Assessment of the environmental impacts of conflict-driven Internally Displaced Persons: A sentinel-2 satellite based analysis of land use/cover changes in the Kas locality, Darfur, Sudan

Abstract

Internal displacement of populations due to armed conflicts can substantially impact a region’s Land Use and Land Cover (LULC) and the efforts towards the achievement of Sustainable Development Goals (SDGs). The objective of this study was to determine the effects of conflict-driven Internally Displaced Persons (IDPs) on vegetation cover and environmental sustainability in the Kas locality of Darfur, Sudan. Supervised classification and change analysis were performed on Sentinel-2 satellite images for the years 2016 and 2022 using QGIS software. The Sentinel-2 Level 2A data were analysed using the Random Forest (RF) Machine Learning (ML) classifier. Five land cover types were successfully classified (agricultural land, vegetation cover, built-up area, sand, and bareland) with overall accuracies of more than 86% and Kappa coefficients greater than 0.74. The results revealed a 35.33% (-10.20 km²) decline in vegetation cover area over the six-year study period, equivalent to an average annual loss rate of -5.89% (-1.70 km²) of vegetation cover. In contrast, agricultural land and built-up areas increased by 17.53% (98.12 km²) and 60.53% (5.29 km²) respectively between the two study years. The trends of the changes among different LULC classes suggest potential influences of human activities especially the IDPs, natural processes, and a combination of both in the study area. This study highlights the impacts of IDPs on natural resources and land cover patterns in a conflict-affected region. It also offers pertinent data that can support decision-makers in restoring the affected areas and preventing further environmental degradation for sustainability.

Full text

RESEARCH ARTICLE
Assessment of the environmental impacts of
conflict-driven Internally Displaced Persons: A
sentinel-2 satellite based analysis of land use/
cover changes in the Kas locality, Darfur,
Sudan
Abdalrahman AhmedID
1,2
*, Brian RotichID
3,4
, Kornel Czimber
1
1Faculty of Forestry, Institute of Geomatics and Civil Engineering, University of Sopron, Sopron, Hungary,
2Department of Forest and Environment, Faculty of Forest Science and Technology, University of Gezira,
Wad Madani, Sudan, 3Institute of Environmental Sciences, Hungarian University of Agriculture and Life
Science, Go
¨do
¨llő, Hungary, 4Faculty of Environmental Studies and Resources Development, Chuka
University, Chuka, Kenya
*ix3mzk@uni-sopron.hu
Abstract
Internal displacement of populations due to armed conflicts can substantially impact a
region’s Land Use and Land Cover (LULC) and the efforts towards the achievement of Sus-
tainable Development Goals (SDGs). The objective of this study was to determine the
effects of conflict-driven Internally Displaced Persons (IDPs) on vegetation cover and envi-
ronmental sustainability in the Kas locality of Darfur, Sudan. Supervised classification and
change analysis were performed on Sentinel-2 satellite images for the years 2016 and 2022
using QGIS software. The Sentinel-2 Level 2A data were analysed using the Random For-
est (RF) Machine Learning (ML) classifier. Five land cover types were successfully classi-
fied (agricultural land, vegetation cover, built-up area, sand, and bareland) with overall
accuracies of more than 86% and Kappa coefficients greater than 0.74. The results revealed
a 35.33% (-10.20 km
2
) decline in vegetation cover area over the six-year study period,
equivalent to an average annual loss rate of -5.89% (-1.70 km
2
) of vegetation cover. In con-
trast, agricultural land and built-up areas increased by 17.53% (98.12 km
2
) and 60.53%
(5.29 km
2
) respectively between the two study years. The trends of the changes among dif-
ferent LULC classes suggest potential influences of human activities especially the IDPs,
natural processes, and a combination of both in the study area. This study highlights the
impacts of IDPs on natural resources and land cover patterns in a conflict-affected region. It
also offers pertinent data that can support decision-makers in restoring the affected areas
and preventing further environmental degradation for sustainability.
Introduction
Land use and land cover (LULC) changes are increasingly being viewed as the primary driver
of global environmental changes, including greenhouse gas emissions, global climate change,
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OPEN ACCESS
Citation: Ahmed A, Rotich B, Czimber K (2024)
Assessment of the environmental impacts of
conflict-driven Internally Displaced Persons: A
sentinel-2 satellite based analysis of land use/cover
changes in the Kas locality, Darfur, Sudan. PLoS
ONE 19(5): e0304034. https://doi.org/10.1371/
journal.pone.0304034
Editor: Bijeesh Kozhikkodan Veettil, Van Lang
University: Truong Dai hoc Van Lang, VIET NAM
Received: January 16, 2024
Accepted: April 14, 2024
Published: May 30, 2024
Peer Review History: PLOS recognizes the
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all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0304034
Copyright: ©2024 Ahmed et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files.

biodiversity, and ecosystem services loss, soil resource loss, and livelihood loss [1–5]. LULC
changes are caused by either natural forces, anthropogenic drivers, or both. Changes in LULC
occur primarily due to human pressures on natural landscapes at various geographical and
temporal scales as influenced by multiple factors that differ by region [6–8]. Natural causes of
LULC changes comprise droughts, natural fires, and general changes and variations in climatic
conditions while the common anthropogenic drivers of LULC changes include agricultural
expansion, deforestation, urbanization, mining, and armed conflicts [9–12].
Armed conflicts are an extreme form of socioeconomic shocks that can shape future land-
use trajectories [13]. These conflicts can have substantial effects on LULC, especially in the
conflict zones, and in areas where the displaced population settles [13]. Globally, armed con-
flicts have resulted in severe demographic, social, economic, and political implications on the
lives and livelihoods of the affected populations. At the landscape level, armed conflicts can
either trigger increased or reduced pressure on land-based resources, especially vegetation and
wildlife [12,14,15]. Warfare can benefit natural systems due to reduced human pressure on
the natural environment in the conflict region thereby reducing contamination and environ-
mental degradation [12,16]. On the other hand, the induction of displaced populations from
conflict zones into new landscapes can result in the over exploitation of natural resources lead-
ing to deforestation, water pollution, agricultural expansion, and land fragmentation [17–20].
Monitoring and mapping changes in LULC is pivotal for environmental management [12].
Satellite Remote Sensing (RS) data integrated with Geographical Information System (GIS) is
an important tool for mapping the extent and spatial distribution of forest and land uses based
on a stable classification system for change and analysis with the integration of field data [21,
22]. It is an adequate tool for monitoring and detecting changes in vegetation cover using
multi-temporal data. Images from previous years can be compared to recent years to measure
the differences in the sizes and extent of forest cover [21,23]. The use of RS technology is
therefore considered a suitable approach for assessing historical and future LULC changes
[23]. The advancement in satellite RS technology has revolutionized the approaches to moni-
toring the Earth’s surface [24]. Since its launch in 2015, there has been a high adoption and
application of Sentinel-2 images in LULC studies. This can be attributed to its free access pol-
icy, high spatial resolution (10 m), and the availability of red-edge bands with multiple applica-
tions [24,25]. Sentinel-2 data can also integrate with other remotely sensed data, as part of
data analysis, which improves the overall accuracy when working with Sentinel-2 images. In
addition, when used with machine-learning classifiers such as Random Forest (RF) and sup-
port vector machine (SVM), Sentinel-2 data produces high accuracies (>80%) [24].
The semi-arid Darfur region of Sudan has experienced a protracted humanitarian crisis,
characterized by armed conflict, widespread displacement, and environmental degradation
since the early 2000s [26,27]. A new wave of violence and insecurity emanating from fighting
involving armed movements, government forces, and armed tribal militia has rocked Darfur
since 2014. This conflict has created an additional humanitarian crisis in the region as it has
led to the displacement of about 322,000 people [27]. The conflict in Darfur has greatly acceler-
ated the processes of environmental degradation that have undermined subsistence livelihoods
and the environmental conditions in the area over the recent decades. The emergence of
numerous internally displaced persons (IDPs) communities around the major towns of the
Darfur region during the conflict has led to the unsustainable utilization of natural resources
significantly impacting the environment through agricultural expansion, overgrazing, and
deforestation leading to environmental degradation [28,29]. This environmental degradation
has not only exacerbated the vulnerability of IDPs but also contributed to broader ecological
and climatic concerns in the region thereby curtailing the efforts in achieving the Sustainable
Development Goals (SDGs) [26,30]. Despite these impacts, little research has been done to
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Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.

quantify and map the recent vegetation cover changes in the region because of IDPs settlement
since the conflict resurgence in the year 2014.
Previous vegetation cover change research in Darfur [29,31,32] was done before 2016. All
those studies were conducted in Central, Western, and North Darfur. Our study is the first in
South Darfur after the launch of Sentinel-2 satellite data for public use This study therefore
aims to provide the latest essential post 2014 spatial data on LULC changes in the Kas locality
of Darfur, which is lacking owing to the ongoing conflicts. The results will further enhance the
understanding of the repercussions of the 2014 conflict-driven IDPs on vegetation cover and
overall environmental sustainability in the Kas locality of Darfur, Sudan. The findings of this
study are crucial for informing decision-makers and guiding efforts to restore the affected
areas, mitigate further environmental degradation, and contribute to the broader objective of
achieving (SDGs) in conflict-affected regions.
Methods
Study area
The study area is located between latitudes 12˚23´ and 12˚36´north and longitudes 24˚9´and
24˚26´east, covering an estimated area of about 1007.13 km
2
(Fig 1). It lies at an altitude of 400
meters above sea level [33]. It is situated in Kas, South Darfur state, around 86 kilometers
northwest of the state’s capital Nyala [34]. The Kas location has an its average temperature of
26˚C and average annual rainfall is 519 mm, which varies from north to south (Fig 2). The
total population of the locality is estimated at 365,000 people, 76,843 of whom are IDPs [35].
The two major natural resource-based livelihood systems in Darfur are rain-fed agriculture
and pastoralism [36]. The Acacia species dominate the forested areas, for instance the Kas For-
est reserve. Darfur’s labor market continues to be heavily reliant on resources from the forest
and rangelands, placing an undue strain on such resources [36].
Data collection and analysis
Sentinel-2 satellite images with a spatial resolution of 10 meters B2 (Blue), B3 (Green), B4
(Red), and B8 (NIR) were used in this study. These images were downloaded from the United
States Geological Survey (USGS) website (http://earthexplorer.usgs.gov/) at Path 178 and Row
52 (Table 1). The dates of the images were chosen to be in the same month and the cloud cover
was less than 1%. Rainfall and temperature data were sourced from power data access from the
National Aeronautics and Space Administration (NASA) website (https://power.larc.nasa.gov/
data-access-viewer/).
Image processing. Sentinel-2 Satellite images were selected for this study because of their
high resolution (10 meters). The two downloaded satellite data were Level-2A products, and
the available Sentinel-2 Satellite images of 8
th
of December 2016 and 2022 were used in this
study (Fig 3). Image processing techniques included atmospheric correction, image classifica-
tion, change detection, and accuracy assessment of the classified images were successfully
achieved. All processing steps were carried out in QGIS 3.22 software. Microsoft Office Excel
2013 was used to compute the land cover changes to show the percentages and change rates.
Machine learning algorithms. A total of three classifiers—random forest (RF), support
vector machine (SVM), and k-nearest neighbor (KNN)—were used to classify the Sentinel-2
images and their classification outputs compared. The RF classifier is an ensemble method
using decision trees as classifiers. RF uses a large number of decision trees each is feature-
aggregated (bagging) by bootstrapping the training samples. The final output is determined by
the majority of the trees. [38,39]. The Dzetsaka classification tool in QGIS was utilized in this
study to apply RF classification to the Sentinel-2A images. The RF is a powerful learning
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technique since it applies feature significance properties and averages several predictions. A
fixed number of 100 trees is set by default, and this has shown to be an appropriate size to
avoid overfitting. [40–42]. During the construction each tree is split at every internal node
using the square root of the number of features (n):
max features ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðn featuresÞ
pð1Þ
From the training data (n_features), which consists of the pixel values defined by the ROIs
(regions of interest), the features are determined and chosen at random. Thus, a suitable size
Fig 1. Map of the study area showing (A) Location of Sudan in Africa (B) Location of SouthDarfur in Sudan (C)Sentinel-2 Image of the study area.
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for the training data set should be ensured. There is no predetermined size for this; it relies on
the specific attributes of each data set and the intended class number. [43–45].
The KNN is a nonparametric memory-based supervised machine learning classifier. Fol-
lowing the calculation of the number of neighbors for which k is an integer value [46] KNN is
used to solve both classification and regression problems [47]. This parameter (number of
neighbors) in the Dzetsaka plugin is selected using a cross-validation technique to optimize
the quality of output. [48]. KNN classification was applied to the study area using QGIS’s dzet-
saka classification tool.
SVM, is a linear model for classification and regression problems that is mainly based on
kernels, it was developed by Cortes and Vapnik [49]. The Gaussian kernel known as the radial
basis function is the one used in Dzetsaka and provides high quality results for classifying tree
species. [48]. SVMs have been utilized in this study as it widely used in remote sensing [50]. A
mathematical formulation of the SVM can be found in Scikit learn [44,46].
Image classification and analysis. Supervised classification was performed on Sentinel-2
satellite images acquired for the years 2016 and 2022. The supervised classification was per-
formed using the Dzetsaka plugin in QGIS. The Dzetsaka classification plugin allows the user
to classify images with several machine learning algorithms. However, to use all the algorithms
in Dzetsaka, some dependencies had to be installed. The Scikit-learn 1.0.1 Python package is
the reference Python library for machine learning. [44,45]. Three classifiers RF, KNN, and
Fig 2. Mean annual rainfall and temperature time series data of the study area from1992–2022 [37].
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Table 1. Details of Sentinel-2 satellite image used in this study.
Sensor Acquisition Date Path/Row Grid cell size (Meters) Bands
Multi-spectral instrument (MSI) 12/02/2016 178/52 10 2,3,4,8
Multi-spectral instrument (MSI) 20/02/2022 178/52 10 2,3,4,8
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SVM were run on both Sentinel 2 images, and the output raster was generated. Default param-
eters of Dzetsaka plugin were used to run all algorithms [43,46]. Before image classification
we used raster calculator tool in QGIS to calculate the NDVI (Normalised Different Vegetation
Index) using Eq (2) [51]. The NDVI index is an index used for evaluation of vegetation health
as it shows biomass production. Note that in Sentinel-2 Scene the Red is band 4, the Near
Infra-Red (NIR) is band 8.
NDVI ¼NIR Red
NIR þRed ð2Þ
The NDVI values range between -1 and +1 where positive values represent healthy vegeta-
tion, values close to zero are bareland, and negative values represent water and clouds.
Observed values from our analysis ranged from 0 to 0.6 (Fig 4).
Manual digitization of vegetation cover, built-up, sand, agricultural land and bareland areas
was performed based on high spatial resolution satellite imagery available through the Google
Earth Pro software using the historical imagery slider to move between the acquisition dates
2016 and 2022, additionally we used different Sentinel-2 Bands colour composite. The NDVI
was assessed and used to increase classification accuracy. The NDVI threshold for the separa-
tion of vegetation and the historical high-resolution images on Google Earth platform were
used as a reference data [41,52] The onscreen digitization approach has been widely used and
reported in previous studies for obtaining LULC classes, and it has been found to be reliable
and accurate [53,54]. We generated 202 samples for the year 2016 and 216 samples for the
year 2022, representing the five LULC classes. Then we extracted the corresponding input pix-
els for both training and testing samples. The training samples were determined using a user
defined number of pixels based on the proportional of the polygons per classes. The images
were successfully classified into five classes namely, vegetation cover, bareland, built-up, agri-
cultural land and sand (Table 2,Fig 5). The Sentinel 2 satellite data used in this study were
Fig 3. Sentinel 2 satellite bands combinations (Red: B8, Green: B4, and Blue: B3) for the year 2016 and 2022.
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analysed using QGIS version 3.28.2, software in combination with Microsoft Office Excel
2016, which was used in computing the LULC changes to show the percentages and change
rates in the study area. A classification report containing the area of each class was then gener-
ated from the Semi-automatic Classification Plugin (SCP) in QGIS.
After the LULC was successfully classified, we subsequently used the Majority/minority fil-
ter in QGIS’s SAGA tool to isolate the built-up class using a 20 pixels radius. A raster buffer
was made 10 km from the center of the IDPs settlements (Fig 6) for comparative analysis of
the vegetation cover in the immediate 10 km and next 5 km buffer zones and to establish
which of the two zones was highly affected. This analysis was conducted to help us better
Table 2. Description of LULC classes.
LULC Class Description
Vegetation
cover
Land covered by forests and shrubs. Forests comprise areas >1.5ha with more than 10% of tree
cover, with a height exceeding 2 meters. Common tree species include Acacia spp,Balanites
aegyptiaca, and Eucalyptus spp. Shrubs are small trees, with heights less than 2 meters, mainly
acacia species such as acacia mellifera.
Agricultural
land
Land with less than 10% of scattered vegetated cover, usually cultivated for crop production by
the IDPs and the host community during the rainy season (June-October).
Built-up Areas covered by temporary structures, semi-permanent buildings, and permanent buildings.
Tents and sheds are examples of temporary structures while semi-permanent buildings comprise
huts established for IDPs settlement. Permanent buildings are mainly occupied by the host
community.
Bareland Areas without vegetation covered by exposed soils, rocks, rough roads, or degraded lands
Sand Lands dominated by sand with no vegetation cover and no agricultural activities. It includes
seasonal water streams (wadies).
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Fig 4. NDVI map.
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Fig 5. Photos showing the different land cover classes; (A) Bareland (B) Agricultural land (C) Sand with vegetation in
the background (D) Built-up (E) Vegetation Cover.
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Fig 6. Raster buffer map, showing the climate change affected region represent lowly affected region and IDPs with climate change affected region
represent highly affected region.
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understand the changes in vegetation cover caused by the IDPs as opposed to other underlying
drivers.
Accuracy assessment. Accuracy assessment is one of the most important steps in the clas-
sification process. The aim of accuracy assessment is to quantitatively assess how effectively
the pixels were sampled into the correct land cover classes. A total of 5092 pixels were collected
for 2016, and 5344 pixels for 2022, then random stratified sample of an equal number of pixels
(50%-50%) was applied in each land cover class to train the classifier, and to test it by calculat-
ing the confusion matrix. Confusion matrix is the most common way to present the accuracy
of classified images [55]. Overall, users’ and producers’ accuracies, and Kappa values were gen-
erated from the error matrices. The user’s accuracy is calculated by dividing the total number
of classified points that agree with the reference data by the total number of classified points
for that class. The producer’s accuracy is calculated by dividing the total number of classified
points that agree with reference data by the total number of reference points for that class. The
Kappa value incorporates the off-diagonal elements of the error matrices and represents agree-
ment acquired after removing the proportion of agreement that could be expected to occur by
chance. Kappa statistic of the agreement provides an overall assessment of the accuracy of the
classification. The Kappa coefficient can be negative and range from 0 (showing no agreement)
to 1 (perfect agreement). The kappa value should be around zero for a fully random classifica-
tion, and negative for a worse classification (than a random classification) [56,57]. The data
processing workflow for this study is summarized in Fig 7.
Results
LULC classification accuracy
Table 3 displays the accuracy of the various classes based on the different classifiers. It was dis-
covered that the range of overall accuracy using various algorithms was 74% to 87% (Table 3).
The RF algorithm, with an overall accuracy of 86.31% and a kappa of 0.75, demonstrated the
best classification performance among the three classifiers in the 2016 LULC classes. Similarly,
with a kappa of 0.71 and an overall accuracy of 85.52%, RF also mapped the 2022 classes. The
Fig 7. Workflow used to analyze land use and land cover changes.
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KNN algorithm had the lowest accuracy in 2016 and 2022, with 82.26% and 85.09% with
kappa 0.68 and kappa 0.69, respectively. The RF results were the best among the three classifi-
ers and were within the acceptable range hence we proceeded and used the classification out-
puts from the RF for this study [56].
Land use land cover classification and spatial distribution
The results of Sentinel-2 satellite image classification showed that the total coverage of the
study area was 1007.13 km
2
. Individual class areas and percentages for the years 2016 and 2022
are summarized in Table 4. In 2016, agricultural land was the dominant LULC class covering
slightly above half (55.56%) of the study area while bareland was the second largest land cover
type at 34.97%. Vegetation cover and built-up areas had the least area coverage at 2.87% and
0.87% respectively. Similarly in 2022, the greatest share of LULC was agricultural land
(65.32%) followed by bareland (28.33%), while built up areas had the least cover (1.39%).
The five LULC classes in Kas’s locality for the years 2016 and 2022 were also mapped, and
their spatial distribution patterns displayed in Fig 8. Built-up areas are located at the center of
the study area while vegetation is mainly found in the eastern part of the Kas locality. Agricul-
tural land dominates the eastern, sand the western and bareland the northern parts of the
study area (Fig 8).
Land use land cover changes
The magnitude and rate of changes in each LULC class between 2016 and 2022 are presented
in Table 5. There was an overall reduction in the area under vegetation cover (-10.20 km
2
),
bareland (-66.85 km
2
), and sand (-26.37 km
2
). Conversely, agricultural land and built-up areas
increased by 98.12 km
2
and 5.29 km
2
respectively. Vegetation cover had the second highest
mean annual decline rate of -5.89% which translates to a loss of 1.70 km
2
of vegetation cover
per annum. Agricultural land gained 16.35 km
2
(2.92%) per year.
The spatial distribution of vegetation cover changes to other classes and vice versa between
2016 and 2022 is visualized on Fig 9
Table 3. Overall accuracy (%) and kappa coefficient values of three different Machine Learning (ML) algorithms used to classify Sentinel 2 satellite imagery.
2016 2022
ML Algorithm Overall Accuracy (%) Kappa coefficient Overall Accuracy (%) Kappa coefficient
Random Forest (RF) 86.31 0.75 85.52 0.71
K-Nearest neighbor (KNN) 82.26 0.68 85.09 0.69
Support vector machine (SVM) 74.31 0.56 87.34 0.74
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Table 4. LULC classes areas and percentages.
LULC class 2016 2022
Area (km
2
) Percent (%) Area (km
2
) Percent (%)
Vegetation Cover 28.87 2.87 18.67 1.85
Bareland 352.21 34.97 285.36 28.33
Built-up 8.74 0.87 14.03 1.39
Agricultural Land 559.63 55.56 657.75 65.32
Sand 57.67 5.73 31.30 3.11
Total 1007.13 100.00 1007.13 100.00
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IDPs and climate change influences on LULC
Results from the two buffer zones analysis (Table 6) show the percentage area and actual area
(in square kilometers) for the different land cover types in both IDPs and climate change
affected areas. It also shows the highly and lowly affected areas, along with the change percent-
ages over the specified time frame. In the highly affected area (0–10 km), there was a -42.57%
decrease in vegetation cover from 2016 to 2022. This indicates a significant reduction in vege-
tation cover within the highly affected zone. In the lowly affected area (10–15 km), a similar
trend was observed with a -35.23% decrease in the area. However, the decrease was less pro-
nounced compared to the highly affected area. Bareland in both highly and lowly affected
areas, decreased from 2016 to 2022. However, the decrease was less in the lowly affected area
Fig 8. The classified maps of Kas locality for 2016 and 2022.
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Table 5. LULC changes in Kas locality between 2016 and 2022.
LULC class 2016–2022
Total area change Mean annual change rate
(km
2
) (%) (km
2
/year) (%/year)
Vegetation cover -10.20 -35.33 -1.70 -5.89
Agricultural land 98.12 17.53 16.35 2.92
Bareland -66.85 -18.98 -11.14 -3.16
Built-up 5.29 60.53 0.88 10.09
Sand -26.37 -45.73 -4.40 -7.62
*Note. (-) = loss, (+) = Gain
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(-17.69%) compared to the highly affected area (-42.57%). Built-up in both highly and lowly
affected areas experienced an increase, the percentage change in built-up areas was substantial,
especially in the lowly affected area (102.56%), indicating rapid urbanization or infrastructure
development in that zone. Agricultural land showed an increase in both highly and lowly
affected areas, with a notable increase in the percentage area the change in agricultural land
was more pronounced in the lowly affected area (17.70%) compared to the highly affected area
(17.29%). There was a decrease in sand coverage in both highly and lowly affected areas, with a
significant decrease in percentage area and actual area, the decrease was more substantial in
the lowly affected area (-51.26%) compared to the highly affected area (-37.50%). The trends of
the changes among different LULC classes suggest potential influences of human activities
especially the IDPs, natural processes, and/or a combination of both in the study area.
Fig 9. Vegetation cover change detection map of 2016–2022.
https://doi.org/10.1371/journal.pone.0304034.g009
Table 6. Land cover areas and changes in the two buffer zones.
CLASS Highly affected area (0-10km) Lowly affected area (10-15km)
2016 2022 2016–
2022
2016 2022 2016–2022
Area (%) Area (km
2
) Area (%) Area (km
2
)
2
Change
(%)
Area (%) Area (km
2
) Area (%) Area (km
2
) Change (%)
Vegetation Cover 2.32 10.01 1.33 5.80 -42. 57 3.47 19.87 2.25 12.87 -35.23
Bareland 37.81 165.51 31.12 135.32 -17.69 32.63 186.71 26.22 150.03 -19.65
Built-up 1.29 5.62 1.77 7.71 37.19 0.55 3.12 1.10 6.32 102.56
Agricultural Land 53.23 231.45 62.43 271.48 17.29 57.35 328.19 67.50 386.30 17.70
Sand 5.35 23.28 3.35 14.55 -37.50 6.00 34.39 2.93 16.76 -51.26
Total 100 434.86 100 434.8 100 572.28 100 572.28
Note: A decline is indicated by red highlights, and an increase is indicated by green highlights. Between 2016 and 2022, there was a noticeable decline in vegetation,
bareland, and sand, and an increase in agricultural land and built-up classes in both highly and lowly affected zones.
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Land cover change transition matrix
For a better understanding of the source and destination of the respective LULC changes, land
cover change matrix for the two years of our study was computed (Table 7). LULC changes
matrix analysis is important as it shows the direction of change and the LULC type that
remains unchanged during the study period [58]. A large proportion of the vegetation cover
(19.80 km
2
) was converted to agricultural land while about 5.50 km
2
of vegetation remained
unchanged between 2016 and 2022. Similarly, significant bareland areas (179.20 km
2
) were
converted to agricultural land. A minor proportion of the bareland was transformed to vegeta-
tion (1.10 km
2
) and built-up area (3.40 km
2
) over the study period Table 7.
Discussion
The LULC classification output of this study were excellent as the overall accuracy were above
the recommended threshold of 80% [56,59]. Additionally, the Kappa statistics of the study
showed a strong agreement of the classified image since accuracy was within the acceptable
range to allow for further LULC changes detection assessment [58]. Five major LULC types
(agricultural land, bareland, vegetation cover, sand and built-up) were classified for this study.
Agricultural land and bareland were the dominant land cover types, with the former being by
far the most dominant land cover type in the study area from 2016 to 2022. There were signifi-
cant shifts in the areas of the different LULC classes with three classes showing a decline (vege-
tation cover, bareland, sand) while agricultural land and built-up areas increased. Vegetation
was the second most reduced land cover type. The land cover class conversion matrix also
revealed that vegetation to non-vegetation conversion increased substantially over the 6-year
study period.
The vegetation cover loss in the Kas locality of Darfur can largely be attributed to resettle-
ment of IDPs from the displaced populations in addition to other underlying factors. This was
supported by the analysis results based on the 2 buffer zones (Fig 6,Table 6) which showed
that vegetated areas within 10 km of IDPs settlements were highly affected relative to areas
away from the settlements. The estimated populations of the IDPs in Kas locality has risen
from between 35,000 to 40,000 in 2004 to about 77,843 by the year 2020 [34,60]. Data from
the United Nations Office for the Coordination of Humanitarian Affairs [27] further shows
that the Darfur region of Sudan has been experiencing newly displaced populations annually
from 2003 to 2014.
The resettlement of IDPs has subsequently increased the human and livestock populations
in the Kas locality. The rise in number of IDPs subsequently resulted in extensive agricultural
activities to meet their food demands hence the expansion of the agricultural land at the
expense of vegetation cover and bareland as is evident in the transition matrix (Table 7). The
increased demand for food production often leads to the conversion of the vegetated areas to
Table 7. LULC transition matrix (km2).
2022
Vegetation Cover Bareland Built up Agricultural Land Sand Total
Vegetation Cover 5.50 2.80 0.50 19.80 0.30 28.90
Bareland 1.10 159.30 3.40 179.20 9.20 352.20
2016 Built up 0.10 2.20 2.80 3.30 0.40 8.73
Agricultural Land 11.90 105.40 6.90 430.80 4.70 559.60
Sand 0.10 15.50 0.50 24.70 16.80 57.70
Total 18.70 285.30 14.10 657.80 31.40 1007.73
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farms through clear felling of trees to increase the land for crop production. This negatively
affects the vegetation cover area while contributing to the expansion of agricultural land [29].
This finding corroborates with that by [13], who reported the establishment of new agricul-
tural areas on Azerbaijani territory, because of refugee migrations due to armed conflicts. A
study by [61] further highlights the expansion of agricultural land, resettlement, and popula-
tion growth as the key drivers of forest fragmentation in the Kaffa Biosphere Reserve of Ethio-
pia. Most IDPs are pastoralists who migrated with their livestock to the new settlement areas.
This leads to increased livestock populations and overgrazing of the shrubs in the vegetated
areas. Palatable tree branches are also cut down for fodder, contributing to deforestation. Simi-
lar findings were reported by [18] in the Afghan refugee camps in northern Pakistan.
Increase in human population due to conflict related displacements is frequently linked to
increased deforestation rates due to overdependence and overutilization of the forest resources
[19]. Increased human population in the neighbouring Zalingei IDP camps has previously
been associated with considerable decrease in woody vegetation within the camp’s vicinity
[31]. A study in four hotspot regions around IDP camps in Darfur showed a correlation
between decreased vegetation cover with logging of trees as well as removal of grass and shrubs
[29]. The vegetation forms a key and readily accessible/available source of energy for the IDPs
in the Kas locality in the form of firewood and charcoal [60]. Unregulated access, however,
leads to overexploitation hence a reduction in vegetation cover. These findings conform with
that by [14,43,62] who cite overexploitation of forest resources for firewood and charcoal pro-
duction as a key driver of vegetation cover loss. The forests and shrubs further provide timber,
building poles and tree branches which are used in the establishment of the temporary settle-
ment structures by the IDPs (built-up areas) which has increased over time. This exerts addi-
tional pressure on the already dwindling vegetation cover in the study area. A study by [14,19]
in the Teknaf region of Bangladesh, showed that a mass influx of refugees coupled with a vast
expansion of refugee camps led to large scale degradation of forestlands from overharvesting
of poles and timber used in building the spontaneous settlements.
The major increase in the built-up areas can be linked to the the renewed civil conflict
which erupted in 2014 emanating from fighting involving armed movements, government
forces, and armed tribal militia which led to the displacement of more people hence more
IDPs settlement. The built-up areas are likely to further increase with the recent conflicts
between the Sudanese Armed Forces, and the paramilitary Rapid Support forces which started
in April 2023, as the more people are likely to go back to the IDP camps. The area under sand
notably far away from the IDPs camps and settlements substantially decreased, indicating that
there has been a vegetation regeneration due to minimal disturbance.
Climate change and extreme negative climate variability have previously been reported in
Sudan [22]. The variability of climatic conditions in the form of increased temperatures and
variability of rainfall in the study area is also likely to play a role in the observed LULC changes
[29]. There have been significant variations in the rainfall patterns and temperatures in the
study area over the past three decades with an estimated 0.4˚C temperature rise per decade
[29,31]. Fluctuations in the seasonal precipitation patterns and temperature impacts the natu-
ral environment as they have the potential of creating drought-prone conditions [12,31].
Although, climatic variations also influence the changes in the LULC, they play a minor role
compared to the direct impacts of IDPs camp-related activities in this region [29,63].
Implications on environmental sustainability
Armed conflict and warfare primarily have a local impact on land systems, but they can also
create tele couplings which can negatively affect the achievement of SDG6 (clean water and
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sanitation), SDG13 (climate action), SDG15 (life on land) and SDG16 (peace justice and strong
institutions) [64]. The armed conflicts in Sudan have resulted in the displacement of human
and livestock populations. This coupled with the drought experienced in the last decade has
also led to a massive displacement of the general population. These migrations have brought
about overexploitation of forests, water resources, arable land, and pastures. The large num-
bers of IDPs living in refugee camps has further resulted in amplified groundwater pollution,
air pollution and a rise in respiratory diseases caused by indoor cooking in the camp areas
[60]. Additionally, there has been increased conflicts between herdsmen and farmers, wors-
ened by the intervention of militias [65]. An effort by the government of Sudan to mitigate
drought and climate change impacts, among others, has focused on the forestry sector with the
establishment of new forest plantations [66]. Forests now are more important than ever before
for they play a significant role in balancing the earth’s carbon dioxide supply and exchange,
thereby acting as a key link between the spheres of the earth [67]. Peacebuilding has the poten-
tial of boosting vegetation conservation; however, most conservation organizations’ core mis-
sion in Sudan is not on conflict reduction. Partnerships between conservation and peace
building organizations is therefore paramount for the reduction of future drastic land cover
changes and the achievement of the above-mentioned SDGs [20].
Conclusions
Using Sentinel-2 satellite data and GIS, this study sought to understand how the vegetation
cover has changed in South Darfur State between 2016 and 2022 largely due to IDPs settlement
among other factors, brought about by renewed armed conflicts in year 2014. Study findings
show a considerable negative impact of the IDPs on vegetation cover, as seen by the negative
change trend. Since the IDPs depend on the forest, shrubs, and scattered trees for their liveli-
hoods, vegetation cover area has experienced a significant reduction due to over exploitation,
which affects the natural regeneration process. Other natural and human driven activities like
climate change, traditional rain-fed agricultural practices and grazing have also led to a reduc-
tion in vegetation cover in the Kas locality.
These findings underscore the urgent need for targeted interventions and policy responses
to address the environmental consequences of conflict-driven displacement. Whereas the con-
sequences armed conflict is typically felt immediately, they have the potential to create long
lasting effects on land cover and the achievement of SDGs. Peace efforts can offer long lasting
solutions as the IDPs will return to their homes thereby reducing the pressure on the vegeta-
tion cover. Implementation of sustainable agricultural management practices and regulated
utilization of natural ecosystems like forests and shrubs can help curb vegetation cover loss in
the Kas locality. The study offers pertinent data that can help decision-makers plan restoration
of the degraded areas and prevent further environmental degradation to promote long-term
environmental sustainability. Recommendations for future studies include a detailed social
survey and key informant interviews in the study area, when peace returns, to establish the
other underlying factors contributing to LULC changes.
Supporting information
S1 File. Stack bands of 2022 S2 image.
(7Z)
S2 File. Training and testing samples.
(ZIP)
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S3 File. Stack bands of 2016 S2 image.
(ZIP)
Acknowledgments
Much gratitude goes to the Sudanese Ministry of Higher Education and Scientific Research for
their academic support, as well as the Tempus Public Foundation and the University of Sopron
for providing a PhD scholarship to the first author.
Author Contributions
Conceptualization: Abdalrahman Ahmed.
Formal analysis: Abdalrahman Ahmed.
Methodology: Abdalrahman Ahmed, Brian Rotich.
Software: Abdalrahman Ahmed.
Supervision: Kornel Czimber.
Visualization: Abdalrahman Ahmed, Brian Rotich.
Writing – original draft: Abdalrahman Ahmed, Brian Rotich.
Writing – review & editing: Brian Rotich, Kornel Czimber.
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PLOS ONE
A sentinel-2 satellite based analysis of vegetation cover changes in the Kas locality, Darfur, Sudan
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