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© 2021 by the authors; licensee Asian Online Journal Publishing Group 
 

Asian Review of Environmental and Earth Sciences 
Vol. 8, No. 1, 18-29, 2021 

ISSN(E) 2313-8173 / ISSN(P) 2518-0134 
DOI: 10.20448/journal.506.2021.81.18.29 

© 2021 by the authors; licensee Asian Online Journal Publishing Group 

  
 

 
 
 
The Dynamics of Land Use Land Cover and its Driving Forces in Mekelle City 
Region, Ethiopia 

 
Shishay Kiros Weldegebriel1   
Kumelachew Yeshitela2   

 

 
( Corresponding author) 

 
1Ethiopian Civil Service University, Addis Ababa, Ethiopia. 

 
2Ethiopian Institute of Architecture, Building Construction and City Development, Addis Ababa University, Addis 
Ababa, Ethiopia. 
 

 
Abstract 

The rationale of this study was to study the spatio-tempo LULC dynamics over the past 47 years 
and identifying the major drivers of these changes. It was conducted in Mekelle city region, 
northern Ethiopia which is highly susceptible to environmental degradation. This landscape-scale 
level study employs a combination of analysis of satellite imageries, information from field studies, 
document review, key informant interview and observation. Digital satellite images were 
processed, classified and analysed by ERDAS Imagine. Computations of the area changes in the 
land use categories was made using supervised classification by applying maximum likelihood 
classifier algorithm and finally post-classification change detection technique was undertaken 
using Arc GIS 10.5.1. For stastical analysis of variables spatial autocorrelation and structural 
equation model was used. During the study period between 1972 and 2019 about 60,705.56 
hectares of the total landscape of the study area was converted from one LULC type to another. 
The findings show increase was observed in cultivated land, built-up area and bushes and shrubs. 
On the other hand, natural forest, water body and bare lands were dramatically declined. Among 
the driving factors; climate variability, population growth, DEM and slope were identified as the 
leading land use and land cover change drivers. Thus, spatial planners need to take these drivers 
into consideration and make sound decisions regarding changes in LULC during decision 
makings. 

 
Keywords: City region, Drivers, ERDAS, Land use/land cover changes, Remote sensing, Spatio-tempo. 

 
Citation | Shishay Kiros Weldegebriel; Kumelachew Yeshitela 
(2021). The Dynamics of Land Use Land Cover and its Driving 
Forces in Mekelle City Region, Ethiopia. Asian Review of 
Environmental and Earth Sciences, 8(1): 18-29. 
History:  
Received: 3 May 2021 
Revised: 7 June 2021 
Accepted: 30 June 2021 
Published: 13 July 2021 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Acknowledgement: My thanks go to Tigray Bureau of trade, industry and 
urban development, Tigray (Ethiopia), bureau of civil service and Ethiopian 
civil service university for giving the opportunity to pursue this study, 
without which this achievement would not have been possible. Authors would 
like to thank all institutions and individuals at national, regional, district and 
city levels who provided them information for this study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 19 
2. Material and Methods ..................................................................................................................................................................... 19 
3. Results ................................................................................................................................................................................................ 21 
4. Discussion .......................................................................................................................................................................................... 26 
5. Conclusion and Recommendations ............................................................................................................................................... 28 
References .............................................................................................................................................................................................. 28 
 

 
 

 

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Contribution of this paper to the literature 
This study contributes to the generation of scientific knowledge for the scientific community on 
understanding environmental degradation using multi-lens approach to sustain the environment, 
improved research tool/ technique and it will have implications for positive social change. 

 
1. Introduction 

The imminent of humankind depends on whether or not we have a vision to guide transition toward 
sustainability, on scales ranging from local landscapes to the planet as a whole [1].The fundamental concept in 
landscape development has always been that people are part of the landscape and that landscapes are changed for 
their benefit [2]. In order to meet increasing human needs natural ecosystems have been converted into different 
land uses since humans first began to manage their environment. However, the changes that have taken place over 
the last 50 years have been especially important and intense as society is becoming increasingly urbanized, while 
natural ecosystems become deteriorated [3]. Land uses/land covers are closely related to the ecosystem services 
supply of provisioning, regulating, supporting and cultural ecosystem services provisions [4].The dynamics of an 
ecosystem are strongly affected by natural and human disturbances, and such changes can have direct and 
cascading effects on the spatial and temporal variations of ecosystems [2]. The notion of driving forces is gaining 
increasing attention in landscape change research [5]. Drivers of degradation can be many and complex, resulting 
from a range of different interactions over time and space [6]. Anthropogenic drivers, such as population growth 
have mostly a short-term and often more perceivable impact than biophysical drivers. Conversely, climate change 
as one of the emerging drivers of land use /land cover dynamics and is difficult to detect and quantify in the short 
term. Subsequently, long-term studies are necessary to provide evidence of climate change [7]. Cities do not 
operate in isolation but within a sphere of dependence on surrounding areas and their ecosystems. Cities depend on 
persisting flows of goods and services coming from other neighbouring non-urban ecosystems to sustain 
fundamental urban functions.Ecological principles of land use advocate to examine the impacts of local decisions in 
a regional context [8]. Mekelle city is dependent on the ecosystem services beyond the city limits. Mekelle city has 
not been studied as a complete network with consideration of all ecosystem sources from its hinterlands. An 
increasing number of studies demonstrate the need of applying a social-ecological system approach in ecosystem 
services. However, there is a lack of empirical research that operationalizes the concept of social-ecological system 
in a spatially explicit. Several efforts have been made to improve the quantification of LULC changes and their 
drivers. From the reviewed literatures there are still deficiencies and methodological inconsistencies. Alternative 
approaches to better understand the problem is useful. Hence, studying landscape dynamics and its driving forces 
in Mekelle city region, northern Ethiopia through a social-ecological systems perspective under different spatial 
and temporal scales can support decision-making in land use to understand past, current and plausible future 
changes in land use. Therefore, the objectives of this study were: to assess land use/land cover dynamics, analyze 
the driving forces of land use/land cover change and suggest possible spatial planning solutions for sustainable 
watershed ecosystem services provision. 

 
2. Material and Methods 
 2.1. Study Area 

Mekelle city and its hinterlands which are significant in terms of watershed ecosystem services for the 
metropolitan city are named as city region (Figure 1). The landscape is located within Tigray region, northern part 
of Ethiopia found in west 39.362942, East 39.687048, North 13.680920 and south 13.342621 about 760.61 km 
north of Addis Ababa and the area covered in this investigation is 897.12 square kilometers (89,712 hectares).  

The study area is characterized by varied topographic conditions. The elevation ranges from 1700 meters in 
the Geba river to 2685 meters Ellala and Gebat river catchment. The core city of the study area Mekelle city is 
2062 m above sea level. The climate is predominantly semi-arid with irregular rainfall and frequent drought 
periods. The mean annual rainfall of the is estimated to be less than 532 mm [9]. Climatically, the area has a semi-
arid climate with little variation and it is knowns by its environmental vulnerability [10]. The agro-climatic zone 
of the study area is mild climatic condition. The monthly mean minimum temperature is 150C and maximum 
monthly temperature may go as high as 280C. The study area population is 556127 [11].  

 

2.2. Data Sources and Processes 
This landscape-scale level study employs a combination of analysis of satellite imageries of 47 years and 

information from field studies, document review and key informant interview. In order to detect LULC dynamics 
data from Landsat series was extracted (Table 1) which is found at (https://earthexplorer.usgs.gov/.Key informant 
selection was made based on the information gathered from knowledgeable local elders; based on their age, duties 
and responsibility in their local community. Remotely sensed data, document review analyses add to validate the 
relevance of the local elderly interview results. In addition to this, the author made observations and described 
about what was observed. A global positioning system was used during the field survey for accuracy assessment. 

The years for analysis were selected based on key signs of LULC change, e.g. land degradation, land policy 
changes, rapid urbanization, rapid population growth, Socio-economic development and finally the availability of 
satellite image. Images from the same period (November–January), i.e., immediately after the rainy season, was 
selected in order to minimize the seasonal effect on the classification results. Probability and non-probability 
sampling techniques was employed. Snowball sampling was employed for reaching local elderly persons. The 
population of LULC was divided into sub-populations of known size, and then random samples was taken from 
each stratum. In each category, random samples were further determined for field investigation. Since it is difficult 
to cover, collect data from whole population of ecosystem from all types of LULC for ground truthing using GPS it 
was selected proportional from all the city region by employing an accuracy assessment framework. The maps were 
further verified from the pre-determined sites through observations and consulting with 15 local residents. The 
450 corroboration points, which were spatially and temporally distributed in the whole study area encompassing all 
nine LULC types Table 2 were sampled through stratified random sample. 

https://earthexplorer.usgs.gov/


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Figure-1. Location Map of the study area. 

Source: Prepared based on ARC GIS online data by the author, 2019. 

 
Table-1. Specifications of satellite images that was utilized in this study. 

Satellite Sensor Path Row Resolution (m) Acquisition date 

Landsat 1 MSS Multispectral C1 level 1 181 50 60 02 November,1972 
Landsat 5 TM Multispectral C1 level 1 169 51 30 22 november,1984 
Landsat 5 TM Multispectral C1 level 1 168 51 30 21 november,1992 
Landsat 5 TM Multispectral C1 level 1 168 51 30 30 November,2001 
Landsat 7 ETM+ Multispectral C1 level 1 168 51 30 20 November,2012 
Landsat 8 Operational Land Imager OLI/TIRS C1 level 1 169 51 30 23 January,2019 

 
Table-2. Descriptions of identified LULC classes.  

LULC class Description 

Built-up area Built-up area encompasses land on which buildings and non-building structures are present, such as urban 
area of Mekelle city  and its suburban area ,developed land lot including roads,rural area, small towns 
and villages  

Grassland Land under grass cover but highly managed for grazing and feeding of domestic animals, selling for various 
purposes. A land dominated by natural grass, small herbs and grazing lands.  

Cultivated land  Areas of land prepared for growing agricultural crops. This category includes areas currently under crop, 
Irrigated agricultural and land under preparation or fallow land.  

Bushes and 
Shrubs 

Land covered with shrubs, bushes, and small trees with little woody vegetation mixed. Specific area 
characterized by scattered bushes and shrubs and trees. Areas covered by low woody of 3 m in height, 
multiple stems, vertical growing of bushes and shrubs with canopy cover between 5 and 50%. Examples like 
Acacia locally known as “Hohot”, Aloe barbadensis, “ika” and Ocimun lamiifolium. 

Water body surface water or all areas of open water areas covered with water either along the river bed or man-made 
including rivers, streams, swales, dams, lakes, wetlands, ponds and reservoirs 

Natural Forest  High and dense natural forest and in churches and reserved Areas. 
Bare lands land left without vegetation cover (devoid vegetation), eroded land due to land degradation and weathered 

road surface and includes rock, including exposed soils, stock quarry, rocks, and areas of active excavation 
and vacant land within Mekelle city 

Plantation 
forest 

Areas covered by man-made trees including sparse forests examples include; Eucalyptus tree, acacia and 
teak 

Riverside vegetation Plants growing in areas adjacent to rivers and streams in both urban and rural (Riparian vegetation include 
trees, shrubs, grasses and trees with canopy.) 

 

2.3. Data Analysis 
Digital satellite images were processed, classified and analysed using ERDAS Imagine remote sensing software 

package 2015. Computations of the area and changes in the land use categories was made using Arc GIS 10.5.1. 
Supervised classification was performed on the image's pixels of the different LULC classes. This was done by 
training the areas (assigning pixels to land use classes) based on the previous knowledge of the area. Before 
classifying, the image was pre-processed in ERDAS software. The image was atmospheric corrected.  

Landsat 7 ETM+ since 2003 to present has gaps in their data due to Scan Line Corrector (SLC) failure. Before 
analysis started scanning line corrector (SLC) was employed using SLC corrected in Arc GIS. The LULC of 
several years was converted to feature class(polygon) using Arc GIS conversion tool and using analysis tool of over 
lay tool using intersecting tool were merged into one polygon and then the 6 polygons were merged into single 
polygon. Then it was used calculate geometry to calculate the changes in hectares.  

The LULC change detection LULC map of 1972 was re-sampled from 60m to 30 meters to match the spatial 
resolution of all the classified maps. Spatial autocorrelation (Moran’s I) was used to derive detailed information of 
the spatial and temporal variation of the land-use land cover dynamics using a spatial statistics tool analyzing 
patterns in Arc GIS. To understand the possible drivers of changes structural equation model using STATA was 
used Table 6.  

https://en.wikipedia.org/wiki/Land
https://en.wikipedia.org/wiki/Building
https://en.wikipedia.org/wiki/Nonbuilding_structure
https://en.wikipedia.org/wiki/Urban_area
https://en.wikipedia.org/wiki/Urban_area
https://en.wikipedia.org/wiki/Suburb
https://en.wikipedia.org/wiki/Land_lot
https://en.wikipedia.org/wiki/Rural_area
https://en.wikipedia.org/wiki/Town
https://en.wikipedia.org/wiki/Village


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3. Results 
3.1. Accuracy Assessment 

To assess the accuracy, first visual inspection for the image acquired from Landsat series was compared with 
LULC map prepared and with shape file the study area, local maps and google earth were used as first necessary 
step. Since this is not sufficient, quantitative accuracy assessmentt have made. To examines the accuracy 
assessment of land use land cover classified was done using ERDAS imagine 2015 accuracy assessment tool, for the 
year 2019 based on user defined 450 points collected referenced data using hand held GPS Table 3. 
Total accuracy =                Number of correct plots  *100     
                                           Total number of reference data    
Accuracy total   = 409/450*100   =   90.88 % 
 

The error percentage, is the number of sites incorrectly classified divided by 450 or 41/450 = error, = 9.12%. 
The overall accuracy and Kappa coefficient of the 2019 map were 90.88% and 91.64%, respectively which is 
acceptable in both accuracy total and Kappa accuracy.  
 

Table-3. Comparing user's and producer's accuracies. 

S. N Class name Reference 
total 

Classified 
total 

Number 
correct 

Producer 
accuracy 

User 
accuracy 

1 Built-up area 50 49 48 96 97.95 
2 Natural forest 50 44 44 88 10 
3 Plantation forest 50 55 47 94 85.45 
4 Water bodies 50 49 49 98 100 
5 Cultivated land 50 56 46 92 82.14 
6 Grass land 50 53 45 90 84.90 
7 Bushes and shrubs 50 49 48 96 97.95 
8 River side vegetation 50 47 39 78 82.97 
9 Bare land 50 45 43 86 95.55 
 Total 450 447 409 90.88 91.87 

 
The user and producer accuracy for any given class typically were not the same. In the above examples the 

producer’s accuracy (It is the number of reference sites classified accurately divided by the total number of 
reference sites for the class) for the built-up class is 96% while the user's accuracy (It was calculated by taking the 
total number of correct classifications for a particular class and dividing it by the row total or the total number of 
classified sites) was 97.95%. This means that even though 96% of the reference built-up areas have been correctly 
identified as built-up area, only 97.95% percent of the areas identified as built-up areas in the classification were 
actually built-up areas. Cultivated land (2) areas were mistakenly classified as built-up areas. By analyzing the 
various accuracy and error metrics we can better evaluate the analysis and classification results.  

 
3.1.1. Land Use/Land Cover Change Dynamics and Change Detection in Mekelle City Region 

The multi-temporal LULC results and the variations for the years 1972, 1984, 1992,2001,2012 and 2019 
(Figure 2 and Table 5) illustrate in 1972 the area was occupied by different classes of built-up area, natural and 
plantation forest, water body, cultivated land, grass land, bushes and shrubs, river side vegetation and bare lands. 
The study area witnessed large amount of agriculture land converted into settlements and other urban 
development activities. Water bodies decreased. Dense forest comprising all land with tree cover of canopy density 
was significantly declined. The results from the classified image of 1972 illustrate that at the beginning of the study 
year 66.78 % of the area was occupied by three land use classes only namely cultivated land, bare land and water 
body consecutively. And 26,403 ha (29.43%), 19936 ha (19.99%),15,577 ha (17.36%),11,132(12.42%),8183 ha 
(9.12%),5543(6.18%),2359 ha (2.63%),2082ha (2.32%) and 497 ha (0.55%) was cultivated land, bare land, water 
body, river side vegetation, bushes and shrubs, natural forest, plantation forest, grass land and built-up area 
consecutively. The end of the study period 2019 reveals that cultivated land, bushes and shrubs and built-up land 
have tremendously expanded at the expenses of other ecosystem types. Currently,64,843 ha (72.27%),13,215 ha 
(14.73%),8897 ha (9.92%),1235(1.38)497 ha (0.55 %),330 ha (0.37%),326 ha (0.36 %),263 ha (0.29), and 106 ha 
(0.13%) are cultivated land, bushes and shrubs, built-up area, river side vegetation, plantation forest, grass land, 
bare land, water body, and natural forest consecutively.  
 

3.1.2. Land-Use and Land-Cover Change Matrix 
The   LULC changes matrix from 1972-2019 (Table 4) showed that; in sum, 749 hectares converted from built-

up to other classes. However, there was no transformation from built-up to natural forest. Another 5427.73 
hectares were converted from natural forest to other classes. This result showed majority of the natural forest was 
converted to cultivated land. Of the changed area, 1585 hectares converted from plantation forest to other classes. 
This result showed majority of the natural forest was converted to cultivated land. There was no transformation 
from water body to bushes and shrubs. In sum, 15314 hectares converted from water bodies to other classes. This 
result showed majority of the water bodies was converted to cultivated land followed by grass land and built-up 
areas. Similarly, 5160 hectares were converted from cultivated land to other classes. This result showed majority of 
cultivated land was converted to built-up areas. Compared to the other LULC, 2029.03 hectares were also 
converted from grass land to other classes. This showed majority of the grass land was transformed to cultivated 
land. Similarly, 2933.8 hectares converted from bushes and shrubs into other classes and majority of the bushes and 
shrubs was converted to cultivated land. On the other hand, 9897 hectares converted from river side vegetation 
into other classes. This result showed majority of the bushes and shrubs were converted to cultivated land. Finally, 
17610 hectares converted from bare lands into other classes. This result revealed majority of the class was 
converted to cultivated land. 
 



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Table-4. Summary of LULC change matrix in hectare from 1972 to 2019. 

To From 

 Built-up area Natural Forest Plantation 
forest 

Water 
body 

Cultivated 
land 

Grass land Bushes 
and shrubs 

Riverside 
vegetation 

Bare 
land 

Total 
(2019) 

Built-up area 42.9 0.74 0.40 1726.6 3516.72 0 1.44 522.31 3676.2 9487.31 
Natural forest 0 12.2 1.30 0.24 1.02 0.55 4.2 0.45 0.15 20.11 
Plantation forest 8 349 539.34 43.43 76 0.90 118 7.45 3.66 1145.78 
Water bodies 439.17 332 63.33 0 0 27.73 0 1519 0 2381.23 
Cultivated land 125.99 3954.2 419.13 11077.93 902.84 1800.8 1614 6277.13 8915.06 35087.08 
Grass land 0.090 1.41 0 2037 13.54 101.3 19.3 2.75 31.51 2206.9 
Bushes and shrubs 117.18 0 138.3 0 4.24 71.92 767.08 1000 1635.8 3734.52 
River side vegetation 14.70 775.37 422.27 174.1 264.76 7.1 383.22 36.73 31.72 2109.97 
Bare land 1 2.81 0.93 254.7 380.88 18.73 26.56 531.18 3315.9 4532.69 
Totals (1972) 749 5427.73 1585 15314 5160 2029.03 2933.8 9897 17610 60705.56 

 
Table-5. Area of classified LULC classes during 1972–2019. 

S.N Class name 1972 1984 1992 2001 2012 2019 

  Area (ha) % area Area (ha) % area Area (ha) % area Area (ha) % area Area (ha) % area Area (ha) % area 

1 Built-up area 497 0.55 536 0.60 541 0.61 1029 1.15 4420 4.94 8897 9.92 
2 Natural forest 5543 6.18 1011 1.13 647 0.72 181 0.20 147 0.16 106 0.13 
3 Plantation forest 2082 2.32 1118 1.25 529 0.59 398 0.44 292 0.33 497 0.55 
4 Water body 15577 17.36 14209 15.83 2024 2.26 1207 1.35 130 0.14 263 0.29 
5 Cultivated land 26403 29.43 40156 44.76 65700 73.23 70003 78 69610 77.59 64843 72.27 
6 Grass land 2359 2.63 1049 1.17 310 0.35 297 0.33 445 0.5 330 0.37 
7 Bushes and Shrubs 8183 9.12 16149 18 12559 13.99 9404 10.48 10982 12.24 13215 14.73 
8 River side vegetation 11132 12.42 9359 10.42 1277 1.42 4907 5.47 1430 1.59 1235 1.38 
9 Bare land 17936 19.99 6134 6.84 6125 6.83 2286 2.58 2256 2.51 326 0.36 
 Total 89712 100 89712 100 89712 100 89712 100 89712 100 89712 100 

 

 



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In the above table the row total sums the amount of land area for each LULC types of the year 1972 and 
column total sums the amount of land area that was converted to each LULC types of the year 2019. The bold 
diagonal values represent the area of each LULC class that remained unchanged while the off-diagonal values 
represent the changed area. The values in each of the cells represent the amount of land that was converted from 
one LULC type to another. The transition probability matrix showed that during the study period between 1972 
and 2019 about 60705.56 ha of the total landscape of the study area was converted from one LULC type to the 
other. From all LULC types, built-up area experienced the lowest persistence with 749 ha conversion to another 
land use type, whereas bare lands and water bodies was the most persistent with 17610 hectares and 15314 
hectares respectively. 
 

 

 

 
Figure-2. LULC map showing the spatial distribution of the 1972, 1984, 1992, 2001, 2012 and 2019 classified images. 

 



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Figure-3.  Spatial autocorrelation of the temporal and spatial trend of LUCC. 

 
From the spatial autocorrelation report, the P-value result indicates there is strong evidence of rejecting the 

null hypothesis. The null hypothesis Ho: there was no significant change in land use pattern of the city region in 
the past 47 years is clearly not rejectable. Hence, the H1: is accepted there was a significant change in the land use 
pattern of the city region. The high z-scores 12.455885 is associated with very small p-values. Morgan’s index is 
0.965981 which is positive spatial autocorrelation and shows values that are clustered. 
 

3.2. Drivers of LULC Dynamics in Mekelle City Region 
In the study area, both population number and population density (Figure 4 ) were increased from 93,163 in 

1972 to 556127 in 2019 [12]. The population density in the Mekelle city region within 47 years increased from 
104 to 620 which is greater than the initial period by 596.15 % persons per square kilometer between 1972-2019 
and higher than the national average in 1972 i.e., 30.14 person per square km. The current population density is 
much higher than the national population density of Ethiopia which is 109.22 people per square km as of 2018. 
 

 
Figure-4. Population growth and density. 

 
The number of populations is continuously increasing. At the same time, the resource consumption per capita 

shows increment. This has leads to an increasing dependency on the city regions on ecosystem service. The city 
still depends on its hinterland for the provision of water, several ecosystem services and the most important and 
largest material flux into urban ecosystems. Demographic change was the most important indirect driver for 
changing ecosystem services in the study area. The land use dynamics increased with population density change. 
 
 
 
 



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3.3. Climate Variability 
The study scrutinizes the historical climatic from 1972–2018(Figure 5) and the consequence these had on 

LULC changes in Mekelle city region. Temporal changeability of climatic conditions, such as temperature 
happened over the last 47 years in Mekelle city region. 
 

 
Figure-5. Climate variability in the study area. 

 
Table-6. Structural equation model regression results 

Fitting target model: 
Iteration 0: log likelihood = -1328.0484  
Iteration 1: log likelihood = -1328.0484  
Structural equation model                                                       Number of observations   = 54 
Estimation method = ml 
Log likelihood = -1328.0484 
OIM 

Coef. Std. Err. z P>z [95% Conf. Interval] 

Structural    
lulcc <-    
tem 0.014197 0.0151089 0.94 0.000 0.015416 0.04381 
rai 0.0000957 0.0001963 0.49 0.000 0.0002891 0.0004805 
pop 0.0027972 0.0003235 8.65 0.000 0.0021631 0.0034313 
dem 0.0010578 0.000098 10.79 0.000 0.0008657 0.0012499 
slope 0.0346079 0.0018498 18.71 0.000 0.0309824 0.0382334 
dis -0.4366216 0.0486337 -8.98 0.000 -0.531942 -0.3413013 
_cons -1.439772 .4447163 -3.24 0.001 -2.3114 -0.5681439 
var(e.lulcc) 0.0177041 0.0034072 0.0121412 0.025816  
LR test of model vs. saturated: chi2(0) = 0.00, Prob > chi2 = 

 

The results show the six drivers namely temperature, rainfall, population, DEM, slope, and distance have 
significantly affected LULC changes in the study area in the past five decades. The biophysical factors terrain 
characteristics (slope and elevation) areas among the main factors causing land-use change. Based on SEM model 
the P-value for each non-stationary LULC driving force indicates for all the six drivers the P-value is < 0.05. 
Hence, there is strong evidence to reject the Ho: Driving forces of land use patterns do not bring LULC changes. 
 

3.3.1. Local Elderly Persons Perceptions on Driving Forces 
The key informants described that there was substantial dependency on wood for house hold consumption and 

this was a threat to various ecosystems like forest, bushes and shrubs, riverine vegetation that cause of 
environmental degradation. Furthermore, farmers clear the forest and change the land into cultivated land.  
According to key informants during the imperial times, the customary land tenure contributed to the expansion of 
settlements and agriculture. They believe the observed speedy expansion of cultivation and built-up expansion 
attributed to the policy and strategy changes. Agricultural encroachment on other lands and expanding built-up 
areas. Such changes in land use patterns have also been observed all part of the study area. Wetlands are destroyed 
and replaced by infrastructure, farmland, housing development.  

According to the qualitative data obtained through interview, the climatic condition has changed significantly. 
They described a trend towards a shorter rainy season that starts later and finishes earlier with a relatively less 
predictable pattern. Particularly, the major events that brought LULC changes include: the severe droughts of 
1984/85. This increased the pressures on the ecosystem, resulting in an expansion of cultivation; increase in dry 
years during the last 3 decades; and rapid population growth that increases the demand for various ecosystem 
services. Furthermore, many churches were built using the wood from these areas.  

One of the local elderly persons from an area known as Shibta stated that “Rain are no longer coming in the 
beginning of June but start to rain in mid-June and end in the mid of August. In the past, they were getting rainfall 
from mid-May/ early June till the end of September and mid-October. And the extent of the rain seasons has 
decreased and only get rainfall for two months or less’. As a result, declining of rivers and streamflow is threatened 
the ecosystem and the provision of drinking water’’. 



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4. Discussion 
Landsat series and semi-structured interview has been used in analyzing LULC changes. This study utilizes 

ERDAS imagine software synchronized with google earth which is currently worldwide used technology. The 
spatio-temporal analysis is not possible through other LULC analysis and mapping software. Because 
synchronization of google earth with ERDAS imagine increase the LULC classification accuracy of different 
resolutions. On the basis of interpretation of remote sensing imageries, field surveys and interview with local 
elderly persons and existing study area geographical conditions, the study have classified the study area into nine 
categories. The different types of the LULC classes have experienced significant changes during past 5 decades in 
the various parts of the study area.  

Many methods of accuracy assessment have been reviewed in the remote sensing literature and the most 
widely promoted and used, is confusion or error matrix [13]. The study used error matrix for accuracy assessment. 
Without accuracy assessment the quality of map or output produced would be of lesser value to the end user. 
Supervised and unsupervised techniques show different levels of accuracy after accuracy assessment was conducted. 
Therefore, this study used supervised image classification based on the knowledge area of the study area to increase 
the accuracy of the remote sensed data. In an accuracy assessment of map data, the map was compared with higher 
quality data, reference data collected by ground survey through sample-based approach.  

In the 1972 the results show that Mekelle city region was rich in various ecosystem types particularly the 
water body such as the rivers and streams were perennial that has continuous flow of water all year round during 
years of normal rainfall, and there was rich river side vegetation and currently the rivers and stream water is 
temporary and flow ceases immediately after the rainy season as per the information obtained from local elderly 
persons and the researcher knowledge of the study area. On the other hand, at the end of the study period 2019 
reveals that cultivated land, bushes and shrubs, built-up   land has tremendously expanded at the expenses of other 
ecosystem types. Despite it is a dominant classis of cultivated land it has showed reduction comparing with 
previous study decades. 

The LULC analysis from remote sensing was supported by observations and perceptions from the ground. The 
observed changes are investigated from satellite images and from the ground through interviews. The remote 
sensed results provide valuable understandings the city region LULC changes, which could not have been captured 
through other sources of data in short period of time. From the interview it is proved that decreased in water body, 
natural forests, river side vegetations while cultivated land and built-up areas has increased tremendously between 
the years 1972 and 2019. The qualitative data generated a deeper understanding of the LULC change and 
discovered the shrinkages of various ecosystem types. The limitations of satellite data were supported with 
qualitative data. The local communities on the LULC change considerably agree with data from satellite images 
analysis. The remote sensing result corresponded quite well with the qualitative data findings from local elderly 
persons. Through comparing both quantitative and qualitative data the differences and similarities between them 
has been proved.  

Various land use/ land cover types are transformed or interchanged into another one or more forms. The 
results of the study agree with other scientific local studies previously conducted on LULC changes at river basin 
level. The study area is part of the great Giba river basin and the results shows there is a significant LULC 
changes. A study by Gebremicael, et al. [14] in Tekeze-Atbara basin, Ethiopia  on  longitudinal land use change 
from land degradation shows 72% of the landscape has changed its category during the past 4 decades but recently 
increasing of vegetation cover resulted from intensive watershed management programs has been recorded. The 
Geba basin is one of the most water stressed areas of Ethiopia, with only a short rainy period from mid-June to 
mid-September due to  consistent erratic rainfall in both time and space in the last decades [15]. In agreement to 
the result of this study Teferi, et al. [16] showed that swap change of the gains and losses among LULC categories 
is more important in recognizing the total change than the net change. According to Abraha [17] a study in Giba 
catchment  the land use is dominated by cultivated land, followed by Bushed, grassland and bare land. Grasslands, 
forests, water bodies, and built-up areas only cover small fractions of the catchments.      

The observed data show several ecosystems are dramatical losing and an increase in built-up area, cultivated 
land, bushes and shrubs due to land use dynamics. According to Millennium Ecosystem Assessment [18] in recent 
years, African grassland, woodland, and other vegetated areas have increasingly been converted into cropland and 
other land uses. In the study area, similar tendency has been found where cultivated land and built-up areas have 
increased tremendously.  

The findings from both quantitative and qualitative data   proves the worth of combining remote sensing with 
local perceptions have principal role in to understand land use dynamics. Qualitative data alone cannot capture the 
large-scale patterns of LULC changes. But quantitative data from remote sensing provides a valuable indication of 
LULC changes, which was explored more in-depth using qualitative data. In recent years few studies have 
attempted to link community levels data, observation to remotely sensed data on LULC changes. The local elderly 
person perception about the last 47 years increased the confidence of the spatial data results. The LULC trend 
changes in the study area have generated ecosystem services sustainability challenges. Here, identifying more 
sustainable land-use options requires solid theoretical foundations on the major causes of those changes. 
Understanding the LULC change from the perspective of socioecological system is vital for designing strategies to 
address the sustainability challenges. 

The complex and diverse interactions among social-ecological systems make hard to identify and quantify the 
main drivers of LULC changes [19]. To fill this gap, the study used biophysical and anthropogonic drivers 
followed by mixed research approach to identify the key drivers for LULC changes. Analysis of LULC changes at 
multiple scales demands conceptual frameworks and analytical methods that are both comprehensive enough to 
capture the dynamics of society-environment interactions at different scales [20]. 

The 1972 satellite image of the study area showed there were relatively undisturbed areas that had been 
serving as a home of various ecosystem with varying levels of density and ground cover. This data is also 
corroborated by the key informants. Based on evidence obtained from the local elderly persons, cultivated land, 
water body, natural forest and bare land were the major LULC classes during in 1970s. During this period the 
ownership of land was feudal and most of the land was rich in ecosystem. The spatial analysis of the 1972 Image 



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revealed that cultivated land constituted the largest proportion of land. This is because, during the 1972 the area 
was characterized as relatively low population. The built-up area increment was huge starting from the year of 
2001 and was increased from 541 ha to 1029 ha of previous study period 1992 which was increased by 90.2%; the 
built-up area of the year 2012 was greater than 2001 by 3391 ha (329.5%) and the increment of year 2019 was 
greater than 2012 by 4477 ha (101.3%). These results show that the built-up area rapidly grow from 2001 onwards. 
The expansion of both rural and urban settlement and infrastructure expansion took the largest share by 
converting other land cover types. This was aggravated due to continuous increase of population number they need 
additional land for settlement area. The expansion of cultivated land as shown on the satellite images was 
confirmed by the key informants that indicated that the land conversion was caused by the high numbers of the 
youths, who were looking for land because no employment opportunities outside agricultural sector was available 
at that time.  

Farmers have been exchanging land among themselves through various arrangements to be able to grow food 
in proximity to their residence. The expansion of cultivated land as shown on the satellite images was confirmed by 
interviews with inhabitants that indicated that the land conversion was caused by the high numbers of the 
population growth. In line with this, globally population growth has been positively associated with the expansion 
of agricultural and urban land, land intensification, and deforestation [21]. This expansion of agriculture was 
supported by government plans and created opportunities to be expanded. Between the mid-1975s and mid-1980s, 
the LULC changes were encouraged by government policy on agriculture and land tenure, and readily bought into 
by farmers accelerated. These drivers contributed to a transformation in the land use patterns.  

During the military government Derg after 1975 made efforts to improve the agricultural systems through the 
establishment of cooperatives and bare land, vegetation areas have been converted into cultivated lands. The 
human population of the study area was increased dramatically in the study period. On the other hand, ecosystems 
like forest and plantation forest, water body, riverine vegetation showed negative increment. The greater 
percentage increase of population number over cultivated, forest, shrub and grazing lands showed that population 
growth is a major driving force for these changes. In this case, the high population growth increased demand for 
agricultural products and result the expansion of cultivated land at the expense other ecosystem types. In line with 
this, a study by Tsegaye, et al. [22] describe demographic factors related to population growth as among the 
primary causes for LULC changes in Ethiopia.  

The results from natural forest analysis show that within 12 years from 1972-1984 a loss of 4532 hectare was 
recorded which is severe destruction in-terms of forest ecosystem. And from 1984-1992 364 hectares was reduced 
which shows continuing deforestation of forests, again from 1992-2001 a reduction of 466 ha was observed. And 
again from 2001-2012 of 34 ha was reduced, and from 2012-2019 a reduction of 41 hectares is recorded. As 
reported from the key informants it is obvious that the most important factor in the destruction of forests is the 
human activities for housing construction and wood for charcoal.  

Both natural and plantation forests amongst other ecosystems have been degraded during the last 5 decades 
continuously. The following drivers or factors are the main causes for such degradation: Changing of land use from 
forest into pasture, agriculture and urban, as a result of population growth and general land scarcity, use of the 
wood as a source of heat and energy in economically poor area and increase in fuel consumption. The plantation 
forest result show there was a reduction of 964 ha between 1972–1984, and continually it shows continuous 
reduction from 1984-1992 reduced by 589 ha,1992-2001 educed by 131,2001-2012 reduced by 106 ha. But from 
2012-2019 it shows an increase of 205 ha (70%). This is due to afforestation programme. The plantations were 
established through government and international aid programs following the increased demand in construction 
and fuel woods [23]. 

The 2007 population and housing census results show that the population of Ethiopia grew at an average 
annual rate of 2.6 percent between 1994 and 2007 and the annual rates of population growth for Tigray regions is 
also almost the same as the national rate [14]. Similarities the study area is one of the highly populated of Tigray 
region. The population growth demanded more land for cultivation, more trees for domestic fuelwood consumption 
and more area for settlement. The population growth led to increased consumption of ecosystem services that 
affected the supply. The population dynamics are so far, important drivers affecting both demand and supply of 
ecosystem services in Mekelle city region. Due to the city’s  rapid urbanization the surrounding forests are cut 
down annually mainly for the purpose of extracting charcoal, fire wood, for the construction of residential area and 
roads as well  as expansion of farm lands in the surrounding areas [24]. 

The water body showed continuously declining of 1368 ha between 1974 and 1984, a dramatic decrease of 
12185 between 1984-1992, from 1992-2001 a decrease of 817, from 2001-2012 a reduction of 1077 ha and from 
2012-2019 a positive increase of 133 ha was observed due to construction different types of water structure of dams 
and ponds. Despite, the recent increment of water body due to construction of water structure like dams and ponds 
this study found that the water body was affected several times by recurrent drought of climate changes. 
Particularly during the 1985 there was heavy drought in northern Ethiopia in Tigray and Wollo. The LULC 
change results show that the periods between 1984-1992 there was severe damage and reduction of ecosystem in 
water body, natural and plantation forest, bushes and shrubs, river side vegetation and grass lands. There was 
severe drought that costs the lives of mankind. Due to the serious drought in 1985, the farmland was intensively 
used for cultivation, as the quality of the land had shown deterioration in quality and as some of the residents were 
come from other parts of Tigray and settled in the study area. 

To substantiate the ecosystem reduction, the climate variability trend analysis obtained from historical 
metrological data was validated by local elderly persons perceptions. The majority of the respondents ranked 
climate related factors as the first order driver. Their perception about rainfall is in agreement with the 
meteorological data analysis. The droughts have negatively impacted the ecosystem causing further degradation, 
as people sought alternative means of survival such as cutting of trees to prepare charcoal. Further, the variation in 
inter-annual rainfall amount causes differences in vegetation. In semi-arid highlands of northern Ethiopia, incidents 
of droughts of varying severity and duration occur. The occurrence of these droughts is associated mainly with the 
seasonal rainfall variability for the period 1954-2008 and the study shows that the northern part of Ethiopia is 
warming faster than the national average of 0.25ºC per decade [25]. 



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Tigray  experiences major environmental hazards of  rainfall instabilities causing droughts and famine, 
earthquakes, invasions of locust swarms, and epidemics [26]. The cultivated land in Mekelle city region expanded 
due to an increase the vulnerability of human environment system to climate fluctuation causes land degradation. 
In Ethiopia, the driving forces, have adverse effects of loss of biodiversity and ecosystem service degradation 
[27].The rainfall in Ethiopia is highly variable and unpredictable in time and space [28]. 

The study finds climate variability have affected various ecosystem particularly the water bodies in the last 47 
years in the study area. The LULC changes and climatic change in combination with soil deterioration alter the 
hydrological cycle, thereby progressively degrading the ecosystem and reducing the quality of water ecosystem. 
Changes in LULC of at catchment level modify the availability of water resources of a basin in complex ways [29].  
This study shows land use dynamics at catchment scale can also affect not only water but also other types of the 
ecosystem services.  

The results from bushes and shrubs prove that between 1972–1984 an increase of 7966 ha was observed. 
Despite this, in the period of 1984-1992 and 1992-2001 there was a reduction of 3590 ha and 3155 ha respectively. 
In between 2001-2012 and 2012-2019 there was an increase of 1578 ha and 2233 ha respectively. This is due to 
policy on environmental rehabilitation through integrated watershed management, and the associated 
implementation programs, has been the key driver behind the observed increasing trend. The numerical result 
from bare land indicates that a continuous reduction from of 1972–1984, 1984–1992, 1992-2001,2001-2012 and 
2012–2019 it reduced by 11802 ha,9 ha,3839,30 and 1930 hectares respectively. This indicate bare lands are 
encroached by urbanization and currently there are 326 hectares of bare lands. Though the ecosystem restoration 
is not fast the period 1992-2001 was a turning point for ecological restoration for river side vegetations and bare 
land significantly reduced. And the year 2001-2012 was a turning point for grass land, bushes and shrubs. 
However, it is very important to realize from 2012-2019 there is a hope for ecological restoration.  

The findings show that the need of social-ecological systems as complex adaptive systems for policy 
development. Understanding the city region as socio-ecological system can have the opportunity to manage its 
resilience towards sustainability. The urban areas and neighbouring rural areas together have to sustain the 
energy, material and ecosystem services flows. Focusing on the local scale of the city has its pitfalls, as it fails to 
take account of those cross-scaling feedbacks. This is because Mekelle city is not self-sufficient in terms of the 
ecosystem services provision on which it depends on a scale that extends well beyond the urban administrative 
boundaries where local interventions take place. Recognising the social and environmental challenges that cities 
need to deal with, long-term urban decision-making under climate change and resource scarcity is crucial to help 
understand the complexity of the interdependencies in ecological, social and economic systems across scales and 
time which could help forecast and avoid unintended effects. These resources range from basics such as water and 
food, to economic goods and information. Cut off these supplies to the modern city of Mekelle can negatively affect 
the total function of the city. Hence, this study concluded complex adaptive system theory have a great 
contribution to understand the relationships between the Mekelle city and its environs to achieve sustainable 
urbanization. 
 

5. Conclusion and Recommendations 
This study analyzed land use dynamics and its driving forces using various sources of data. Using a mixed 

methods approach helped to gain a wide perspective on the identified research problem. The change detection 
analysis showed that substantial land cover change that had occurred. Significantly, the LULC features are 
dynamically transformed to another land uses due to anthropogonic requirement of the local people and climate 
variability. From this study it is inferred that there were significant changes in vegetations and water bodies. 
Unfortunately, most of the LULC features are transformed to build-ups and settlements and other land uses 
without considering their negative impacts on ecosystems with increasing rate of vulnerability to environmental 
degradation. The significant changes in LULC have contributed to dwindling of numerous ecosystems. Based on 
the analyses there is a very high confidence that population growth and climate variability in both urban and rural 
is a major driver of LULC change. The ecosystem services are experiencing continuous loss. This study also found 
that both factors are drivers for land use dynamics in Mekelle city region. But they differ in their rank.  

To sustain the ecosystem which are vital for the benefit of the people and economic development of the study 
area, appropriate spatial planning policy should be issued. Thus, understanding how LULC changes drivers affect 
ecosystem service is important to communicate with decision makers, ecologist and land planners to craft 
appropriate land management legal frameworks. This study can be useful for efficient watershed management since 
Mekelle city depend on distant watershed ecosystem services. Monitoring the negative consequences of LULC 
while sustaining the supply of essential ecosystem services is vital. Therefore, there is a need to design and 
implement appropriate spatial planning policies in city region by decision makers. 

A determined and a renowned approach is required to sustain the continuum provision of ecosystem services. 
In this regards the following measures are suggested to ensure sustainable provision of ecosystem services and 
ensure sustainable landscape management in Mekelle city region, the application of spatial planning strategies is 
vital. In this regard, it is suggested that:((1) Continuous monitoring on land use dynamics and strengthening 
ecological restoration (2) Adopting watershed-based approach to improve the quality and quantity of ecosystem 
services (3) Launching regional green infrastructure plans and regional ecological networks (4) river side 
restoration (5) Further deep investigations on assessing the major drivers of LULC changes (6) Strengthening 
measures for the Implementation of regional spatial planning (7) Introducing regional metropolitan plan and (8) 
Sectoral integration of watershed management in Mekelle city region. 
 

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