analyze trends and drivers of land use land cover dynamics

Volume 02 Issue 02 March 2021 ISSN (Online) 2708-2830 ISSN (print) 2707 – 7640

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Analyze Trends and Drivers of Land Use Land Cover Dynamics in Dawa River Basin since

1989, Southern Ethiopia

Fekadu Temesgen Tesfaye Bule Hora University, Bule Hora, Ethiopia College of Social Sciences and Humanities

Department of Geography and Environmental Studies, Bule Hora University, Ethiopia Email address:[email protected]

ABSTRACT

Land cover alteration is major issue of global environmental change. This change is due to human

intervention for various activities. The change is a gradual process and it takes time depending on

the drivers. Basin level land cover dynamic has been supposed to be the most earliest of all human

that brought environmental changes. Thus, trends and drivers of land use land cover dynamics in

basin are the key considerations in the research. To detect the trends of the change, TM (1989),

ETM+ (2004) and ETM – OLI (2019) Land sats were used. The trends of land cover dynamics that

happened from 1989 to 2019 were analyzed using ERDAS 2015 and arc map 10.5. In addition, the

drivers that supposed to accelerate the changes were identified by questionnaire, focus group

discussion and interviews. In the period comparisons between 1989 and 2019, the farmlands &

settlements and bush & shrub lands are increased by 4684.36 and 10496.28 km2 with rate of 104.09

and 233.25 km2 per year respectively. In contrast, the forest areas and grasslands & degraded lands

are decreased 6770.20 and 8410.44km2 with rate change of 528.83 and 186.89 km2 per year

respectively. Regarding to the factors that accelerated the changes are population pressure, open

grazing activities, lack of professional natural resource managements, weak governmental regulations

on forest resources, effect of climate change, and increasing of unemployment. Thus, it is

recommended that strategies should be designed for the pastoralists sector due to decreasing trends

of forest and grassland in the basin.

Key words: Drivers, Dynamics, Land Cover, Land Use, River Basin, Trends

1. Introduction

1.1. Background of the Study

Land use and land cover alteration is major issues of global environmental change (Parksam, 2010)

due to human intervention as a driver for various processes (Batar et al., 2017; Meneseset al., 2017;

Baral et al., 2009; Woldeamlak, 2004). Such as for agricultural, settlement, transportation,

Analyze Trends and Drivers of Land Use Land Cover Dynamics in Dawa River Basin since 1989,

Southern Ethiopia www.bhu.edu.et/jikds


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infrastructure and manufacturing, park recreation uses, mining and fishery (Ellis 2011). Starting from

the domestication of plant, animals and control of fire, humans have been changing the topography

in an irreversible state (Bone et al., 2017; Lambin et al., 2003). Nonetheless, in the world history, an

abrupt and huge land use change has been begun in the 17th century related with agricultural

expansion and industrial revolution (Goldewijk & Ramankutty, 2003).

Land use land cover change in the river basin has been supposed to be the most earliest of all

human that brought environmental changes (Solomon et al., 2017; Thapa-Magar and Shrestha, 2015;

Gebresamuel et al., 2010). In the globe, land use land cover history in the river basin is quite

dynamic (Nejadhashemi et al., 2011) both temporally and spatially with respect to the sustainable

socio economic development of human beings (Koch et al., 2012). For example the developed

countries, land use land cover change in river basin level had been become stable before fifty years

ago (Wang, 2006). Ironically, the land use land cover change process in the river basin in the

developing world is still in progress, which devastated their natural resource, facing a challenge on

their sustainability (Goldewijk & Ramankutty, 2003).

In Ethiopia, land degradation and deforestation are the most agents for land use land cover change

(Moges and Holden, 2009; Gebresamuel et al., 2010; Zeleke and Hurni, 2001; Alemayehuet al.,

2009). Empirically, deforestation rate is guessed to be between the range of 150,000 and 200,000

hectare per year (Zeleke and Hurni, 2001). This makes the average annual rate of deforestation in

the country 0.8 up to 1.05 % per year (Earths Trend, 2003). In the Ethiopian highlands, because of

population growth and shortage of agricultural land, encroachment of farm lands (Woldeamlak,

2004) has been shifted to semi marginal and marginal lands in the last fifteen years than ever before

(McKee, 2000; Tesfaye, 1999). For instance, in southern Ethiopian, in the Abaya-Chemo sub-basin

of the study conducted by Mengistu (2009) showed that a significant rate of change from forest land

into farmland. The farmland and settlement had increased by about 72% while the forest cover

decreased by 17% within less than a decade. Land cover changes are caused by a number of human

driving forces (Meyer and Turner, 1994), which is also true for southern Ethiopia (Mengistu 2008;

Genene and Ramachandra, 2015). In relation to this, traditional and modern way of mining

exploration becomes one of the driver of land use land cover dynamic in southern Ethiopia (Reid et

al. 2000). In addition, the rate of deforestation in southern parts of Ethiopia is by far reaching climax

point at the moment time due to encroachment of farm lands, firewood, lumbering and home

construction (Mengistu 2008; Dessie & Christiansson, 2008). Thus, an understanding of the trends

and drivers of land use land cover change is essential for tackling future prospect of land use and

Fekadu Temesgen Journal of Indigenous Knowledge and Development Studies (JIKDS)


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land cover dynamics driven impacts on socio-economic development (Pullanikkatil et al., 2016;

Bhattarai et al., 2012; Schreier et al., 2003; Rechard, 2005).

Having derived by these views, this study assesses the drivers, trends, patterns and, extents of land

use land cover dynamics in the south eastern Ethiopia with particular reference to Dawa river basin.

The study area is one of the drought prone areas where land use and land cover change is reaching

of a critical stage due to drivers. As the result of the existing driving forces of land use land cover

change in the study area, forest coverage and grass land areas are greatly decreased and imposed

critical influences for grazing on the pastoralist (Dessie and Kleman, 2007; Dereje, 2007; Mengistu,

2008; Dessie & Christiansson 2008).Furthermore, the area is marked by diverse topographic

conditions, which provides an opportunity to analyze trends and drivers of land use land cover

dynamics in Dawa river basin since 1989.

2. Materials and Methods

2.1. Location and Descriptions of the Study Area

The study lies between 405’8’’ to 60 27’ 18’’N and 3802’ 48’’ to 4102’ 34’’E. In administrative term, it

covers 11 Woreda from both Oromia and Ethio-Somali Regional states. These are Uraga, Bule

Hora, Yabelo, Arero, Odoshakiso, Liben, Filtu, Moyale, Dire, Bore and Adolana Wadera Woreda.

The study area has special coverage of 42,202km2 and the basin is located around 567km southwest

of Addis Ababa and forms the parts of the southeast highlands of Ethiopia. The following Map

helps clearly to show the location of the study area.

The study area is consisting of different topographic conditions. The elevation ranges from 323 to

3011 m.a.s.l. The study area is characterized by mountainous and highly dissected stretches of land

with steep slopes at the upper stream part while an undulating terrain and gentle slopes at the

downstream part. Mixed types agriculture on subsistence scale is the major livelihood of the people

in the study area. Land and livestock are the most important assets of the people, with which they

lead a sedentary life. A Variety of crops are produced by a house hold.

According to the National Meteorological Agency as measured at Negele town (5019’ 58’’N and

39019’ 27’’E with elevation 1496m), the climate condition of Dawa basin is generally characterized

by sub-tropical and tropical agro ecology with the mean rainfall of 600mm to 1250mm (for the year

between 1989 and 2019) and the average temperature of the study area is 15 to 22.80c (for the year

between 1989 and 2019). The study has four seasons. These are summer, winter, autumn and spring.

Summer is the main rain rainy season while winter is a dry season in the study area.




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The study area is consist of different topographic conditions, which is characterized by mountainous

and highly dissected stretches of land with steep slopes at the upper stream part while an undulating

terrain and gentle to level slopes at the surface part. The elevation ranges from 323m to 3011 meter

above sea level. According to FAO, 1999 classification the topography of the study area consists of

level slope 19822.36km2 (46.97%), very gently slope 9483.16km2 (22.47%), gentle sloping

6902.92km2(16.35%), sloping 4148.44km2(9.83%), strongly slope 1611.64km2(3.82%), moderate

steep 221.16km2(0.53%) and very steep 12.16km2 (0.03%). Most parts of the study area are found

within level slope while small parts of the study area are just found in very steep slope.

The livelihoods of the majority of the settlers in the study area are mainly depending on pastoral

practices. In addition, mixed types agriculture on subsistence scale is the major livelihood of the

people in the study area. Land and livestock are the most important assets of the people, with which

they lead a sedentary life. A Variety of crops like Maize, Enset, Coffee, banana and etc. are produced

by house hold. Moreover, settles who live in urban areas depend on trade.

Figure 1. Location map of the study area (Source: authors, 2019).

2.2. Data Sources and Methods of Collection

2.2.1. Satellite Data

The data used to create the spatial data base used for this study were obtained from the Ethiopia

Map Agency which were taken in 1989 (TM landsat-5), 2004 (ETM+ Landsat-7) and 2019 (ETM

landsat-8 OLI) during February months. These years and months were selected in order to have a

clear difference with in each land use and land cover categories of the area for supervised



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classification. ASTER DEM 30 m*30 m resolution was downloaded from USGS website and was

used for basin delineation and slope generation. Even though all the land sat images are freely found

in the website addresses, due to lack of experience and internet access to extract and down load the

images, the necessarily data were directly purchased from EMA.

2.2.2. Field Data Measurements and Observations

The field data collection were done randomly to verify the classified image and to collect the

necessary land use and land cover data for accuracy assessment. In addition, random field data,

ground control points, were collected to validate the pour points of the streams in the basin.

Ground control points were collected using Garmin Global Position System model 72 devices.

2.2.3. Sampling Techniques and Size Determination

The intended objective was achieved from the result of focus group discussion, interview and

questionnaire. Focus group discussion and interview were selected than other methods of obtaining

information because the researcher strived to lead the discussion by moderator. Thus, to identify the

possible deriving factors of land use land cover change in the earlier times, elders of the local areas

(kebele leaders, administrative officials, illiterate farmers who have changed their crop land to other

land use types and the like), were planned to discuss and interviewed. The main reason that the only

elders of the local areas were selected for the discussion and interview than other social group was

that they have lived a long periods of time and have a chance to observe a significant temporary land

use land cover change starting from earlier time within a given study time interval in the study area.

Questionnaire also distributed to the target population in the study area. This was to gather

additional causative factors of LULCC from the social groups in the study area and the

questionnaires were distributed in the selected districts. Hence, in the process of selecting sample

population for the purpose of questionnaire, purposive sampling technique was considered. In the

study area there were 11 Woredas. From 11 Woredas in the study area, 6 districts were selected

purposefully based on the strength of the LULCC exhibited in the study area.

SS =

.........................................................................................................................................................1

Whereas, SS is the sample size,

N is the population size, and

E is the level of precision (0.1) (Yamane, 1967).




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The study area has a total of 843,343 populations. Hence, the sample size was 100 (Table 1). Finally,

In order to determine the weight of sample size in each Woreda, the total sample populations were

distributed proportionally in to the respective Woreda’s by using proportionality scientific formula

(Cochran, 1977) given below.

Finally, the number of population for each district was collected from the respective Woreda’s

administrative offices. In order to determine the sample size in each district, the total sample

populations were distributed proportionally in to the respective districts by using proportionality

scientific formula given below (Cochran, 1977).

ni=

..........................................................................................................2

Where, ni=the number of sample population on each sample district.

Ni=the number of population in each district.

S= total number of sample population.

N= ∑Ni i.e. number of population in districts (Cochran, 1977).

Thus, the possible Drivers land covers land change in the study area identified by the selected

sample size populations. Finally, the results of the group discussion, interview and questionnaire

were become an input for the identification of possible drivers of Land use land cover change

performed in the Dawa river basin.

Table 1. Sample size determination

Distribution of Distribution of Sample percentage

Name of Sample district population(Ni) sample size(ni)

No. districts

1 Bule Hora 262,659 31.15 31.15

2 Yabelo 84,563 10.03 10.03

3 Moyale 159,909 18.96 18.96

4 Dire 46,569 5.52 5.52

5 Arero 64,289 7.62 7.62

6 Odoshakiso 225,354 26.72 26.72

Total 843,343 100 100

The possible deriving factors of land use and land cover change data required for the research was

collected using structured questionnaires which consist of open ended questions. The questions

were set to generate information related to the possible deriving factors of LULCC in the study area.



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The questionnaires were pretested and administered by the researcher, extension workers (DA’s)

and high school teachers who were recruited from each sample districts and a total of 25 individuals

involved in answering the pretest questionnaires.

2.2.4. Methods of Data Analysis

2.2.4.1. Satellite Data and Image Classification

Pre-processing: The pre-processing activities such as layer stacking, haze correction and

topographic correction were carried out on the satellite images using ERDAS IMAGINE 2015. The

land sat images were extracted by mask with the study area DEM by arc map 10.5.

Image Classification: Supervised image classification systems were carried out using ERDAS

IMAGINE 2015 after preprocessing activities. Finally, farmland and settlements, forest, grassland

and degraded land, bush and shrub lands were produced corresponding to the three reference years

and the result sent to Arc GIS 10.5 software to display, quantify and interpret. The cultivated land

and settlements were merged in to one category because it was awkward to recognize the scattered

rural settlements as a separate land cover type. Also, cultivated land exists around homesteads and

would have to be recognized as such. Hence, for practical reasons, the two land cover types were

merged in to one category. In addition, grassland and degraded land includes both open grazing

lands and exposed lands. It was difficult to distinguish because the bare lands were identical with

grazing lands, which were heavily degraded in the study area. As a result, they were categorized

together (Table 2).

Table 2. Descriptions of land cover classes in Dawa basin (1989-2019)

No. Land cover types Description

1 Farmland and settlements

Areas used for crop cultivation, and the widespread rural settlements

2 Forest

Areas covered with close growth of trees like eucalyptus and junipers

3 Grass and degraded lands

Savanna grass areas used for pastoral grazing, that has degraded lands

4 Bush and shrub lands Area covered with bush and shrub lands

2.2.4.2. Field Data Analysis

Ground control points were collected for the purpose of undertaking accuracy assessment to

validate and compare the classified images with the true geographical phenomena. After the ground

control points were collected by GPS from the field, it was recorded in Microsoft office Excel 2010

spread sheet so as to put the latitudes and longitudes coordinate points as they represented the types




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of geographic phenomena. Finally, after land use and land cover change of the study area processed,

analyzed and interpreted, the drivers of the land use land cover changes supported with the result

obtained from questionnaire, informal interview and focus group discussions.

Accuracy Assessment: After the supervised classification has taken place on the land sat image,

accuracy assessment has also under taken to compare the classified images with geographical data

that they are assumed to be true. The accuracy assessment of the LULC map have been under taken

by comparing the field data collected by GPS with the classified images in ERDAS IMAGNE 2015

software . Four lands used and land cover classes were identified using visual image interpretation

and field survey. Thus, from the supervised digital image classification, in this study, accuracy

assessment was done for the 2019 satellite images. The overall accuracy was calculated by summing

the number of pixels classified correctly and divided it with the number of pixels. Thus, the overall

accuracy of 2019 satellite image was 97.76.

Land Use and Land Cover Detection: The amount of different land use and land cover types of

the study area quantified from the produced maps, which depicted in percentiles. A combination of

information collected from field, local people, elders and natural resource experts through informal

interviews and focus group discussions with an information gained from satellite images were used

in the analysis of land use and land cover change detection. The land cover maps for the three

period series of images were analyzed based on land use and land cover types area comparison. The

changes over 30 years were analyzed and rate of change for each land use land cover type is

calculated. In the meantime, the rate of land use and land cover changes for the three period’s

from1989 - 2004 and 2004-2019 and 1989-2019 were computed using the following formula (Geist

and Lambin, 2004).

R =

………………………………………3

Where R= Rate of change, Q1=initial year land use/ land cover in km2, Q2 = recent year land use/ land cover in km2, T= time interval between initial and recent years.

2.2.4.3. Possible Drivers of Land Use and Land Cover Change Interpretation

Having collected the socio economic data for the study, the researcher analyzed those by using

qualitative techniques. That implies, after all the possible drivers of the LULCC in the different time

intervals had identified from the society, it was interpreted using descriptive methods by matching

with the change identified from the imageries.



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3. Results and Discussions

3.1. General Background of the Respondents 3.1.1. Demographic Characteristics of Respondents by Sex Out of the total number of sample size, 66 percents are male and 34 percents are female

respondents. Thus, the majority of respondents participated in the study are males (Table 3).

Table 3 Characteristics of Respondents by Sex

Sex of Respondents Number of Respondents Percentage

Male 66 66

Female 34 34

Total 100 100

Source: Field Survey, 2019

3.1.2. Demographic Characteristics of Respondents by Age

According to the collected data from field survey, out of the total sampled size, 30 percents, 17

percents and 15 percents of the sampled respondents are the most economically active groups

respectively, while the remaining 18 percents of the respondents are economically inactive and 20

percent are elder (table 4).

Table 4.characteristics of sample households by age

Source: Field Survey, 2019 3.1.3. Marital Status of the Respondents

Majority (74%) of the marital status of respondents are married who have expected to have family

members that depend on them. On the other hand, most of the respondents are married (74%) and

significant proportions of them are divorced (4%) and widowed (4%) (Table 5).

Table 5.Marital Status of the sample household

Number of

Percentage Marital Status Respondents

Single 18 18 Married 74 74 Divorced 4 4 Widowed 4 4

Total 100 100


Age of Frequency

Percentage Respondents Respondents

20-25 30 30 26-30 17 17 31-35 15 15 36-40 18 18 >41 20 20

Total 100 100




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3.1.4. Occupations of the respondents

Regarding to their occupation, 33% of the respondents are farmers while 35% of them are

merchants. The remaining 21 %, 6 % and 5% of the respondents are daily labor, government

employers and others respectively in the study area (Table 6).

Table 6.Occupations of the respondents

Occupation

Number of

Percentage Respondents

Farmer 33 33

Merchant 35 35

Daily labor 21 21

Government employees

6 6

Other 5 5

Total 100 100


3.1.5. Educational status of the respondents

In their educational status, 25% of the responds are able to read and write while 20% of them are

illiterate. In addition, from the total sampled size 16 and 14 percent of the respondents are

completed elementary and high school education. Similarly, 10 percent of respondents are achieved

preparatory school and 15 percents are completed grade 12 and above. (Table 7).

Table 7.Educational levels of the respondents

Education Level No. respondents Percentage

Illiteracy 20 20

Read and Write 25 25

1-8 16 16

9-10 14 14

11-12 10 10

>12 15 15

Total 100 100


3.1.6. Family sizes of the despondences

Majority (41%) of the respondents have a medium (3-4) number of family members while 32% of

the respondents have small number of family (1-2). On the other hand, 18 percent of respondents

have 5-7 family size and 9 percent of them owned large number of family size which is greater than

8(Table 8).



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Table 8.Family sizes of the respondents

Family sizes Frequency Percents (%)

1-2 32 32 3-4 41 41

5-7 18 18

>8 9 9

Total 100 100


3.2. Spatial Temporal Distribution of Land Use and Land Cover and Accuracy Assessment

3.2.1. Land Use Land Cover Classification for 1989

As information obtained from land sat, the land cover classification of 1989 was identified as bush

and shrub lands, farmland and settlements, forest, grass and degraded Lands. Moreover, based on

the spatial distribution, most of the farmland and settlements (534.32 km2 (1.27%)) were found in

the north parts of the study area. Most parts of the study area were covered by grass and degraded

lands (30171.64 km2 (71.49%)) and spatially they were found in the most central and south eastern

parts of the study area. In addition, the bush and shrub lands (6988km2 (16.56%)) were mostly

concentrated in the south western parts of the study area and forests (3008.04km2(10.68)) also found

in the northern parts of the study area (Figure 2 1989A).


The land use and land cover classifications of 2004 were identified as bush and shrub lands

(22988.28km2 (54.47%)), farmland and settlements (2443.08km2(5.79%)), forest (2237.12km2(5.3%)),

and grass and degraded lands (13033.52km2 (34.44%)). During this time, most parts of the study area

were covered by the bush and shrub lands. Spatially, the bush and shrub lands are dominantly

situated in the central parts of the study area. Similarly, the grass and degraded lands also located

mostly in the southern edge in the study area. In the same manner, the farmland and settlements are

mostly found in most northern edge of the study area (Figure 2 2004B).


Furthermore, land use and land cover classifications of 2019 were identified as bush and shrub lands

(26741.44km2 (63.37%)), farmland and settlements (4764.72km2(11.29%)), forest (694.72km2

(1.65%)) and grass and degraded lands (10001.12km2 (23.69%)). During this time, most parts of the

study area were covered by the bush and shrub lands. Spatially, the bush and shrub lands are

dominantly situated in the central parts of the study area. Similarly, the grass and degraded lands also




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mostly located in the southern edge in the study area. In the same manner, the farmland and

settlements are mostly found in the northern edge of the study area (Figure 2 2019C).

Figure 2. Dawa river basin land use land cover classification map

Accuracy Result: The result of the of overall accuracy assessment of 2019 year classified Landsat

imagery was analyzed (Table 9). As its name implies, the accuracy assessment depicted the degree of

correctness of assigning a pixel to the predefined land use and land cover classes. Therefore, the

base line requirement of the overall accuracy assessment is ≥80 (Congalton, 1991).Higher accuracy

result had obtained on the latest imagery in the software matched with the land cover classes on the

ground.

Table 1. Classification accuracy statistics of LULCC classes (2019)

Class name

Accuracy (%)

2019

Producers users

Bush and shrub lands 100 90.91

Farmland and settlements 100 100

Forest 100 100

Grass and degraded lands 100 100

Over all classification 97.76



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3.3. Land Use and Land Cover Change Detection 3.3.1. Land Use Land Covers Change Detection between1989 and 2004

In 1989 the farmland and settlements covered 534.32km2 and increased to2443.08 km2 in2004.So,

within 15years, 532 km2 remained constant from being changed. But, it lost 11.12 km2 to the other

class types and again it gained 1911.08 km2 from the other land class types. Generally, the farmland

and settlements showed a total incensement of class change (1908.76 km2) in 15 years. Similarly, the

bush and shrub lands covered an area of 6988 km2 in 1989 but it increased in to 22988.28 km2 in

2004. In these years, 6702km2 remained constant from being changed. But, it lost 286 km2 to the

other land class types and 16286.28 km2 again gained from the other classes’ type. Generally, the

bush and shrub lands showed a total reduction of class change (16000.28 km2) in 15 years (Table

11A).

In contrast, in the same study years, the grass and degraded lands covered an area of 30171.64 km2

in 1989and it decreased in to 13033.52 km2 in 2004. So, in 15 years, 14056 km2 remained constant

from being changed. But, it lost 16115.64 km2 to the other class types and again it gained 477.52 km2

from the other mentioned classes. Generally, the grass and degraded lands showed a total decrement

of class change (15638.64 km2) in 15 years. Likewise, the forest covered an area of 3008.04 km2 in

1989 but it decreased in to 2237.12 km2 in 2004. In these years, 2226 km2 remained constant from

being changed. But, it lost 2272 km2 to the other land class types and it gained 11.12 km2 from the

other classes’ type. Generally, the forest showed a total reduction of class change (3170.92 km2) in

15 years (Table 11A).

3.3.2. Land Use Land Covers Change Detection between 2004 and 2019

In 2004 the farmlands and settlements covered an area of 2443.08km2 but in 2019 it increased to

4764.72 km2.This implies that 2443.08km2 was remain unchanged to the other classes and it lost

nothing km2 to the other class types within 15 years while2321.64 km2 gained from other classes.

Generally, the farmlands and settlements showed a total increment of class change (2321.64 km2) in

15 years. Similarly, in 2004 the bush and shrub lands covered an area of 22988.28 km2 but in 2019 it

increased to 26741.44km2. This indicated that 22415.44 km2 remain unchanged until 2019. Within

the 15years, it lost 572.72 km2 to the other classes and again it gained 4326 km2 from the other class

types. Generally, the bush and shrub lands showed a total increment of class change (3753.16 km2)

in 15 years (Table 11B).

In contrast, the forest covered an area of 2237.12 km2 but it decreased to 694.72 km2 in 2019. This

indicated that 694.72km2 remained constant from being changed until 2019. Within the 15years, it




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lost 1542.4 km2 to the other classes and again it gained nothing km2 from the other class types.

Generally, the forest showed a total reduction of class change (1542.4 km2) in 15years. Similarly, in

2004 the grass and degraded lands covered an area of 13033.52 km2 but in 2019 it declined to

10001.12 km2. This indicated that 9928.84 km2 remain constant from being changed until 2019. So,

within 15 years, it lost 4605.24 km2 to the other classes and it again gained 72.84km2 from the other

class types. Generally, the grass and degraded lands showed a total reduction of class change (3032.4

km2) in 15 years (Table 11B).

3.3.3. Land Use Land Covers Change Detection between1989 and 2019

The overall change in land use and land cover in the last four decades was also assessed. There were

an increment of farm land and settlements and bush and shrub lands but a decrease in the area

coverage of grassland/degraded land and forests. This was just the general impression of land cover

dynamics based on comparison of individual land cover type.

The farmland and settlements portrayed increasing pattern of change during the period comparisons

of between 1989 and 2019 .It increased from 80.36 km2 to 4764.72 km2 in 1989 and 2019

respectively. Within 30 years, farmland and settlements was converted from bush and shrub lands,

forests, grass and degraded lands accounted for 3508 km2, 1148 km2 and 55.72km2 respectively.

Thus; within 30 years, it gained 4711.72 km2 from the other classes. In addition, bush and shrub

lands was showed increasing pattern of changes during the periods between 1989 and 2019. It

increased from 16230.16 km2 to 26741.44 km2 and lost 3552.92 km2 to the other land types mainly

to farmland and settlements (Table 11C).

In contrast, the forest portrayed decreasing pattern during the period comparisons of between 1989

and 2019. It decreased from 7464.92 km2 to 694.72km2 in 1989 and 2019 respectively. Within the

entire study time, the forest was changed mainly to bush and shrub lands which account 5622.2 km2

and 1148 km2 respectively. Similarly, grassland and degraded area showed decreasing pattern during

the period comparisons between 1989 and 2019. It decreased from 18411.56 km2 to 10001.12km2

between 1989 and 2019 respectively. Within the 30 years, it lost 4882.72 km2 to other land types

mainly to bush and shrub lands and farmlands and settlements (Table 11C).

Generally, during in the period comparisons among 1989, 2004 and 2019, the farmlands and

settlements were increased by 4684.36 km2with rate of 104.09 km2 per year. In addition, within 30

years, bush and shrub lands were increased to 10496.28 km2 with rate change of 233.25 km2 per year.



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In contrast, the forest lost 6770.20 km2 within the entire time intervals with rate change of 528.83

km2 per year. Similarly, the grasslands and degraded areas lost 8410.44 km2 within 30 years with rate

of changes 186.89 km2 per years.

Table 2. LULCC matrix between 1989 and 2004; 2004 and 2019; 1989 and 2019

A 2004

1989

Class Name

BS FS F GD Total

km2 % km2 % km2 % km2 % km2 %

BS 6702 29.15 104 4.26 - 182 1.2 6988 16.56

FS - 532 21.78 11.12 0.5 - 534.32 1.27

F 1041 4.53 936.72 38.34 2226 99.5 295.52 2.1 3008.04 10.68

GD 15230.28 66.32 870.36 35.62 - 14056 96.7 30171.64 71.49

Total 22988.28 2443.08 2237.12 13033.52 42202.00

Class Change

+16000.28 +1908.76 -3170.92 -15638.64

B

2019

2004

Class Name

BS FS F GD Total

km2 % km2 % km2 % km2 % km2 %

BS 22415.44 83.82 500 10.49 - 72.84 0.72 22988.28 54.47

FS - 2443.08 51.28 - - 2443.08 5.79

F 93 0.34 1449.4 30.42 694.72 100 - 2237.12 5.30

GD 4233 15.84 372.24 7.81 - 9928.84 99.27 13033.52 34.44

Total 26741.44 4764.72 694.72 10001.12 42202.00

Class Change

+3753.16 +2321.64 -1542.4 -3032.4

C

2019

1989

Class Name

BS FS F GD Total

km2 % km2 % km2 % km2 % km2 %

BS 12692.24 47.

9 3508 73.62 - 44.92 0.46

16230.16

38.49

FS - 53 1.14 - 27.36 0.27 80.36 0.19

F 5622.2 21 1148 24.09 694.72 100 - 7464.92 17.69

GD 8427 31.

1 55.72 1.15 -

9928.84

99.27 18411.56

43.63

Total 26741.44 4764.72 694.72 10001.12 42202.00

Class Change

+10496.28 +4684.36 -6770.20 -8410.44




16

3.4. Drivers of Land Use and Land Cover Change between 1989 and 2019

Land cover changes are caused by numbers of natural and human deriving forces (Li et

al.,2016;Myer and Turner II, 1994).Whereas natural effects such as climate change are felt only over

a long period of time; the effects of socio-economic factors are immediate and radical for LULCC

(Woldeamlak, 2004). Moreover, population growth was the most important derivers for land cover

change of the human factors in Ethiopia (Hurni, 1993), as it was generally the case in

underdeveloped countries (Hurni, 1993).

According to questionnaire, focus group discussion and interviews made with local people, the

most influential socio- economic, political and natural factors that have speeded up the conversion

of specific class type to the other from the year between 1989 and 2019 were population pressure;

open grazing activities, lack of professional natural resource managements, deforestation, weak

governmental regulations to wards forest resources, effect of climate change and the day to day

increasing of unemployment in the basin.

4. Discussions

4.1. Drivers of Land Use and Land Cover Change

Farmland and Settlements: This class has included cultivated land and settlements. The land cover

types were merged in to one category because it was awkward to recognize the scattered rural

settlements as a separated land cover type. Also, cultivated land exists around homesteads and would

have to be recognized as such. Hence, for practical reasons, the two land cover types were merged in

to one category. The areas under the farmland and settlements have showed a slow and persistent

increase over the period under the study. Though this was a general trend, the rate of increase was

quite small while compared with the total area of the basin. This land use land cover showed

increasing pattern of change.

The farmland and settlements showed increasing pattern of change during the first period

comparisons of 1989 to 2004.The farmland and settlements have converted mainly from grass

/degraded lands, forest areas. According to questionnaire, focus group discussions and interview

conducted, the farmland and settlements increased between 1989 and 2004 due to purely population

pressure occurred in the study area for the purpose of cultivation. Due to above mentioned reasons

the farmland and settlements were increased during the period of comparison had made.

Similarly, the farmland and settlements showed increasing pattern of change during the second

period comparisons of 2004 to 2019. Farmland and settlements have converted mainly from



17

woodlands, grass lands and forests. According to questionnaire, focus group discussions and

interview conducted, the farmland and settlements were increased between 2004 and 2019 due to

the illegal encroachment of the local population in to the grass land and forest areas for cultivation

purpose. Due to the above stated reasons the farmland and settlements were increased during the

period as comparison had made. Generally, the farmland and settlements were increased in between

2004 and 2019 due to purely population pressure. To sum up, the justification of the informants is

similar with Haile (2004) and Gebresamuel et al. (2010), which caused much of the open grazing

land and forest to be shifted in to cropland illegally.

Forest: The area under the forest showed a persistent decrease in the first two years study intervals

and increase in the last study interval over the period under the study. Though this was a general

trend, the rate of decreased was quite high when compared with the total area of the basin.

The area of land that was occupied by forest depicted decreasing situation in the periods between

1989 and 2004. The forest has mainly converted to farm lands and bush/shrub lands. According to

questionnaire, focus group discussions and interview conducted, the forest decreased between 1989

and 2004 owning to illegal cutting of the forest for personal consumption. This justification strictly

matched with Yigremew (1997), which has helped the people to open the way to extract out the

natural indigenous forests for the purpose of constructing the dwelling houses for the whole family.

Due to above mentioned reasons the forest was decreased during the period as comparison had

made.

Similarly, the forest showed decreasing pattern of change during the second period comparisons of

2004 to 2019. The forest was changed mainly to bush lands and farmland/settlements. Based on

information conducted through questionnaire, focus group discussions and interview, forest lands

decreased during this period due to the most recent advancement of charcoal ways of production by

the local community in the basin. This reason is strictly matched with Yigremew (1997). In addition,

the existing government also exploited the forest resource from the basin area forests for the name

of rehabilitating the war devastating regions and this situation opened the way for further

deforestation for the local community. Moreover, according to questionnaire, focus group

discussions and interview conducted in the study area, the community considered the forests

resource as the source of income gaining area via selling the forest for the purpose of fire wood to

the nearby urban areas and the increasing demand of the forest charcoal by the urban dwellers paved

the way of forest destruction by the local community as a daily income especially by the youngster




18

ladies. This justification agreed with Woldeamlak (2004).Due to the above stated reason the forest

areas were decreased during the period as comparison had made.

Grass and Degraded Lands: Degraded land included both open grazing lands and exposed lands.

This is because, it was difficult to distinguish open bare lands from grazing lands as they were

heavily degraded in the study area. As a result, they were categorized together.

The area under the grass and degraded land showed a decreasing pattern of change during the first

period comparison in between 1989 and 2004. The grassland and degraded land have converted to

bush and shrub lands; farmland and settlements. According to information obtained from

questionnaire, focus group discussions and interview the grassland was converted mainly to bush

and shrub lands, and farmlands between 1989 and 2004 due to the effect of climate change and

population pressure happened in the basin. Due to above mentioned reason the grassland and

degraded lands were decreased during the period as comparison had made.

Moreover, Grass lands and degraded areas showed decreasing pattern during the second period

comparisons of 2004 and 2019. The grasslands have converted mainly to farmland/settlements,

bush and shrub lands. Based on questionnaire, focus group discussions and interview made with the

local people in the basin, the grass and degraded lands converted to bush and degraded lands due to

mainly the effect of climate change in the basin. This resulted in the favorable condition for the

encroachment of bush and shrub by intermingling deep in to the grass and degraded lands

overwhelmed it. Thus, it decreased the grass and degraded lands. The other main reason for the

decreasing of the real extent of the grass and degraded lands was population pressure, which have

caused much of the open grazing land shifted in to cropland. This agreed with the findings of the

previous conducted findings (Amsalu et al., 2007; Gebresamuel et al., 2010; Zeleke and Hurni,

2001). The remaining area now consists largely of bare ground, which is overgrazed and

characterized by exposed rocks, thus; decreased the grass and degraded lands.

Bush and Shrub Lands: These types of vegetation have covered the fringe of forest vegetation and

sprawled towards the grassland and degraded land. They showed increasing pattern of change during

the first period comparisons of 1989 and 2004. The bush and shrub lands have converted from

grasslands/degraded lands and forests.

Based on questionnaire, focus group discussions and interview made with the local people in the

basin, the bush and shrub lands increased in between 1989 and 2004 owning to the climate change

occurrence in the basin. This favorable condition assisted to the encroachment of bush and shrub by



19

intermingling deep in to the grass and degraded lands, resulted in the grass and degraded land areas

automatically transformed in to bush and shrub lands, thus; expanded the bush and shrub lands.

The bush and shrub lands converted mainly from grass and degraded lands and forests. It increased

during the second period comparisons of 2004 and 2019. Based on questionnaire, focus group

discussions and interview made with the local people in the basin, the bush and shrub lands

converted from grass and degraded lands due to mainly the recent serious occurrence of climate

change in the basin. This has resulted in the favorable condition for the encroachment of bush and

shrub by intermingling deep in to the grass and degraded lands. Owing to this conversion of the

grass land to the bush and shrub lands frequently grass shortage was occurred year from year on

pastoralist.

Based on questionnaire, focus group discussions and interview made with the local people in the

basin, the bush and shrub lands converted from forest due to population pressure. Population

pressure is one of the headache agents of deforestation in the basin. In the basin, the community

considered the forest resources as the source of income gaining area via selling the forest products

for the purpose of fire wood to the nearby urban areas and the increasing demand of the forest

charcoal by the urban dwellers paved the way of forest destruction by the local community as a daily

open income gaining area especially by the youngster ladies. This illegal deforestation made the

forest area to be degraded and changed in to bush and shrub lands. Thus, it increased the bush and

shrub lands.

5. Conclusions

In Dawa river basin land use and land cover changes have occurred since 1989. As a result, during in

the periods comparisons among 1989, 2004 and 2019, the farmlands and settlements were increased

by 4684.36 km2 with rate of 104.09 km2 per year. In contrast, the forest lost 6770.2 km2 within the

entire time intervals with rate change of 528.83 km2 per year. Similarly, the grasslands and degraded

areas lost 8410.44 km2 within 30 years with rate changes 186.89 km2 per years. Moreover, during the

periods of comparison, bush and shrub lands increased 10496.28 km2 with rate change of 233.25

km2 per years. Thus,the major change has observed in increasing of farmlands and settlements at the

expense of other identified land classes especially on woodlands, grasslands and forests in the study

area. According to the key informants, focus group discussion and questionnaire the major drivers

that had accelerated the change were effect of climate change, deforestation, population pressure,

and lose governmental policy. Generally, as the result of the existing driving forces of land use land




20

cover change in the basin, forest coverage and grass land areas are greatly decreased and imposed

critical influences for grazing on the pastoralist.

6. Recommendations

The findings of the study gives clue for the concerned body including researchers and other

stakeholders for further better investigation on the issues and bring possible intervention

mechanisms for the dramatic land use land cover change in the study area.

According to the result of the study done in Dawa river basin, the following recommendations has

been forwarded. The study tries to see starting from 1989 to 2019. Thus, it needs to be assessed

using before 1989 imageries. In addition, the study points that settlement & farmlands and grass

/bare lands are merged together during image classifications due to low image resolution. Thus, it

needs to be classified independently using high resolution SPOT images. Moreover, the study tries

to investigate only the drivers of land use land cover change in Dawa watershed but it needs to be

investigated on the socio-economic impacts of land use land cover change. Similarly, the study tries

to identify the drivers of land use land cover change in some selected districts but it needs to be

identified in all districts of the study area. Finally, forest and grasslands areas greatly decrease from

time to time than the other class types in the study area. Thus, strategies should be designed for

these specific cover types to recover back in their coverage in the basin.

Acknowledgements

First of all, I would like to express gratitude to the almighty God, for giving me strength to complete

the study and for all things done in all my life. Secondly, I want to express my deepest gratitude to

Bule Hora University for the permission, financial support, guidance and inspirations to do more for

the best of this study. Thirdly, I want to express my great appreciation to Ethiopian Mapping

Authority, United Nation Geological Survey, all the district office staffs found in river basin,

respondents. Finally, I want to express my gratitude to all my colleagues and staff members of the

department of Geography and Environmental Studies for sharing their experience, material and

valuable cooperation in the study.



21

References Alemayehu, F., Taha, N., Nyssen, J., Girma, A., Zenebe, A., Behailu, M., Deckers, S. and Poesen, J. 2009. The impacts

of basin management on land use and land cover dynamics in Eastern Tigray (Ethiopia). Resources, Conservation and Recycling, 53, 192– 198.

Amsalu, A., Stroosnijder, L., Graaff, J. D. 2007. Long-term dynamics in land resource us and the driving forces in the Beressa basin, highlands of Ethiopia. Journal of Environmental Management, 83, 448–309.

Batar,A.K.;Watanabe,T.;Kumar,A.2017.Assessment of land use/ land cover and Forest fragmentation in the Garhwal Himalayan Region of India, Environment,4,34.

Baral SK, Malla R, Ranabhat S. 2009. Above-ground carbon stock assessment in different forest types of Nepal. Banko Janakari 19: 10–14.

Bhattarai TP, Skutsch M, Midmore DL, Rana EB. 2012. The carbon sequestration potential of community-based forest management in Nepal. The International Journal of Climate Change: Impacts and Responses 3: 234–253.

Bone, R.A.; Parks, K.E.; Malcolm, D.H.; Tsirinzeni, M.; Willcock, S.2017. Deforestation since independence: A quantitative assessment of four decades of land-cover change in Malawi. South. For. J. For. Sci., 79, 269–275.

Cochran, W.G.(1977).Sampling techniques(3rd edition)new York: John Wiley and Sons. Congalton, R.G.1991.A Review of Assessing the accuracy of Classifications of Remotely Smensed Data. Remote Sensing

of Environment 37. Dereje T, 2007. Forest cover change and socio-economic drivers in SW Ethiopia. MSc thesis, Centre of Land

Management and Land Tenure, Technical University Munich, Germany. Dessie G & Christiansson C, 2008. Forest decline and its causes in the south-central rift valley of Ethiopia: Human

impact over a one hundred year perspective. Ambio 37(4): 263–71. Dessie, G. and Kleman, J. 2007. Pattern and magnitude of deforestation in the South Central Rift Valley region of

Ethiopia. Mountain research and development, 27, 162–168. Earth Trends Country Profiles.2003. Forests, Grasslands, and Drylands—Ethiopia. Ellis E, 2011. Land-use and land-cover change. Encyclopedia of Earth. Available at

http://www.eoearth.org/article/Land-use_and_land-cover_change. Gebresamuel, G., Singh, B. R., Dick, Ø. 2010. Land-use changes and their impacts on soil degradation and surface

runoff of two catchments of Northern Ethiopia. Acta Agriculturae Scandinavica, Section B – Plant Soil Science, 60, 211–226.

Geist, H.J. and Lambin, E.F. 2004. Proximate Drivers and underlying driving forces of tropical deforestation. Bioscience, 52 (2), 143-154.

Genene B., P. Ramachandra R., 2015.Study of Vegetation Composition of Magada Forest, Borana Zone, Oromia, Ethiopia.

Goldewijk. k and Ramankutty. N. 2003. Land Use Land Cover Change during the Past 300 years. Haile, S. 2004. Population, Development, and Environment in Ethiopia. Special report. Retrieved from

http://www.wilsoncenter.org/sites/default/files/ecspr10_specialreport.pdf August 15, 2010 from http://www.future-agricultures.org/pdf%20files/SG_paper_2.pdf

Hurni, H., Tato, K., Zeleke, G. 2005. The implications of changes in population, land use, and land management for surface runoff in the upper Nile Basin Area of Ethiopia. Mountain Research and Development, 25, 147–154.

Koch, F.J., Ann, V.G., Stefan, U., Sirak, T., Teferi, E. 2012. The Effects of Land Use Change on Hydrological Responses in the Choke Mountain Range (Ethiopia). A New Approach Addressing Land Use Dynamics in the Model SWAT. International Environmental Modelling and Software Society, International Congress on Environmental Modelling and Software,Germany.

Lambin.E, Helmut .J and Lepers.E.2003. Dynamiocs of Land Use Lnad Cover Change in Tropical Regions. Li, X.; Wang, Y.; Li, J.; Lei, B.2016. Physical and socioeconomic driving forces of land-use and land-cover changes: A

case study of Wuhan City, China. Discret. Dyn. Nat. Soc. Mckee.J .2007.Ethiopia: Country Environmenta profile. Addis Ababa, Ethiopia Mengistu DA, 2008. Remote sensing and gis-based land use and land cover change detection in the upper Dijo river

catchment, Silte zone, southern Ethiopia. Working Papers on Population and Land Use Change in Central Ethiopia, nr. 17, Addis Ababa University.

Mengistu, K. T. 2009. Basin Hydrological Responses to Changes in Land Use and Land Cover, and Management Practices at Hare Basin, Ethiopia. Dissertation, Universität.Siegen Fakultät Bauingenieurwesen, Research Institute for water and Environment, Germany.

Meneses, B.M., Reis, E., Pereira, S., Vale, M.J., Reis, R.2017. Understanding Driving Forces and ImplicationsAssociated with the Land Use and Land Cover Changes in Portugal. Sustainability 9, 351

Meyer W, Turner II. 1992. Human Population Growth and Global Land-Use/Cover Change:Advances in 1.5 Decades. Sustained International Research. Clark University, USA.

http://www.wilsoncenter.org/sites/default/files/ecspr10_specialreport.pdf




22

Moges, A. and Holden, N. M. 2009. Land cover change and gully development between 1965.and 2004 in Umbulo Catchment, Ethiopia. Mountain Research and Development, 29, 265– 276.

Nejadhashemi, A.P., Wardynski, B.J., Munoz, J.D. 2011. Evaluating the Impacts of Land Use Changes on Hydrologic Responses in the Agricultural Regions of Michigan and Wisconsin. Hydrology and Earth System Sciences, 8, 3421-3468.

Prakasam, C. 2010. Land use and land cover change detection through remote sensing approach: A Case study of Kodaikanal taluk, Tamil nadu. International journal of Geomatics and Geosciences, 1(2): 150-158.

Pullanikkatil,D; Palamuleni,L.G.; Ruhiiga,T.M. 2016.Land use/land cover change and implications foretcosystems services in the Likangala River Catchment, Malawi.Phys.Chem.Earth 93,96-103.

Reid RS, Kruska RL, Muthui N, Taye A, Wotton S, Wilson CJ & Mulatu W, 2000. Land-use and land-cover dynamics in response to changes in climatic, biological and socio-political forces: The case of southwestern Ethiopia. Landscape Ecology 15(4): 339–55.

Richard, B. 2005. Basin Management Research: A Review of International Development Research Centre (IDRC) Projects in Asia and Latin America.

Schreier, H., Brown, S., Schmidt, M., Shah, P, Shrestha, B., Nakarmi, G., Subba, K., Wymann, S. 2003. Gaining forests but losing ground: a GIS evaluation in a Himalayan basin.

Solomon,N.,Birhane,E.,Tadesse,T.,Treydte,A.C,Melese,K.2017.Carbon stocks and sequestration potential of dry forests under community management in Tigray,EthiopiaEcol.Process.6,20.

Tesfaye Teklu .1999. Environment Stress and Increased Vulnerability to Impoverishment and Survival in Rural Ethiopia: A synthesis from Existing Empirical Evidence.

Thapa-Magar KB, Shrestha BB. 2015. Carbon stock in community managed hill Sal (Shorea robusta Gaertn.) forests of central Nepal. Journal of Sustainable Forestry 34: 483–501.

Wang, C., 2006. Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. Forest Ecology and Management, 222(1-3): 9-16.

Woldeamlak, B. 2004.Land cover dynamics since in the 1950s in Chemoga basin,Blue Nile Basin, Ethiopia,Mountain Research and Developent, 22 (3), 263-269.

Yamane,T.(1967).Statistics:An introductory Analaysis,2nd Edition. New York, Harper and Row2nd ed. YigremewA.1997.Rural land holding readjustment in west Gojjam, Amhara Region.Ethiopia Journal of Develeopment

Research 19(2):57 – 89.

Zeleke, G. and Hurni, H. 2001. Implications of land use and land cover dynamics for mountain resource degradation in

the northwestern Ethiopian highlands. Mountain Research and Development, 21, 184–191.



23

APPENDICES

Appendix 1: Questionnaire Format.

Bule Hora University

College of Social Sciences and Humanities

Department of Geography and Environmental Studies

This questionnaire is designed to assess the deriving factors of land cover land use change in Dawa river basin for the

purpose basin management in the watershed. The data provided by the respondents have greater role for the success of

this research work. The data are only used for academic purposes and will be kept in confident. I would like to extend

my appreciation in advance for your cooperation in answering these questions.

Thank you!!!!!

District Name: _________________________________________

Direction: Dear respondents!, The following questions are focused on the deriving factors of land use land cover

change of Dawa river basin starting from 1989 to 2019 G.C. Thus, you are kindly requested to jot down the deriving

factors of Land use land cover change of 1989, 2004 and 2019 in the Dawa river basin.

NB. No need of writing your name!

In order to answer the following questions put a right sign (√) in the boxes provided

I. Personal information of Respondents

1. District: _____________

2. Kebele: ______________

3. Sex: Male Female

4. Age: A. 25 – 30 B. 31 -35

C. 36 – 40 D. 41- 45 E. > 46

5. Marital status:

A. Single B. Married

C. Divorced D. Widowed

6. Occupation:

A. Farmers

B. Merchant

C. Daily labor

D. Government employees

E. Others (Specify) __________

7. Level of education:

A. Illiterate

B. Write and read

C. 1- 8 (primary school)

D. 9-10 (high school)

E. 11- 12 (preparatory school)

F. 12 Complete and above

8. Number of family size

A. 1- 2

B. 3 -4

C. 5 -7

D. > 8




24

Question1. What were the main possible deriving factors of Land Cover Land Use Changes from the year1989 to 2004

G.C in your locality?

_____________________________________________________________________________________________

_____________________________________________________________________________________________

_____________________________________________________________________________________________

___________________________________________________

Question1.1. Based on question1; if these were the main possible deriving factors of Land Cover Land Use Changes

from the year1989 to 2004 G.C in your locality which land use type would change to which LULC types?

_____________________________________________________________________________________________

_____________________________________________________________________________________________

_____________________________________________________________________________________________

___________________________________________________

Question1.2. Based on question1.1; if a certain LULC types were changed in to the others LULC types from the year

1989 to 2004 G.C, what would be the main agents for these conversions in your locality?

_____________________________________________________________________________________________

_____________________________________________________________________________________________

_____________________________________________________________________________________________

____________________________________________

Question2. What were the main all possible deriving factors of Land Cover Land Use Changes from the year2004 to

2019 G.C in your locality?

_____________________________________________________________________________________________

_____________________________________________________________________________________________

_____________________________________________________________________________________________

___________________________________________________

Question2.1. Based on question2; if these were the main all possible deriving factors of Land Cover Land Use Changes

from the year 2004 to 2019 G.C in your locality, which land use type would change to which LULC types?

_____________________________________________________________________________________________

_____________________________________________________________________________________________

_____________________________________________________________________________________________

___________________________________________________

Question2.2. Based on question2.1; if a certain LULC types were changed in to the others LULC types from the year

2004 to 2019 G.C, what would be the main agent for these conversions in your locality?

_____________________________________________________________________________________________

_____________________________________________________________________________________________

_____________________________________________________________________________________________

__________________________________________________



25

Appendix 2: Dawa Watershed Ground Control Points, 2019.

Used GCPs Used GCPs

No. LAT LONG ALT No. LAT LONG ALT

1 6.241489 38.526874 2789 271 4.951016 40.43036 1084

2 6.193338 38.49418 2575 272 4.881787 40.35896 850

3 6.146066 38.456956 2529 273 4.929386 40.28948 855

4 6.090737 38.441305 2458 274 4.976841 40.25109 1035

5 6.068256 38.405902 2449 275 4.959884 40.18787 1006

6 6.007545 38.377362 2536 276 4.942775 40.10882 963

7 5.955347 38.352791 2241 277 4.951964 40.02048 1030

8 5.940833 38.321899 1983 278 4.945414 39.95505 1102

9 5.889841 38.302662 2060 279 4.960405 39.90342 1125

10 5.846429 38.296602 2365 280 4.970749 39.84319 1214

11 5.774704 38.305523 2196 281 4.95433 39.77341 1327

12 5.691108 38.302954 2046 282 4.980695 39.71766 1345

13 5.622981 38.293334 1755 283 5.029812 39.65379 1365

14 5.56314 38.269009 1931 284 5.107297 39.64317 1462

15 5.491789 38.243238 1810 285 5.159192 39.56044 1310

16 5.441172 38.19627 1675 286 5.176663 39.49747 1175

17 5.403798 38.164802 1713 287 5.217537 39.43379 1262

18 5.335996 38.153828 1690 288 5.259051 39.37774 1210

19 5.26965 38.147394 1776 289 5.307828 39.32495 1373

20 5.211103 38.130969 1702 290 5.363649 39.26667 1332

21 5.13041 38.128934 1701 291 5.420071 39.2117 1293

22 5.028958 38.153336 1646 292 5.495663 39.16356 1623

23 4.963691 38.148551 1548 293 5.553242 39.10209 1730

24 4.87829 38.177441 1612 294 5.646385 39.03222 1756

25 4.861983 38.225528 1543 295 5.701509 38.98206 1735

26 4.861529 38.261715 1612 296 5.750043 38.90402 1733

27 4.830457 38.284659 1534 297 5.800093 38.82926 2177

28 4.969881 38.118273 1630 298 5.838549 38.74514 1869

29 4.890296 38.105748 1694 299 5.935959 38.68085 2026

30 4.848828 38.09225 1932 300 5.870677 38.64663 1816

31 4.816456 38.144735 1840 301 5.784835 38.62551 1643

32 4.805541 38.199109 1588 302 5.701453 38.5819 1758

33 4.7951 38.241165 1678 303 5.615143 38.5399 1312

34 4.779682 38.284174 1589 304 5.503457 38.53501 1356

35 4.76952 38.302203 1581 305 5.427468 38.51182 1264

36 4.71454 38.361171 1573 306 5.291473 38.49993 1290

37 4.632423 38.382475 1526 307 5.184597 38.45489 1425




26

38 4.566942 38.426491 1404 308 5.144214 38.40769 1421

39 4.498352 38.410736 1426 309 5.098348 38.41237 1505

40 4.44003 38.474807 1403 310 5.138724 38.36259 1460

41 4.422131 38.526301 1355 311 5.092989 38.36344 1480

42 4.395805 38.546033 1385 312 4.982286 38.38563 1633

43 4.34403 38.599328 1472 313 4.880839 38.43001 1500

44 4.293222 38.662329 1420 314 4.772937 38.46294 1506

45 4.255697 38.722121 1301 315 4.703717 38.48398 1470

46 4.226273 38.787124 1286 316 4.608071 38.52496 1369

47 4.237837 38.849327 1399 317 4.515528 38.56835 1292

48 4.279033 38.875898 1381 318 4.443516 38.61835 1366

49 4.291433 38.931572 1284 319 4.399323 38.66636 1322

50 4.320817 38.985713 1224 320 4.377416 38.72833 1288

51 4.393459 39.043787 1145 321 4.407383 38.77874 1225

52 4.459174 39.10798 1115 322 4.491311 38.83001 1169

53 4.50833 39.197415 1155 323 4.545847 38.88301 1170

54 4.512796 39.268095 1123 324 4.591732 38.93447 1198

55 4.53094 39.342175 1059 325 4.619623 38.99245 1126

56 4.554004 39.427958 1026 326 4.667766 39.02227 1087

57 4.582117 39.529635 971 327 4.687184 39.10153 975

58 4.591078 39.628183 851 328 4.706609 39.18138 919

59 4.581223 39.707378 1016 329 4.716552 39.2506 894

60 4.590688 39.806239 1224 330 4.721807 39.34354 959

61 4.609754 39.906186 1207 331 4.726608 39.42503 717

62 4.591167 39.980652 1229 332 4.751188 39.51835 749

63 4.558627 40.02678 1215 333 4.74635 39.59638 963

64 4.531383 40.076676 1195 334 4.76639 39.71416 815

65 4.514134 40.160973 1151 335 4.741889 39.76399 637

66 4.483494 40.239184 1117 336 4.746723 39.86957 661

67 4.439522 40.277461 1091 337 4.751887 39.95197 776

68 4.41397 40.347308 1036 338 4.72254 40.03128 913

69 4.401734 40.426154 1012 339 4.678276 40.1311 1249

70 4.359929 40.484623 957 340 4.630597 40.20977 1183

71 4.318206 40.513038 945 341 4.612476 40.27616 1112

72 4.332048 40.58856 837 342 4.5944 40.33451 1083

73 4.350291 40.658298 757 343 4.576431 40.3925 1008

74 4.385948 40.721721 587 344 4.540663 40.47345 1011

75 4.461501 40.799283 706 345 4.514529 40.5515 871

76 4.506711 40.859259 779 346 4.520511 40.61694 642

77 4.566004 40.885646 804 347 4.588964 40.58539 911

78 4.631612 40.883324 870 348 4.669889 40.53229 834

79 4.634912 40.787615 822 349 4.718497 40.47916 882

80 4.690527 40.730248 780 350 4.787459 40.44784 811



27

81 4.753703 40.685369 891 351 4.761118 40.38035 825

82 4.804227 40.680439 920 352 4.704034 40.30479 1152

83 4.874734 40.626823 1094 353 4.701244 40.37462 1010

84 4.910146 40.561023 1031 354 4.65416 40.45205 1051

85 4.984147 40.537894 1112 355 4.605358 40.42416 1044

86 5.020954 40.425166 1152 356 4.632226 40.34417 1099

87 4.98281 40.341636 934 357 4.66461 40.26045 1172

88 5.020168 40.285385 925 358 4.777361 40.20925 1089

89 5.030099 40.212585 1151 359 4.793226 40.29805 882

90 5.039721 40.123033 1137 360 4.875067 40.18175 601

91 5.071594 40.047652 1244 361 4.81274 40.13864 995

92 5.08115 39.95312 1308 362 4.767553 40.12966 532

93 5.085391 39.849769 1445 363 4.802415 40.05857 877

94 5.084666 39.771009 1403 364 4.900046 40.06413 1145

95 5.135286 39.693884 1572 365 4.836572 39.97049 1328

96 4.695273 38.338076 1580 366 4.781536 39.96607 937

97 4.613866 38.344317 1523 367 4.815728 39.85021 1281

98 4.544248 38.336914 1563 368 4.881942 39.90612 1270

99 4.485392 38.338813 1558 369 4.896703 39.78284 1222

100 4.432092 38.354488 1497 370 4.810808 39.7394 1159

101 4.384115 38.376221 1489 371 4.820514 39.6686 1377

102 4.332817 38.394848 1461 372 4.875977 39.67807 1231

103 4.286608 38.422985 1398 373 4.981342 39.6445 1260

104 4.237143 38.455036 1403 374 4.908001 39.60273 1041

105 4.220678 38.493971 1382 375 4.831192 39.55799 960

106 4.220378 38.536553 1356 376 4.857028 39.50645 1042

107 4.220049 38.582665 1362 377 4.966714 39.50219 1000

108 4.195636 38.618976 1390 378 5.091331 39.53505 1263

109 4.171411 38.661944 1445 379 4.956767 39.38877 949

110 4.139778 38.69526 1433 380 4.842157 39.38339 765

111 4.151324 38.756062 1357 381 4.826565 39.2848 841

112 4.174114 38.822737 1350 382 4.79121 39.2194 865

113 4.185722 38.887755 1384 383 4.76097 39.15845 964

114 4.205599 38.923943 1380 384 4.745995 39.10482 1019

115 4.25429 38.982044 1284 385 4.731107 39.03591 1048

116 4.304057 39.030503 1239 386 4.686151 38.95899 1155

117 4.34618 39.084313 1169 387 4.695065 38.86161 1574

118 4.397523 39.167634 1120 388 4.619699 38.80948 1238

119 4.423615 39.220929 1094 389 4.536725 38.79026 1192

120 4.45446 39.293012 1083 390 4.500283 38.74684 1207

121 4.48119 39.354954 1081 391 4.50065 38.67212 1250

122 4.51287 39.424912 1055 392 4.528258 38.63382 1273

123 4.522166 39.50704 1046 393 4.616408 38.59998 1410




28

124 4.503984 39.559261 1089 394 4.659656 38.55017 1472

125 4.517252 39.619154 1077 395 4.762367 38.52389 1527

126 4.525988 39.690423 1153 396 4.767383 38.52297 1504

127 4.544214 39.777402 1230 397 4.885284 38.51347 1412

128 4.539671 39.829716 1179 398 4.959889 38.47964 1498

129 4.544256 39.871303 1227 399 5.1321 38.49803 1334

130 4.521417 39.914644 1208 400 5.25488 38.54903 1278

131 4.503532 39.961777 1257 401 5.39517 38.57856 1264

132 4.47231 39.999915 1234 402 5.501878 38.63788 1293

133 4.450261 40.02742 1210 403 5.619096 38.65761 1655

134 4.446131 40.078692 1196 404 5.692876 38.68431 1674

135 4.446649 40.126804 1169 405 5.781447 38.74266 1788

136 4.434085 40.184361 1162 406 5.703349 38.84405 1897

137 4.390908 40.219058 1150 407 5.580586 38.94269 1689

138 4.374259 40.271535 1078 408 5.487318 39.03123 1795

139 4.36625 40.325045 1033 409 5.431494 39.11896 1700

140 4.324553 40.394449 1007 410 5.37691 39.20947 1093

141 4.296195 40.458065 964 411 5.290657 39.28685 1328

142 4.237749 40.397253 1005 412 5.206466 39.37896 1179

143 4.213739 40.4249 983 413 5.137099 39.44744 1137

144 4.230577 40.474271 969 414 5.104178 39.35222 1012

145 4.247641 40.527625 956 415 5.104727 39.2524 995

146 4.269076 40.589479 899 416 5.005907 39.15466 920

147 4.282527 40.650133 851 417 4.926295 39.20801 812

148 4.287699 40.70559 750 418 4.904521 39.3016 804

149 4.305412 40.757078 891 419 4.81673 39.18639 906

150 4.352764 40.805447 667 420 4.791023 39.10928 1010

151 4.405064 40.855653 645 421 4.765843 39.03284 1091

152 4.409684 40.886424 679 422 4.78431 38.92145 1426

153 4.449093 40.889358 755 423 4.758402 38.83451 1739

154 4.498231 40.92474 779 424 4.666726 38.75726 1267

155 4.557315 40.948014 804 425 4.578141 38.75053 1234

156 4.603837 40.952884 877 426 4.536668 38.7077 1285

157 4.646435 40.948895 934 427 4.606347 38.66275 1481

158 4.66535 40.919046 930 428 4.702751 38.57742 1571

159 4.660147 40.871238 891 429 4.827905 38.55959 1523

160 4.712376 40.848858 975 430 4.970187 38.55512 1509

161 4.750324 40.798795 916 431 5.097523 38.54164 1343

162 4.804827 40.775706 1065 432 5.201248 38.5833 1286

163 4.865687 40.749374 1066 433 5.296179 38.62414 1171

164 4.906972 40.722091 1104 434 5.368306 38.69624 1313

165 4.964332 40.677124 1055 435 5.380506 38.70333 1332

166 5.017754 40.646795 1143 436 5.499615 38.7586 1344



29

167 5.049921 40.603402 1213 437 5.630333 38.77895 1810

168 5.077208 40.566973 1257 438 5.55896 38.71528 1450

169 5.104918 40.534443 1249 439 5.731595 38.77644 1762

170 5.110215 40.489436 1211 440 5.62292 38.8479 1609

171 5.104146 40.45085 1157 441 5.527968 38.92523 1554

172 5.086866 40.398166 1178 442 5.455466 39.00288 1517

173 5.10334 40.358981 1125 443 5.375576 39.10083 1626

174 5.125722 40.299521 1152 444 5.292358 39.20513 936

175 5.119423 40.252667 1076 445 5.178523 39.2659 1177

176 5.124162 40.190493 1052 446 5.143988 39.28412 1101

177 5.140709 40.150473 1086 447 5.144104 39.26319 1120

178 5.145712 40.100653 1172 448 5.149828 39.25456 1105

179 5.145059 40.058628 1225 449 5.139024 39.17554 883

180 5.149897 40.004501 1279 450 5.084053 39.09575 881

181 5.166357 39.955383 1327 451 5.029007 39.02171 898

182 5.171396 39.909448 1380 452 4.957148 39.05645 912

183 5.176679 39.867688 1491 453 4.857353 39.08446 1022

184 5.199491 39.826795 1559 454 5.016323 38.91654 1657

185 5.204829 39.780552 1597 455 5.082123 38.80615 1266

186 5.192707 39.733589 1583 456 5.021985 38.75193 1260

187 5.193033 39.678784 1580 457 4.88281 38.73858 1447

188 5.216806 39.620163 1520 458 4.904891 38.66698 1335

189 5.27041 39.557004 1369 459 5.02916 38.68085 1243

190 5.306869 39.492746 1371 460 5.210263 38.75187 1413

191 5.325322 39.436587 1339 461 5.185878 38.80161 1207

192 5.380054 39.361861 1554 462 5.316094 38.88924 1670

193 5.462271 39.311198 1368 463 5.395141 38.88543 1330

194 5.54091 39.268278 1376 464 5.320552 38.97091 1370

195 5.600827 39.198137 1511 465 5.266364 38.9844 1293

196 5.680624 39.116423 1730 466 5.207291 38.93469 1524

197 5.800888 39.048117 1765 467 5.126496 38.86757 1371

198 5.837529 38.975076 1719 468 4.939623 38.81571 1573

199 5.874021 38.905996 1648 469 4.759559 38.76766 1382

200 5.926282 38.82971 1984 470 4.815666 38.98505 1222

201 5.98796 38.746257 1900 471 4.325255 38.86634 1334

202 6.011432 38.661545 1965 472 5.214689 39.17531 939

203 6.057846 38.602126 2308 473 4.961525 38.89806 1530

204 6.150831 38.614061 2363 474 4.871325 38.80425 1521

205 5.967661 38.561702 1963 475 4.831618 38.70202 1483

206 5.850347 38.526914 1981 476 4.716137 38.70353 1344

207 5.73029 38.480549 1994 477 4.615201 38.70338 1320

208 5.641272 38.458588 1585 478 4.69742 38.6365 1395

209 5.510715 38.440087 1463 479 4.837542 38.6335 1416




30

210 5.377307 38.406709 1624 480 5.024303 38.62397 1349

211 5.29232 38.361966 1538 481 5.177354 38.63359 1330

212 5.203312 38.307146 1627 482 5.253196 38.68626 1161

213 5.099355 38.291215 1706 483 5.288075 38.76312 1360

214 5.239368 38.261151 1597 484 5.383977 38.81231 1379

215 5.094392 38.246323 1615 485 5.484528 38.82725 1541

216 5.005093 38.290702 1605 486 5.470259 38.88651 1453

217 4.966345 38.360276 1581 487 5.375122 38.97906 1365

218 4.855236 38.387067 1506 488 5.27827 39.06041 1011

219 4.753386 38.419628 1488 489 5.231505 39.11916 1027

220 4.627375 38.463918 1407 490 5.168451 39.04763 1184

221 4.534237 38.519762 1336 491 5.061716 38.96939 1321

222 4.470358 38.59509 1340 492 4.490471 40.67845 748

223 4.470941 38.513179 1347 493 4.500041 40.70696 358

224 4.352123 38.659623 1413 494 5.763511 39.16214 1745

225 4.32642 38.718105 1307 495 5.294358 39.58292 1473

226 4.385586 38.814706 1217 496 5.318959 39.53579 1397

227 4.406947 38.899718 1253 497 5.34976 39.50124 1384

228 4.540948 38.992574 1098 498 5.399345 39.47928 1407

229 4.558952 39.111417 1048 499 5.417639 39.44412 1456

230 4.577212 39.223455 1085 500 5.461354 39.40391 1560

231 4.581791 39.340484 1070 501 5.519942 39.34491 1507

232 4.577179 39.257479 1110 502 5.579771 39.32073 1544

233 4.563467 39.141249 1058 503 5.639662 39.29161 1467

234 4.477115 39.080591 1108 504 5.695479 39.26206 1644

235 4.384846 39.022711 1166 505 5.743505 39.23707 1595

236 4.376361 38.95438 1191 506 5.799293 39.23465 1862

237 4.619616 38.88574 1219 507 5.842605 39.19053 1999

238 4.60123 39.058754 1054 508 5.843844 39.13412 1858

239 4.657996 39.176725 953 509 5.844643 39.07772 1747

240 4.663035 39.307012 1036 510 5.86025 39.02484 1729

241 4.648405 39.388051 936 511 5.897153 38.96522 1692

242 4.653053 39.468465 965 512 5.957587 38.93135 2177

243 4.658024 39.525988 834 513 5.987615 38.8759 2199

244 4.657937 39.57588 747 514 6.017017 38.83939 2154

245 4.662434 39.645058 690 515 6.017549 38.78145 2044

246 4.657908 39.721717 679 516 6.048063 38.74417 2189

247 4.672445 39.806946 1241 517 6.118593 38.70166 2364

248 4.677436 39.918948 822 518 6.205835 38.65144 2516

249 4.658434 39.998186 1226 519 6.270976 38.61312 2661

250 4.629731 40.052887 1279 520 6.222929 38.5746 2554

251 4.605927 40.118905 1255 521 6.143958 38.54114 2380

252 4.588095 40.196658 1147 522 6.066618 38.51819 2228



31

253 4.565474 40.250527 1102 523 6.005945 38.5031 1958

254 4.538706 40.318413 1071 524 6.006625 38.44992 2300

255 4.498184 40.345923 1061 525 5.992031 38.41374 2395

256 4.462531 40.358854 1049 526 5.925087 38.3963 2048

257 4.450336 40.453336 1001 527 5.851961 38.39469 2053

258 4.421177 40.529564 953 528 5.787697 38.37783 2355

259 4.409386 40.586491 866 529 5.710605 38.3777 1651

260 4.402217 40.662664 841 530 5.642377 38.35348 1890

261 4.451662 40.737126 357 531 5.56275 38.34562 1645

262 4.54251 40.807714 683 532 5.497632 38.31337 1782

263 4.579224 40.801356 642 533 5.478665 38.27707 1926

264 4.553738 40.680946 728 534 5.421903 38.24888 1642

265 4.613747 40.682451 842 535 5.35391 38.2325 1549

266 4.680035 40.600048 572 536 5.305243 38.22559 1534

267 4.758929 40.608116 771 537 5.251562 38.19436 1665

268 4.828318 40.565981 1055 538 5.192934 38.18199 1718

269 4.853226 40.518655 1008 539 5.129757 38.16698 1642

270 4.925018 40.478762 992 540 5.078823 38.16223 1604

J J A S O N D

analyze trends and drivers of land use land cover dynamics

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