Journal of Applied Biosciences 211: 22300 – 22318
ISSN 1997-5902
Analysis of land use changes in the commune of Kaour from 1990 to 2024 (Sedhiou region / Senegal).
Fode Amata Drame1*, Hyacinthe Sambou1, Seydou Ndiaye2
1Institute of Environmental Sciences, Faculty of Science and Technology, Cheikh Anta Diop University, Dakar, Senegal
2Agroforestry and Ecology Laboratory, Agroforestry Department, Assane Seck University, Ziguinchor, Senegal
Corresponding author: Fode Amata Drame
Email addresses: amata10184@gmail.com (Fode Amata Drame), hyacinthe.sambou@ucad.edu.sn (Hyacinthe Sambou), seydougdp@gmail.com (Seydou Ndiaye)
Submitted 03/07/2025, Published online on 31/07/2025 in the https://www.m.elewa.org/journals/journal-of-applied-biosciences https://doi.org/10.35759/JABs.211.2
ABSTRACT
Objective: This study aims to contribute to a better understanding of the level of spatial change in the municipality of Kaour, by analysing territorial evolution under the influence of climatic and anthropogenic changes over two distinct periods (1990-2006 and 2006-2024).
Methodology and results: The analysis is based on the use of satellite images processed with ENVI 5.6 and ArcGIS 10.5 software, supplemented by field visits and a diagnostic survey of resource persons. Between 1990 and 2006, the inhabited environment expanded by 85.76% (+447.22 ha), while agricultural land shrank by 45.57% (-853.27 ha). During the period 2006-2024, the populated environment lost 701.02 ha, offset by the expansion of the vegetated (+764.35 ha) and agricultural (+909.75 ha) environments.
Conclusion and application of results: This research reveals complex spatial dynamics in the commune of Kaour, characterised by major transformations between the different types of land use. The period 1990-2006 shows rapid urbanisation at the expense of agricultural land, suggesting growing demographic pressure. Conversely, the period 2006-2024 shows a partial conversion to agricultural activities and plant regeneration, possibly linked to rural development policies or changes in land use practices.The causes identified, both natural and man-made, require an integrated approach to territorial management. The results provide a decision-making tool for local authorities and planners, enabling land-use policies to be geared towards sustainable development. The study recommends setting up a system for continuous monitoring of changes in land use and developing adaptive strategies to anticipate future territorial transformations.
Keywords: Analysis, Change, Land use, Kaour
INTRODUCTION
Natural ecosystems are constantly changing under the influence of climate and human-induced changes. Yet, Senegal has embarked on a new dynamic of socio-economic development over the medium and long term and to ensure sustainable well-being for the men, women, young people and other vulnerable groups that make up its population by 2035 (MEDD, 2015). This requires, on the one hand, an identification of environmental dysfunction factors and, on the other, an analysis of changes in land use units, quantifying them in order to master territories to promote sustainable development. However, the phenomenon is profound and universal. Today, all regions of the world are faced with the deterioration of natural resources, increasing pressure on ecosystems and the loss of biological diversity (Brun, 2018; FAO-ODD, 2017). According to Paturel et al., 2004, all regions of West and Central Africa, both dry and humid, were affected by a decrease in annual rainfall around the 1970s. The causes often cited are population growth and falling rainfall Lawrence et Chase, 2010). In his 2020 research, Tchibozo demonstrated that the spatio-temporal dynamics of soil are dependent on human activity, agriculture, and urban planning. As a result, in Sudanian regions facing socio-environmental changes (Alexandre et Mering, 2019), land cover monitoring is essential to establish a diagnosis and better understand the causes of vegetation cover modification and its consequences. In this respect, maps of land use and changes in surface conditions are genuine planning and decision-making tools, especially for the management and preservation of natural resources and ecosystems. This situation is a major constraint for rural populations in Senegal, where 50% of rural populations derive their livelihood from the land (ANSD, 2013). The regression of vegetation in the Bakoum watershed in Sedhiou between 2000 and 2010 is striking. It fell from 81% in 2000 to 12.11% in 2010 (Dramé et al., 2023).
MATERIAL AND METHODS
Description of study environment: Kaour became a municipality on 28 December 2013.The commune of Kaour is located in the Sedhiou region, Goudomp department and Djibanar arrondissement. It is the capital of the rural community of the same name. Its territory extends between the Casamance River to the north, the Ziguinchor region to the south and west, and the municipality of Djibanar to the east. It is crossed by Route Nationale N°6, which links Ziguinchor and Kolda. Its surface area is approximately 7141 hectares (Figure 1). In 2013, the rural Commune of Kaour had a population of 4785 inhabitants (ANSD, 2013). This population now stands at 5701 inhabitants according to the preliminary report of the (ANSD, 2013).
Figure 1: Geographical location of the Commune of Kaour in 2024
The Casamance region, which covers our study area, belongs to the South-Sudanese zone. The commune of Kaour, in the Sedhiou region, is in the continental South-Sudanese zone. In this area, the rainy season begins in May and ends in October, with the predominance of the monsoon. The dry season, from November to April, is marked by the continentalized maritime trade winds and the continental trade winds or harmattan (Sagna, 2005). From 1971 to 2000, the rate of change in rainfall was -22% in Sedhiou. In contrast, between the period 1971-2000 and the decade 2001-2010, the rate of change was 12% (Diédhiou, 2019).
Data collection: Data collection was carried out in two phases: we downloaded satellite images and supplemented them with interviews with resource persons. In addition to these approaches, we visited the site to see the current state of certain land-use units and to take landmarks in order to facilitate processing. The satellite images were acquired using platforms (https://earthexplorer.usgs.gov and Google Earth) from (Landsat 5, Landsat 9) (Table 1). The dates 1990, 2006 and 2024 were chosen for a number of reasons. Firstly, the effects of climate change began to manifest themselves in the 1990s, and the pressure of human activity on the ecosystem (interviews). In addition, the displacement of local villagers due to the repression of the Casamance conflict towards localities such as Goudomp and Kaour. This population flow has also contributed to transformations in the Commune’s ecosystem. In fact, the early 2000s were marked by illegal logging. This took place at an unprecedented rate in the department of Bignona, as in most of the border areas of the northern part of Lower, Middle and Upper Casamance, bordering the Republic of Gambia (Ba et Descroix, 2021). The availability and quality of the images should also be noted in the choice of dates.
Table 1. Satellite image metadata
To gain a better understanding of the situation, its spatial and temporal evolution and the factors responsible for its dynamics, we conducted interviews with some of the Commune’s notables.
Data processing: Data processing is carried out using tools such as ENVI, ArcGIS and Excel. These were used to process satellite images, calculate surface areas, draw up maps and produce statistical tables.
Index calculation: NDVI: is a vegetation index defined as the normalized difference of spectral reflectance measurements acquired in the ‘Near Infrared’ (PIR) and ‘Red’ wavelength zones (Yin et al., 2023). The normalized vegetation index (NDVI) is used to characterize different types of vegetation in relation to the intensity of their photosynthetic activity (Zhang et al., 2024). The formula used combines the near-infrared and red bands (Rouse et al., 1974). NDVI = 𝑃𝐼𝑅-𝑅𝑜𝑢𝑔𝑒 / 𝑃𝐼𝑅+𝑅𝑜𝑢𝑔𝑒. It varies between -1 and +1. The NDVI has a range of values between -1 and +1. This scale makes it possible to distinguish between non-vegetated areas (negative or zero values) and dense, healthy vegetation, which has high indices, typically between 0.7 and 0.8 (Griffith et al., 2002). The use of the latter and images from Google Earth provided us with a basis for photo-interpretation, enabling us to identify the different land-use classes.
Image corrections: The images we download are not always of good quality. They are often subject to what are known as radiometric and/or geometric errors. We need to pre-process them in order to use them. The technique used to improve the visual quality of our images after each colour composition is called enhancement. Contrast enhancement involves using the full range of available colour intensities (65,535 levels) to visualise the data on the screen. This enhancement simply redistributes the available colour palette across the image so that certain elements stand out more clearly.
Supervised classification: Performing a supervised classification of a satellite image implies that you have a very good knowledge of the area where the image was taken. This knowledge can come from several sources: a field survey or photo-interpretation of the image, provided you are sufficiently confident in your judgement. In our case, we carried out a field survey. This fieldwork and photo-interpretation enabled us to define the land-use classes in our study area, based on spectral signatures after colour composition. In total, we identified seven (07) land-use unit classes (Water, Buildings/Bare Soil, Open Vegetation, Dense Vegetation, Burnt, Cultivated Area and Tanne. The merger of the Buildings and Bare Ground classes is explained by the similarity of their spectral signatures. Most of the roofs on the buildings in this zone were either straw or zinc. The classifications were validated using arc GIS software. This involves establishing a number of points on the site or in google earth for each class. Based on these validation points, an error matrix is established. The latter is a table of figures identifying the number of objects assigned to a classification category, relative to the actual number in that category, but verified by reference data. We thus obtain the accuracy (producer and user) of each classification, the overall accuracy and the Kappa index (K).
Spatio-temporal change methodology: Change analysis is based on the combination of land use results from different dates. A cross-referencing of two land-use maps from two different periods (1990 and 2006, 2006 and 2024) yielded change maps and matrices showing the evolution of the different classes between these dates. This approach can highlight changes in land use. It is based on comparing the classifications of two scenes acquired at different dates (Mas, 2000; Lu et al., 2024). To understand and see the dynamics of landscape change at Kaour over time. We superimposed the land-use maps at two different dates, in ArcGIS software (using the “Intersect” algorithm of the Geoprocessing extension), and finalized the processing of the matrices in Excel. To facilitate and synthesize the spatial analysis and study of changes, the thematic classes were grouped into four (4) thematic clusters: Water, Inhabited Environment, Vegetation Environment and Agricultural Environment.
RESULTS
Analysis of land use units: In 1990, six land-use units were detected: Water, Buildings/Bare ground, Light vegetation, Dense vegetation, Cultivated area and Tanne. At this date, the Kaour area is characterized by a significant presence of vegetation, i.e. 29.17% dense vegetation and 14.03% light vegetation. These two units cover an area of around 3084.94 ha. They are followed by the cropland class with a spatial coverage of 26% of the study area. The least representative class is that of tannes, with 0.22% of occupied space.
Figure 2: Representation of land-use units in the Commune of Kaour in 1990
Table 2: Area of Kaour land-use units in 1990
| Classes in 1990 | Area (ha) |
| Water | 1627,23 |
| Buildings/Ground | 556,44 |
| Dense vegetation | 2082,77 |
| Growing zone | 1856,77 |
| Clear vegetation | 1002,17 |
| Tanne | 15,62 |
| Total | 7141 |
In 2006, land-use units went from 06 to 07 classes, with slash-and-burn occupying seventh place. This occupies an area of some 1,434.41 ha, or 20.09%, behind the light vegetation class (20.39%) and the water class (20.55%). Dense vegetation fell from 29.17% in 1990 to 7.32% in 2006, ahead of the tannes class, which occupied the lowest proportion in 2006 (3.78%).
Figure 3: Representation of land-use units in the Commune of Kaour in 2006
Table 3: Kaour land-use unit areas in 2006
| Classes in 2006 | Area (ha) |
| Buildings/Ground | 1032,52 |
| Brûlis | 1434,41 |
| Water | 1681,90 |
| Tanne | 270,13 |
| Clear vegetation | 1456,26 |
| Dense vegetation | 522,75 |
| Growing zone | 743,03 |
| Total | 7141 |
Kaour’s land cover in 2024 is dominated by vegetation (50.26%) compared to the other classes. This includes dense vegetation with a share of 22.14% and light vegetation covering 28.12% of the area. We also note a decrease in tanne areas between 2006 and 2024 (ref Table 2 and 3). And, an increase in cultivated areas, i.e. a 6.94% increase in space between 2006 and 2024.
Figure 4: Representation of land-use units in the Commune of Kaour in 2024
Table 4: Kaour land-use unit areas in 2024
| Classes in 2024 | Area (ha) |
| Buildings/Ground | 584,15 |
| Water | 1591,22 |
| Tanne | 137,97 |
| Clear vegetation | 2007,82 |
| Dense vegetation | 1580,76 |
| Growing area | 1239,07 |
| Total | 7141 |
Analysis of Spatio-temporal changes: The results of the method applied to changes in land-use units allow us to move on to a much more detailed description and analysis of the spatio-temporal evolution of Kaour from 1990 to 2024. The changes are well described on the maps. And the analysis of these is enhanced by the transition matrices, statistical tables and graphs derived from these matrices. The change maps show the recurrence of each land-use unit and their expansion over time. The transition matrices allow us to see in greater detail and understand the different evolutions within and between classes. The transition matrix shows the various ways land-use units change from one period to another. It describes this evolution either by conversion (the passage from one category to another, e.g. cultivated areas becoming inhabited areas), or by modification (changes occurring within the same land-use unit, e.g. dense vegetation becoming open vegetation). It also highlights the stability of classes over time, i.e. without change. On the other hand, this matrix can be explained in another way, using the concepts of gain and loss. Gain is the conversion of any other thematic class, in whole or in part, in favour of the evolution of a target class. Loss is the conversion of a target class, in whole or in part, in favour of other thematic classes.
To better understand the study of changes in Kaour, we have chosen to detect changes over two periods:
- The period (1990 – 2006) of the manifestation of change factors and/or the beginning of human pressure on Kaour’s environment;
- And the period (2006 – 2024) that shows the current state of Kaour’s occupation units and their evolution between these two dates.
Change detection 1990 – 2006: Several changes have taken place between the different zones of the Kaour commune. These are described as conversion or modification. Using the change map and matrix, we were able to identify and quantify the different forms of change.
Figure 5: Change in land use between 1990 and 2006
This map shows the recurrence of each environment or thematic group, as well as the expansions that have taken place between them. In all, we have eight major expansions (Figure 5), which translate into conversion. The transition matrix has enabled us to differentiate forms of conversion through the concepts of gain and loss.
Table 5: Transition matrix of land-use units between 1990 and 2006
Between 1990 and 2006, the overall rate of change in the agricultural environment shows a loss of around -45.57% of its area, or -853.27 ha, despite having benefited from 612.25 ha. It lost in favour of the built-up and bare ground known as the inhabited (427.20 ha) and vegetated (1038.31 ha) environments. The vegetation environment gained the most, with 1143.04 ha, via the agricultural environment (1038.31 ha) and the inhabited environment (104.73 ha). However, the highest rate of change was in the settled environment, with an expansion of 85.76% compared to its initial area. The transformation of vegetation (360.84 ha) and agricultural land (427.20 ha) in its favour has enabled it to gain more space. In terms of modification, this is estimated at around 1974.34 ha, or 27.65% of the total area within the agricultural and vegetation environments. A small negative change was detected in the agricultural environment. 2.37% of the cultivated area class was converted to tannin. Tanne is known for its salt and/or acid content, which is generally unfavourable to agriculture. Dense vegetation recorded the biggest change, with 45.36% of its area converted to slash-and-burn and 32.61% to light vegetation. From light vegetation to dense vegetation, we have 11.61% change in area, which is a good warning in the vegetation environment. However, 18.61% of this same area has changed in favour of slash-and-burn.
Figure 6: Changes in thematic classes between 1990 and 2006
Change detection 2006 – 2024: During this period, we witnessed ten 10 major expansions between thematic groups. These expansions are quantified in the transition matrix table. They accelerated between 2006 and 2024. 88 ha of the study area’s total surface area went from submerged land (water) to inhabited (57.83 ha), vegetated (0.88 ha) and agricultural (29.30 ha) environments. More than 701.02 ha were lost to the inhabited environment, while the vegetated and agricultural environments covered 764.35 ha and 909.75 ha respectively.
Table 6: Transition matrix of land-use units between 2006 and 2024
Figure 6: Change in land use between 2006 and 2024
Significant changes also took place during this period. An area of 834.11 ha of burnt land became open vegetation, while the latter also lost space (300.24 ha) to dense vegetation. In addition to this expansion of dense vegetation, it increased its area by 359.53 ha through the loss of slash-and-burn.
Table 7: Changes in thematic classes between 2006 and 2024
| Thematics groups | Changes in thematic classes between 2006 and 2024 | Area (ha) |
| Farming environment | Growing area – Tanne | 1,07 |
| Tanne – Growing area | 76,79 | |
| Vegetable environment | Dense vegetation – Light vegetation | 22,06 |
| Light vegetation – Dense vegetation | 300,24 | |
| Brûlis – Light vegetation | 834,11 | |
| Brûlis – Dense vegetation | 359,53 |
Factors in land use dynamics: Kaour underwent a significant change in its environment between 1990 and 2024. The causes of this disaster are both natural and man-made. As far as the main natural factors are concerned, we have:
Decreasing rainfall: According to Dramé, 2019, rainfall analysis of the Sedhiou station over the period (1951 to 1967) shows an abundance of rainfall with a predominance of wet years. Between 1967 and 2000, the region experienced a rainfall deficit marked by a predominance of dry years.
Image 1: Tanne in 2006 at Kaour
The rise of the salt tongue has brought about a significant change in the nature of the soils in the Kaour lowlands. Formerly made up of clay soils, this area is now dominated by tannins.
Silting / Erosion: Water erosion is the consequence of intense runoff. The most obvious result is the progressive physical and chemical degradation of arable land. This phenomenon not only changes the nature of agricultural land; it also disrupts the regeneration of woodland cover.
Image 2: Sand layer in the lowlands at Kaour
Anthropogenic causes: Population dynamics: The radicalization of the Casamance crisis has led to significant population flows towards more secure localities such as Kaour and Goudomp. Recall that the inter-census growth rate (1988-2002) of Sedhiou was low (1.8%) (ANSD, 2004) during that time in the department. However, the following years marked a rapid increase in the population of its localities. We counted 17654 inhabitants in our study area in 2013, including 4785 inhabitants for the rural commune of Kaour and 12869 inhabitants for the urban commune of Goudomp (ANSD, 2013). This population now stands at 21694 inhabitants according to the preliminary report of the (ANSD, 2023), of which Kaour 5701 inhabitants and Goudomp 15993 inhabitants.
Timber harvesting: Porous borders combined with the persistent insecurity of the Casamance conflict in this sector led to the growth of illicit timber trafficking around 2000. In addition to this, cultivation practices such as land clearing and bush fires, etc., have become more widespread.
Image 3: Wood cutting at Kaour
Image 4: Traces of fire in 2006
DISCUSSION
The results of a diachronic analysis of land use in the rural commune of Kaour (Sedhiou Region, Senegal) between 1990 and 2024 reveal complex spatial dynamics that merit scientific discussion. This study is based on transition matrices and Kappa coefficients greater than 0.50, thus guaranteeing the reliability of the classifications used to assess changes.
Spatio-temporal dynamics
Period 1990-2006: The period 1990-2006 was characterized by a remarkable expansion of the inhabited area (+85.76%, or +447.22 ha), mainly at the expense of agricultural land, which declined by 45.57% (-853.27 ha). This conversion of agricultural land was to the benefit of buildings and bare soil (427.20 ha) and vegetation (1038.31 ha). This dynamic suggests a phenomenon of increasing urbanization, comparable to what Tabopda et Fotsing, 2010 have observed in other regions of West Africa, where peri-urban agricultural land is gradually being converted to residential areas under demographic pressure.
Period 2006-2024: The following period (2006-2024) shows a different dynamic, with a significant loss of inhabited area (-701.02 ha), while plant and agricultural areas expand by 764.35 ha and 909.75 ha respectively. This trend could be explained by agricultural conversion and reforestation policies, as highlighted by Van Ackern et Detges, 2022 in their work on Climate Change, Vulnerability and Security in the Sahel.
Explanatory factors for the observed mutations
Natural factors: Land use changes in Kaour are influenced by several natural factors:
– Decreasing rainfall: In line with the observations of Nicholson et al., 2018 on rainfall variations in West Africa, decreasing rainfall directly impacts land productivity and forces the adaptation of farming systems.
– Rise of the salt tongue: It results in the salinization of the land. This leads to consequences such as decreased yields, abandonment of land, or the practice of shifting agriculture (Ndiaye, 2018).
– Silting: Processes that affect soil quality and their agricultural suitability, as demonstrated in the Master 2 thesis Dramé, 2025 on soil degradation in the Bakoum valley in Senegal.
Anthropogenic factors
The human causes of Kaour’s spatial mutations are:
– Demographic dynamics: Population growth and movements create pressure on natural resources and increased demand for habitable land. This is an issue raised by Greenfield, 2024 in her study on “the effects of population growth on the environment.” According to her, one of the most significant effects of population growth is the destruction of the natural ecosystem for agricultural expansion. This change destroys forests and harms wildlife, causing animals and plants to disappear. Urban expansion driven by demographic growth further aggravates this issue by converting natural ecosystems (Greenfield, 2024).
– Logging: These practices, often linked to household energy needs, contribute to deforestation. Badji et al., 2014 highlighted the impact of this practice on forest cover in the southern regions of Senegal.
– Bush fires: Used as a clearing technique or occurring accidentally, they considerably modify the vegetation landscape. This was demonstrated (Mbow et al., 2014) in their work on Sudano-Sahelian ecosystems.
The methodology employed in this study, based on spatial remote sensing, represents a significant advance for monitoring natural resources in Senegal, as highlighted by Boschetti et al., 2020. However, the late adoption of these technologies has limited the ability to effectively monitor land-use changes in the region. This limitation reflects what (Sambou et al., 2015) refer to as a lack of equipment and means in environmental management in West Africa, where access to accurate spatial data remains a challenge. As suggested by Thiaw et al., 2022 land use and land cover maps are true decision-making tools, especially in urban areas where the advance of the urbanization front poses a significant problem for urban planning, particularly in developing countries.
CONCLUSION AND APPLICATION OF RESULTS
This research allowed us to evaluate land use unit modifications in Kaour. Several changes were noted between the different zones of the Kaour commune. These changes are described as conversion or modification. Between 1990 and 2006, there were eight major expansions resulting in conversion, and five modifications within the land-use units themselves. During the second period, changes accelerated. We detected ten 10 mutations between different land-use units and six mutations within them. This strongly affected the agricultural environment through loss of space, and disrupted the stability and regeneration of vegetation. It would therefore be important to involve all the players concerned through a participatory approach in order to promote integrated and sustainable management.
ACKNOWLEDGMENTS
We thank the Institute of Environmental Sciences of the Cheick Anta Diop University of Dakar for facilitating this study.
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