ASSESSMENT OF LAND USE LAND COVER CHANGE AND FARMER

PERCEPTION TOWARDS LAND COVER CHANGE IN SETEMA DISTRICT

OF JIMMA ZONE SOUTH WEST ETHIOPIA

MSc THESIS

BY

DASSALEGN TINTAGO MENGESHA

OCTOBER, 2O23

JIMMA, ETHIOPIA

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 College of Agriculture and Veterinary Medicine
Department of Natural Resource Management

MSc. Thesis

By

Dassalegn Tintago Mengesha

Major Advisor: Alemayehu Regassa (PhD)

Co-Advisor: Dessalegn Obsi (PhD)

October, 2O23

Jimma, Ethiopia

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 College of Agriculture and Veterinary Medicine
Department of Natural Resource Management
MSc Thesis Frist draft Submission Form
Name of student: Dassalegn Tintago Mengesha ID No: 0673/14

Program of study: Degree of Masters of Science (M.Sc.).In Land use planning and
Management

Title: Assessment of land use land cover change and farmer perception towards land
cover change in Setema district of Jimma zone south west Ethiopia

I declare that the MSc Thesis work has not been done anywhere else before.

Dassalegn Tintago Mengesha Signature _____________ Date _______________

We have agreed to supervise the proposed research work. We have evaluated the content of
the MSc Thesis, found to be satisfactory, complete and according to the standards and formats
of the University. We have also verified that the work has not been done anywhere else
before.

Major Advisor: Alemayehu Regasa (PhD) Signature ____________Date ______

Name
Co- Advisor: Dessalegn Obsi (PhD) Signature ____________ Date ____________
Name

Decision/suggestion of Department Graduate Council (DGC) _________________________

___________________________________________________________________________

Chairperson, DGC Signature____________________ Date _________________________

Chairperson, CG Signature __________________ Date ___________________________

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 BIOGRAPHICAL SKETCH

The author was born to his father Tintago Mengesha and his mother Assegedu Kosa in
Setema district, Jimma Zone in June 15/1979EC. He attended his elementary school at Setema
Primary School and completed his secondary school at Gatira Secondary School. Then he
joined Holeta Agricultural Technical and Vocational Education Training College (ATVET)
College in October 2001EC and graduated with a diploma in Plant Science in January 10
/2003EC.
After his graduation he joined Setema woreda, Agricultural and Natural Resource
Management Office as a development agent and served as field for three years (2003-
2006EC) worked until he joined Jimma University in October 2007EC and graduated with
BSc. degree in Plant Science in June 30/2008EC. After awarded his BSc. He served in Setema
District of Agriculture and Natural Resource Management Office as an Expert of crop
protection and Agronomist until he joined the graduate study of Jimma University, College of
Agriculture and Veterinary Medicine to pursue a Master Science degree in land use planning
and management.

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 STATEMENT OF THE AUTHOR

I declare and confirm that this Thesis is my original work by signing below. I followed all
technical and ethical scholarship in the preparation, data collecting, data analysis, and
compilation of this thesis. A literature used in the thesis has been duly acknowledged.
This thesis is being submitted as part of the MSc. degree requirements at Jimma University
College of Agriculture and Veterinary Medicine (JUCAVM). The thesis is archived in the
JUCAVM Library and is accessible to anyone who persuades the criteria of the Library. Thus
I declare that this Thesis has not been submitted to any other school or the awarded of any
academic degree, diploma, or certificate. Without specific permission, brief quotations from
this thesis may be made provided that the source is accurately and completely acknowledged.
Requests for permission to cite from or reproduce this thesis in whole or in part may be
granted by the Head of the School or Department if the suggested use of the material is in the
interest of scholarship. Permission must be acquired from the author of the Thesis in all other
cases.

Name: Dassalegn Tintago Mengesha

Signed: __________________
Date of Submission: _________________
Department: Natural resource management

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 ACKNOWLEDGEMENT
First and for most, I would like to thank the almighty God for keeping me safe and healthy so
that I could complete this work successfully. I would want to express my gratitude and
Admiration to Dr. Alemayehu Regassa, my major advisor, and Co Advisor Dr. Dessalegn
Obsi. For investing in countless hours of supervision and advice during this research. The
high level of professional recognition they conferred on me during the study really
strengthened my self-confidence and desire to overcome any non-academic obstacles and
complete the primary work on time. I learned not just expertise but also professional ethics
and seriousness from them committed personality and dynamic attitude. They were at my side
when I was going through difficult periods and ups and downs in school life, from which I
could have faced the worst of the effects sooner or later. Only because of their Collaborative
efforts and intelligent comments did my thesis take shape. I was able to cope with the
multidisciplinary character of the study because to the open contacts I had with them. I really
express my personal respect and gratitude to them for rationally being a part of meat both
convenient and inconvenient stages of my studies. Finally, my heartfelt gratitude goes to my
wife, Ms. Meseret Ermias my children; Natnael Dasslegn, Tsion Dassalegn and to my lovely
brother Teka Tintagu for the love, patience, Prayer and emotional support. Lastly, my
acknowledgment also goes to the Jimma University College of Agriculture and Veterinary
Medicine, Land use Planning and Management and the Department of Natural Resource for
financial support and the provision of The opportunity to conduct this study.

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 TABLE OF CONTENTS
BIOGRAPHICAL SKETCH..................................................................................................II
STATEMENT OF THE AUTHOR......................................................................................III
ACKNOWLEDGEMENT.....................................................................................................IV
TABLE OF CONTENTS.........................................................................................................V
LIST OF TABLES................................................................................................................VII
LIST OF FIGURE...............................................................................................................VIII
LISTS OF ACRONYMS AND ABBREVIATIONS...........................................................IX
ABSTRACT..............................................................................................................................X
1. INTRDUCTION....................................................................................................................1
1.2. Objective..........................................................................................................................4
1.2.1. General objective.......................................................................................................4
1.2.2. Specific objectives.....................................................................................................4
1.2.3. Research question......................................................................................................4
2. LITERATURE REVIEW....................................................................................................5
2.1. Land..................................................................................................................................5
2.1.1. Land use land cover change.......................................................................................6
2.1.2. Driving Forces of Land Use/Land Cover Changes...................................................7
2.1.3. Land use land covers change detection.....................................................................8
2.1.4. Land Surface Temperature........................................................................................8
2.1.5. Farmer perception in land use land covers change dynamics...................................9
2.1.6. Role of GIs and remote sensing...............................................................................10
3. MATERIAL AND METHOD............................................................................................12
3.1. Description of the study area..........................................................................................12
3.1.1. Location of the Study area.......................................................................................12
3.1.2. Demographic and socio economic characteristics of study area.............................13
3.1.3. Biophysical characteristics of the study area...........................................................13
3.1.4. Land use system of the study area...........................................................................14
3.2. Method of Data Collection.............................................................................................14
3.2.1. Google earth data collection....................................................................................14
3.2.2. Socio-economic data collection...............................................................................15
3.2.3. Focus group discussion...........................................................................................16

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 3.3. Sampling technique and sample size..............................................................................19
3.3.1. Study design............................................................................................................20
3.2.4. Land Use and Land Cover Classification................................................................20
3.2.5. Accuracy Assessment..............................................................................................21
3.2.6. Kappa coefficient.....................................................................................................22
4. RESULTS AND DISCUSSION.........................................................................................24
4.2. The rate and extent of LULC change pattern over the past thirty one...........................28
4.3. Identify the major drivers and factors contributing to land use and land cover change 29
4.4. To identify the farmer perception toward LULCC........................................................32
4.5. Demographic information of the sampled households...................................................32
5. CONCLUSION AND RECOMMENDATION................................................................35
5.1. Conclusion......................................................................................................................35
5.2. Recommendation............................................................................................................36
5. REFERENCE......................................................................................................................38

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 LIST OF TABLES
Table 1. Number of key Informants participants......................................................................16
Table 2. Number of FGD participants Members of FGD participants/Kebeles.......................17
Table 3. Demographic and Socio economic characteristics of house hold in study area.........18
Table 4. Sampling frame and sample size................................................................................19
Table 5.Categories and patterns of Land Use/Land Cover of Setema districts........................26
Table 6. Land use land covers change......................................................................................27
Table 7. Rate of Land Use/Land Cover Change in 1992-2002, 2002-2013, 2013-2023, 1992-
2023............................................................................................................................28
Table 8. The reasons for land use land cover changes..............................................................29
Table 9. The major drivers and factors contributing to land use and land cover changes........30
Table 10. The major problems related to wetland....................................................................33

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 LIST OF FIGURE
Figure 1: Study area..................................................................................................................12
Figure 2: Flow Chart.................................................................................................................23
Figure 3: Spatial distribution of LULCC in Setema district (1992–2023)...............................24
Figure 4: Figure of Land use land cover classification map of1992, 2002, 2013 and 2023.....26
Figure 5: Changes of LULC classes between 1992-2002, 2002-2013, 2013-2023&1992-2023
....................................................................................................................................27
Figure 6: Illegal settlement and Charcoal production (Phato2023) Left and Right..................32
Figure 7: Wetland converts into agricultural land (Photo2023)...............................................34

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 LISTS OF ACRONYMS AND ABBREVIATIONS
DA Development Agents
ERDAS Earth Resource Data Analysis System
FAO Food and Agriculture Organization
GIS Geographic Information System
LST Land Surface Temperature
LULC Land Use Land Cover
LULCC Land Use Lands Cover Change
M.b.sl Meter below sea level
MSS Multi-Sensor Scanner
RS Remote Sensing
SWoARD Setema Woreda Office Agricultural Rural Development
TM Thematic Mapper
USGS United State Geological Survey
CSV Central Statistical Agency
KM Keyhole Markup Language
GTP Growth and Transformation Plan
MoFED Ministry of Finance and Economic Development
MSS Multi-Sensor Scanner

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 ABSTRACT

LULCC is the result of the long-time process of natural and anthropogenic activities that has
been practiced on the land. Assessments of land use land cover change and farmer perception
towards land cover change over the last 30 years (1992-2023). The study has initiated due to,
loss of biodiversity (used for agriculture, fuel wood, construction materials, etc.) and wetland
conversion to agricultural land. The General objective of this study was concentrated on
determine the rate and extent of LULC change pattern over the past thirty years in Setema
district South west Ethiopia. The study area was classified into five LULCC categories on the
basis of field study, geographical conditions, and remote sensing data. The supervised
classification maximum likelihood algorithm in ERDAS Imagine was applied in this study to
classify land cover using multispectral satellite data obtained from Landsat 5, 7, and 8 for the
periods 1992, 2002, 2012,2013 and 2023 In establishing the main drivers of land use/land
cover change, the study utilized household survey, Key informant interview and focus group
discussion. A total of 192 respondents were selected from the four kebeles by stratification
and purposely based on the criteria of Coffee grower kebeles, cereal crop grower kebeles,
kebeles changes wet land to agricultural land illegally, the most kebeles proximity to forest
land by more discussion with kebele administrator “kabines” and DAs. From the
kebele’sSusa Atila, Gido, and Done and Setema Kecha were selected which satisfies the
criteria. Random selection of reference pixels was used to reduce the biases of using the same
pixels for testing classification. 102 ground control points or references were collected
randomly from the study area by GPS. 35 points from agriculture, 30 points from forest, 17
points from grass land, 8 points from settlement, and 12 points from wetland were collected
randomly from the study area. Survey interpretation SPS, and Microsoft excel 2010 were used
The LULCC classification result revealed that at the base period of 1992 Land sat imagery,
forest land (40.5%), grass land (7.4%), Agricultural land(49.7%) , wet land (1.6%) and
Settlement land(0.9%) were identified with their respective percentage. On the contrary in the
recent period of 2023 land sat imagery forest land(28.0%),, grass land(4.4%),, wet
land(0.4%) were decreased respectively. The result analysis of household’s survey, Focus
group discussion and key informant interview were used and identified the major proximate
drivers and underlying drivers such as fuel wood extraction, illegal wetland conversion to
agricultural land, and illegal timber production, and agricultural expansion, extraction of
wood for house construction, population growth and corruption. The outcome of this study
shows that there would be decreasing of forestland; grassland wet land and increasing of
agricultural land and settlement area. Lastly, further study is required to identify role of
wetland for LULCC.

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 1. INTRDUCTION

People are utilizing the land in multi-dimension to meet their daily needs, which leads to land
use land cover (LULC). The LULC refers to several land use categories and various forms of
land cover (Thonfeld, et al., 2020). The term "land cover" refers to the substance that
physically protects the earth's surface (Mathewos et al., 2022). Land use which refers to
human activity on the land. Knowledge of lands and LC has become extremely relevant and
important as the nation attempts to address problems such as disorganized, uncontrolled
development, deteriorating environmental quality, loss of prime agricultural lands, destruction
of important wetlands, and loss of fish and wildlife habitat.

Land use data are needed for such an analysis of environmental processes and problems that
must be understood in terms of improving or maintaining living conditions and standards at
current levels (Parvin et al. 2017). One of the most critical elements for better land use
understands existing land patterns and changes in land time (Forkuor, 2011). One of the main
processes of LULC change in Ethiopia is the conversion of forest land to agricultural use. The
major direct drivers of land use and land cover are the need for the collection of fuel wood,
the extraction of timber, commercial agriculture, and the production of charcoal, whereas
the indirect drivers are population growth, commodities, governance, and economic growth.

Regardless of the fact that LULC change also had a huge negative effect on Ethiopia's
agricultural development, the country developed the 2011 Climate Resilient Green Economy
strategy and the Growth and Transformation Plan (GTP) by the Ministry of Finance and
Economic Development (MoFED).The LULC change assessment is a study of environmental
changes closely associated with post-agriculture, settlement expansion (Wang et al., 2018),
rapid urbanization, and deforestation (Yousafzai et al., (2022). LULC changes include forest
fragmentation and cover, land degradation, biodiversity loss (Mengist. et al., and Feyisa,
2022), and degraded habitat quality (Hassan et al., and Erum, 2016). The changes also have
significant ecological consequences for variability in local climate conditions, lowering of
water tables, and changes in surface runoff (Shelemay, 2022). The LULC change was more
associated with the livelihoods of rural people, which depend on mixed agriculture of crop
production and livestock (Miheretu and Yimer, 2018).

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The LULC change was caused by the expansion of agriculture through unplanned and
inappropriate land management practices to meet the food needs of local communities
(Temesgen et al., 2022). In many areas of developing countries, LULC changes caused by
deforestation have increased agricultural production in rural communities (Berihun et al.,
2019) as their livelihoods depend on natural resources (Kalema et al., 2015). It also has
significant impacts on the functioning of socioeconomic and ecological systems, with trade-
offs for sustainability, food security, and biodiversity (Lesschen et al., 2005).The LULC
changes in Ethiopia, particularly in the highland areas, are caused by a combination of
different factors, although this depends on the area's conditions (Tilahun and Shiferaw, 2022).
Most studies conducted in Ethiopia showed that LULCC was mainly the conversion of natural
vegetation into agricultural land and grazing land due to the high demand for agricultural food
production and livestock grazing (Yahya et al., 2019). Dagnachew et al., 2020) showed that a
significant reduction in natural vegetation cover at the expense of open grasslands and
cultivated land could exacerbate the problem of land degradation. In addition, LULCC in
Ethiopia plays a critical role in climate change and variability through increased droughts and
changes in precipitation patterns that cause flooding and reduce lake size (Zemenu, 2022)
According to FGD participants, the major reasons for the decline in forest cover were the
expansion of agricultural (crop and livestock) production activities, and the cutting of trees for
timber, fuel wood, and charcoal production, in line with this Assefa [32] indicated that the
decline of forest cover is mainly caused by expansion of agricultural land and cutting of trees
for house construction and charcoal production.

In Ethiopia, deforestation of forest land and conversion to agricultural land is one of the main
processes of LULCchange. Fuel wood collection, logging, commercial agriculture, and
charcoal production are the primary direct drivers, while indirect drivers of land use are land
cover, and population growth, which is essential for natural resources, governance, and
economic growth (Kissinger et al., 2012). Remote sensing data and statistical analysis
integrated into geographic information systems (GIS) are powerful tools to identify, analyze
and understand LULCC patterns (Negassa et al., 2020). Therefore, this study focuses on the
application of remote sensing data and GIS techniques integrated into the variables and
assessing LULCC and farmer perceptions of land cover changes occurring over 30 years in
the Jimma Zone of south-western Ethiopia in the Setema District take place

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1.1. Statement of the problems

At the beginning of the 19th century, 40% of the land in the country was covered by forests
(Kupkov et al., 2021).Both urban and rural areas are experiencing rapid population growth,
overgrazing, deforestation, unplanned land use for settlements, agricultural expansion, and
other activities increasing from time to time, and the earth's environment is a dynamic system
comprising many interacting components that are constantly changing change (Leta et al.,
2021). The LULC change poses significant global economic and environmental risks
(Spruce et al., 2020) and changes in LULC impact the overall functioning of the Earth system
at local, regional, and global scales (Esgalhado et al ., 2020).

In Africa, the land-use change resulted in the conversion of 75 million hectares of forest to
cropland and rangeland between 1990 and 2010 (Gibbs et al. 2010). (Ajibola et al., (2020))
explained that the slow development of agricultural technology in Africa is because slash and
burn cultivation has become commonplace, resulting in forests being harvested for firewood
and charcoal to fuel to respond to the growth of cities, and due to population growth, the
fallow periods became shorter. More importantly, in East Africa, nearly 13 million hectares of
original forest were lost over the same 20-year period, and the remaining forest remains
fragmented and under threat (Mango et al., 2011).
In most East African countries, Ethiopia has a fragile highland ecosystem that is currently
under stress due to increasing population pressures, severe deforestation and loss of
biodiversity and ecosystem services, land degradation, land, air, and water pollution (Tsegaye,
2019), and traditional agricultural practices such as cultivation on steep slopes without
protection, climate and land use, and cover changes (Nigatu, 2014). Consequently, this would
lead to a loss of forest cover and greater hydrological variability (Kalhor et al., 2019). In
Ethiopia, the problem of LULCC is more severe in the highlands (Demissie, 2022.). Several
studies conducted at the national and local levels of Ethiopia on LULCC showed that under
Ethiopian conditions, land-to-agricultural relocation is the main driver of LULCC
(Hailemariam et al., 2016). Something is true in the Setema district the natural environment is
degraded over time due to several factors. The main challenges related to land cover changes
are rapid population growth and scarcity of land for agriculture, loss of biodiversity and
during the expansion of agricultural land coverage from forest land coverage, illegal wetland
expansion to agricultural land, deforestation of the forest, illegal logging, and illegal

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Settlement around the forest as the result of population pressure. Despite this, there are no
reliable studies intentionally conducted to assess LULC change and farmers' perception of this
change in the Setema district of Jimma Zone in southwestern Ethiopia.

1.2. Objective

1.2.1. General objective

The main objective of this research is to assess the extent of land use land cover change and
farmers perception toward LULC change in Setema district, south western.

1.2.2. Specific objectives

1. To determine the rate and extent of LULC change pattern over the past thirty years in
the study area.
2. To identify the major drivers and factors contributing to land use and land cover
changes in the study area.
3. To identify the farmer perception toward LULCC in the study area.

1.2.3. Research question

1. What is the rate and extent of LULC change pattern of the study area over thirty
years?
2. What are the major factors affecting land use land cover change in the study area
3. What is the knowledge of the farmers toward LULCC in the study area?

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 2. LITERATURE REVIEW

2.1. Land
Land is a fundamental natural resource. Throughout human history, humans have obtained
most of their food and much of their fuel, clothing, and shelter from the land (Zerga, 2016).
The land was man's inhabitant and habitat; it was a matter of life and death, survival or
starvation (Federici, 2018). The land is a resource in agricultural production but is ultimately
available within a nation and even globally (Wolford et al., 2013). The fixed nature of land
supply distinguishes it from other factors of production (Xu and Wang, 2020). The land is the
most physically immobile, although it can be used in different ways and each piece of land
differs in its economic potential (Zerga, 2016). Land is a capital stock, fixed asset, or
investment and a measure of wealth (Barbier, 2013).

The value of land reflects not only the expected rate of return on land as a capital investment
in agricultural production, but also the need to hold it as a means of livelihood, financial
security, a transfer of wealth over a generation, and a resource for consumption purposes
(Kochar, 2004). The land price reflects all these requirements, services, and uses (Zerga,
2016). The land is a foundation of all human activity and its proper management is key to
creating and sustaining a civilized society (Blaikie and Brookfield, 2015). The land is a
commodity that can be assigned a value and traded through land markets (Tian and Ma,
2009). It is also a commodity that can be taxed to generate revenue that supports good
governance (Moore, 2007).

A country can be viewed as a cultural entity. It has a cultural dimension that lies at the heart
of all nations. People often have an emotional relationship with the land they claim to own
and the place they live, which is why proper stewardship of the land is necessary for a stable
society and social justice (Abu-Lughod, 2015). The land is becoming a scarce resource due to
population growth and industrialization (De Janvry, 2010). The rapid growth of mining
activities can also be considered one of the reasons behind the decline and degradation of
land. Therefore, it becomes an important task to regulate mining areas for sustainable
development and environmental protection. According to (Austin et al., 2019), minerals
usually occur under features such as forested areas or agricultural land where mining activities
have to be carried out and the costs of degrading that forested or agricultural land. Therefore,
it becomes imperative to monitor such changes on the Earth's surface.

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2.1.1. Land use land cover change

Although the terms 'land use' and 'land cover' are sometimes used interchangeably, each has a
distinct meaning. Land cover is the bio-physical layer covering the earth surface, while land
use represents the human utilization of the land cover (Nedd et al., 2021). Land cover
includes earth's land surface distribution of vegetation, water, desert and ice as well as the
biota, soil, topography etc. in the immediate subsurface, and it also includes human activity
areas, such as settlement, mine exposure (Patgiri and Amin, 2017). LULC are defined by
many scholars and organizations. According to (Genet, 2020), Land use is the total of all
arrangements, activities and inputs that people undertake in a certain land cover type”. In
contrast, Land covers “is the observed physical and biological cover of the earth’s land as
vegetation, rocks, water body or man-made features” (Mengstu, 2016).

Similarly, presented the term land cover to the observed biophysical cover of the earth’s
surface are Water surface, soil, rock, vegetation and manmade features and land use refers to
peoples activity on the land cover (Olorunfemi et al., 2020). According to (Shao et al., 2016)
land cover as the basic parameter to evaluate the content of earth surface, are important
factors that affect ecosystem condition and function. In addition, it is also as a biophysical
state of the Earth, which can be used to estimate the interaction in form of biodiversity,
biosphere-atmospheric and geo-sphere-atmospheric interactions. Hence, land cover analysis
plays an important role in many environmental applications nowadays (Sen and Kumar,
2017).

According to (Korbu et al., 2020) three main factors appear to have influenced both the use of
the land and the natural vegetation cover type as they are presently expressed in Ethiopia -
climate, terrain and population. In other word Land is the most important natural resources,
which comprises soil and water and associated flora and fauna, thus involving the total
ecosystem (Singh and Singh, 2013). Knowledge of the spatial distribution of land use and
land cover is essential for the planning and management activities (Arnous et al., 2017). Land
use is characterized by the planning, activities and inputs people undertake in a certain land
cover type to produce, change or maintain it.(Bui,et al., 2021) Also LULC change contributes
significantly to climate change, reduction in forest cover, and biodiversity loss (Bufebo and
Elias, 2021). In addition, LULC change is one of the factors that influence runoff, soil loss,
and stream flow (Yin et al., 2022).

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2.1.2. Driving Forces of Land Use/Land Cover Changes

Land-use/land-cover changes occurred due to natural and anthropogenic factors (Melese,

2016). As (Zia et al., 2016) states there were three main driving causes for LULCC. These
are; natural processes, such as wildfire, climate, and atmospheric changes, and pest
infestation, direct effects of human activity such as deforestation, soil erosion, and reduction
of biodiversity which brings land degradation, and construction like road and buildings and
indirect effects of human activities, like depletion or lowering of the water table and
contamination of groundwater.

Also anthropogenic factors are the major driving forces of LULCC (ABDULA, 2021) even
though there is also a contribution from the natural processes. LULCC is a very complex
process due to its causes and impacts are very closely related; for example, land degradation
(Alemu, 2015). Currently, the human-related causes of LULCC are very serious (Abdula et
al., 2021). For instance, expansion of agricultural land (Hailu et al., 2015); (Bimrew et al.,
2019) due to rapid population growth responsible for the massive collapse of natural
vegetation, loss of biodiversity, and land degradation. Extreme transformation of forests,
grassland, and shrub land brings a reduction of plant species diversity and continuously
shrinking of natural wildlife (Starik et al., 2020). Intensification of agriculture such as crop
and pastoral land towards the natural ecosystem which is related to population growth also
contributes to extreme changes of LULC and environment (Thekkeyil et al., 2022).

Moreover, rapid population growth reduces forest areas and woodlands (Wang et al., 2020).
This destruction affects biological diversities and functions ecosystems (van der Plas, 2019)
and causes climate change which raises the risk for wildfire (Scheller et al., 2018) also agreed
that population expansion causes deforestation and creates pressure on forest resilience
(Smith, et al.,2016). This extreme destruction of forestland is a root for climate change at the
local, regional and global levels (Seymour and Busch, 2016). Deforestation also affects the
process of atmosphere and thermodynamics at the earth-atmosphere interface and water
storage capacity and soil hydraulic conductivity (Bimrew and Oda, 2019). Besides, changes in
LULC affect hydrological cycles and their parameters (Arabameri et al., 2019).

Additionally(WoldeYohannes et al., 2018) also desertification, loss of biodiversity, habitat

destruction, soil degradation, and a reduced ability of the watershed to sustain natural
resources and ecosystem services are the consequences of land use/land cover change. The

7
consequences of land use change challenge conservation, management, and rehabilitation
activities. Ayele and Sathishkumar (2014) the relationship between land use/land cover
change and its driving factors is complicated and dynamic. Some of the previous studies
suggest that demographic changes contribute more than any other causative factors of land
use/land cover changes (Bufebo and Elias, 2021).Other studies suggest economic factors to be
the major drivers of LULC change (Bufebo and Elias, 2021.

The LULC change has impacts on hydrology and changes the quality of water and water
flows, causing surface water pollution, depletion of groundwater aquifers (Han et al., 2017).
Further, LULCC due to urban expansion causes urban heat island (Balew and Korme, 2020)
that has adverse social, economic and environmental effects both at the local, regional and
global scale (Zheng et al., 2014.). Thus, higher urban temperatures increase the demand for
air conditioning, change urban thermal environments and ultimately lead to thermal
discomforts and incidence of heat-related illnesses (Balew and Semaw, 2022).

2.1.3. Land use land covers change detection

Change detection is valuable in many applications related to LULC changes detection

including cultivation, urban expansion and landscape changes (Fetene and Amede, 2022).
Understanding landscape patterns, changes and interactions between human activities and
natural phenomenon are essential for proper land management and decision improvement
(Rawat, and Kumar, 2015). Geographic Information Systems (GIS) and remote sensing (RS)
are powerful and cost-effective tools for assessing the spatial and temporal change of LULC
(Munthali and Adeola, 2019). Nowadays, remote sensing data are applicable and valuable for
land use and cover change detection studies (Rajasekhar et al., 2017). Remote sensing data is
the most common source for detection, quantification, and mapping of LULC patterns due to
its repetitive data acquisition, suitable for processing, and accurate geo-referencing
(Abualgasim et al., 2022).

2.1.4. Land Surface Temperature

Land surface temperature (LST) is the fundamental climatic parameter in determining the
surface radiation and the energy exchange (Weng and Fu, 2014. It denotes the skin
temperature of the earth's surface phenomena (Zandi et al., 2022). LST also an important
variable in land atmosphere interactions and a climate change indicator that varies over space

8
and time as a function of vegetation cover, surface moisture, soil types, and topography
(Mundia et al., 2014). Land-use change alters the thermal environment; the LST is a proper
change indicator to show the thermal changes concerning land-use changes (Youneszadeh et
al., 2015). The changes of LST is related to many factors, including changes in land use, land
surface parameters, seasonal variation, climatic condition, and economic development
(Mohammad et al., 2022). In other words, the change of land use is the important reason
leading to an increase in LST (Liu et al., 2018).

The concept of LST has been widely used by many researchers across the globe for
unpredictable rainfall, temperature fluctuations, and vegetation patterns are aspects that alter
the LU/LC in a region (Tariq et al., 2020.). The shifting of this land use/land cover is
attributed to anthropogenic activities that alter the physical characteristics of the land surface
and abrupt changes in temperature in a particular region (Tariq et al., 2023). It is well
documented that as land surface cover changes, the surface temperature of that particular area
also changes (Buyadi et al., 2013.).

2.1.5. Farmer perception in land use land covers change dynamics

The studies on farmer perceptions in Ethiopia showed that deforestation targets the expansion
of agricultural activities ranging from small to large scale commercial farming systems (Kassa
et al., 2017), weak law enforcement (Gifawesen et al.,2020) , and drought prevalence (Gobie,
and Miheretu, 2021) have been identified as crucial factors driving LULC changes in
Ethiopia. Informants to the current study claimed that greater population growth has driven
urbanization, forest collection, and large numbers of livestock overgrazing and agricultural
land (Mariye and Li, 2022).Population size change is often considered to be one of the main
drivers of land use change (Alaro Dutebo., 2021). It was also discussed that population
dynamics and related issues are important drivers for LULC changes. Similarly, a study from
central Malawi reported that population growth; fuelwood collection, charcoal production,
and poverty are the main drivers of LULC changes (Abebe and Ewunetu, 2022).

Similarly, van der Esch (2017) discussed that the conversion of forests and forests to
cultivated land is mainly due to the desire for land for crop production to meet the food needs
of the ever-growing human population, which ended in the loss of Soil productivity and soil
degradation. AGIZE and Sishaw (2020)) showed that the expansion of arable land in the
Borana rangeland was a major contributor to the conversion of grassland management

9
practices to arable land. Rapid population growth led to land fragmentation and small farm
size, which in turn led to land use change (Anteneh, 2022). According to Mariye and Li
(2021), rural poor households depended on the sale of firewood and charcoal for additional
income, which also contributed to the eradication of forests and shrubland.

A study by Wubie and Nicolau (2016)) reports that the increased demand for firewood in the
absence of alternative energy sources has led to the destruction of forests. Similarly,
continued logging activities in the forest reserve led to its loss in Nigeria (Mariye and Li,
2022). Deforestation by local people for various purposes caused land use changes in the
southern part of the country (Muk, 2019). ). The LULC change was more associated with the
rural people livelihood that depends on mixed farming of crop production and livestock
(Miheretu and Yimer, 2018).LULC change was caused by the expansion of agriculture
through unplanned and inappropriate land management practices to meet the food demand of
the local communities (Temesgen et al., 2022). In many areas of developing countries, LULC
changes caused by deforestation have increased the agricultural production of rural
communities (Berihun et al., 2019)

2.1.6. Role of GIs and remote sensing

Geographic information systems and RS techniques have been widely used around the world
for the study of historical changes in LULC and other surface characteristics (Dang et al.,
2012). Furthermore, understanding the correlation between LST and LULC is important to
managing the land. It provides a large variety and amount of data about the earth’s surface for
detailed analysis and change detection with the help of various airborne and spaceborne
instruments (Gergel and Turner, 2017). With the availability of historical remote sensing data,
the reduction in data cost, and increased resolution from satellite platforms, remote sensing
technology appears ready to make an even greater impact on monitoring land cover change
(Dang et al., 2012).

Land-use and land-cover change can be analyzed over a period using Landsat sensors such as
MSS data and TM data using image classification techniques (El Jazouli et al., 2019). Since
1972, Landsat satellites have provided repetitive, synoptic, global coverage of high-resolution
multispectral images. Their long history and reliability have made them a popular source for
documenting changes in LULC over time (Kuntla, 2021). and their evolution is further

10
marked by the launch of Landsat 7 (Enhanced Thematic Mapper Plus) sensors by the United
States in 1999. According to Abdula (2021), the following are four LULCC detections, which
are important when monitoring natural resources: distinguishing the nature of the change;
detection or finding of the changes that have occurred; measuring the area extent of the
change; and assessing and investigating the spatial pattern of the change. The basis for using
remote sensing data for change detection is that changes in land cover result in changes in
radiance values, which can be remotely sensed.

11
 3. MATERIAL AND METHOD
3.1. Description of the study area

3.1.1. Location of the Study area

The study area, Setema district, is one of the Jimma zones located in the southwestern part
and has 21 kebeles and 1 town kebele. Setema is bordered on the south by Gera, on the west
by Sigmo, on the north by Didesa woreda (Bunno-Bedele) Zone, and on the southeast by
Gomma. The administrative center of the district is Gatira. Annual RF averages 1200mm to
2000mm/year. Average temperature: maximum 21 and minimum 14 °C.The geographical
location of the district is 7°58'51''N and 36°12'36"E, at a distance of 457 km and 100 km
southwest of Addis Ababa and Jimma, respectively. The altitude of Setema district ranges
from 2,250 to 3,010 m a.s.l., with the highest points being in the Damu Sika Mountain
(Source: from Setema district of Agriculture and natural resource office 2023)

Figure 1. Study area

12
3.1.2. Demographic and socio economic characteristics of study area

According to CSA (2007), the total population of the district is 103,221, of whom 50,744
were men and 52,477 were women; 4,729, or 4.58% of its population, were urban dwellers.
The majority of the inhabitants were Muslims, with 96.91% of the population reporting they
observed this belief, while 2.67% of the population said they practiced Ethiopian Orthodox
Christianity. The study area is located about 457 kilometers away from Addis Ababa, the
capital city of Ethiopia, and 100 kilometers north-west of Jimma. The farmers found in this
district grow crops such as teff (Eragrostis teff), maize (Zea mayes) for domestic
consumption, as well as coffee (not grown extensively), which is also an important cash crop,
covering less than 20 square kilometers (7.7 square miles).

Agriculture is the main economic activity and is dominated by small-scale and mixed-crop
and livestock farmers. More than 90% of the district population depends on agricultural
activities. Crop production is mainly rain-fed. Coffee plays a major role in income generation
in the area. Maize (Zea mayes), teff (Eragrostis teff), and sorghum (Sorghum bicolor) are the
major crops grown in the area. Pulses crops, such as beans and peas, are grown to a lesser
extent in the area (Guinand, and Lemessa, 2000) Maize (Zea mayes) and enset (Ensete
ventricosum) are the major staple food crops, and they are strategic crops substantially
contributing to the food economy of the district. Setema district has 35 kilometers (22 mi) of
year-round road, for an average road density of 31.6 kilometers per 1,000 square kilometers (5
mi/100 sq mi). About 60% of the urban and 9.6% of the rural population have access to
drinking water (https://en.wikipedia.org/wiki/setema).

3.1.3. Biophysical characteristics of the study area

Setema district has a mean annual rainfall of 1665 mm/year and annual average maximum
and minimum temperatures of 27.9°C and 11.9°C, respectively, as well as perennial rivers
such as Onja, Salako, Gidache, and Gabba. The district's land cover is 27.2% arable or
cultivable (20.8% annual crops), 13.1% pasture, 55.1% forest, and the remaining 4.6% is
degraded or built up (Girma et al., 2016). The western and south-western parts of the country
have such a unimodal rainfall pattern, with October to January ("Birra") defining the end of
the long rainy fall season, followed by a medium-to-short dry season during the same period,
and February to May ("Bona") the start of the long rainy springtime (SWoARD).

13
3.1.4. Land use system of the study area

As in many developing countries, most rural people in Ethiopia depend on land for their
livelihood, and they grow rapidly and have effects on resource bases and natural vegetation by
changing to other land uses as a result of population growth and scarcity of land ( Degefu et al.,
2019). The percentage of land used for agriculture in Ethiopia has been increasing since the
beginning of the 20th century. Though the land use of the study area is found in the Jimma
zone, which grows coffee more, the study area does not focus on the production of coffee; it
produces cereal crops, and most of the land is used for agricultural production such as teff
(Eragrostis teff), sorghum (Sorghum bicolor), and pulse crops such as beans and peas, which
are grown to a lesser extent in the area.

3.2. Method of Data Collection

The data was collected from both primary and secondary sources. The primary data was
collected from local community, community leaders and Woreda expert by using a
questioner’s direct interviewing, group discussion and observation and Elderly people, land
resource administration experts, and forest protection experts was selected for key informant
interviews. Also From the study area data is collected by using GPS (for collecting point
data) for ground verification to do accuracy assesement of land use.On the other hand, the
secondary datas for this study was collected from free available satellite image archives and
related documents. Other secondary data sources include various non-governmental
organizations, distributed and unpublished sources, for example, reports, articles, diaries, day
by day daily papers, records, maps etc. table.1,shows the dataset and source of data that was
used for this study.

3.2.1. Google earth data collection

Google Earth is a computer program that renders a three-dimensional representation of the

earth based on satellite imagery (https://en.wikipedia.org/wiki/Google earth).Consequently,
Google Earth was used to extract information where the location was inaccessible to take a
sample point, to take the information for previous downloaded information, whereas
for overlaying of sample pointcollectedfromthe studyarea,whichwas first converted to
CSV and KML by showing and moving the time slider of Google Earth.

14
3.2.2. Socio-economic data collection

Household Surveys

The semi-structured questionnaires were conducted to gather the type of LULC change. To
determine the rate and extent of LULC change pattern over the past thirty years in the study
area with local language “Afan Oromo “communication to cross-check and to support the
downloaded image. Consequently, to carry out household surveys, a total of four DA
enumerators were recruited and trained to administer the questionnaire because the HHs knew
them and to obtain clear information from the HHs. Before the activity was carried out, pre-
test interviews were carried out with DAs to make some comments and make the work more
clearly for them.

Sampling techniques for household survey

The gathering of depth information for assessment LULCC of study area, two stage
sampling methods and designs were used, which are stratification methods (four strata for
each Kebeles) and purposeful methods depending on the criteria of the cereal crop grower
Kebeles, Kebeles changing wet land to agricultural land illegally, and Kebeles proximity to
forest land by more discussion with kebele administrator “kabines” and DAs. Then, from the
stratified kebele, four Kebeles were selected purposefully depending on the most proximity to
the forest land, the most deforested forest land for farmland expansion (Done, Setema
Kecha, a Gido), and the most illegal conversion of wetland to agricultural land (Susa Atila)
Kebeles.

Key informant interview

KII involves interviewing a select group of individuals who are likely to provide needed
information, ideas, and insights on a particular subject to obtain in-depth information that
contains, as a rule of thumb, 15 to 35 key informants ( Kumar et al., 1989). Therefore, 15 key
informant people, such as four DAs, four kebele leaders and administrators, and four elder’s
people, were selected from each purposefully selected Kebeles of Setema Kecha, Done, Susa,
and Gido, whereas one district land use land administrator expert, one forest expert, and one
OFWE expert from the study district (Table.1), depending on their criteria of depth
knowledge of what is going on in the Kebeles, lived for a long period of time around the study

15
area to collect the detailed information of the assessment of LULCC and farmer perceptions
towards land cover changes in the study area with the help of DAs and Kebeles administrator.

Table 1. Number of key Informants participants

T/L Setema Kecha Done Susa Gido Total
1 DA, 1 1 1 1 4
2 kebele leader/administrator 1 1 1 1 4
3 Forest Expert, 1 0 0 0 1
4. Woreda land use land administration
expert 1 0 0 1 1
5 OFWE expert 1 0 0 0 1
6 Elder’s peoples who live for a Long 1 1 1 1 4
Total 15

Source: Author, 2023

16
3.2.3. Focus group discussion

A focus Group discussion is a group of individuals who carefully participate (5-10), but 10 is
preferred per group, similar types of people, environmentally comfortable, and circle
seating (Dale et al., 2002.).Therefore, to accomplish the objectives of the study areas, one
FGD was selected for each of the four Kebeles selected purposefully, which have three elder
people members, two youths members, and one religious member to cross-check and validate
the information collected from the KII and household survey depending on the
criteriaofknowledgeaboutthearea, ability to respond the question, group proximity to
the study area, and deep discussion with the Kebeles administrator and DAs.The participants'
members of the FGDs were asked to identify the farmer's perception toward LULCC in the
study are.

Table 2. Number of FGD participants Members of FGD participants/Kebeles

T/L Type of FGD Setema Kecha done Susa Gido Total

1 Elder people 3 3 3 3 12

2 Youths 2 2 2 2 8

3 Religious leader 1 1 1 1 4

4 Total 6 6 6 6 24

Photo taken by Author, (2023)

17
Table 3. Demographic and Socio economic characteristics of house hold in study area
Age No of Respondent %
20-35 47 24.5
36-45 70 36.5
46-55 52 27
56-70 23 12
Total 192 100
Gender Sex of respondent
Male 157 81.7
Famel 35 18.3
Total 192 100

Demographic and Socio economic characteristics of house hold are very important variables
in Assessment of land use land cover change and farmer perception towards land cover
changes. Shows that about 47 (24.5%) of the respondents were in the age group of 20-
35years, 70 (36.5%) respondents were in the age of 36-45, 52 (27. %) of respondents were in
the age group of 46-55 years, 23 (12%) of respondents were in the age group of 56-70years.
From this one can conclude that the majority of the respondents were in the age group of 35-
46 years and Shows that about 81.7% were male respondents, while about 18.3% were female
respondents. Thus, from the above information the majority of respondents were
male respondents.

Table.4: Education Status of respondents

villageNot attend elementary Highdchool Diploma above

male Femal Total male Fema Total male Fema Total male Femal Total
l l
Setema 1 1 2 23 5 28 14 3 17 5 3 8
Kecha
Done 2 - 2 18 2 20 14 1 15 4 1 5
Susa 4 1 5 22 3 25 11 3 14 3 2 5
Gido 2 3 5 16 2 18 13 5 18 4 1 5
Total 14 91 64 23

The educational level of the society affects household decisions that determine the welfare of
the society, such as income, health, and their attitude towards using land. It may also enable
the household to have a broad vision of the surrounding environment. Regarding the
educational status of the sample households, the survey data collected from the study area

18
shows that half of the population is in elementary school (91,47.4%), while the respondents
are in high school (64,33.3%), the respondents are in diploma and above (20,10.4%), and they
cannot read and write (14,7.3%).

3.3. Sampling technique and sample size

The study used a purposive random sampling method with a total sample size of 192 to select
representative population distribution of LULCC kebele and then selection of representative
households. Then four kebele were selected from the total of 22 (21 local and 1 Woreda)
kebele that have LULC in the area. In the second stage, the total of 192 samples was
proportionally allocated to each kebeles (i.e., Setema-kecha kebele 55, Done 44, Gido 46, and
Suusa Atilla 47). Households were drawn from the kebeles using simple random sampling by
the lottery method. Using selects using the sampling formula by Yamane (1967)

N
n= −−−−−−−−−−−−−−−−−−−−−−−−( 1 )
1+ N ( e 2 )

Where, n = sample size, N = population size, e = (error margin7%). The sample size will draw
on the 2877 population with 97% confidence level and error margin of 7% is 192. Of which,
192, members from the Setema-Kecha kebele55, Done-Kebeles 44, Gido kebele 46 and Susa
Atilla 47 members from Kebeles households will be determines to be respondents for the
questionnaire distributed. The list of households is obtained from Setema Woreda Office
Agricultural and rural development (SWROD)

Table 5. Sampling frame and sample size

Name of sampled Kebeles Total households # # of sample household

Head # %
Setema-Kecha 828 55 28
Done 648 44 23
Gido 691 46 24
Susa Atilla 710 47 25
Total 2877 192 100%

19
3.3.1. Study design

Four Kebeles, namely Gido Bari, Susa Atilla, done, and Setema Kecha, were intentionally
selected from the sum of 21 Kebeles in the Setema district. Three highland kebele and one
lowland kebele, or Dega and Weinadega, respectively. Using information from the very worst
Setema district administration office and Setema district agricultural office, kebele were
particularly selected for this study as they represent scenarios in LULCC. In the second stage,
each village found in the chosen Kebeles has been categorized into two parts based on how
far away the forest edge was (and over (or less than 1 km) and 50 m) away. After which, two
villages are chosen specifically from each category. There are various research methods.
However, the mixed research method was chosen for the purpose of the study. In order to
improve the quality of the information from a multitude of sources during analysis and
interpretation, the mixed method gathers sources and applies the triangulation method. The
quantitative, technical, and qualitative phases actually occur one after the other, with the
quantitative or technical phase receiving higher priority and mixing taking place at the data
interpretation stage, as indicated in Debesa et al., 2020. The quantitative data received
precedence, and the two methods were integrated during the study's interpretation stage.
Priority was given to the quantitative data, and the two methods were embedded during the
interpretation stage of the study.

3.2.4. Land Use and Land Cover Classification

To classify an image pixel as belonging to a spectral class, the supervised technique was
employed. Post processing images, such as classification, was done. The classification scheme
has to have classes that are used for the study and discernible from the available data. For this
purpose, Anderson's (1976) classification scheme was applied to the land use and land cover
classification. The supervised classification maximum likelihood algorithm in ERDAS
Imagine was applied in this study to classify land cover using multispectral satellite data
obtained from Landsat 5, 7, and 8 for the periods 1992, 2002, 2012,2012 and 2023. The study
area is classified into five major land cover classes. These are agricultural land,wetland, forest
land, grassland, and settlements.

From the parametric and non-parametric decision rules in a supervised classification system,
the parametric decision rule was selected as it depends on the statistical descriptors (mean and

20
covariance matrix) of the pixels assigned as a training sample for the land cover class.
Maximum likelihood parametric was used to classify the land cover of the study area.
Maximum likelihood considers the variance and covariance of class signatures to assign each
object or pixel to a class (Sisodia and Kumar, 2014).

3.2.5. Accuracy Assessment

The accuracy assessment reflects the real difference between classifications and the reference
map or data (Negassa et al., 2020). If the reference data is highly inaccurate, the assessment
might indicate that classification results are poor. Random selection of reference pixels was
used to reduce the biases of using the same pixels for testing classification. 102 ground
control points or references were collected randomly from the study area by GPS. 35 points
from agriculture, 30 points from forest, 17 points from grass land, 8 points from settlement,
and 12 points from wetland were collected randomly from the study area.

Producer’s accuracy is the map of accuracy from the point of view of the map maker (the
producer). This is how often are real features on the ground correctly shown on the classified
map or the probability that a certain land cover of an area on the ground is classified as such.
It is also the number of reference sites classified accurately divided by the total number of
reference sites for that class (Eikelboom et al., and Widimsky, 2017).... (E. 2)

User’s accuracy is the accuracy from the point of view of a map user. The User's accuracy
essentially tells us how often the class on the map will actually be present on the ground. The
User's accuracy is the complement of the commission error; the User's accuracy is equal to
100%-commission error. The User's accuracy is calculated by taking the total number of
correct classifications for a particular class and dividing it by the row total (Tripathy; Anand
et al., 2017). (Eq. 3)

Overall accuracy will be used to calculate a measure of accuracy for the entire image across
all classes present in the classified image. The collective accuracy of the map for all the
classes can be described using overall accuracy, which calculates the proportion of pixels
correctly classified (Wickham et al., and Baer, 2017).

21
3.2.6. Kappa coefficient

The Kappa coefficient, which measures a classification agreement, can also be used to assess
the classification accuracy. It expresses the proportionate reduction in error generated by a
classification process compared with the error of a completely random classification
(Congalton, 1999). The Kappa coefficient (K) is calculated using the equation given by Foody
(2004). (Eq. 5)Where, Xii

Where: r = is the number of rows in the matrix; Xii = is the number of observations in rows i
and column i (along the major diagonal); Xiy = the marginal total of row i (right of the
matrix); Xi+1 are the marginal totals of column i (bottom of the matrix); N is the total number
of observations.

Therefore, to get the kappa coefficient of the classification process, the Congalton formula
was applied (1999). …….. (Eq.6)

Image classification

Different studies showed that image classification is an important process for quantifying the
location, extent, and trends of LCLUC (Hano, 2013). Similarly, Reddy (2008) reported that
image classification is a procedure to automatically categorize all pixels in an image of a
terrain into land cover classes. So, image classifications were conducted for the study area in
order to classify all pixels of satellite imagery into land use and land cover based on
reflectance characteristics of features by using the basic visual image interpretation elements
(color, tone, texture, size, shape, structure, association, shadow) and the prior knowledge of
the area. Accordingly, supervised classification was employed to classify the study area into
different land use and land cover categories and to assess the trend and rate of change of the
LULCC.

22
Figure 2: Flow Chart

23
 4. RESULTS AND DISCUSSION

To determine the rate and extent of LULC change patterns over the past thirty years in the
study area Four LULC maps were produced for the years 1992, 2002, 2013, and 2023, and
five LULCC were identified and classified: agricultural land, forest land, grass land, wet land,
and settlement. Fig, 4.1

Figure 3. Spatial distribution of LULCC in Setema district (1992–2023)

Generally, over thirty years (1992-2023), the gross changes in hectares and percentage change
in the study area varied from one LULC class to another, whereas the transition matrix also
varied from period to period, which was computed using the MOLUSCE plug-in extension
version 3 (Table:4.1 4.2, and 4.3).

In the study area at the base year (1992) from the total area of 116474.8 hectare the area were
covered by forest 47134.3ha-1 (40.5%) followed by grassland 8588.8ha-1 (7.4%) and

24
wetland1890.0ha-1 (1.6%); the other LULC of agricultural land 57868.0ha-1(49.7%) and
settlement land 993.8ha-1(0.9%) however in 2002 forestland from 47134.3ha-1(40.5%) to
45508.8 (39.1%) followed by grassland from 8588.8ha-1(7.4%) to 5442.8ha-1 (4.7%) and
wetland from 1890.0ha-1 (1.6%) to 856.0(0.7%) the other LULC of agricultural land from
57868.0ha-1(49.7%) to 62881.0(54.0%) and settlement land from 993.8ha-1(0.9%) to
1685.7(1.4%) while in 2013fost land from45508.8ha-1(39.1%) to 35302.6ha-1(30.3)grass
land from 5442.8ha-1 (4.7%) %) to 6685.9ha-1 (5.7%) wetland from 856.0(0.7%) to741.9ha-
1(0.6%),agriculture land from 62881ha-1(54.0%)to71669.8ha-1(61.6%) and settlement from
1685.7ha-1(1.4%) to1974.7ha-1(1.7) the recent year (2023) forest cover 32617.5 ha-1
(28.0%) were declining in alarming rate followed by grass land cover 5066.7 ha1 (4.4%) and
wet land 428.0ha-1 (0.4%) (Table.5). The analysis of land use and land cover change during
the period of 1992–2002 and 2013–2023 showed that there was a significant decrease in
forest, with a consequence of an increase in agricultural land and settlement land (Table 6).
Between 1992 and 2023, agricultural land and settlement area increased from 49.7% to 65.3%
and from 0.9% to 2.0%, respectively (Table 5).

During the same period, the rate of forest land, grass land, and wetland decreased from
40.5%) to 28.0%), from 7.4% to 4.4%, and from 1.6% to 0.4%, respectively (Table 5). In the
period between 2002 and 2013 (Table 5), the total area of forest land cover, grass land cover,
and wet land cover increased from 39.1% to 30.3%, from 4.7% to 5.7%, and from 0.7% to
0.6%, with a magnitude area and percentage change of -927.8 ha-1 (-1.1%), 113.0 ha-1 (-
0.9%), and 10.4 ha-1 (-0.6%), respectively (Table 6). The annual decreasing rate of forest
land cover, grass land cover, and wetland cover change between 2002 and 2013 was -0.1%, -
0.8%, and -0.054%, respectively, per year in the study area (Table7). Table (5) In the time
period from 2013 to 2023, the change in land use and land cover of the study shows that there
is an increase in area coverage/proportion of agriculture land cover, settlement/build up from
71669.8 ha-1 (61.6%) to 75984.3 ha-1 (65.3%), from 1974.7 ha-1 (1.7%) to 2277.3 ha-1
(2.0%), respectively. During this period, there is also a decline in forest land cover, grass land
cover, and wet land cover (Table 5). changed from 35302.6 ha-1 (30.3%) to 32617.5 ha-1
(28.0%), from 6685.9 ha-1 (5.7%) to 5066.7 ha-1 (4.4%), from 741.9 ha-1 (0.6%) to 428.0
ha-1 (0.4%), and with the area change and percentage change of -268.5 ha-1 (-0.395%),-
14516.7 ha-1 (-106.3%), and-161.9 ha-1 (-1.377%). -3522.05 ha-1 (-151.9%), -31.4 ha-1 (-
2.682%), and -1461.97 ha-1 (44.7%), respectively (Table 7).

25
 Between 2013 and 2023, the annual decreasing rates of forest land cover, grass land cover,
and wet land cover were 26.85 ha-1 (-0.045%), 16.2 ha-1 (-0.147%), and 3.14 ha-1 (-0.27%),
respectively, whereas the annual increasing rates of agricultural land cover and settlement
cover were 431.5 ha-1 (0.292%) and 30.3 ha-1 (0.711%), respectively, per year. (Table 7).
Generally, forest land is the major LULC of the area in the 1992 period in relation to the total
coverage area of the land, whereas agricultural land is the major LULC of the area in the 2023
period in relation to the total area coverage of the LULCC.

Table 2.Categories and patterns of Land Use/Land Cover of Setema districts.

1992 2002 2013 2023
no C/name Area(ha) Area% Area(ha) Area% Area(ha) Area% Area(ha) Area&
1 AL 57868.0 49.7 62881.0 54.0 71669.8 61.6 75984.3 65.3
2 FL 47134.3 40.5 45508.8 39.1 35302.6 30.3 32617.5 28.0
3 GL 8588.8 7.4 5442.8 4.7 6685.9 5.7 5066.7 4.4
4 WL 1890.0 1.6 856.0 0.7 741.9 0.6 428.0 0.4
5 ST 993.8 0.9 1685.7 1.4 1974.7 1.7 2277.3 2.0
116474.8 100.0 116374.32 100.0 116374.8 100.0 116373.8 100.0
7
Note: AL=Agricultural FL = forest land, land GL= grass land; WL = wetland; SL= settlement land

70
65.3
61.6
60
54
50 49.7
39.1
40 40.5 1992
30.3 2002
30 28 2013
2023
20

10 7.4 4.7 5.7

4.4 1.6 0.7 0.9 1.4 1.7 2
0 0.6 0.4
AL FL GL WL SL

Figure 4. Figure of Land use land cover classification map of1992, 2002, 2013 and 2023.

26
Table 3. Land use land covers change
LULC Land use land cover change
1992-2002 2002-2013 2013-2023 1992-2023
area( ha) area % area (ha) area % area (ha) area % area (ha) area %
AL 501.3 0.4 799.0 0.6 431.5 0.292 18116.3 22.7
FL -162.6 -0.1 1974.7 -1.1 -268.5 -0.395 -14516.7 -106.3
GR -314.6 -0.3 113.0 0.9 -161.9 -1.377 -3522.05 -151.9
WL -103.4 -0.1 -10.4 -0.6 -31.4 -2.682 -1461.97 -44.7
ST 69.2 0.1 26.3 0.7 30.3 0.711 1283.5 39.2
Note: AL=Agricultural FL = forest land, land GL= grass land; WL = wetland; SL= settlement land

200
0 22.7% 39.2%
-106.3% -44.7%
-200 AL FL GL WL ST
-400
-600
-800 1992-2023
-1000 2013-2023
-1200 2002-2013
-1400 1992-2002
-1600 -1519%

Figure 5. Changes of LULC classes between 1992-2002, 2002-2013, 2013-2023&1992-2023

27
Table 4. Rate of Land Use/Land Cover Change in 1992-2002, 2002-2013, 2013-2023, 1992-
2023
LULC Land use land cover change
1992-2002 2002-2013 2013-2023 1992-2023
area( ha) area % area (ha) area % area (ha) area % area (ha) area %
AL 50.13 0.04 72.63 0.054 43.15 0.03 584.46 0.73
FL -16.26 -0.01 -84.34 -0.1 -26.85 -0.045 -468.3 -3.429
GR -31.46 -0.03 10.27 0.8 -16.2 -0.147 -113.61 -4.9
WL -10.34 -0.01 -0.94 -0.054 -3.14 -0.27 -47.158 -1.44
ST 6.92 0.01 2.4 0.063 3.03 0.071 41.4 1.3
Note: AL=Agricultural FL = forest land, land GL= grass land; WL= wetland; SL= settlement land

The overall accuracy of the classified images in 2013 and 2023 was 91% and 91.17%,
respectively, with kappa coefficients of 0.88 and 0.882, respectively, which is a perfect kappa
coefficient (0.81–1.00) according to Congalton (1991).The reason why the producer's
accuracy and the user's accuracy were computed is because the overall accuracy of the map
does not always represent the accuracy of individual classes. For instance, the higher users
accuracy of agricultural land (91.17%and the lower producer's accuracy implies that there is a
gain in agricultural land in map classification and a loss in reference data, whereas the higher
producers accuracy of forest land (93.3%) and the lower user’s accuracy (89%) implies that
there is a greater forest gain in map classification and a loss in reference data.

4.2. The rate and extent of LULC change pattern over the past thirty one

From 1992 & 2023 the study area experienced significant LULC change as a consequence of
increasing agricultural activities & populations. Although just over one-third of respondents
(22.9%) said that there is no LULCC, the majority of respondents (77.1%) stated that there is
a LULCC. However, Wetland area coverage has declined highly in this years; a continuous
decline was observed in the three consecutive decades of the study period Forest land cover
appears that it had decreased most significantly. The most significant decrease is forest land
(50.5%) wet land (29.7%) and grazing land (19.8%) respectively. The result shows how, over
the previous three decades, the study's area's forest land use and land cover changed
significantly. According to respondent viewpoint, the extent and rate of LULC changes differs
significantly. Agriculture land and settlement land were significantly increased. Forest land,

28
grazing land, and wet land, on the other hand, were greatly reduced. In addition to key
informants, focus groups, discussions and house hold assessments, the highest land use land
cover change took place. Land cover changes are caused by agriculture expansion (crop and
livestock) agriculture activities (39.6%), and population density (27.6%), Overgrazing
(21.4%) and illegal settlement (11.5%), respectively. This agricultural expansion, expansion
of infrastructure (road and settlement) and fuel wood extraction result was in lined with the
findings of (Shiferaw, and, Suryabhagavan, 2019)

Land cover changes can occur in response to both human and climate impacts. Demand for
more settlements, for example, can frequently result in the permanent destruction of natural
and operational areas, this might result in localized deviations in weather patterns,
temperature, and precipitation. The discussion with KII and FGD focused on the types of
vegetation they have in their surroundings, the uses of these vegetation, how they conserve it
(soil, water, and genetic resource), what their preference is, the trend of the vegetation over
time, how they use their land (for what purpose), how they overcome the problem of drought,
the condition of their livestock, and the type of wild life. Furthermore, all previous published
and unpublished literature on land use land cover, and policy issues has been collected and
reviewed.

Table 5. The reasons for land use land cover changes

The reasons for land use land cover changes Frequency Percent

Agriculture expansion 76 39.6

Population density 53 27.6

Illegal settlement 22 11.5
Over grazing 41 21.4
Total 192 100.0

4.3. Identify the major drivers and factors contributing to land use and land cover
change

The expansion of agricultural land has been a major driver of LULC change, in response to
population growth and the need for food security, there has been a significant conversion of
forests, grasslands, and other natural habitats into croplands and in the study area has
experienced substantial deforestation and forest degradation over the years. Factors such as
subsistence agriculture, commercial logging, fuel wood collection, and infrastructure

29
development have contributed to the loss of forest cover. This has resulted in habitat
fragmentation, biodiversity loss, and increased carbon emissions.

According to KII and house hold reported and ranked that Currently, the human-related
causes of LULCC are very serious, For instance, expansion of agricultural land due to rapid
population growth responsible for the massive collapse of natural vegetation, loss of
biodiversity, and land degradation. The most serious underlying drivers of land use land cover
change KII and FGD information’s were Increase demand for agriculture activities (34.9%),
population growth (28.1%), Charcoal production (24%) and Illegal settlement (13.0%)
respectively. which is confirmed by 192 respondents (Table 9). In addition forest land use has
directly driven or being caused by charcoal production (35.9%), firewood collection (29.2%),
timber extraction (21.9%) and house constructing (13%) respectively. According to key
interviews and focus group desiccation suggest that Extreme transformation of forests,
grassland, and wetland land brings a reduction of plant species diversity and continuously
shrinking of natural wildlife and Intensification of agriculture such as crop and pastoral land
towards the natural ecosystem which is related to population growth also contributes to
extreme changes of LULC and environment This which Overall these findings are in
accordance with findings reported by (Shiferaw,and Suryabhagavan, 2019)

Table. 6. The major drivers and factors contributing to land use and land cover changes

the major drivers and factors contributing to land use and Percent
land cover changes
Population growth 54 28.1

Increase demand for agriculture activities 67 34.9

Valid Charcoal production 46 24.0
Illegal settlement 25 13.0
Total 192 100.0

Land use and land cover change (LULCC) is influenced by a variety of drivers and factors.
These factors can vary in importance depending on the geographic location, socio-economic
conditions, and specific circumstances of a given region. Here are some of the major drivers
and factors contributing to LULCC. According to respondent’s opinion there are the major

30
drivers and factors contributing to land use and land cover change are Climate change
introduces additional challenges to grazing lands. Shifts in temperature and rainfall patterns
can affect the growth and availability of forage, potentially leading to feed shortages for
livestock. Extreme weather events, such as droughts or floods, can further exacerbate grazing
land degradation and reduce livestock productivity. socio-economic factors, including
poverty, income inequality, and market dynamics, can influence land use decisions, poverty
and limited economic opportunities can drive unsustainable land use practices, such as illegal
logging or land encroachment also according to key interview and focus group Discation
Land use policies, land tenure systems, and governance frameworks play a crucial role in
shaping LULCC. Ineffective land management policies, weak enforcement of regulations, and
lack of land-use planning can contribute to unsustainable land use practices and unplanned
development. Addition to the construction of roads and other infrastructure projects can result
in land clearance and fragmentation. These developments open up previously inaccessible
areas, leading to land cover change and associated impacts on ecosystems.

The respondents suggest that as Climate Change (18.2%), Socio-economic Factors (27.6%),
Policy and Governance (31.3%) and Infrastructure Development (22.9%) respectively. The
expansion of legal and illegal settlements, cultivated land, and illegal fire following
encroachment by cultivation, seasonal grazing and firewood collection were prioritized as the
top significantly (p<0.001) ranked drivers (Table::9). According to house hold respondents
reports the extraction fuel wood has been used for heating, cooking and selling. In addition to
HHs according Key informant’s interview and deep discussion with FGD the proximate
causes of land use land cover change in the study area were agricultural expansion, fuel wood
extraction, and expansion of settlement, illegal logging, charcoal production and wood for
house construction in which agricultural land expansion from wetland and forest land is the
serous land use land cover change followed by fuel wood extraction, expansion of settlement
illegal logging and charcoal production This agricultural expansion, expansion of
infrastructure (road and settlement) and fuel wood extraction result was in lined with the
findings of ( Nigussie, et al,. 2023)

31
Figure 6. Illegal settlement and Charcoal production (Phato2023) Left and Right

4.4. Perceptions among farmers regarding Land Use and Land Cover
Change (LULCC) 4.5. Demographic information of the sampled households
The age of the sampled household heads (N=192) ranged from 20 to 70 years old, and more
than fifty percent of respondents ‘age category (20-45 years) was in the productive region.
Approximately 82 % of the sampled households were men. With respect to their education
status, 92.7 % of the respondents attended formal education, and 7.3 % do not attended formal
education. (Table 10).

To identify perception of major LULC change types and other associated variables of change
showed statistical significance (p<0.001). The local communities provided confidential
evidence for the decline of forest and other land use change in the study area and its
surroundings. In this case, according to the respondents, about the knowledge of farmers
towards LULCC in the study area reveals that they have seen the results of LULCC as
Conversion of natural vegetation into agricultural land, Land degradation, Climate change and
variability through increased drought and Biodiversity loss with percentages of 38.5, 29.7,
17.2 and 14.6 respectively. The remedy or solutions for this LULC change have also given by
the respondents. According to the respondents protecting the remaining forest, reforestation
and facilitation credit and cooperation on apiculture/beekeeping are thought as solutions for
LULCC, with respective percentages of 44.3 %, 31.3 % and 24 %.

32
There are varied problems related to wetland in the study area and around that environment.
Wetlands are highly valuable ecosystems that provide a wide range of ecological benefits.
However, they are also facing significant challenges and problems. Wetlands are being
drained, filled, and converted for agriculture, urban development, and infrastructure projects.
This destruction disrupts the natural hydrology and leads to the loss of habitat for numerous
plant and animal species. Wetlands support a diverse array of plant and animal species, many
of which are specialized and dependent on wetland habitats. The destruction of wetlands
directly leads to the loss of critical habitat for these species, resulting in declines in
biodiversity and the potential extinction of certain wetland-dependent species. Wetlands play
a crucial role in water purification and filtration. However, they are vulnerable to pollution
from various sources such as agricultural runoff, industrial discharges, and urban wastewater.

According to the respondents, the major problems related to wetland are revealed as changing
the wetland into agricultural land, urban expansion, and distribution of wetland to landless
and changing wetland into grazing land, with percentages of 33.9%, 20.8 %, 29.7% and
15.6% respectively. Table 10

Table 7. The major problems related to wetland

the major problems related to wetland Frequency Percent

Agricultural land 65 33.9

Urban expansion 40 20.8

Distribution of wetland to landless 57 29.7

Changing wetland into grazing 30 15.6

Total 192 100.0
Also, the FGD and KII revealed that there are insertions of pollutants and garbage in the
wetland that change into sediment and finally dry the wetland. In addition to this with the
damage and drying of wetland, different types of animal species such as birds and mammals
("Dolle" and "Kellejje") were disappeared from the wetlands around the study area. In terms
of grazing land use land cover change, the respondents also suggested the major problems
change of this land use land cover change causes ; Rapid population growth when pasture

33
over grazing, conversion of grazing land into agricultural land, overgrazing and bare soil
susceptible for erosion. Overgrazing occurs when livestock graze on rangelands beyond their
carrying capacity, leading to the degradation of vegetation and soil. According to respondent
information, the result of over grazing loss of plant cover, soil erosion, and decreased
productivity of the grazing land. Overgrazing often occurs due to inadequate pasture
management, lack of rotational grazing systems, and increased livestock numbers without
proper land management practices. Others Improper grazing management can lead to
increased soil erosion, especially in areas with steep slopes or fragile soils. When vegetation
cover is removed or degraded by overgrazing, the soil becomes more vulnerable to erosion by
wind and water. Addition to Soil erosion can cause sedimentation in water bodies, leading to
reduced water quality and affecting aquatic ecosystems.

They are recorded as problems for change of grazing land use type with percentages of 39.1
%, 28.1 %, 18.2 % and 14.6 % respectively. Solutions suggested to solve the problems related
to grazing land are proper management of animal (34.9 %), followed by shifting to other ways
of feeding livestock (28.1 %), which is followed by preventing over grazing (24.0 %) and
lastly avoiding the act of grazing too early (13.0 %). The type of lands which are vulnerable to
land degradation are also discussed and replied by the respondents.

Figure 7. Wetland converts into agricultural land (Photo2023)

34
 5. CONCLUSION AND RECOMMENDATION

5.1. Conclusion
The study examined LULC changes, their driving forces, and their implications on land
resources between 1992 and 2003 in the Setema district of southwest Ethiopia. Remote
sensing and socioeconomic data sources were used as major inputs for the study. The results
showed that there have been substantial changes in LULC in the Setema district and its
adjacent agro-ecosystem. The major changes were expansions of cultivation and settlement
land, the conversion of wetland into land, agricultural land, and a decline in grazing.
Conservation and management of natural resources in the study area are not adequate to
alleviate the problem of local land degradation. As a result, the livelihood of the local
community and the normal function of the ecosystem are under threat. Apparently, it tells us
that it will continue to be a development challenge for land use, land cover changes, and the
nation at large. Thus, sustainable land management is vital to remove unsustainable practices
and create a sustainable environment for all concerned. Therefore, overall land use
management is essential to safeguarding an environment that will result in sustainable natural
resource management and development in all dimensions of the study area.

Based on information from satellite-classified images integrated with GIS, social survey
analyses (key interviews and focus group discussions, and HHs field), field observation, and
ground control points, the study was conducted to identify LULCC, analyze the rate and
extent of land use land cover change, the driving force of land use land cover change, and
farmer perceptions for 30 years (19192–2023). Therefore, forest land, agricultural land, grassy
land, wetland, and settlement land were assessed and identified in the 1992–2023 LULCC.
This understanding of the rate of land use land cover change, the driving forces, and LULCC
farmer perceptions towards land cover changes for the study area are very important for
LULCC processes and spatial trends, which provide relevant knowledge that is a useful
guideline for decision‐makers at the national and local government and civil society.
Therefore, based on the downloaded satellite image classification and social survey analyses
of KII, FGD, and HHs at the base year (1992), from the total area of 116474.8 hectares, the

35
area was covered by forest at 47134.3ha-1 (40.5%), followed by grassland at 8588.8ha-1
(7.4%), and wetland at 1890.0ha-1 (1.6%), whereas the LULC of agricultural land was at
57868.0ha-1 (49.7%) and settlement land at 993.8ha-1 (0.9%), respectively. The increase in
land use and land cover was due to proximate drivers and underlying drivers such as fuel
wood extraction, illegal wetland conversion to agricultural land, illegal timber production,
agricultural expansion, extraction of wood for house construction, population growth, and
corruption, which showed that there was a significant decrease in forest as a consequence of
an increase in agricultural land and settlement land (Table 6). In the recent year (2023), forest
cover of 32617.5 ha (28.0%) was declining at an alarming rate, followed by grass land cover
of 5066.7 ha (4.4%) and wet land 428.0 ha (0.4%) (Table 5).

5.2. Recommendation
The findings of this study on assessing the rate of land use change and its driving forces show
that there was an increase in agricultural land and settlements, whereas there was a decrease
in forest land, grassland, and wetland land. Because wetland was changed to agricultural land,
“Kellejje” and “Dolle” species disappeared, and as was explained by the key informants, the
decline of forest cover caused a decline in the number of wild animals. In some cases, animals
such as tigers, lions, and antelopes (gafarsa), which were commonly found in the study area
over 30 years ago, disappeared. This problem is caused by proximate drivers and underlying
driver factors.

Based on the household survey, FGD, and key informants’ perceptions, the drivers of LULCC
were identified, and based on the study area results; the following recommendations could be
promoted: All kebeles will develop "Gafo" practices in which to the a small group of people
around the forest tie their traditional hive tree and nobody can cut the tree since it is forbidden
in their culture. Even if the trees are removed by others, they inform the Kebele administrator.
Developing proper and organized land use planning and management has a great opportunity
to solve land use and land cover change problems. The government will start generating off-
farm activities for landless youth, such as credit and cooperation on apiculture/beekeeping,
dairy farms, and poultry, rather than distributing wetland as an alternative solution, even
though unemployment is the reason for the change of wetland. Society will be decided to
make aware of the consequences of Land use land cover changes and its effects. Inspire
people to cultivate fast-growing species close home gardens and farmland boundaries for fuel

36
wood. Institutions should collaborate as well as provide integrate to identify the problem,
plan, solutions that use society for sustainable land use management. The government should
increase awareness of corruption and inspire citizens to fight it. The community should
participate in participatory forest management (PFM), in which the forest provides higher
long-term forest income benefits to the community and the community manages and uses the
forest in a sustainably. Conservation activities should be carried out in rural areas of the study
area,

37
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