Leveraging Spatial Analysis for Sustainable Land Use Change

Management: A Case of the Mountain Elgon Region

Isdore Paterson Guma

, Agnes Semwanga Rwashana

, Benedict Oyo

and Daniel Waiswa

Department of Computer Science, Gulu University, Gulu, Uganda

College of Computing and Information Science, Makerere University, Kampala, Uganda

College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda

Keywords: Synergies, Land Use, Digital Elevation Model, Crammer’s Coefficient, Associations, Environmental

Sustainability.

Abstract: Anthropogenic activities such as agriculture, deforestation and expansion of infrastructure have significantly

changed land use land cover. These changes have raised environmental concerns, including soil erosion,

landslides, water-catchment degradation and loss of biodiversity, with adverse consequences for food

production and thus livelihoods. This study sought to explore how the associations between slope, elevation,

distance to roads and rivers, population growth and hillshade influence spatial and temporal variations in land

use change. The methodology involved integrating remote sensing, geographic information systems and

spatial modelling. The study found that deforestation is a persistent phenomenon, with forest cover falling

from 32.34% (2014) to 14.40% (2054). Similarly, the rangeland coverage is projected to decrease significantly

from 17.74% in 2014 to 8.91% in 2054.Urbanization, on the other hand is rapidly increasing, tripling from

18.27% in 2014 to 48.55% in 2054. It has been shown that population growth, distance from roads, elevation

and slope are strongly correlated, with the latter being very strong. Among the identified potential synergies,

built up areas are expected to almost reach 50% by 2054 at the expense of deforestation, land degradation and

water loss. Based on the identified synergies, it is recommended that a balance between economic growth and

environmental sustainability be sought to promote land use change management.

1 INTRODUCTION

Over the years, anthropogenic activities have

significantly altered land use and land cover (LULC)

(Ojelabi et al., 2025). These changes have triggered

environmental concerns, including soil erosion,

landslides, water catchment degradation, and

biodiversity loss (Aduku et al., 2024), and have had

undesirable effects on food production, thus

threatening livelihoods, especially in developing

countries (Luwa et al., 2024). These changes have

incited global debate as they directly affect

sustainable development and human well-being

(Aduku et al., 2024).

Spatial analysis, GIS, and remote sensing support

informed land use decisions by revealing connections

across sectors and balancing environmental, social,

https://orcid.org/0000-0002-8282-2993

https://orcid.org/0000-0002-4824-0260

https://orcid.org/0000-0003-0859-9858

and economic priorities. This approach supports the

integration of geographic data, predictive modeling,

and decision support tools, thereby advancing

planning, policymaking, and sustainability efforts

(Bielecka, 2020).

Additionally, spatial analysis provides

geographic specificity, enhancing the realism of

models by incorporating factors such as topography,

climate, and infrastructure (Oztuna, 2023).

Consequently, extensive research has been

undertaken at various spatial and temporal scales for

diverse purposes highlighting the essential role of

spatial analysis in land use change management.

Between 1987 and 2015 in Côte d’Ivoire, 1.44%

of forestland and 3.44% of dense forest were

converted to agricultural and degraded forest areas,

respectively (Kouassi et al., 2021). In Ethiopia,

cultivated and settlement areas increased by 6.4% and

382

Guma, I. P., Rwashana, A. S., Oyo, B. and Waiswa, D.

Leveraging Spatial Analysis for Sustainable Land Use Change Management: A Case of the Mountain Elgon Region.

DOI: 10.5220/0013644800003970

In Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2025), pages 382-389

ISBN: 978-989-758-759-7; ISSN: 2184-2841

6.5%, while grassland and forest cover are projected

to decline by 22.3% and 63.8% by 2050 (Mathewos

et al., 2022). Similarly, urban areas in Southwestern

Nigeria expanded from 341.7 km² to 520.6 km²

between 1984 and 2019 (Fashe et al., 2020). These

rapid LULC changes pose major challenges to

sustainable development, affecting forest cover,

increasing flood risks, complicating urban planning,

and straining agriculture and water resources

(Akinyemi, 2021).

In Uganda's Mt. Elgon region, various studies

have addressed LULC dynamics. Luwa et al. (2020)

used intensity analysis to assess change patterns;

Opedes et al. (2022) examined land cover change and

subsistence farming; and Bamutaze et al. (2021)

evaluated erosion risk via Global Positioning System

data. However, these studies explicitly underscored

the associations between population growth,

proximity to rivers, proximity to roads, slope, digital

elevation model (DEM), hill shade, and aspect. This

study explored how the associations between these

factors influence spatial and temporal variations in

land use change.

2 MATERIALS AND METHODS

2.1 Study Area

The study was conducted in Mbale, Bududa,

Manafwa and Namisindwa (Figure 1). The study area

is positioned between 0°40'0"N and 1°10'0"N latitude

and 34°10'0"E and 34°30'0"E longitude. The region

encompasses a total area of 320 km² with a population

of approximately 1,338,178 people. The topography,

climatic conditions and socio-economic activities

provide a complex context for analysing LULC

changes, making ideal setting for this research.

Figure 1: The Study Area.

The methodological approach integrates remote

sensing with geographic information systems (GIS)

to assess LULC dynamics in the region.

2.2 Data Collection

2.2.1 Satellite Imagery

Multi-temporal satellite images from Landsat 8, with

a spatial resolution of 30 m, were acquired from the

United States Geological Survey (USGS) Earth

Explorer (https://earthexplorer.usgs.gov/) for the

years 2014, 2019 and 2024. The imagery was chosen

based on phenological considerations, seasonality

and minimal cloud cover to ensure precise analysis.

2.2.2 Cramer's V Analysis of Driver

Variables

To evaluate the strength of association between

selected drivers and LULC changes, Cramer's V

values were computed using the R-Processing plugin

in QGIS. Cramer's V, ranging from 0 (no association)

to 1 (strong association), quantifies relationships

between categorical variables. This analysis revealed

how spatial factors such as population growth,

proximity to rivers and roads, slope, elevation

(DEM), and hillshade contribute to LULC dynamics.

The results highlighted which variables most strongly

influenced land use changes in the Mount Elgon

region.

2.2.3 Ancillary Data

Roads and rivers were derived from OpenStreetMap

(http://www.openstreetmap.org/), providing essential

infrastructure data. Population growth data were

sourced from WorldPop (http://www.worldpop.org),

offering insights into demographic pressures.

2.3 Data Pre-processing

2.3.1 Geometric Correction

Geometric correction was conducted on all satellite

images to ensure spatial alignment using reference

layers such as DEM, slope, and distance from roads.

The correction process utilized the MOLUSCE

plugin in QGIS to preserve data consistency across

temporal layers.

2.3.2 Image Classification

A supervised classification technique, specifically the

maximum likelihood algorithm within ArcGIS, was

Leveraging Spatial Analysis for Sustainable Land Use Change Management: A Case of the Mountain Elgon Region

383

employed to categorize LULC types. Training

samples were selected based on expert knowledge

and field validation, ensuring the accuracy of the

classification process. To assess the reliability of the

classified maps, their accuracy was evaluated using a

confusion matrix and kappa coefficient, comparing

the classified outputs with ground-truth data. This

approach ensured a robust validation of the

classification results.

2.4 Analysis of Spatial and Temporal

Variations

2.4.1 Land Use Change Detection

The LULC maps for 2014, 2019, and 2024 were

analyzed to detect spatial and temporal changes. The

MOLUSCE plugin in QGIS was used for change

detection, specifically employing post-classification

comparison to identify transitions among different

LULC categories over time. This method was chosen

because it allowed for accurate identification of land

use changes by comparing classified maps from

different years, making it ideal for assessing spatial

dynamics and temporal trends in LULC.

2.4.2 Spatial Variable Analysis

This study examined how factors such as DEM,

slope, proximity to rivers and roads, and population

influenced land use change in the Mount Elgon

region. DEM and slope determine land suitability by

identifying areas vulnerable to erosion and landslides,

thereby guiding human activities. Proximity to rivers

impacts agriculture and settlement patterns due to

water availability and flood risk, while proximity to

roads drives urban expansion, agricultural

development, and resource extraction. Population

growth increases the demand for farmland and

settlements. Together, these factors shape the spatial

and temporal dynamics of land use change in the

region. Spatial analysis tools in ArcGIS were

employed to calculate proximity metrics and generate

thematic layers.

2.5 Future Land Use Land Cover

Prediction

The MOLUSCE module in QGIS was used to predict

future LULC changes using a Cellular Automata and

Neural Network (CA-ANN) model. This approach

utilizes historical LULC maps for 2014, 2019 and

2024 as input layers, integrating spatial variables to

simulate future scenarios. The projected maps offer

insights into potential LULC dynamics based on

observed trends.

Table 1: Area statistics of LULC classes for the years 2014, 2019 and 2024 and percentages of change.

LULC

Classes

2014 2019 2024

Chan

e in

2014-2019

Chan

e in

2019-2024

Chan

e in

2014-2024

sq.k

% sq.k

% Δ % Δ % Δ %

Wate

0.04 0.00 0.33 0.02 0.08 0.01 0.02 -0.018 0.00

Trees 443.56 32.34 410.69 29.95 316.71 23.09 -2.40 -6.852 -9.25

Crop lan

433.95 31.64 506.14 36.91 434.02 31.65 5.26 -5.259 0.00

Built areas 250.54 18.27 276.73 20.18 418.23 30.50 1.91 10.318 12.23

Rangelan

243.31 17.74 177.52 12.94 202.36 14.76 -4.80 1.812 -2.99

Table 2: Classification Accuracy Assessment of each Land use Maps.

LULC Classes 2014 2019 2024

Producer's

Accurac

User's

Accurac

Producer's

Accurac

User's

Accurac

Producer's

Accurac

User's

Accurac

Trees 0.97 1.00 0.97 1.00 0.96 0.77

Crop land 1.00 1.00 1.00 0.93 0.57 0.93

Built areas 0.96 1.00 0.92 1.00 0.79 0.54

Rangeland 1.00 0.86 1.00 1.00 0.90 0.69

Overall accuracy (%) 97.00

80.00

98.00

Kappa coefficient 0.97

0.96

0.64

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3 RESULTS

3.1 Land Use Land Cover

Classification

The LULC area statistics are shown in Table 1. The

total surface area of the analysed area is 1,371.4028

. The distribution of the Elgon LULC classes

shows that in the year 2014, 32.34% of the area was

forested, 31.64% cropland, 18.27% built-up, 17.74%

rangeland and 0% water body. Whereas the year 2019

analysis shows that 36.91% of the Elgon region is

agricultural land (cropland), 29.95% forest, 20.18%

built-up, 12.94% pasture and 0.02% water body. In

the year 2024, the agricultural, built-up, forest,

rangeland and water bodies were 31.65, 30.50, 23.09,

14.76 and 0.01 percent respectively.

3.2 Classification Accuracy Assessment

The classification accuracy assessment (Table 2) for

the LULC maps of 2014, 2019, and 2024 reveals a

clear decline in both overall accuracy and class-

specific reliability in 2024. While the Producer’s and

User’s Accuracies for most land cover classes

remained high in 2014 and 2019 with Overall

Accuracy values of 97% and 98%, and Kappa

Coefficients of 0.97 and 0.96, respectively, a

substantial drop was observed in 2024. The Overall

Accuracy fell to 80%, and the Kappa Coefficient

dropped markedly to 0.64, indicating a notable

decrease in agreement between the classified and

reference data.

Class-specific performance in 2024 illustrates the

nature of this decline. Built-up areas showed a

pronounced reduction in both Producer’s Accuracy

(0.79) and User’s Accuracy (0.54), implying

increased confusion with other classes and potential

overestimation of urban expansion. Cropland also

exhibited a major drop in Producer’s Accuracy from

1.00 (in 2014 and 2019) to 0.57 in 2024, indicating

significant misclassification, even though its User’s

Accuracy remained stable at 0.93. Trees and

rangeland, while more stable, also showed decreased

User’s Accuracy, suggesting reduced reliability in

mapping these categories.

The reduced classification quality in 2024 may

have compromised the accurate detection of land

cover changes between 2019 and 2024.

Misclassification of built-up or cropland areas could

have resulted in either exaggerated or underestimated

land transitions during this period.

Table 3: Change of area between LULC classes for the years 2014-2019, 2019-2024 and 2014-2024.

Changes between LULC classes 2014-2019 (sq.km) 2019-2024 (sq.km) 2014-2024 (sq.km)

Water to Water 1.00 0.22 0.86

Water to Trees 0.00 0.08 0.01

Water to Croplan

0.00 0.60 0.07

Water to Built-up areas 0.00 0.09 0.02

Water to Rangeland 0.00 0.02 0.04

Trees to Water 0.00 0.00 0.00

Trees to Trees 0.82 0.71 0.67

Trees to Cropland 0.05 0.05 0.07

Trees to Built-up areas 0.05 0.14 0.14

Trees to Rangeland 0.09 0.10 0.11

Cropland to Water 0.00 0.00 0.00

Cropland to Trees 0.03 0.01 0.02

Cropland to Cropland 0.86 0.75 0.74

Cropland to Built-up areas 0.06 0.16 0.19

Cropland to Rangeland 0.04 0.08 0.05

Built areas to Wate

0.00 0.00 0.00

Built areas to Trees 0.05 0.00 0.00

Built areas to Cropland 0.08 0.03 0.05

Built areas to Built-up areas 0.86 0.96 0.94

Built areas to Rangeland 0.01 0.01 0.01

Rangeland to Wate

0.00 0.00 0.00

Rangeland to Trees 0.09 0.10 0.04

Rangeland to Cropland 0.37 0.14 0.28

Rangeland to Built-up areas 0.06 0.10 0.15

Rangeland to Rangeland 0.48 0.66 0.5

Leveraging Spatial Analysis for Sustainable Land Use Change Management: A Case of the Mountain Elgon Region

385

Moreover, since future LULC projections up to

2054 are based on trends derived from historical and

current maps, the 2024 dataset serves as a critical

input. Lower classification confidence in this dataset

may propagate uncertainty into the projection model,

potentially distorting forecasts of land cover change

particularly for rapidly evolving classes like urban or

agricultural land.

3.3 Change of Area Between Land Use

Land Cover Classes

When the change values are examined (Table 3);

shrinkage was detected in water, trees, cropland, and

rangeland by 0.02%, 9.25%, 5.26%, and 2.99

respectively with greater shrinkage in the trees

(forested area) by 9.25% in the year 2014-2024,

whereas expansion was observed in built-up areas

throughout the years with greater expansion values by

12.23%, for the period 2014-2024.

The change analysis of the LULC classes showed

that the expansion in the built-up areas had arisen

from water bodies, Trees, Cropland and rangeland

where 0.09, 0.14,0.19, and 0.15 Km

of respective

LULC classes were transformed into built-up areas

from the year 2014 to the year 2024. Over a 30-year

period (2024-2054), the built-up area is projected to

increase from 30.5 to 48.6 and the trees, croplands

and rangelands to decrease from 23.1 to 14.4, 31.7 to

28.1 and 14.8 to 8.9 percent respectively, with

minimal changes in water bodies.

3.4 Projected LULC Area Statistics for

2054 Relative to the Baseline (2014)

Figures 2 and 3 illustrate LULC changes between

2014 and 2024, along with projections for 2054.

These figures highlight trends across various land

cover categories, revealing a steady transformation of

natural landscapes into urban and built-up areas.

A key trend is deforestation, with forest cover

decreasing sharply from 32.34% in 2014 to a

projected 14.40% by 2054. This persistent loss is

driven by urban expansion, agricultural

encroachment, illegal logging, and land degradation.

Urbanisation is another prominent trend, with

built-up areas projected to nearly triple from 18.27%

in 2014 to 48.55% by 2054. This surge is likely fueled

by population growth, increased housing demand,

infrastructure development, and rural-to-urban

migration affecting green spaces and croplands,

greater pollution, urban heat island effects, and

heightened pressure on water and waste management

systems.

Cropland initially increased between 2014 and

2019 due to agricultural expansion but is expected to

decline from 2024 onward due to urban

encroachment, soil degradation, and the impacts of

climate change on agricultural productivity. The

shrinking cropland base poses risks to food security

and signals a shift in economic focus from agriculture

toward industry and services.

Rangelands have also seen a significant decline

from 17.74% in 2014 to a projected 8.91% in 2054.

Contributing factors may include overgrazing, land

degradation, urban encroachment, and climate

change affecting pasture availability. This threatens

livestock farming and may exacerbate soil

degradation if unsustainable grazing continues.

Figure 2: Visualizing Land Use Land Cover changes from

2014 to 2054.

Figure 3: Spatial distribution of the LULC for the years

2014, 2019, 2024 and Prediction for 2054.

3.5 Association Between Driving

Forces of Land Use Land Cover

Change

Table 4 presents the explanatory power of each driver

variable influencing LULC changes, measured using

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386

Table 4: Cramer's V Values of Driver Variables.

Cramer's Coefficient

Variable Cramer's V Value Interpretation

Population Growth 0.5316 Strong association

Distance from Rivers 0.3801 Moderate association

Distance from Roads 0.5337 Strong association

Slope 0.7205 Very strong association

DEM (Digital Elevation Model) 0.5236 Strong association

Hillshade 0.2973 Weak association

Aspect 0.4171 Moderate association

Figure 4: Distribution of driver variables.

the Cramer's V coefficient. All variables

demonstrated significant associations, with Cramer's

V values exceeding 0.15. Among them, distance from

rivers (0.3801) and aspect (0.4171) exhibited

moderate explanatory power. Hillshade showed a

weaker association (0.2973), while population

growth, distance to roads, elevation (DEM), and slope

demonstrated strong correlations, slope showed a

very strong relationship. Following the identification

of these key drivers, the specified land cover

transitions were modeled within a unified transition

sub-model. This process produced transition potential

maps, which demonstrated accuracy levels ranging

between 40% and 95%.

4 DISCUSSION OF RESULTS

Land use and land cover change at global level is a

problem for the environment and for development

because of its complex nature and local occurrence.

For the coming decades, the world population is

expected to continue growing, causing major

problems (Unger & Lakes, 2023). Land demand is

reflected in different land-use interests, which can

lead to land-use synergies that are manifested locally.

Satellite data allow continuous monitoring of land

use change at various scales. It should also be noted

that understanding the interrelationships between the

various land use factors and their effects is conducive

to optimising land use patterns and promoting land

use sustainability. However, spatial differences and

the drivers of synergies between the various land use

factors in the region have not been well studied.

4.1 Historical Land Use Land Cover

Change Dynamics Analysis

Historical changes in the LULC (Table 1) shows that

agricultural land and rangelands have been reduced

between 2014 and 2024. Trees have declined over the

years, while built-up areas have increased steadily.

Agricultural expansion (Alshari & Gawali, 2022) and

urbanisation (Gündüz, 2025) has caused more than

28.6% of forest cover to be lost in the last 10 years.

Similarly, studies in the region have reported that the

conversion of forests and the reduction of rangelands

are indicators of an increase in anthropogenic activity

and food demand (Ojelabi et al., 2025). Historical

findings on LULC dynamics align with studies

showing that agricultural and urban expansion are

driving the loss of natural vegetation (Alshari &

Gawali, 2022).

4.2 Spatial and Temporal Land Use

Change Trends

The analysis of Cramer's V values provided insights

into the key drivers influencing LULC changes in the

Mount Elgon region. These results are consistent with

a study conducted in Sana’a City in Yemen (Ouma et

al., 2024). Slope exhibited the strongest association

(0.7205) (Xu et al., 2021).

Leveraging Spatial Analysis for Sustainable Land Use Change Management: A Case of the Mountain Elgon Region

387

Distance from roads (0.5337) and population growth

(0.5316) also had strong associations, defining

infrastructure development and urban expansion

(Rahnama, 2021). DEM (0.5236) and distance from

rivers (0.3801) influenced land use patterns (Abbas et

al., 2021; Gharaibeh et al., 2020), while hillshade

(0.2973) showed the weakest association (Ouma et

al., 2024). These findings are consistent with the

findings of Abijith et al. (2025) where DEM, slope

and distance from roads including population growth

contribute to the change in land use.

5 CONCLUSION AND FUTURE

WORK

The study modeled LULC changes by analyzing key

environmental and human-driven factors such as

elevation, slope, distance from roads and rivers, and

population growth. Accurate LULC modeling

requires careful selection of relevant predictors and

the use of spatiotemporal data to capture complex

dynamics. Among the variables analyzed, slope

showed the strongest influence, followed by distance

to roads, elevation, and population growth. Distance

to rivers and aspect had moderate associations, while

hillshade had the weakest. Despite these insights, the

study acknowledges limitations in simulating human

behaviour and policy influences. To enhance

predictive accuracy, future research should

incorporate integrated models, scenario-based

simulations, and advanced techniques like machine

learning or Artificial Intelligence. Incorporating

socio-economic drivers is also essential, as human

activity significantly shapes LULC patterns.

DECLARATION OF COMPETING

INTEREST

The authors declare that there were no known

conflicting interests that could have influenced the

work reported in this paper.

FUNDING

This research received financial support from

Building Stronger Universities (BSU IV) project,

Gulu University and Makerere University Research

and Innovation Fund.

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