EXCEL and R software were used
for processing and analysing data obtained from RS and social survey respectively. The finding
of the study revealed that, during 33 years period agriculture land and urban
buildings/settlements increased by 7828ha (10.82%) and 15471.92ha (21.39%) respectively
with equivalent area of 3887.85ha (5.37%) and 17502.55ha (24.2%) decline in forests and
shrub land. Throughout the study periods, steady net increasing rate of expansions observed for
urban buildings/settlements and agriculture land by 468.8ha and 237.27ha per annual. In
contrary, a net decline rate noted for shrub lands and forests by 530.38ha and 117.8ha per year.
The main findings of this study disclosed that, a resume increase in agriculture land and urban
buildings and settlements at the expense of forests and shrub lands throughout investigated
periods (1985-2018). The major proximate and underlying drivers of forest cover change
identified through HHS and FGDS are agricultural land expansion, fuelwood extraction,
charcoal production, urban expansion, expansion of rural settlements, extended dry period,
infrastructural development, high rate of population growth, landlessness, low institutional
enforcement and others. Hence, in order to revoke the problem of forest cover change and its
impact, proper measures had been forwarded which can be implemented both in the long and
short-term commitment of concerned stakeholders in the district and national level.

Key words: Accuracy, Land use/Land cover change, Magnitude, Rate, Trends

xii
1. INTRODUCTION

1.1 Background

Forests make up one of the world’s most important precious natural resource and play a crucial

role in global ecological balance (Torahi, 2013). The forests of the world cover about 4 billion

hectares (FAO, 2010). They are vital for the conservation of ecosystem, maintenance of water

quality, prevention and reduction of natural hazards such as floods, erosion, landslides, avalanches,

and drought and hence in regulating the climate on the regional level (Rashid and Iqbal, 2018).

Forests provide support for one billion people that live in far beyond the norm poverty around the

world, and provide emolumentive employment to more than one hundred million as Violini (2013)

cited in (FAO,2011).

In Africa forests are very crucial for protecting water catchments and for enhancing conservation;

for regulating rainfall; for preventing landslides and are an in important of biodiversity pool (FAO,

2011). The most important use of forest resources from the viewpoint of the population in Africa

is as an energy source. Wood fuel is used by over 60 percent of the population for cooking and

generates 29 times as many jobs as the forestry/wood products sector (Rametsteiner and

Whiteman, 2014). Employment numbers and the importance of the informal forest economy of

both the Sub‐ Saharan Africa region and the North Africa put up to 0.1% to the overall career. The

highest contribution in any region to share of GDP is found in sub-Saharan Africa, which accounts

for 1.8 % (Agrawal et al., 2013).

According to Global Forest Resources Assessment (GFRA) Ethiopia’s forest cover is 12.4 million

hectares (11.5 percent) (FAO, 2015). The importance of natural forest ecosystems to human Well-

being cannot be exaggerate (Bamlak Ayenew and Yemiru Tesfaye, 2015). Forest-based ecosystem

1
services are directly available as products derived from and within forests and those that indirectly

support other production landscapes. Forest fragments in southwest Ethiopia have higher

ecosystem service richness where 85% of all forest-based ecosystem services described by local

people were found in the landscape ( Getachew Tadesse et al., 2014). According to (Sisay Nune

Hailemariam et al., 2012) cited in MOFED (1995–2005) the forestry sector contributed on average

5.7% of the total GDP in Ethiopia over the years 1995–2005.

1.2. Statement of the problem

Despite their significant pertaining to both economic and ecological services, forests are currently

under serious threats both from anthropogenic and natural destructives (Worku Zewdie and

Csaplovies, 2017). For several centuries the world‘s forests have been under streak due to

alarmingly increase human population. These activities have resulted in loss of biodiversity,

degradation of water catchments and increase in greenhouse gases, which have far-reaching

effects(Wachiye et al., 2013). Total area of 4128 million ha has covered by forests in the 1990

and by the end of 2015; this has reduced and recorded to 3999 million ha. There is a globally

decline in forest cover from 31.6% to 30.6% (Rashid and Iqbal, 2018).

Deforestation is most noticeable in tropical regions such as Africa. Africa accounted for a net loss

of 4.0 million hectares per year (Kero Alemu et al., 2018). Deforestation is very serious issue in

developing countries. It has been occurring at rapid rates, primarily to clear land for agriculture

and for production of fuel wood for domestic use. Highly concentrated agriculture and immoderate

tree felling for the use of energy lead to a serious deforestation problem among the most of African

countries (Yasar Arfat, 2010). FAO (2009) indicates that Africa’s forests cover is about 21.4% of

2
the total land area. In East Africa, forests and woodlands thus making these resources quite limited

and threatened cover around 13% of the land mass.

Ethiopia is part of the dynamic land cover change where more than 90% of the country’s highlands

once forested, and currently the percentage of forest cover is less than 12% (FAO, 2015). In

Ethiopia, several studies had carried out to estimate forest cover change. The country has suffered

drastic historical deforestation, primarily due to agricultural expansion coupled with population

growth (Bongers and Tennigkeit, 2010). Due to massive exploitation, the forest resource of the

country has marginalized itself to small remnants on the highlands particularly, almost all located

at unreachable areas. However in relation to the available information of forest cover in the

country, there is still no adequate documented information on the location, extent of the remaining

forest cover of the country and the rate at which this resource is expended.

In the study area, Lume district, there is high forest cover change due to agricultural expansion,

energy production (fuel wood and charcoal), settlement, the establishment of infrastructure such

as road; industries and urbanization also contribute for forest cover change. Furthermore, like in

many other parts of the country, the problem of forest cover change is a very serious environmental

problem such as deficit precipitation, extreme temperature, flooding and unseasonal rainfall. In the

study area, forests and far-reaching areas of forest cover including shrub lands have been

deforested.

However, the rate and an actual extent of the forest cover change has not well studied to date.

Thus, for a sustainable forest resource management and reduce deforestation it is necessary to

estimate forest cover change on large spatial and temporal scales. In addition, it is crucial to assess

3
and monitor the trends of forest cover change and the drivers of the change in the district to

acquaint consistent and sustainable forest conservation strategies.

1.3. Objectives:-

1.3.1. General objective

✓ To investigate the long-term spatiotemporal forest cover change and its driving forces in
Lume district, Central Ethiopia (1985-2018)

1.3.2. Specific Objectives

✓ To investigate the magnitude and rate of forest cover change within each periods

✓ To examine forest cover trends and management system in the district

✓ To Identify the driving forces of forest cover change in the district

1.4. Research Questions

1. How much forest area gain and/or lose coincidence in the study area?
2. What is the rate, extent and magnitude of forest cover change within specified period in the

district?

3. What type of patterns and management system experienced in the study area?

4. What are the major driving forces of forest cover change in the district?

4
1.5. Significance of the study

The study will be stress how forest cover change mapping and explicitly identification of drivers

is indispensable for decision-making and for forest resource management. In developing countries,

deforestation is very serious issue. It has been occurring at rapid rates, primarily to clear land for

agriculture and production of fuel wood for domestic use and interaction of proximate and

underlying drivers that accelerate the dynamics of land use/land cover change.

Therefore, the study will be initiate to overcome these problems and to bring a hint for the forestry

sectors and planners about forestland cover data that are important for sustainable natural resource

management and standards to maintain the current and future land use/land cover managements.

On the other hand, information on forestland cover change and other land use in the form of maps

and statistical data is very crucial for special planning, management and utilization of land for

agriculture, forestry, pasture, urban-industrial, environmental studies etc. The study further

contributes to scientific knowledge related to extracting information from remotely sensed data

and stipulates perspective analysis techniques to fully exploit these data for better forest resource

monitoring. It is expected that the results of this study will be of ultimately crucial for policy

makers and natural resource managers in the district.

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2. LITRATURE RIVIEW

2.1. Forest cover change in Ethiopia

Forest cover changes is actively moving, pervasive and accelerating process, mainly passionate by

natural phenomena and anthropogenic activities, which in turn drives changes that would strongly

influence natural ecosystem (Melaku Melese, 2016). In recent year, conservation of biodiversity

and management of tropical forest have become a major issue in developing countries. According

to data provided by the U.N. Food and Agricultural Organization, approximately 4,168 million

hectares of the earth’s terrestrial surface was covered by woodlands and forest cover in the 1990s

(Deb and Mishra, 2016)

However, continued access to forest resources is increasingly become challenged through

deforestation and forest degradation (Wachiye et. al., 2013). Despite their crucial importance in

livelihood and climate regulation, forest resources all over the globe are subject to enormous

pressure resulting in deforestation and degradation because of the increase in human and cattle

population and extensive rural poverty (Negasi Solomon et. al., 2018).

According to FAO (2015) Ethiopia’s forest, cover (FAO definition) is 12.4 million ha (11.5%),

clearly underestimated compared to the IPCC definition of 17.2 million ha (MEFCC, 2017). Forest

cover indicate a decline from 15.11million ha in 1990 to 12.4 million ha in 2015, during which

2.65% of the forest cover was removed (Moges et.al, 2010). FAO’s appraises and the findings of

the individual studies, predict the forest and woodland cover change in Ethiopia indicate that the

average annual rate of deforestation is greater than 0.25% (Hansen, et. al., 2010).

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2.2. Change Detection of forest area

Change detection is the process of identifying departure in the state of an object or phenomenon

by observing it at different times. Remote sensing based change detection pertain comparison of a

set of temporal images covering period of interest using specific change detection algorithms

(Abiyot Yismaw et al., 2014). According to the IHDP/IGBP report, digital change detection

studies make an effort to assess the information about the processes of forest cover change, their

tessellation and human interactions to forest cover change (Deb and Mishra, 2016).

Digital change detection fundamentally consist of the quantification of temporal phenomena from

multidate imagery that most commonly secured by satellite-based multispectral sensors (Running

and Bauer, 1996). Change detection analysis stipulate a thematic view to understand the natural

and artificial behaviour of changes in land (Sommer et al., 2011). Information on land and land

cover change in the form of maps and statistical data is very vital for special planning, management

and utilization of land for agriculture, forestry, pasture, urban-industrial, environmental studies,

economic production (Abineh Tilahun and Bogale Teferie, 2015).

2.3. Accuracy Assessment

The accuracy assessment is a comparison of a classification with ROI or ground-truth data to

evaluate how well the classification represents the real world (Lillesand, 2004). Accuracy

assessment has vital role in remote sensing studies dealing with image processing and change

detection processes. It is very important for the analysis of results as well as decision-making

7
The accuracy assessment has done by a confusion matrix that delivers the relationship between the

samples taken as reference data and the corresponding samples on classified image. The accuracy

assessment consists of overall accuracy, producer’s accuracy, user’s accuracy and kappa

coefficients (Fichera et al., 2012).

The overall accuracy is the ratio of total number of correctly classified samples and total number

of samples. The ratio of total correct samples in a class and the total number of reference samples

in that class is name as producer’s accuracy, which shows the way reference samples of the ground

are classified. Whereas, the user’s accuracy is define as the ratio of number of correctly classified

sample to the total number of sample classified in that class. User’s accuracy signifies the

probability of a pixel to be classify in a particular category representing on ground (Russell and

Plourde, 2001). One more accuracy assessment parameter is kappa coefficient that is the basis for

statistical significance of a confusion matrix in any classification (Munoz and Bangdiwala, 1997).

2.3. Drivers of forest cover change

A broad range of factors such as agricultural expansion, insecure land tenure, international
markets, colonization, infrastructure and road building, urbanization, mining, grazing,
uncontrolled fire, political unrest, fuelwood extraction, and timber logging influences
deforestation (Ferretti-Gallon and Busch, 2014). Deforestation is the major source of forest cover
change in the tropics including Ethiopia and this is due to several factors (Rahman and Sumantyo,
2010).

The main causes of deforestation in Ethiopia are the rapid population growth, underslung

agricultural productivity, the impoverished economic performance of the country, shifting

agriculture, livestock production and fuel in drier areas (Tigabu Dinkayoh, 2016). Two types of

drivers: proximate (direct drivers) and underlying drivers.

8
Proximate (direct drivers): of deforestation and forest degradation are human activities

and actions that straightly affect forest cover and result in ruin of carbon stocks. Agriculture is

account to be the proximate driver for around 80% of deforestation worldwide (Kissinger et al.,

2012). Direct deforestation drivers in Ethiopia are expansion of smallholder traditional agriculture

following population growth in forest areas, expansion of enormous-scale development activities,

population growth, wood extraction and other forest products collection and forest fires (Melaku

Melese et al., 2015).

Underlying (indirect drivers): Underlying drivers include range of political, cultural and socio-

economic factors, including unsound policies, weak governance and lack of law enforcement,

landlessness and unclear allocation of rights, rural poverty, lack of investment and financial

resources, population growth and migration, and civil conflict (Arevalo, 2016). According to

Ashebir Mengistu (2018) cited in (Lambent et al., 2003; Lambin and Geist, 2003) in Ethiopia, the

underlying drivers are characterized by a complex social, political, economic, demographic,

technological, cultural and biophysical variables that are considered to be vital forces underpinning

the proximate causes function at a much broader scale.

As Mersha Gebrehiwot (2013) cited in (Labin and Geist, 2006a; Geist and Labinb, 2002) in

Ethiopia, both proximate and underlying drivers of change often comprise more fold factors and

drivers. Those are deed jointly rather than single-factor causation, as most of the world’s tropical

deforestation has influenced by an interaction of economic, institutional, technological, cultural

and demographic variables where economic factors are prominent.

9
Figure 1: This diagram shows the direct and underlying causes of forest decline.

Adapted from Contreras-Hermosilla (2000), Underlying causes, CIFOR, p. 5

10
2.4. Application of GIS and RS in Ethiopia

Remote sensing refers to acquiring information about objects or areas by using electromagnetic
radiation (light) without being in direct contact with the object or area (De Jong, 2004). According
to Awange and Kiema (2013) cited in ( Trigal, 2015) a GIS is a set of tools made up of hardware,
software, data and users, which allows us to capture, store, manage and analyse digital information,
as well as make graphs and maps, and represent alpha numeric data.

For the past few decades the application of remote sensing (RS) not only completely changed the

way data has been collected but also notably enhanced the quality and accessibility of important

spatial information for natural resources management and conservation. The quick approval of the

use of remote sensing for conservation and nature protection corresponds with the frequent

reporting of wide spread adaptation of natural systems and destruction of wildlife habitats during

the past three to four decades (Bedru Muzein, 2006). The parallel advance in the trustworthy of

Geographic Information System (GIS) has allowed the processing of the large quantity of data

produced through remote sensing (Lunetta, 1999). In Ethiopia, recent GIS applications have

included site preference for village schools, oil and gas discovery in the Ogaden desert, agriculture

and forestry development, research activities in poverty reduction, drought management and

irrigation suitability studies (Kebede Ganole, 2010).

Recent advances in geographic information system (GIS) and remote sensing (RS) instruments

and techniques allow researchers to essentially model urban growth. Satellite Remote Sensing

images provide excellent data sources from which thorough information about land use and land

cover can be efficiently extracted, analysed and predicted (Afera Halefom et al., 2018). Take into

account the importance of remote sensing and geographic information system (GIS) in evaluating

the changes in landscape cover, this technique is use for the present study. Remote sensing

provides a relevant means of detecting and analysing temporal changes. Since early 1970s, satellite

11
data have been ordinarily use for detecting these changes over large landscapes (Mary Tahir et al.,

2013).

According to Mary Tahir et al. (2013), cited in (Mohan, 2005 and Jaiswal et al., 1999) the

development of spatial data infrastructure is a vital to sustainable land development. Whereas

Information on existing LULC, its spatial distribution and change are important prerequisite for

planning. Remote Sensing and GIS technologies now stipulated the potential for mapping and

monitoring the spatial extent of the built environment and the related urban land use changes in

Ethiopia.

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3. RESEARCH METHODOLOGY
3.1. Description of the study area
3.1.1. Location
Lume is one of the district in the Oromia Region of Ethiopia. Part of the East Shoa Zone located

in the Great Rift Valley. Lume is border on the south by the Koka Reservoir, on the west by Ada'a

Chukala, on the northwest by Gimbichu, on the north by the Minjar District, and on the east by

Adama. Mojo is the capital of the district; which is located 70 kms Southeast of Addis Ababa.,

other towns include Ejere, Ejersa and Koka. Due to the geographical proximity of Mojo to Addis

Ababa, it has a great advantage for market access for both agricultural and industrial products

(Kassahun Melese et al., 2014). The district is found between the coordinate of the following figure

(2) with an altitude ranged from 1500 to 2300 meters above sea level (Tesfaye Moreda, 2016)

13
Figure 2: Map of Lume district

14
3.1.2. Climate
Agro- ecologically, the study district is classified as Moist Woina Dega (30%), Woina Dega (45%)

and Kola (25 %). Annual temperature and rainfall varies between120C to 280C and 500mm to

1200mm, respectively (Lume district agricultural office report, 2015/16). Soil is defined as a

natural body consisting of layers or horizons of mineral and/or organic constituents of variable

thickness, which differ from the parent material in their morphological, physical, and chemical

properties and their biological characteristics (Davidson, 1980). According to FAO soil

classification, the soils of Lume District were grouped into seven soil types which is mainly

dominated by Eutric Vertisol (44.84%), Mollic Andosols (21.69%) and Luvic Phaeozems

(14.76%) (Ahmed Kamil and Bekele Ayalew, 2017).

3.1.3. Demographic Characteristics

According to Lume district Finance and Economic Development Office 2015/16 report, the district

cover 75,220.32 ha of land, the total cultivated land of the district is 43,713 ha, for livestock

grazing 361.08 ha, for irrigation 6,497 ha, for forest 2,462.38 ha and unproductive land was

22,186.86 ha. The district total population 142,288 of which 72,973 (51%) male and 69,315 (49%)

female and urban human population was 72,105 of which 31,570 male and 40,535 female

respectively (CSA, 2013).

3.1.4. Agricultural Practice

Livestock is considered as an important component of the prevailing crop-livestock mixed farming

systems of the study district. Smallholder farmers of the study area owned various livestock species

such as; cattle, sheep, goat, chicken and equines. According to report of Agricultural office of

Lume district (2015), the study district is reported to have a total population of 33,797 for cattle,

which (33, 148 local and 649 exotic cattle), 10,953 for sheep and goat, 12,699 for equine, 31, 984

15
for chicken, which (26,852 local and 5132 cross and exotic breed chicken). Vegetables are an

important cash crop. Koka Lake is the major lake, which gives economic importance in the district.

It is mainly use during the dry season for the production of horticultural crops, mainly vegetables

(WFEDO, 2015/16). The main crops cultivated in the Lume district are tef, wheat, chickpea,

barley, faba beans, lentil, pea, maize and bean.

3.1.4. Vegetation cover

Major types of natural vegetation and manmade found in the district are forest, shrubs & bushes.

Natural vegetation combines Acacia woodland and savannah. Across the district, grain crop and

livestock farming are dominant, whereas in areas adjacent to the Rift Valley Lake (Koka) and river

(Mojo river), irrigated vegetable farming and horticulture are practiced. Within the grain–livestock

areas, the combination of crop and trees shows some variation: teff–wheat with Faidherbia albida

to maize–beans–sorghum with Acacia tortilis across the north south transect. Moreover, teff–

wheat with F. albida to teff–maize–sorghum and with A. tortilis across the west– east transect.

Mountainous parts of the district mostly covered with shrub species and acacia species. The

highland part of the district more commonly covered with Eucalyptus plantation forest. The shrubs,

bushes, woodlot (around settlements and towns), natural forest and plantation forest together cover

25.7% of the total land use of the district.

3.2.1. Material and Method

I. Method for GIS and Remote Sensing

3.2.1. Satellite Data Acquisition

Land use/land cover change analysis for the last 33 years were done with the help of Landsat multi-

spectral data (i.e. years 1985, 1999, 2013 and 2018). The images have had downloaded from

16
NASA Landsat series distributed by United States Geological Survey (USGS) with the required

specifications: satellite type, acquisition date, path/row, spatial resolution, cloud/scene cover and

others. To ensure complete coverage of the study area and obtain precise forest cover change, four-

(4) cloud and scene free Landsat L1TP image were acquired for 1985, 1999, 2013 and 2018

periods.

Table1: Type and Characteristics of Satellite images

Sensors Resolution Path Row Acquisition Source Periods

date 1985 1999 2013 2018
Landsat5 TM 30m*30m 168 054 1985/01/02 USGS ✓

Landsat5 TM 30m*30m 168 054 1999/01/25 USGS ✓

Landsat8 30m*30m 168 054 2013/12/01 USGS ✓

OLI_TIRS
Landsat8 30m*30m 168 054 2018/02/01 USGS ✓
OLI_TIRS
Source: Researcher

Standard Terrain Correction (L1TP) is Landsat Level-1 data products, which is radio metrically,

calibrated and orthorectified using ground control points and digital elevation model (DEM) data

to correct for relief displacement. These are the highest quality level-1 products suitable for pixel

level, time series analysis. Dry period had selected for the acquisition of satellite image to obtain

cloud free image and to distinguish the spectral reflectance between forest and seasonal

agricultural crops.

Landsat5 TM and Landsat8 OLI_TIRS multispectral satellite data were used for change detection

of two consecutive periods. Landat7 ETM+ was not used for satellite data since it was full of strip

and blurred to distinguish spectral value of different landscapes. The time interval between the
17
first two periods is 14 years and 5 years for the last period since there is no Landsat5 and Landsat8

image from 2000-2010. Change detection was starts from 1985 and ends in 2018 to compare the

forest coverage between the dergue regime and the current government at institutional level and

policy.

Table2: Quantitative data collection

No. Data Type Data Source

1 Primary data
Training and validation points Ground control points using GPS
Differentiate homogenous sample of landscapes Field observation
2 Secondary data
Satellite images USGS
Shape files Internet, CSA
Aerial photo Google Earth
Base map Google Satellite
Source: Researcher

3.2.2. Image Pre-processing and Classification

The image pre-processing tasks were performed in Semi-Automatic Classification plugin for the

area of interest (AOI) using Landsat satellite images. Image processing involves manipulation and

interpretation of digital images. The spatial resolution of images were enhanced using resolution

merge technique that integrates images of different spatial resolution or pixels. Radiometric

enhancement, however, improve the area of image classification by addressing stripping and

banding errors that occur when the detector goes out of adjustment. Each image was assigned to

be classified into 7-9 land classes based on specific Digital Number (DN) values or spectral

reflectance of different landscape elements.

Maximum likelihood algorithm employed for supervised classification of images. The spectral

signature of each class was obtained from the raster images. This has done through selection of

18
ROI for each of the LULC category. The ROI helps in producing the map by defining an area in

the map based on the colour assign to that category and the spectral homogeneity of the pixels of

chosen area. This classification had yield a good result after subjecting the classified maps to a

confusion matrix. The change analysis was carried out by using MOLUSCE software in QGIS.

Table3: Description of land use/land cover types

Id Land Use type Description
1 Forest land Land spanning more than 0.5 hectares with trees higher than 5 meters
and a canopy cover of more than 10 percent.

2 Shrub land A shrub land is a specific type of ecosystem and Land with
shrubs/bushes canopy cover ≤ 10%
3 Lake A lake is a body of water that is surrounded by land/and or A lake is a
very slow flowing body of open water which occupies a land depression
4 Settlement area Refers to the physical spaces and environments in which households are
sheltered, and how one shelter relates to others.
5 Woodlot Is any track of land, regardless of shape or size that supports naturally
occurring or planted trees
6 Bare land Areas with little or no “green” vegetation present due to erosion,
overgrazing and crop cultivation.
7 Agricultural land Land allotted for crop cultivation both annual and perennial crops
Sources: (Danilo et al., 2014), (Mittermeier et al., 2018) and (Hannon and Cotterill, 1998)

2.4. Forest cover change detection

3.2.3. Change detection analysis

In this process, images of every year had been classified and labelled separately. After

classification, ground verification were done in order to check the precision of the classified

LU/LC (forest cover) map. Based on the ground verification necessary correction and adjustments

was made. Following classification and verification reclassification was proceed to classify land

19
cover categories into forest and non-forestland cover. The reclassified images were then compare

to determine the change that has taken place between the two images using a change matrix. This

enable the changed areas to be extracted and by how much through the computation of change

maps and change matrix statistics. With this information, it was easy to quantify and explain the

change LULC as well as in the forest and non-forestland cover.

The map from t1 (e.g., 1985) was compared with the map produced at time t2 (1999) as well as the

map at t2 was compared with the map produced at t3 (2013), whereas the map at t3 was compared

with the map t4 (2018) and a complete matrix of categorical change was obtained.

3.2.4. Determination of magnitude of change

The magnitude of change is a degree of expansion or reduction in the LULC size. A negative

value showed a decrease in LULC size while a positive value indicated an increase in the size of

LULC class (Mahmud and Achide, 2012).

• The magnitude of change (K) is calculated by the simple equation:

𝐾 = 𝑄2 − 𝑄1 … … … … … … … … … … … … … … … … … (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛1)

• The percentage of change (A) is calculated by the formula:

𝑄2−𝑄1
𝐴= × 100% … … … … … … … … … … … … … … … … (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛2)
𝑄1

• The rate of change (r ) is estimated by:

𝑄2−𝑄1
r= ………………………………………………………………………… (Equation3)
𝑡

Where, r= Rate of Change

Q2= Recent year forest cover in ha

Q1= Initial Year forest cover in ha and

20
 t= Interval year between Initial year and Recent year

K = magnitude of change

A = percentage of change

3.2. 5. Accuracy Assessment

For accuracy assessment, maximum number of validation point were randomly distributed on

both supervised classification image and high-resolution image such as Google Earth, which show

the ground truth of each land classes clearly. In addition, 372-ground control points were collected

153 from agriculture, 121 from urban and settlements, 27 from woodlot, 38 from bare land, 22

from shrubland and 16 from forestland were collected based on proportional area of each LU/LC

using GPS for validation. (Lillesand et al., 2004). Then, each points were coded and assigned for

the land uses on supervised classification and inter into accuracy software to produce confusion

matrix.

The accuracy assessment has done by a confusion matrix that delivers the relationship between the

samples taken as reference data and the corresponding samples on classified image. A minimum

of 85 percent accurate classification at the 95 percent confidence level was recommended for

research (Stan Aronoff, 1982). In this study, all the accuracy assessment parameters (i.e. overall

accuracy, producer’s accuracy and user’s accuracy) have determined for the classification images

of year 1985, 1999, 2013 and 2018 respectively. More complete measure of the classification

accuracy is Kappa coefficient, also known as Kappa hat or K-hat. Minimum value of kappa hat

between 0.61and 0.80 was recommended which is substantial agreement (Tymków, 2009)

According to (Neiser et al., 2013) accuracies were calculated as:

𝐶𝑖𝑖 𝑚
• User’s accuracy= where, (Nri) = ∑𝑗=1 𝐶𝑖𝑗……………………………..1
𝑁𝑟𝑖

21
 𝐶𝑖𝑖 𝑚
• Producer’s accuracy= where, (Ncj) =∑𝑖=1 𝐶𝑖𝑗 …………………………2
𝑁𝑐𝑖

1
• Overall accuracy = ∑𝑚
𝑖=1 𝐶𝑖𝑖 where, N=∑ 𝐶𝑖𝑖 ………………………………..3
𝑁

N ∑m m
i=1 Cii- ∑i=1 Nri.Nci …………………4
• Kappa coefficient (K-hat) = 𝑁𝟐−∑𝑚 𝑖=1 𝑁𝑟𝑖.𝑁𝑐𝑖

Where, N- total number of pixels in the classification image (N)

i- The sum of any row of the confusion matrix gives the total number of pixels

j- The sum of any column of the confusion matrix gives the total number of pixels

Cii/Nri- is the %age of correct classification of class i, based ground truth

Cii/Nci- is the %age of correct classification of class j, based on ground truth

Cii- The major diagonal elements or correctly classified pixels

K- Kappa coefficient

II. Method for Social Survey

3.2.6. Sampling design and data collection techniques

Both purposive and random sampling techniques were employed. Three (3) representatives

kebeles in the study area were selected depend on their potential of forest coverage, proximity,

accessibility and agro ecological conditions (Dega, woinadega and kola) in relatives to others

kebeles in the district. Hence, Kara Fincawa, Nanawa and Kolba Gode were selected purposefully.

Here proportionality was not a concern since the target is to capture areas with specific criterion.

In other ways three (3) data collection techniques were employed namely key informants (KIS),

Focus Group Discussions (FDGs) and household survey (HHS).

Key informants ( elders, and government bodies (kebele leaders)) were selected purposefully,

since it was expected that they have knowledge, experience, profession and background in
22
management system, drivers of forest cover change, forest cover trends and current situation of

the forest relatively. Eighty (18) key informants were selected purposefully, six (6) individuals

from each kebeles.

For household survey, open ended and close-ended questions had been asked to avoid, restricting

the participants and give respondents control over what they wish to say and how they wish to say

it. The interviews involve sex, age, socioeconomic, major forest cover change driving force,

management, status and trends of the forest cover. In total, 384 households were randomly selected

using Participatory Rural Appraisal (PRA) tools, where, Participatory rural appraisal (PRA) is a

set of participatory and largely visual techniques for assessing group and community resources,

identifying and prioritizing problems and appraising strategies for solving them. Random sample,

imply that all members of the community have equal chance of being involved to avoid bias in

favour of specific groups. Additional members (people reserved in contingency) were interviewed

when the initial sampled members were not available.

In addition to household survey and key informants, the researcher made six (6) group discussions

with PFM cooperatives, households and development agents and kebele leaders. This focus group

were selected purposefully based on their experience and knowledge of the existing LUCL types,

the major driving forces of forest cover change and identifying mostly increasing/decreasing land

use/land cover type. According to many authors such as (Pripathy and Pripathy, 2017; Robinson,

1999 and Jakasekara, 2012) based on the variability of sample population and interest of the

researcher the size of participants in FGD ranges 8 to 12. Hence, each group was comprises of 8

to 12 individuals and the topics for the discussion were related to their perception of forest cover

change and drivers of forest cover change.

23
3.2.7. Sample size determination

The study were conducted in three (3) kebeles, which had selected purposefully. In order to collect

the data (qualitative & quantitative) close ended and open-ended interviews were administered to

the sample population and the number of households sampled from each kebeles were determined.

The following assumptions had made to determine the minimum sample size for the study.

-Estimation of population percentages or proportions were 50%, as this was result in the
maximization of variance and produce the maximum sample size.

-A marginal error of 5% (SE) will take assuring a 95% level of confidence (Z). Accordingly,
the following formula given by (Taherdoost, 2017) was used to determine the sample size.

o The sample (n) = (p(1-p))/[(SE/Z)]2 where, P=0.5, Z=1.96 & SE= 0.05
n= (05(1-0.5))/ [(0.05/1.96)]2
n= 0.25/0.00065

n=384

Ten percent (10%) contingency: 0.1*384=38.4 then,

Total (N) = 422

• Population size for Kara fincawa=1235, for Nanawa=1532 and for Kolba Gode=1654
➢ Total(N) = 4421

384∗1235
Therefore: Sample size for Kara Fincawa kebele (n) = = 107
4421

384∗1532
Sample size for Nanawa kebele (n) = = 133
4421

384∗1654
Sample size for Kolba Gode kebele (n) = = 144
4421

24
Plate 1: Field Works. (By Hailu Wondu, February 2019).

25
Figure 3: Flow-chart for the general methodology. Adopted from Huang et al., (2010) and
Esayas (2015) with some modification.

26
Table 4: Qualitative data collection

Study Data type Data source Sampling Total Sample size per
kebeles technique Sample site
1.Nanawa KIIS Purposefully 18
Size 6 from each
kebeles
2.Kara Primary HHS Randomly 384 107, 133 and 144
Finchawa from the three
kebeles
3.Kolba FGDS Purposefully 6 2 from each
Gode kebeles
Socioeconomic Formal Full profile 1 document
Secondary data requesting of the
district Source: Researcher

3.2.8. Data Analysis

3.2.8.1. Data analysis for GIS and Remote Sensing

To improve the interpretability of the images, appropriate pre-processing procedures including

radiometric correction were applied by using Semi-automatic Classification Plugin of QGIS.

Classification of land use land cover has performed in Semi-Automatic classification plugin

algorithm of QGIS and Using training samples, maximum likelihood supervised classification was

performed. Then reclassification of the image was proceeded to produce forest and non-forest land

classes. For accuracy assessment, the validation point from ground truth distributed on the maps

randomly and coded as 1-7 for LULC in the attribute table. Then this coded data were feeds to

accuracy software in QGIS to produce error matrix of producer’s accuracy, user’s accuracy, overall

accuracy and kappa coefficient.

Using MOLUSCE software from QGIS, a real change and changed map were obtained for two

consecutive period maps. Areas that were converted from one class to any of the other classes was

27
computed and the change directions were determined. The values has been computed in terms of

hectares and percentages. Finally, the result has been summarized and presented by tables, graphs,

maps and charts.

3.2.8.2. Data analysis for social survey

Following data collection, validation and coding of data collected through the interviews were

carried out for their easier capturing and analyzing. Two computer programs namely Microsoft

Excel and R-software were used in performing the analysis of differences in perceptions among

respondents in different kebeles concerning the forest cover change and drivers of forest cover

change. A descriptive statistical method such as frequency and percentage were also employed to

analyze the data collected. Table, pi-chart, histogram and graph were used for presentation and

summarize the data analyzed by frequency and preferred soft wares.

28
4. RESULTS AND DISCUSSION

4.1. Investigating the magnitude and rate of forest cover change (1985-2018)

LULC categories and change in the study area during the past 33 years is condensed in Table 5

and 6. The result showed that, rapid increments of urban buildings and settlements as well as

agricultural land in the area for 33 years investigated (Table 5 and 6, Figure, 4, 5, 6 and 7). In the

reverse, shrub land and forestland have highly declined. In between 1999 to 2013 and 2013 to

2018 Agricultural lands has shown increment in 8.33% and 2.76% respectively. In contrary

forestland has shown decline to -0.37% and -0.3% respectively it might be due to conversion into

cultivation land, which has lead agricultural land expansion at its expense. This reported in Daniel

Jaleta et al., (2016), as bush land has reduced by 11.8% during the study period. This is due to

expansion of cultivated land and in Biniyam Alemu et al., (2015) suggested that, the land use and

land cover changes that were detected in all study areas revealed, in general, the greater areas of

wood land, shrub land and grazing land were transformed into agricultural land, bare land and

settlement.

In the years between 1985-1999 and 2013- 2018 there was significant shrub land area reduction -

12275.74 and -5519.21ha respectively, which has directly converted to agricultural land and

settlements and/or urban buildings. This result is similar with, (Mikias Biazen, 2015) which

revealed that, over the entire study period, the annual rate of the cropland area increased. While

the rate of the woodland and shrub/bush land, area declined and showed a fluctuating trend

between the study years. Throughout the study periods (1985, 1999, 2013 and 2018) urban

buildings indicate an increasing tendency of 13.38%, 22.92%, 24.21% and 30.06% respectively

that might be due to settlement area expansion for the sake of high rate of population growth and

29
 infrastructural development due to urbanization. Agricultural land reduces only in the period

between 1985 and 1999 by -0.27% that could be converted to settlement area due to resettlement

program of the dergue policy following 1984/5 famine.

As the result depicted, (table 6 and figure 8) from the period 1985 to 2018 there has been a net

gain for woodlot, Agriculture land and urban buildings and settlements by 1369.11ha, 7828.73ha

and 15471.92ha respectively, in contrary, there has been a net loss for forestland, shrub land, lake

and Bare land by-3887.85ha, -17502.55ha, -2690.69ha and -2690.69ha respectively. This might

be due to the increasing demand for land in expansion of agricultural land and space for settlements

and buildings including bare land/open space with relevant rapid rate of population growth. This

confirmed in, Biniyam Alemu et al., (2015). The net gain from woodlot might be due to the

increasing demand of tree planting around home for fuelwood consumption with respective

expansion of settlement area.

Table 5: Categories and patterns of Land Use/Land Cover in the study area

1985 1999 2013 2018

Land Cover classes area(ha) % area(ha) % area(ha) % area(ha) %
Forest 6779.3 9.37 3377.59 4.67 3107.24 4.29 2891.45 3.99
Woodlot 3663.69 5.06 8306.5 11.48 3223.55 4.45 5032.8 6.96
Agriculture Land 20588.36 28.46 20392.91 28.19 26418.07 36.52 28417.09 39.28
Lake 3876.69 5.36 4110.76 5.68 4227.43 5.84 3288.01 4.54
Shrub land 21477 29.69 9201.27 12.72 9493.67 13.12 3974.46 5.49
Urban Buildings and
6275.79 13.38 16580.77 22.92 17511.45 24.21 21747.71 30.06
Settlements

Bare land 9678.4 13.38 10369.44 14.33 8357.81 11.55 6987.71 9.66
Source: Researcher

30
 Source: Researcher

Figure 4: LULC map of Lume district in 1985

31
Figure 5: LUCL map of Lume district in 1999

Source: Researcher

Figure 6: LUCL map of Lume district in 2013

32
 Source: Researcher

Figure 7: LULC map of Lume district in 2018

Table 6: Magnitude of Land Use /Land Cover change

1985-1999 1999-2013 2013-2018 1985-2018

Land cover
Δ Δ Net Δ Net Δ
Area(ha) Δ% Area(ha) Δ % Δ Area(ha) Δ% Area(ha) %
classes
Forests -3401.71 -4.7 -270.35 -0.37 -215.79 -0.3 -3887.85 -5.37
Woodlot 4642.81 6.42 -5082.95 -7.03 1809.25 2.5 1369.11 1.89
Agriculture
Land -195.45 -0.27 6025.16 8.33 1999.02 2.76 7828.73 10.82
Lake 234.07 0.32 116.67 0.16 -939.42 -1.3 588.68 -0.82
Shu bland -12275.74 -16.97 292.4 0.4 -5519.21 -7.63 -17502.55 -24.2
Urban Buildings
and Settlements 10304.98 14.24 930.69 1.29 4236.25 5.86 15471.92 21.39
Bare land 691.04 0.95 -2011.63 -2.78 -1370.1 -1.89 -2690.69 -3.72
Source: Researcher

33
 LULC change
30
Magnitude of change in %

20

10

0
1985-1999 1999-2013 2013-2018 1985-2018

-10

-20 Time interval in years

-30
Forests
Woodlot
Agriculture Land
Lake
Shurbland
Urban Buildings and Settlements
Figure 8: LULC change of Lume district (1985-2018)

Source: Researcher

The amount of changes varied among the LULC types. For instance, out of 6779.3ha forests in

1985, only 924.13ha (13.63%) was unchanged during the study period, wich implies about 86%

of forestlands were converted to others LULC. From 86% converted forestland, 3.4% converted

to agricultural land, 37.6% converted to woodlot and 20.24% were converted to settlement area.

Similarly in this period, out of 21477ha only 4986.3ha (23.22%) remained unchanged shrubland

and the remaining 77% of shrubland were converted to others LULC. From 77% shrubland

converted, 13.8% converted to agriculture, 15.5% converted to woodlot and 40.72% converted to

settlement area (Table 5 and 7). This might be due to increasing expansion of settlement area with

the respective high rate of population growth and highly increasing demand for food crops and

34
fuelwood production. Similarly, recent researches have revealed that the expansion of agricultural

land has been at the expense of lands with natural vegetation cover (Belay Woldeamlak, 2002).

Major gained trends of LULC changes were observed from the conversion matrix for agriculture

land and urban buildings/settlement area. Agriculture land replaced about 7828.73ha the land that

used to be covered by other LULC types. The main conversion were from bare land (2643.4ha),

shrubland (843.3ha), woodlot (756.94ha) and forestland (165.74ha). Therefore, agricultural land

gained an increase of 138% throughout the study period. In addition, there was a conversion to

urban buildings and settlement area from other LULC types. However, the conversion from

agriculture land and shrubland were the highest, which were about 5542.89ha and 2540.6ha

respectively.

Moreover, forest, woodlot and bare land were also contributed to gained conversion of urban

buildings and/or settlement area by 454.18ha, 520.66ha and 215.74ha consecutively. As a result,

urban buildings and/or settlement area indicated an increment by 346.5% of its areal coverage at

initial study period in 1985 (Table 8 and 9, Figure 13, 14 and 15). This might be due to the

increasing demand space for residential and urban infrastructures and technological transformation

in the scene of urbanization and fulfilling social and economic needs of urban and rural

communities. Similar reports in Abineh Tilahun et al., (2015) when population pressure increases

there is a demand for settlements. This has a two-way effect on the environment. On one hand

there is a need for settlement area through burning of bush lands, on the other hand, there is a need

for housing construction material particularly wood, and hence farmers cut trees. Similar

suggestion in (Mahendra and Karen, 2019), noted that, unmanaged urban expansion increases the

costs of service provision, deepens spatial inequities, and imposes heavy economic and

environmental burdens.

35
 Table 7: LULC conversion matrix of 1985-1999

Urban Buildings
LULC Forest Woodlot Agriculture Lake Shrub and Settlements Bareland
Forest 924.13 2549.98 230.87 46.9 1631.4 1372.37 25.76
Woodlot 841.2 1373.2 84.7 91 648 419.6 7.8
Agriculture 501.7 739 14368.2 49.45 1064.4 231.8 3624.4
Lake 11.8 4 1.7 3855.4 4 1.35 0.63
Shrub 962.5 3335.4 2956.8 65.3 4986.3 8745.4 408.5
Urban Buildings
and Settlements 107.65 222.8 2795.3 5 609.93 5082.2 1154.9
Bareland 29.64 82.6 3648.5 0 254 517.3 5150
Source: Researcher

Table 8: LULC conversion matrix of 1999-2013

Urban Buildings
LULC Forest Woodlot Agriculture Lake Shrubland and Settlements Bareland
Forest 1134.46 618.13 279.78 168.81 495.35 675.15 6.94
Woodlot 804.77 1187.8 955.1 32.6 2067.7 3236.8 22.34
Agriculture 171.15 138.72 16288.14 20.26 1303.5 302 3162.3
Lake 61.16 7.8 49.2 3979.73 5.6 9.6 0
Shrub 470.85 736.8 2462.63 16.84 2540.6 3915.3 55.13
Urban Buildings
and Settlements 454.183 520.66 5542.89 10.8 2754.64 10064.36 215.74
Bareland 12.25 14.32 4830.3 0.9 324.65 289.87 4899.8

Source: Researcher

36
 Table 9: LULC conversion matrix of 2013-2018

Urban Buildings
LULC Forest Woodlot Agriculture Lake Shrubland and Settlements Bareland
Forest 727.3 550.66 979.89 20.9 46.75 762.9 7.66
Woodlot 604 850.6 745 0 129.7 888.64 5.58
Agriculture 165.74 756.94 24,016.04 23.96 843.3 152.4 2646.4
Lake 112.15 92.87 568.5 3244.95 73.14 129.98 8.3
Shrubland 837.4 1252.56 2300.55 0 1697.3 3434.2 201.3
Urban Buildings
and Settlements 173.4 1507.4 4773.7 0 1196.53 15,932.17 164.4
Bareland 12.25 11.08 3461.77 0.18 31.8 269.25 3143.52

Source: Researcher

In the year between 1985 and 1999, forestland declined by -3401.71ha (-4.7%) and shrubland

reduced by -12275.74ha (-16.97%) whereas woodlot and settlement area increased by 6.42% and

14.24% respectively. This insists that forestland and shrubland decreased at the rate of 243ha and

876ha per year respectively, while woodlot and settlement area increased by rate of 331.63ha and

736ha per annum consecutively (table 6 and 7, Figure 8 and13). According to discussion with

FDGS, there was high rate of deforestation during this period since it was transition period (1990-

1991) for the downfall of dergue regime, political unrest and the coming of current government.

Hence, there was no responsible institutional and legal framework for conservation of forests and

other natural resources. Similarly this reported in, Amogne Asfaw (2014) which proposed that, the

majority of these 'community forests' were destroyed during the conflict and transition after the

downfall of the Dergue (1991) because they were undertaken without the consent of the locals

with the exception of the few cases.

37
 In the second and third period also the declined of forest and shrubland cover continued by rate

of 19.31ha, 43.2ha and 20.88ha, 1103.84ha per annum consecutively, whereas agriculture land

and urban buildings and settlements keep on increasing in the remaining periods (1999-2013 and

2013-2018) by the rate 430.4ha, 399.8ha and 66.5ha, 847.25ha per year respectively (Table 7). It

might be due to high demand for food crop production and space for buildings and settlements

with corresponding high rate of population growth. Similarly reported in, (Ebrahim Esa Hassen

and Mohamed Assen, 2017) which disclosed that, the area devoted to farmland and settlement

showed a steady expansion by about 33.44% (370.3 ha/year) in this third period of analysis.

Table 10: Rate of Land Use/ Land Cover Change (1985-2018)

1985-1999 1999-2013 2013-2018 1985-2018

Land Cover %age rate of %age rate of %age of rate of %age rate of
classes of (A) Δ /yr. of (A) /yr. (A) /yr. of (A) /yr.
Forests -0.5 -243 -0.08 -19.31 -0.07 -43.2 -0.57 -117.8
Woodlot 1.3 331.63 -0.61 -25.93 0.56 361.85 0.37 41.48
Agriculture
Land -0.009 -13.96 0.3 430.4 0.75 399.8 0.38 237.23
Lake 0.06 16.72 0.03 8.33 -0.22 -187.88 -0.15 -17.84
Shurbland -0.57 -876.84 0.03 20.88 -0.58 1103.84 -0.81 -530.38
Urban Buildings
and Settlements 1.64 736 0.05 66.5 0.24 847.25 2.46 468.8
Bare land 0.07 49.36 -0.19 -143.69 -0.16 -538 -0.28 -81.5
Source: Researcher

The result indicated (table 11) the extent of areal share of forest lands from the total land cover of

the district which implies maximum share in 1985 of which 6779.3ha (9.37%) and minimum share

in 2018 of which 2891.45ha (4%) . Furthermore, it showed the amount of forest cover converted
38
to non-forest land (other land uses) in the specified period. In between 1985 and 1999 high amount

of forestland 3401.71ha (4.7%) was converted to non-forestlands and in between 2013 and 2018

relatively low amount forest area 215.79ha (0.3%) was converted to non-forestland (Table 9,

Figure 13, 14, 15 and 16). This might be due to enhancing forest management by Ethiopian

government commitment through establishing various institutions such as Ministry of

Environment Forest and Climate Change in 2012, Regional forest enterprises: Oromia Forest and

Wildlife Enterprise in July 2009 and Amhara Forest Enterprise in November 2009. This involves

Participatory Forest Management and which increase ownership and responsibility of the local

community for forest conservation. The result confirmed the finding in Mulugeta Lemenih et al.,

(2015) which noted that, today PFM is formally recognised in forest proclamations of Ethiopia’s

Federal Government and several regional states. The approach has expanded significantly.

For example, according to information from OFWE (2018) branch in Lume district, 1500.5ha of

plantation forest is managed by Enterprise in the study district. It could be also due to the

elaborated increment of awareness creation by Development Agents throughout the country

recently. However, a net loss of 3887.85 ha (5.37%) forest area was recorded throughout of study

periods 1985-2018 (Table 9, Figure, 9, 10, 11 and 12).

39
Table 11: Patterns of Forest and Non Forest cover in the district (1985-2018)

1985 1999 2013 2018

Land
Cover type area(ha) % area(ha) % area(ha) % area(ha) %

Forest 6779.3 9.37 3377.59 4.67 3107.24 4.3 2891.45 4

Non Forest 65559.93 90.63 68961.64 95.33 69231.99 95.7 69447.78 96

Source: Researcher

Source: Researcher
Figure 9: Forest and Non Forest cover map of Lume district in 1985

40
Figure 10: forest and Non-forest cover map of Lume district in 1999

Source: Researcher

41
 Figure 11: Forest and Non Forest cover of Lume district in 2013

Source: Researcher

Figure 12: forest and Non-forest cover of Lume district in 2018

Table 12: Magnitude of Forest and Non Forest cover change (1985-2018)

1985-1999 1999-2013 2013-2018 Net change

Land Δ Δ Net Δ Net

Cover type Area(ha) Δ% Area(ha) Δ% Δ Area(ha) Δ % Area(ha) Δ%

Forest -3401.71 -4.7 -270.35 -0.37 -215.79 -0.3 -3887.85 -5.37

Non Forest 3401.71 4.7 270.35 0.37 215.79 0.3 3887.85 5.37
Source: Researcher

42
Figure 13: Changed map of Lume district between 1985 and 1999

Source: Researcher

43
Figure 14: Changed map of Lume district between 1999 and 2013

Source: Researcher
Figure 15: Changed map of Lume district between 2013 and 2018

Source: Researcher
Figure 16: Magnitude of Forest cover change in Lume district (1985-2018).

44
 The computed result (table 13 and Figure 17) showed that the average rate of forest cover change

in between 1985 and 1999 was declined by 243ha per year, between 1999 and 2013 it was

decreased by 19.3ha per annum and between 2013 and 2018 reduced by 43.16ha per annum. This

result supported, the findings of Abiyot Yismaw et al., (2014) which noted, rate of forest cover

change from year 1973 to 1986 is -245.2 ha per year (6044.4ha –2855.9ha/13 years) and from year

1986 to 2003, it was -24 ha annually (2855.9-2446.9ha/ 17years). The annual rate of forest cover

change throughout the assessment period was -117.8ha. This could be due to alarming rate of

population growth in needs for high food security and space for settlements combined with low-

income source forced farmers to deforestation.

Table 13: Rate of change for Forest and Non Forest (1985-2018)
Source: Researcher
1985-1999 1999-2013 2013-2018 1985-2018
Land Cover %age of rate of %age rate of %age rate of %age rate of
type (A) /yr. of (A) /yr. of (A) /yr. of (A) /yr.
Forest -50.18% -243 -8 -19.3 -6.94 -43.16 -57.3 -117.8
Non Forest 5.18 243 0.39 19.3 0.31 43.16 57.3 117.8

Source: Researcher

45
 Rate of change
243

117.8

43.16
19.3
-19.3
1985-1999 1999-2013 2013-2018
-43.16 1985-2018

-117.8

-243

Forest
Non Forest
Source: Researcher

Figure 17: Rate of Forest cover change in Lume district (1985-2018)

Note:-The rate of forest cover change and LULC was computed using Equation (3)

Percentage of forest cover and LULC change was computed using Equation (2)

4.2. Examining forest Cover trends and management system in the district

(1985-2108)

The trend analysis of forest cover and other land use/land cover disclose that a change in area of

each LULC through the investigation of 33 years period (Table 5, Figure 18 and 19). The change

happened in the district were reduce in forest and shrubland due to deforestation, Agricultural land

expansion, expansion of settlements and urban buildings were the major changes encountered in

this period. Urban buildings and settlements undergoes the most increment change during the study

period. This confirmed the finding in (Pratik and Ashok, 2017); in which settlement experienced

46
a most positive change during the 21-year study period. This might be, as result of ongoing

population growth, socio economic activities for livelihood, urbanization and technological

transformation. This support the finding in Asirat Tolosa (2018) suggested that, the increase of

aerial coverage for cropland and grassland was due to an increase of population pressure, demand

for cultivated land in the highland and intervention of soil conservation practice by different NGOs

and Governmental Organization.

Hence the area coverage of urban buildings and settlements 6275.79ha (13.38%) in 1985 increased

to 21747.71ha (30.06%) in 2018, which was a dramatic change from LULC existed in the district

and followed by agricultural land which covers 20588.36ha (28.46%) in 1985 and 28417.09ha

(39.28%) in 2018. For more emphasize, the net gain in percent for settlement area is 21.39% and

10.82% for agricultural land area (Table 6 and 9). Forestland cover indicate a negative change over

the 33 years of study period. In 1985, 9779.3ha (9.37%) and declined to 2891.45ha (3.99%) in

2018 (Table 8, Figure 18 and 19). This might be due to expansion of agricultural land, urban

growth and expansion of settlements, rapid rate of shrubland reduction that can developed to forest

gradually and due to improper management system.

The transition matrices displayed that, forests and shrub lands are the most exposed to the future

LULC change. Currently, bare land has the highest probability (0.43%) of being converted to

agricultural land, while forest has a probability of 0.29% to convert into agricultural land and shrub

land has a probability of 0.36% to convert into urban buildings and settlements (Table 8). Similar

findings reported in (Pratik and Ashok, 2017).

47
 Trends of LULC
30000
Area in hectares

25000

20000

15000

10000

5000

0
1985 1999 2013 2018
Years
Forest
Woodlot
Agriculture Land
Lake
Shrubland
Urban Buildings and Settlements
Bareland

Source: Researcher

Figure 18: LULC trends in Lume district (1985-2018)

Areal cover trends of forest and non forest

80000
68961.64 69231.99 69447.78
70000 65559.93
Area in hectares

60000

50000
40000

30000

20000

10000
6779.3
3377.59 3107.24 2891.45
0
1985 1999 2013 2018
Forest Years
Non Forest

Source: Researcher

48
Figure 19: Forest and Non Forest cover trends in Lume district (1985-2018)

According to information from key informants, every year there is a plantation program which

mobilize and involving massive participation of local communities starting from the mid of July

to the beginning of August. However, the survival rate is low due to water deficiency in long dry

period (November to April) since it is a period when seedlings needs excessive amount of water

and low participation and responsibilities of local communities for post plantation activities.

Similarly, in the district water and soil conservation practices are performed yearly from the mid

of January to beginning of March through mobilization of local communities and concerned

stakeholders including DAs, woreda experts and cadre from administration office.

During this period, various soil and water conservation structures were established in agricultural

land and forest area. Furthermore, awareness creation nourished to local communities by different

experts at the end of daily physical work to increase their knowledge concerning forest

conservation, their security and other natural resources management.

Series interview held with KIIS depicted that, forest plantation, soil and water conservation and

awareness creation and PFM are most commonly practiced forest management system in the

district (Table11). Currently Nanawa and Kara Finchawa plantation forest are managing under

OFWE through PFM, but the benefit sharing between each stakeholder is still not fair, since it was

40% for local community, 10% for administrative kebeles and 50% for government. This

confirmed findings in Amogne Asfaw (2014), So as long as the intervention is to enhance the

productivity of nature and to improve the livelihood of local community, locals have to actively

participate right from the outset to the completion of the program; and they have to be the number

one beneficiaries.

49
Table 14: Investigated Forest Management System in the District

Major Forest Management System in area Respondents Proportion Rank

Forest Plantation 4 22% 3

Area Closure 1 6% 5

Soil and Water conservation 5 28% 1

Participatory Forest Management 3 17% 4

Awareness Creation 5 28% 1

Total 18 100%
Source: Researcher

4.3. Accuracy assessment for classified images of 1985-2018

Accuracy assessment is an important step in the process of analysing remote sensing data. Remote

sensing products can used as the basis for political as well as economical decisions. Potential users

have to know about the reliability of the data when face up with maps derived from remote sensing

data. In order to increase the result of overall accuracy, images of different land use/land cover

divided into more parts. For instance, in this study agricultural land divided into five different parts

to increase homogeneity of pixels and finally categorize as agricultural land. Thus, 86.16%, 84.7%,

85.8% and 85% overall accuracy were achieved for 1985, 1999, 2013 and 2018 respectively, which

is satisfactory level for GIS and RS research. Furthermore, 0.756, 0.807, 0.787 and 0.779 kappa

coefficient were attained for 1985, 1999, 2013 and 2018 consecutively, which is substantial

agreement, that produced by accuracy assessment of error matrix/confusion matrix (Table 15, 16,

17 and 18). The Kappa coefficient lies typically on a scale between zero and one, where the latter

indicates perfect agreement.

50
 The overall map accuracy is not always representative of the accuracy of individual classes. High

overall map accuracy does not guarantee high accuracy for forest and others land cover losses.

Therefore, both producer’s and user’s accuracy for all single classes need to be considered. For

instance a higher user’s accuracy (85.4%) and low producer accuracy (81.1%) implies that more

forest loss in the map was also loss in the reference data (Table 15). In contrast to the overall

accuracy, the Kappa coefficient considers also non-diagonal elements. It measures the proportion

of agreement after chance agreements have been removed from considerations. Therefore, always

the value of kappa coefficient is less than overall accuracy. The result in (Table 15, 16, 17 and 18)

also confirmed the fact of this statement. This also reported in (FAO, 2016).

Table 15: Confusion matrix for LULC of 1985

References Data
Urban
Buildings
Classification Agriculture and Bare User's
Data Forest Woodlot Land Shrubland Lake Settlements land Total accuracy
Forest 258 32 47 1 61 0 3 302 85.4%
Woodlot 65 107 39 3 12 0 0 176 60.8%
Agriculture
Land 45 24 1460 3 168 36 105 1541 94.7%
Lake 0 0 0 253 0 0 0 253 100%
Shrubland 41 16 226 6 769 24 9 897 85.7%
Urban
Buildings and
Settlements 6 3 85 5 26 157 37 249 63.1%
Bare land 3 2 121 0 10 37 459 531 86.4%
Total 318 134 1684 296 849 184 503 4019
Producer's
accuracy 81.1% 79.8% 86.7% 85.50% 90.6% 85.32% 91.2%

Source: Researcher

Overall accuracy=86.16% Kappa coefficient =75.6%

51
 Table 16: Confusion matrix for LULC of 1999
Column1 Reference Dataolumn12
Urban User’s
buildings accuracy
Classification Agriculture Shrub and Bare
Data Forest Woodlot Land Lake land Settlements land Total
Forest 53 3 8 4 15 10 0 69 76.8%
Woodlot 21 257 35 4 45 39 0 311 82.6%
Agriculture Land 7 15 1294 3 17 188 145 1383 92.8%
Lake 0 0 0 276 0 0 0 276 100%
Shrubland 0 47 38 0 296 92 5 393 75.4%
Urban buildings 75.6%
and Settlements 7 54 226 0 97 713 25 943
Bareland 0 0 136 0 3 20 515 574 89.7%
Total 64 286 1437 312 383 872 590 4019
Producer’s 77.3
accuracy 82.8% 89.7% 90% % % 81.8% 87.3
Source: Researcher
Overall accuracy=84.7% Kappa Coefficient=80.7

Table 17: Confusion matrix for LULC of 2013

Reference Data
Urban User’s
buildings accuracy
Classification Agriculture and Bare
Data Forest Woodlot Land Lake Shrubland Settlements land Total
Forest 46 0 44 0 2 26 0 106 43.4%
Woodlot 3 54 20 0 7 7 0 59 91.5%
Agriculture 94.84%
Land 9 10 1746 9 80 136 141 1841
Lake 0 0 0 278 0 0 0 278 100%
Shrubland 8 15 110 0 353 70 1 387 91.2%
Urban 69.5%
buildings and
Settlements 1 19 343 0 135 672 4 967
Bare land 0 0 90 0 0 27 299 316 94.6%
Total 55 66 2057 312 407 729 345 4019
Producer’s
accuracy 83.6% 81.8% 84.88% 89.1% 86.7% 92.2% 86.7%
Source: Researcher
52
 Overall accuracy= 85.8% Kappa Coefficient=79.7%

Table 18: Confusion matrix for LULC of 2018

Reference Data
Urban
Buildings
Classification Agriculture Shrub and Bare User's
data Forest Woodlot land Lake land Settlements land Total accuracy

Forest 73 14 5 15 10 4 0 84 87%

Woodlot 3 213 67 4 19 52 0 227 93.8%

Agriculture land 6 72 1524 8 94 259 136 1608 94.7%

Lake 0 0 0 231 0 0 0 231 100%

Shrubland 14 28 67 15 247 62 2 259 95%

Urban buildings
and Settlements 18 126 494 10 104 782 11 1223 64%

Bare land 0 3 115 0 3 18 297 329 90.3%

Total 77 325 1772 283 301 850 338 3961

Producer's 81.6
accuracy 94.8% 65.5% 86% % 82% 92% 87.7%

Source: Researcher

Overall accuracy= 85% Kappa Coefficient= 78.7

53
4.4. Major Driving Forces of Forest cover change in the study district

4.4.1. Socio-economic characteristics of study population

In the study area, 90% of the participant were male headed and only 10% were female headed.

Mostly it is expected that women are involved in fuel wood collection and other forest products

from the forest, hence the effect is less. This is in line with, Sunderland et al., (2014) which

noted that, in many places, particularly in Africa; it is women and girls who are the main

collectors of fuelwood. Respecting age of participants 65% of sampled population were in

between 35 and 50 years, which were enough matured to easily understand the use of forest and

participate in forest management activities rather than deforestation. 62% of the respondents

were owned 0.5 to 1.5 ha of land and only 4% of sampled population has land 3 to 6ha. This

insist that, 62% of the farmers forced to secure others income source to change and support their

life properly (Table 19).So they might be involved in deforestation to expand their farmland or

cut trees for sale of fuelwood and charcoal production.

Regarding income, 48% of the participants were generating 15000 to 25000 birr per year, which

is not satisfactory in the current life situation. Hence, these farmers also forced to search for

others income source alternatives including forest products and expanding of agricultural land

through deforestation of forests and shrub lands. This in line with, (Jane and Charles, 2008)

which suggested that, a decomposition of income shares by source and wealth groups show that

the lowest income group derive higher income from forest crop farming . From below socio-

economic data, displayed (Table 19) land holding size and income has a negative impact on

expansion of forests and shrub lands.

54
Table 19: Socio-Economic Characteristics of Sampled Population

No. Variables Categories Frequency Percentage (%)

1. Sex Male 347 90
Female 37 10
2 25-35 93 24
Age 35-50 246 65
50-65 45 12
3. Education Illiterate 120 31
Literate 264 69
4. Family Size 1-3 66 17
3-6 229 60
>6 89 23
5. Land Holding(ha) 0.5-1.5 238 62
1.5-3 130 34
3-6 16 4
6 Income Per Year 5-15 35 9.
(Birr in 1000) 15-25 184 48
25-35 149 39
>35 16 4
Source: Researcher

4.4.2. Proximate Drivers

The consecutive interview and discussion held with HHS and FGDs in the study sites depicted

that four major proximate drivers of forest cover change existed in the districtwide.1)

Agricultural land expansion 2) Fuelwood/charcoal production 3) Urban Expansion and

Settlements and 4) Extended dry period. The perception from HHS indicate that, agriculture is

the main life supporting practices in the district since the agro ecology of the district is more

suitable for diversity of crop production and livestock rearing. So, most of rural farmers are

dependent on agriculture for their livelihood and income generation. Nevertheless, the

55
population size of the study site increase from time to time, which needs more additional food

crops from agriculture. Thus, highly demand for food security combined with expensive living

condition leads to farmers to expand their lands by destructing forests and shrub lands for the

use of agriculture. Therefore, agricultural land expansion (314) is the major driving force in the

district (Figure 20). This also reported in Kissinger et al., (2012), as most of the smallholder

farming resettlement schemes are established on forestlands and therefore environmentally

unfriendly.

According to the interview with HHS and discussion with FGDS, fuelwood and charcoal were

the primary energy sources (145) in the rural area (Figure 20). To fulfil the needs of energy,

many rural farmers planting trees around home and settlements. However, the demand for

energy increase with respective high rate of population growth and this forced forest

surrounding community to exploit additional fuelwood and charcoal production from forest

trees. Similar reports in Tigabu Dinkayoh (2016) which suggested that, trees or derived charcoal

can be sold as a commodity and used by humans, while cleared land is used as pasture,

plantations of commodities and human settlements. The result also confirmed the finding in

(Negasi Solomon et al., 2018) which noted that, elders pointed out that they are dependent on

the selling of fuelwood as an immediate source of income during decline or failure of crop

production because of drought years.

The discussion with FGDS revealed that, the increasing tendency of urban expansion and

settlement area (244) were very high due to high rate of population growth, immigration and

strategic location of the district for technological transformation and investment (Figure 20 and

Plate 2). Recently, the district became a station for Ethiopian dry port (Mojo dry port), Addis

56
Ababa to Adama high way which is passed through the district, Hawassa to Mojo high way,

Addis Ababa to Djibouti rail way and others urban buildings and infrastructures combined with

highly increasing demand of space for residence and settlements increase the encroachment of

agricultural land and shrub lands. This result also supported finding in Mary Tahir et al., (2013),

which proposed that, the magnitude of land cover change reflected in the city was basically due

to an increase in the human population density coupled with an increase in residential, industrial

and institutional building at the expense of bare lands and agriculture lands This portrayed that,

farmers need to search another new land through deforestation of forests and shrub lands to

replace land lost by urbanization and settlements. The result is in line with, Eshowe et al., (2019)

which suggested that, both settlement expansion and road transport were found to be more

frequently occurred specific factors that caused land cover changes.

The perception from farmers (78) insists, in the district large amount of seedlings were planted

annually, but the survival rate of seedlings were very low due to the recent climate change and

long term dry period (November to April) usually observed in the district. They also said, the

drought happened in 2016/17 had damage high amount of planted seedlings and regeneration of

natural seedlings.

57
 Major Proximate Driver
Respondent Frequencies 350 314
300
244
250
200
145
150
100 78

50
0
Agricultural Land Fuelwood/Charcoal Urban Expansion and Extended dry period
Expansion Production Settlement

Drivers

Source: Researcher

Figure 20: Major proximate drivers in Lume district

58
Plate 2: The spatial relation of urban, agriculture land, shrubland and Forests in Lume district.
Screen shot from google satellite (by Hailu Wondu, April 2019).

The arrow indicate how urbanization leads to encroachment of agricultural and shrub lands
and the expansion of agricultural land result in deforestation of both forests and shrub lands.

4.4.3. Underlying Drivers

The above-discussed proximate drivers were passionate by various types of underlying causes

such as lack of awareness, weak law enforcement, landlessness, high rate of population growth,

poverty, technological transformation and policies that were identified by HHS and FGDS

(Figure 22). According to the responses from FGDS and HHS (39%), the population of the

district raises steadily from time to time (Figure 21) due to immigrants from different parts of

the country, since the district is potential for various industries and factories that can provide

job opportunity for many people.

In addition, population size increases due to early marriage that leads high fertility rate without

considering family planning. The demand of space for residence and settlements, high supply

of food crops, energy (fuelwood and charcoal), infrastructures (school, health centre and roads)

and construction materials increases with the respective growth of population size. Hence this

result in encroachment of forests and shrubland for increase agricultural land, expand settlement

area and secure the required energy and construction material as well. The result confirmed the

finding in Hosonuma et al., (2012) which noted that, as the increased population has also meant

more demand for food items and hence more pressure to clear forestland to provide for the

demanded food. The finding also supported the result in Meshasha et al., (2016), which noted

that, rapidly growing of population brought shortage of land, removal of forest cover and soil

erosion and land degradation

59
The discussion with HHS and FGDS (25%) depicted that, in the district landlessness became

increasing recently, due to large amount of land were gave for investment, technological

transformation (flower company, tanary, meat processing industries and others), infrastructures

and urban expansion. This situation affect youth (15-25 years) of the community part, mostly

that failed from different educational level and remained jobless (Figure 22). This in turn,

triggered those people to encroach forestlands and other communal lands such as shrub lands

and bare lands to generate income through crop production, charcoal production and fuelwood

collection.

P o p u l a t i o n g ro w t h ( 1 9 9 8 - 2 0 1 3 )

160000
140000
Population size

120000
100000
80000 142,288
117,080 126,933
60000 110,357
40000
20000
0
1998 2007 2010 2013
Years

Figure 21: Population growth trends of Lume district 1998-2013

Source: (CSA, 1998; 2007 and 2013) and (WFEDO, 2010)

The information from HHS and key informants (19%) insisted that, the implementation of laws

for forest protection and management was low (Figure 22). Until now, some people cutting trees

illegally and they were not punished tantamount of their damage and this result in frequent

illegal forest destruction continued in the district. For example, As stated in forest development,
60
conservation and utilization proclamation No. 542/2007 (FDRE, 2007), in order to properly

conserve, develop and utilize the forest resources of the country, major forestlands should be

designated as state forests, their boundaries should be demarcated with the participation of the

local community and they should be registered as protected and productive forests (article 8:1);

forests shall be protected from forest fire, unauthorized settlement, deforestation, undertaking

of mining activities and other similar dangers (article 9:7).

However, deforestation of both forest and shrubland, free grazing of protected area and illegal

settlement in and around forests continued in the study district and many parts of country to

date. According to information from key informants (19%), in the district DAs and other

concerning bodies were nourished training for farmers regarding the use and management of

forests, but can’t address all stakeholders (women, youth and elders) at equal level, mostly the

training focused on adults. Thus, the attitude of some community members still not changed

worrisome of environmental benefit and sustainable use of the forest.

Underlying drivers

19% Lack of Awareness

39%
17% weak law enforcement
25%
landnessness

High Rate of Population

Growth

Source: Researcher

Figure 22: Major indirect drivers of LULC in Lume district

61
 Total identified drivers in the district by HHS
and FGDS

Underlying
Proximate Drivers Drivers

Agricultural land expansions

Fuel wood extraction Lack of awareness
Charcoal Production Low institutional enforcement
Urban Expansion Poverty
Extended dry period High Rate of population growth
Rural Settlements Policy
Infrastructural Development Political situation
Landnessness
Technological Transformation

Source: Researcher
Figure 23: Summary of proximate and underlying drivers

62
5. CONCLUSION AND RECOMMENDATION
5.1. CONCLUSION

Forest cover change in the form of deforestation is the major environmental problem

demonstrated in Lume district. The main findings of this study disclosed that, a resume increase

in agriculture land and urban buildings and settlements at the expense of forests and shrub lands

throughout investigated periods (1985-2018). From the analysed results, the extent of land

use/land cover in general and forest cover change in particular was fundamentally changed

between 1985 and 2018. Specifically dramatic expansion of urban buildings/settlements and

awesome decline of shrub lands as well as forests were monitored in the district.

The study demonstrated that, areal coverage of forests and shrub land were declined from time

to time. The finding revealed that, maximum areal share of forestlands and shrub land at starting

of study period and minimum share was recorded at the end of study time due to conversion into

agricultural lands and urban buildings and/or settlement area. Throughout the study periods,

steady net increasing rate of expansions observed for urban buildings/settlements and

agriculture land annually with the respective high rate of population growth and urbanization.

In contrary, a net decline rate noted for shrub lands and forestlands per year due to deforestation

for the use of agriculture and settlement area. The assessment of KIIS revealed that, awareness

creation, soil and water conservation, forest plantation and PFM were the major forest

management system existed in the district.

Forest cover change in Lume district is an outcome of various interactions between direct and

indirect drivers. The major proximate drivers of forest cover change identified through HHS and

FGDS are agricultural land expansion, fuelwood extraction, charcoal production, urban

63
expansion, and expansion of rural settlements, extended dry period and infrastructural

development. In addition, lack of awareness, low institutional enforcement, and poverty, high

rate of population growth, policy, political situation, landlessness and technological

transformation are the main underlying drivers recognized in the district. In conclusion, high

rate of population growth is the most triggered factors, which resulted in expansion of

agricultural land, demand for fuelwood and charcoal production, urban expansion,

infrastructural development for public service, expansion of settlement area and others socio-

economic needs. Hence, this situation leads for more depletion of forest resources and shrub

lands at the expense of fulfilling demand for growing population.

5.2. RECOMMENDATION

Eventually, from general study it had been understood that forest cover of Lume district has

been conjugated. Hence, to protect the forest resources and shrub land from extra expenditure

and to utilize these irreplaceable natural resources in sustainable basis, the following feasible

suggestion are forecasted based on the findings and conclusion drawn.

1. Improved urban planning and design should be prepared and implemented by urban

planners. The plan should be based on emphasize urban greening, reducing costs of service

provision, integrating existing informal or regularized settlements within the town’s formal

authority. Moreover, it should be based on improving and maintaining residents’ social and

economic networks while reducing the need for more urban land and promoting upward

growth to reduce pressure from agriculture land and shrub land is crucial.

2. Alleviation of food crops demand from agricultural land is important by increasing the

productivity of land through using agroforestry technologies such as planting suitable

64
 horticultural crops with trees and livestock around homestead, growing of nitrogen fixing

trees in farmlands, expanding integrated agriculture to all farmers and using of new

agricultural inputs that facilitated by agricultural and irrigation sectors.

3. Fuelwood energy and charcoal burning is as one problem of forest cover change. Hence,

Planting of trees around homestead and periphery of agriculture land is important for

household fuelwood supply and introduction and distribution of improved stoves for fuel is

indispensable for the reduction of pressure from forests for the use of fuel energy.

4. Diversified job opportunity and income generation through the district macro and micro

enterprise sector for youth and landlessness farmers minimize strongly dependency on

agricultural and forest products. To address the increasing population pressure, awareness

as well as service provision of family planning to all local communities via integrated

extension service is exhaustedly crucial.

5. Further studies is required on suitable tree species production that can tolerate the long-term

dry period, specifically planting indigenous species that could adopt the environment should

be considered as a strategy to increase productivity of multipurpose trees and expand spatial

coverage forests and shrub lands.

6. To reduce further depletion of forest and to understand the impact of deforestation working

on the knowledge of farmers through awareness creation and training of the concerned

stakeholders, on community forest production and sustainable forest management is crucial.

7. To improve the efficiency of existing local PFM projects, to promote creation of additional

project members and to reduce complain of stakeholders concerning benefit sharing, it is

strongly suggested that, project planners should revise the existing management plan and

design, which will benefit local community as number beneficiary and increase ownership

65
as well as responsibility of local communities towards forest conservation is extremely

important.

66
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APPENDICES

Annex 1: Questionaries’ on drivers of forest cover change and local community’s

perception about forest cover change

Interview number/code: ___________

Name of the Interviewer______________ Signature_____________________

Location: Region______________Zone__________Woreda_________Kebele___________

Altitude____________ Latitude____________ Longitude_____________

Date of Interview: Day ________ Month ________Year____________

A. Personal information and questions for House Holds

1.1. Name of household head: ______________________

1.2. Respondent’s name (if different from the head): ____________________________

1.3. Gender of head (1) M = ____ (2) F = _____

1.4. Age of respondent____________

1.5. Educational status (year of schooling) _________________ If illiterate record zero; if

literate record one

1.6. House hold family size: __________________________

1.7. Land holding size: ______________

1.8. Mean household income in birr: ____________________

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2. What are the major uses of forests in your area? A. Used for construction B.

Fuelwood/energy C. Soil and water conservation D. Timber production E. Others

specify_____________

3. Do you think that deforestation is the major problem in your locality? A. Yes B. No

4. How is today’s coverage of the forest when compared to the conditions before 1985? A.

Declined B. Increased C. No change

5. Do you think, severe and rapid forest cover change observed today? A. yes B. No

6. If the answer to question number ‘5’ is yes, what were/are the major causes of

deforestation? And how?

________________________________________________________________

A. Agricultural land expansion B. Fuelwood/ Charcoal production C. Settlement area D.

Extended dry period

7. What is your major source of income? A. Sale of cash crops B. Sale of wood and charcoal

C. Sale of livestock and livestock products D. Others specify_________

8. What do you think about the possible solution to alleviate the current problem of deforestation

and to use forest resources in a sustainable manner? A. Participatory Forest Management B.

Use of improved stove. C. Sustainable timber production D. Others specify____

9. What are the existing efforts to reduce deforestation and forest degradation in the study

district?

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A. Afforestation/ Reforestation B. Increase the distribution of improved stove C. Integrated

agricultural practice D. Other specify____________

10. What are the challenges in implementing the efforts to reduce deforestation and forest

degradation in the area? A. Lack of awareness B. low institutional enforcement C. Poverty

D. High rate of population growth

B. Checklists for Key Informants

1. Have you noted any change in the land use/land cover in your area over the past 33 years?

A. Yes B. No

2. If your answer to question number 2 is yes, what changes did you observed?

Increase/decrease in: A. Agricultural land B. Forest cover C. Woodland D. Scrub land E.

Settlement and infrastructure

3. What are the causes behind their increase/decrease? I. Direct causes II. Indirect (root)

causes Specify and discuss____________________________________________________

4. Participation of the local communities, government and non-government organizations

in resource conservation and management activities and how they are participating?

_____________________________________________________________

4. Do you think national policies and institution implemented starting from 1985 until today

have responsibility for land use/land cover change? A. Yes B. No,

If Yes how? _______________________________________________________

6. What major natural calamities occurred in your area in the last 33 years?

______________________________________________________________________

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C. Checklist for Focus Group Discussion (FGDS)
1. What are currently existing land use/land cover types in your locality?

List them.______________________________________________________

2. Which land use/land cover type is increasing and which is decreasing starting from 1985?

Why? _______________________________________________________________

3. What are the direct/proximate drivers of land use/land cover change over the last 33 years,

between 1985 & 1999, 2013 & 2018? And how?

A. Infrastructure development and urban expansion

B. forest encroachment for illegal and legal settlement,

C. Agricultural expansion,

D. Unsustainable harvest of forest products (like firewood, charcoal, logging)

4. What are the underlining causes along each proximate driver? How?

A. Complex social characteristics

B. Political Situation

C. Economic factors

D. Demographic characteristics

E Technological transformation

F. Cultural and biophysical variables

H. Others specify_________________

79