Influence of Climate Variability on The Dynamics of Land Use Land Cover in the Sub-Soudanian Sector: The Case of the Badenou Classified Forest, Northern Côte D'ivoire

International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 11 November 2025 DOI: 10.47191/ijcsrr/V8-i11-28, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5716 *Corresponding Author: KOUASSI N'Zibla Roch-Ghislaine Volume 08 Issue 11 November 2025 Available at: www.ijcsrr.org Page No. 5716-5731 Influence of Climate Variability on The Dynamics of Land Use Land Cover in the Sub-Soudanian Sector: The Case of the Badenou Classified Forest, Northern Côte D'ivoire KOUASSI N'Zibla Roch-Ghislaine1*, KOUAKOU Amani Abell Mike2, SILUÉ Pagadjovongo Adama3, NANAN Kouassi Kouman Noël4, YAO N'Guessan Olivier5, Bohoussou Cristel Natacha6 1West Africa Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Graduate Research Programme on Climate Change and Biodiversity, Université Félix Houphouët-Boigny, 22 BP 582 Abidjan 22, Côte d'Ivoire 2West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Graduate Research Programme on Climate Change and Land Use, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana 3Département de Biologie Végétale, UFR Sciences Biologiques, Université Peleforo Gon Coulibaly (UPGC), BP 1328 Korhogo, Côte d'Ivoire. 4,6Natural Environment Laboratory and Biodiversity Conservation, Department of Bioscience Université Félix Houphouët-Boigny, 22 BP 582 Abidjan 22, Côte d'Ivoire 5Laboratory of Systematics, Herbariums and Botanical Muses, National Floristic Center, Department of Bioscience Université Félix Houphouët-Boigny, 22 BP 582 Abidjan 22, Côte d'Ivoire ABSTRACT: In a context of global change marked by climate evolution, tropical forest ecosystems are under increasing pressure that threatens their integrity and biodiversity. This study quantifies the impact of climatic parameters on the evolution of land use/cover in the Badenou Classified Forest (northern Côte d'Ivoire) between 1990 and 2022. By cross-referencing Landsat images and climatic data (temperatures, precipitation, PDSI, SPI) via Google Earth Engine, and applying statistical tests (Spearman correlations, PCA, regressions), significant relationships were highlighted. The results show a distinct vulnerability of natural ecosystems to climatic stresses. Dense dry forests and galleries regress with drought (PDSI: ρ = -0.502, p = 0.003). The low density shrub savannah declines sharply with rising temperatures (Tmax: ρ = -0.613, p < 0.0001). Water bodies decrease during dry periods (PDSI: ρ = -0.545, p = 0.001). Anthropogenic dynamics present contrasting responses. Fallow lands decrease with temperature (Tmax: ρ = -0.413, p = 0.017), while perennial crops expand their reach under these same conditions (Tmax: ρ = +0.413, p = 0.017). An increase in bare soils and built-up areas is correlated with humid conditions (SPI: ρ = +0.362, p = 0.039). This research demonstrates that climatic variables, particularly temperatures and drought indices, are major explanatory factors for landscape transformations. These quantified results provide an essential scientific basis for the development of adaptive management policies, reconciling biodiversity preservation and local development in a context of global change. KEYWORDS: Badenou Classified Forest, Climate change, Land Use Land Cover change, Ivory Coast, Sub-Sudanese zone. I. INTRODUCTION In 1979, the international community, concerned by the threat of climate change linked to anthropogenic emissions, organised a World Climate Conference in Geneva (IPCC, 2023). It was during this conference that the Intergovernmental Panel on Climate Change (IPCC) first defined the term 'climate change'. According to the IPCC (2021), climate change is manifested by a perceptible increase in the intensity and frequency of extreme temperatures, notably heatwaves and heavy precipitation, as well as agricultural and ecological droughts in some regions. Most scientific analyses have shown that global warming is largely caused by anthropogenic activities, particularly the release of greenhouse gases into the atmosphere (Ardoin et al., 2003; Sighomnou, 2004). As a result, concerns have grown significantly in recent decades regarding the increase in this global surface temperature (United Nations Environment Programme, 2022). Because of this situation, countries and territories are vulnerable depending on their specific climatic conditions. Climate variability is a global phenomenon that significantly affects forest ecosystems by altering their structures and dynamics. This International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 11 November 2025 DOI: 10.47191/ijcsrr/V8-i11-28, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5717 *Corresponding Author: KOUASSI N'Zibla Roch-Ghislaine Volume 08 Issue 11 November 2025 Available at: www.ijcsrr.org Page No. 5716-5731 phenomenon is particularly acute in Africa, where the consequences are manifested through desertification (Cornet, 2022; Yao et al., 2018). Côte d'Ivoire is not spared from these major current climatic upheavals, which cause floods and droughts in forested areas. Indeed, according to Diawara et al., (2014), the major impacts on the country's ecosystems over the past three decades are due to changing temperatures, altered rainfall patterns, longer drought periods, and increased evapotranspiration. If no action is taken to mitigate these effects, the country will face the combined impact of rising temperatures (+2 degrees Celsius), varying rainfall (-9% in May and +9% in October), and rising sea levels (30 cm) by 2050 (World Bank, 2018). In this context, various studies have been conducted in Côte d’Ivoire. These have focused on the combined effect of climate variability and anthropogenic activities on land (Ogbuji and Adejuwon, 2006), on biodiversity and ecosystem services (Achieng et al., 2016), and on ecosystems in Africa (Al Hamndou & Requier-Desjardins, 2008; Omotoso et al., 2023). Furthermore, this study was initiated to assess specifically the impact of climate variability on the spatio-temporal dynamics of the Badenou Classified Forest. Indeed, due to its location in northern Côte d'Ivoire, this classified forest is likely to be influenced by various climatic variables. Similarly, research in the sub-Sudanian sector has revealed changes in drought periods, which have become increasingly longer over recent decades. Based on this observation, it is necessary to conduct a study to understand the contribution of climate variability to land use dynamics in order to anticipate and plan current and future measures. To this end, remote sensing proves to be the ideal methodology, enabling a reliable classification and description of land use and land cover, to which climatic variables can be correlated. This study will rely on satellite data and field surveys to quantify variations in forest cover and to understand biodiversity dynamics in response to climate variability. The objective of this study is to analyse the influences of climate variability on the spatio-temporal dynamics of the Badenou Classified Forest over a period of more than three decades. (1) We will examine how the Badenou Classified Forest adapts to climate change; this can provide valuable information for long-term conservation management and adaptation strategies in the face of future climate scenarios. (2) To understand the ecosystem dynamics of the Badenou Classified Forest, which can contribute to guiding sustainable development policies by integrating ecological considerations into regional and national planning. II. MATERIALS AND METHODS A. Study Area The Badenou Classified Forest is located 30 km from Korhogo and covers 26,980 hectares. The forest stands like a verdant oasis in the heart of the Ivorian savannah. The GPS coordinates are 9° 41' 63" to 9° 51' 63" North latitude and 5° 32' 06" West longitude (Figure 1). It was established by Decree N°3499/SE/5 on 29 November 1937, and its management is entrusted to SODEFOR. The forest lies within a Sudano-Guinean climate, characterised by two distinct seasons, which is typical of the subSudanian phytogeographical zone (Guillaumet and Adjanohoun, 1971). The mean annual temperatures range between 26.07°C and 28.60°C, with peak heat in February-March (reaching up to 29°C) and cooler periods in August (dropping to 24°C). Precipitation, on the other hand, mean 1178 mm per year, shaping a landscape where lush vegetation thrives. Under the influence of this generous climate, the Badenou Classified Forest is home to a fascinating mosaic of vegetation landscapes. Gallery forests, dry dense forests, open forests, wooded savannahs, tree savannahs and shrub savannahs coexist, creating a rich and valuable biodiversity. This diversity of habitats attracts abundant and varied fauna. Numerous mammals, birds, reptiles and amphibians inhabit the forest, contributing to the fragile balance of this unique ecosystem. Furthermore, it contains several rivers, including the Vaka, Badenou, Kodjalogo, Loua, Nafounloho and the Bandaman. It derives its name from the Badenou River which flows through its central part. The Badenou Classified Forest, with its natural beauty and ecological richness, constitutes an invaluable treasure for Côte d'Ivoire. Its protection and sustainable management are essential to preserve this unique natural heritage for future generations. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 11 November 2025 DOI: 10.47191/ijcsrr/V8-i11-28, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5718 *Corresponding Author: KOUASSI N'Zibla Roch-Ghislaine Volume 08 Issue 11 November 2025 Available at: www.ijcsrr.org Page No. 5716-5731 Figure 1. Geographical location of the Badenou Classified Forest B. Cartographic and Climatic Data The cartographic data consist of the digital contour of the study area, which is the Badenou Classified Forest, and various other layers of vectors (road network, localities, hydrographic network, and administrative division) extracted from the BNETD database. Regarding the satellite images, they were downloaded from the United States Geological Survey (USGS) website (http://earthexplorer.usgs.gov/). The satellite images used date from the 1990s, 2002, 2012, and 2022. An average interval of 10 years was chosen between the dates, as this is the minimum duration for perceiving changes in vegetation. They are derived from scene 197-53 for Badenou, and from sensors (Landsat TM for the year 1990, Landsat ETM+ for the years 2002 and 2012, and Landsat OLI for the year 2022 (Table 1). These images date from the period of the major dry season, when the cloud cover and cloudiness rates are the lowest (Chatelain, 1996). Furthermore, they were acquired during the same period to reduce issues related to solar angles, phenological changes in vegetation, and differences in soil moisture. Moreover, data relating to climatic parameters such as rainfall (monthly and annual) and maximum, minimum and mean temperatures (Tmax, Tmin, Tmean) were collected at the Korhogo weather station for those that were available. Missing data were collected from the Climate Engine website (ClimateEngine.org). Evaluation of the dynamics of climatic variables from 1990 to 2022 in the different study areas. The climatic variables considered in this study are precipitation (monthly and annual), maximum, minimum, and mean temperatures (Tmax, Tmin, Tmean) and drought indices such as the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI). These parameters are generally considered as components of the climate and environment that most influence the behaviour of forest fires and the dynamics of vegetation (Guiguindibaye et al., 2013; Ago, 2016; Vissin, 2007). Indeed, temperature is considered one of the main factors influencing the rate of plant development. Higher temperatures predicted by climate change and the risk of more extreme thermal events will impact plant productivity. According to Hatfield and Prueger (2015), the latter dries them out and weakens them in the face of water stress. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 11 November 2025 DOI: 10.47191/ijcsrr/V8-i11-28, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5719 *Corresponding Author: KOUASSI N'Zibla Roch-Ghislaine Volume 08 Issue 11 November 2025 Available at: www.ijcsrr.org Page No. 5716-5731 Table 1. Landsat image data from 1990 to 2022 used for the Land use/cover (LULC) dynamics of Badenou Classified Forest (BCF) YEARS 1990 2002 2012 2022 Categories Landsat 4 TM Landsat 7 ETM+ Landsat 7 ETM+ Landsat 9 OLI/TIR Acquisition date 29/12/1990 22/12/2002 16/01/2012 13/12/2022 Path/Row 197/53 197/53 197/53 197/53 C. Methods Assessment of Climatic Variables dynamics from 1990 to 2022 To assess the potential climatic drivers of land use and land cover change in the Badenou Classified Forest, a temporal analysis of key meteorological variables was conducted for the period 1990–2022. This analysis focused on temperature regimes, precipitation patterns, and derived drought indices to characterise the climatic stresses acting on the forest ecosystem. Temperature and precipitation regimes Time-series data for maximum, minimum, and mean monthly temperatures, alongside total monthly precipitation, were acquired for the study area. A descriptive statistical analysis was performed to quantify interannual variability and identify significant long-term trends. For temperature, linear regression models were applied to the annual time series to determine the rate of warming over the 33 years. Precipitation data were aggregated annually and seasonally to evaluate fluctuations in total rainfall and its intra-annual distribution. Drought Indices Calculation To move beyond raw precipitation and capture periods of hydrological deficit critical to vegetation health, two established drought indices were computed: Standardised Precipitation Index (SPI) The SPI was calculated to characterise meteorological drought. This index quantifies precipitation anomalies at multiple timescales (McKee et al., 1993). This allows for the classification of conditions into discrete categories of wetness and dryness (Bergaoui and Alouini, 2001). The SPI's utility lies in its ability to directly link precipitation deficits a primary driver of vegetation stress to potential changes in forest cover dynamics (McKee et al., 1993). The interpretation of the SPI calculation results was made based on the SPI classes and their degree of drought or humidity (Table 2). Negative SPI values correspond to a dry year, while positive values indicate wet years. The SPI was evaluated using the following equation: SPI = Rainfall Index for year i; Pi = the total rainfall for year i; Pmean = average annual rainfall observed over the entire series; σ = Standard deviation of the annual rainfall observed for a given series. Table 2. Classification of drought according to SPI values (Mckee, Doesken and Kleist, 1993). SPI Classes SPI˃2 1.5˂SPI˂1.99 1.0˂SPI˂1.49 -0.99 ˂SPI˂0.99 -1˂SPI˂- 1.49 - 1.5˂SPI˂- 1.99 SPI˂-1.99 Level of drought or humidity Extreme humidity (IL) High humidity (WH) Moderate humidity (WM) Near to the normal Moderate drought (DM) High drought (DH) Extreme drought (DE) Palmer Drought Severity Index (PDSI) To provide a more comprehensive assessment of soil moisture availability, the Palmer Drought Severity Index was also calculated. Unlike the SPI, which is based solely on precipitation, the PDSI incorporates a simplified water balance model that accounts for temperature-influenced evapotranspiration and soil water recharge. Thus, it is classified as a meteorological drought index and quantifies the departure of water from the soil surface (Svoboda and Fuchs, 2016). SPI = 𝑷𝒊−𝑷𝒎𝒆𝒂𝒏 𝝈 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 11 November 2025 DOI: 10.47191/ijcsrr/V8-i11-28, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5720 *Corresponding Author: KOUASSI N'Zibla Roch-Ghislaine Volume 08 Issue 11 November 2025 Available at: www.ijcsrr.org Page No. 5716-5731 This makes it particularly relevant for assessing ecological and agricultural drought, as it more directly reflects the moisture stress experienced by vegetation. The standardised measure of the PDSI (Table 3), ranges from −4 (dry) to +4 (wet), with values below −3 representing severe to extreme drought (Palmer, 1965). Descriptive and trend analyses were evaluated in the same manner monthly over the period (1990-2022) through time series data. The PDSI can be formulated according to the following equation: X(i) is the PDSI result for the i-th month, z(i) is the moisture anomaly index for the i-th month, X(i-1) is the PDSI amount for the previous month, α and β are the climatic coefficients of the PDSI. Table 3. PDSI categorisation of drought severity (Palmer, 1965) PDSI Values Drought Categories 4.00 ou more Extremely wet 3.00 to 3.99 Very wet 2.00 to 2.99 Moderately wet 1.00 to 1.99 Slightly wet 0.50 to 0.99 Beginning of a wet period 0.49 to -0.49 Close to normal -0.50 to -0.99 Beginning of drought -1.00 to -1.99 Slightly drought -2.00 to -2.99 Moderate drought D. Analysis of the spatiotemporal dynamics of Land Use/Cover Pre-processing through radiometric and atmospheric correction made it possible to correct certain data errors caused by the time lag during image acquisition and extraction of the study area. These corrections provided clear images for calculating indices such as NDVI, Tasseled cap and PCA, and for applying colour compositions (Table 4). The colour composition allows the establishment of the 327 training plots. These plots were carried out through the identification of the different land use/cover classes. Each plot was assigned a label corresponding to the class to which it belongs (Aka et al., 2022). The classes of land use types in Badenou Classified Forest are Gallery Forest, Dense dry forest, Open Forest/Wooded savanna, Tree savanna/Shrub savanna, Low dense shrub savanna, Fallow land, Perennial crop, Bare soil/Rock outcrop/Agricultural development and water body. Among the classification algorithms, maximum likelihood has been used. This method consists of searching for objects similar to reference objects (Journaux, 2006). The classifications were first carried out based on the training points that guided the choice of regions of interest (ROIs) for the land cover classes produced on the most recent Landsat 9 OLI/TIRS images from 2022. Then the Assessment of the mapping result was possible using the confusion matrix. The classifications obtained in raster format were exported to ArcGIS 10.8 software for conversion to vector format. This stage was followed by the production of statistics and cartographic editing. The statistical analyses focused on calculating the area of Land Use/Cover of each classes. Table 4. Bands used for the colour compositions in the Badenou Classified Forest Years 1990 2002 2012 2022 Colour composition (bands) 4/ 7/ 3 4/ 7/ 3 4/ 5/ 3 5/ 7/ 4 X(i) = 𝚭(𝒊) 𝜶+𝜷𝑿(𝒊−𝟏) International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 11 November 2025 DOI: 10.47191/ijcsrr/V8-i11-28, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5721 *Corresponding Author: KOUASSI N'Zibla Roch-Ghislaine Volume 08 Issue 11 November 2025 Available at: www.ijcsrr.org Page No. 5716-5731 E. Analysis of the Influence of Climatic Variability on Land Use/Cover Dynamics A Spearman’s rank order correlation analysis was conducted to evaluate the influence of key climatic parameter specifically minimum, mean, and maximum temperature, total annual precipitation, the Palmer Drought Severity Index (PDSI), and the Standardized Precipitation Index (SPI) on the dynamics of Land Use Land Cover (LULC) within the Badenou Classified Forest. The response variables consisted of the areal extent of each LULC class across three distinct transition periods (1990-2002, 20022012, and 2012-2022). This non-parametric method was selected due to its suitability for capturing monotonic, potentially nonlinear relationships without assuming normality in the data, and for its robustness to outliers (Sokal and Rohlf, 2012). These characteristics are particularly advantageous in ecological studies, where threshold driven responses and non-normal data distributions are common. The resulting correlation matrix identifies significant monotonic associations between climatic variability and changes in LULC areas, providing insight into the potential climatic drivers of observed landscape transformations. III. RESULTS A. Climate variability of the Badenou Classified Forest Temperature fluctuation By analysing the temperatures within BCF, it is observed that the mean, minimum, and maximum temperatures display varied but relatively similar trends over the studied period (Figure. 2). The mean annual temperatures between 1990 and 2022 range from 26.07°C (1992) to 28.60°C (2021), corresponding to an overall increase of 2.5°C over the 30 years. The year 2021 recorded the highest temperature in the series, while the years 1992 and 2012 showed values below the mean. The analysis of minimum temperatures from 1990 to 2022 reveals significant interannual variability, with fluctuations around an average of 20.89°C. The lowest values were recorded in 1992. Maximum temperatures from 1990 to 2022 show marked interannual variability, with notable fluctuations over time. The average maximum temperature over this period is 35.69°C, indicated by a reference line on the graph. Dynamics of Precipitation The analysis of annual precipitation between 1990 and 2022, highlights significant interannual variability in the BCF region (Figure. 3). The recorded values range from 888.8 mm in 2015, the driest year, to 1410.9 mm in 2003, which represents one of the highest rainfall peaks. The mean precipitation over the entire studied period is 1178 mm, with a standard deviation of 111.8 mm, 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Temperature (°C) Years Minimum Temperature (° C) Mean Temperature (°C) Maximum Temperature (°C) Figure 2. Trend curves of temperature variables in the Badenou Classified Forest from 1990 to 2022 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 11 November 2025 DOI: 10.47191/ijcsrr/V8-i11-28, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5722 *Corresponding Author: KOUASSI N'Zibla Roch-Ghislaine Volume 08 Issue 11 November 2025 Available at: www.ijcsrr.org Page No. 5716-5731 indicating a humid tropical climate. Some years stand out for particularly low levels of precipitation, such as 2015 and 2017, with 888.8 mm and 976.3 mm, respectively. Conversely, the years 2003 and 2018 recorded precipitation levels significantly above the mean, reaching 1410.9 mm and 1334.6 mm, respectively. These fluctuations reflect an alternation between periods of high humidity and episodes of water deficit. Over the entire 32 years analysed, no clear trend of increasing or decreasing precipitation appears. The standardised precipitation index (SPI) Figure 4. Curve of precipitation fluctuations in the Badenou Classified Forest from 1990 to 2022 The standardised precipitation index (SPI) values at BCF fluctuate regularly, illustrating climatic cycles where some years are characterised by a water deficit while others record a precipitation surplus (Figure. 4). The years 2005 (SPI = -1.33, moderate drought), 2015 (SPI = -2.59, extreme drought), and 2017 (SPI = -1.80, severe drought) are distinguished by strongly negative indices, indicating periods of marked drought. While 2000 (SPI = 1.48, moderate humidity), 2003 (SPI = 2.08, extreme humidity), 2018 0 200 400 600 800 1000 1200 1400 1600 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Precipitation (mm) Years -3 -2 -1 0 1 2 3 1990 1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 SPI Years SPISPI+ Figure 3. Trend curve of the SPI in the Badenou Classified Forest from 1990 to 2022 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 11 November 2025 DOI: 10.47191/ijcsrr/V8-i11-28, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5723 *Corresponding Author: KOUASSI N'Zibla Roch-Ghislaine Volume 08 Issue 11 November 2025 Available at: www.ijcsrr.org Page No. 5716-5731 (SPI = 1.40, moderate humidity) and 2022 (SPI = 1.08, moderate humidity) show distinctly positive values, indicating episodes of excessive precipitation. The distribution of extreme values shows an intensification of rainfall variability after the 2000s, with more pronounced alternations between droughts and water surpluses. Palmer Drought Severity Index (PDSI) The evolution of the Palmer Drought Severity Index (PDSI) at BCF between 1990 and 2022, highlights significant fluctuations between periods of drought and humidity. Before 2015, the values oscillated around zero, with a relatively balanced alternation between drier and wetter phases (Figure. 5). However, the PDSI classification table (Table V) highlights a predominance of drought periods, representing 63.63% of the years studied, with a notable distribution between mild droughts (18.18%), moderate droughts (18.18%), severe droughts (15.15%) and extreme droughts (12.12%). Table 5. Distribution of years of study according to PDSI categories at BCF PDSI Categorie s Light Humidity Beginning of Humid Period Near Normal Beginning of Drought Mild Drought Moderate Drought Severe Drought Extrem e Drough t Light Humidit y Years 1991, 1995, 2010 2019 1994, 1996, 2001, 2003, 2004, 2005, 2008 2011 2002, 2007, 2009, 2014, 2015, 2018 1990, 1993, 1999, 2000, 2006, 2012 1992, 1997, 2013, 2017, 2020 1998, 2016, 2021, 2022 1991, 1995, 2010 Number of Years 3 1 7 1 6 6 5 4 3 Proportio n (%) 9.09 3.03 21.21 3.03 18.18 18.18 15.15 12.12 9.09 Figure 5. Trend curve of the PDSI in the Badenou Classified Forest from 1990 to 202 -4 -3 -2 -1 0 1 2 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 PDSI Years PDSIPDSI+ International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 11 November 2025 DOI: 10.47191/ijcsrr/V8-i11-28, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5724 *Corresponding Author: KOUASSI N'Zibla Roch-Ghislaine Volume 08 Issue 11 November 2025 Available at: www.ijcsrr.org Page No. 5716-5731 B. Dynamics of Land Use Land Cover in the Badenou Classified Forest The mapping of the Land Use Land Cover (LULC) of the vegetation at BCF discriminated 8 classes for the years 1990, 2002, 2012 and 2022 (Figure. 7). These are gallery forest/dense dry forest (GF/DSF), open forest/wooded savannah (OF/WS), tree savannah/shrub savannah (TS/SS), sparsely populated shrub savannah (LDSS), fallow (FL), perennial crop (PC), bare land/rock outcrop/ Agricultural development (BL/RO/AD) and water bodies (WB). The overall accuracies of the various classifications for the 1990, 2002, 2012 and 2022 images are 95.98%, 95.60%, 92.65% and 94.39% respectively for Landsat TM, 7 ETM, 7 ETM and OLI-TIRS. The Kappa coefficients are valued at 0.93; 0.91; 0.87 and 0.87 respectively for images from 1990, 2002, 2012 and 2022. The spectra of proportions (%) and areas in hectares of land use/cover in the BCF vary from year to year (Table 6). In 1990, the BCF was characterised by a strong predominance of tree savannah/shrub savannah, occupying 43% or 14121.73 ha of the total area (Figure. 8). In 2002, the landscape was still characterised by a predominance of savannah environments, with the tree savannah/shrub savannah class covering 42% equivalent to 13641.77 ha of the total area. At the same time, the forest cover shows a notable reorganisation. In 2012, a significant change in vegetation cover was observed, with a marked increase in the tree savannah/shrub savannah class, covering 51% or 1,686.12 ha of the total area. gallery forests/dense dry forests continue to decline, now accounting for 13%, while open forests/wooded savannahs have increased significantly to 21%. In 2022, the landscape is characterised by the marked predominance of tree savannah/shrub savannah, which occupies more than 53%, equivalent to 17555.26 ha of the study area. In terms of gallery forest/dense dry forest, we note a slight increase, accounting for around 19% of the surface area, while open forest/wooded savannah fell from 21% to 6%. Figure 7. LULC classification map of BCF in 1990, 2002, 2012 and 2022 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 11 November 2025 DOI: 10.47191/ijcsrr/V8-i11-28, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5731 *Corresponding Author: KOUASSI N'Zibla Roch-Ghislaine Volume 08 Issue 11 November 2025 Available at: www.ijcsrr.org Page No. 5716-5731 25. PNUE/UNEP. (2020). Emissions Gap Report. Disponible à : Emissions Gap Report 2020 26. Sighomnou D. (2004). Analysis and redefinition of Cameroon's climatic and hydrological regimes: prospects for the evolution of water resources. Doctoral thesis, University of Yaoundé 1, Cameroon, 279 p. 27. Sintayehu, D. W. (2018). Impact of climate change on biodiversity and associated key ecosystem services in Africa: A systematic review. Ecosystem Health and Sustainability, 4(9), 225-239. https://doi.org/10.1080/20964129.2018.1530054 28. Sokal, R.R., Oden, N.L. & Thomson, B.A., (2012). A Problem with Synthetic Maps. Human Biology, vol. 84, no. 5, pp. 609–621. doi:10.1353/hub.2012.a503922. 29. Svoboda, M.D. & Fuchs, B.A., (2016). Handbook of Drought Indicators and Indices, vol. 2. Geneva, Switzerland: World Meteorological Organization. 53 p.https://digitalcommons.unl.edu/droughtfacpub/117/ 30. Timité, N., Koua, K. A. N., Kouakou, A. T. M., & Barima, Y. S. S., (2023). Spatio-temporal dynamics of agroforestry parks in the Sudanian zone of Côte d’Ivoire from 1990 to 2020 in a context of cashew expansion. International Journal of Biological and Chemical Sciences, 17(2), 484-504. 31. United Nations Environment Programme. (2022). Spreading like wildfire – The rising threat of extraordinary landscape fires. A UNEP Rapid Response Assessment. https://www.unep.org/resources/report/spreading-wildfirerising-threatextraordinary-andscape-fires. 32. Vissin, E. (2007) Impact de la variabilité climatique et de la dynamique des états de surface sur les écoulements du bassin béninois du fleuve Niger. Thèse Doctorale, Université de Bourgogne, Spécialité : Hydro climatologie, Bourgogne, 311 p.https://theses.hal.science/tel-00456097 33. World Bank. (2021). République de Côte d’Ivoire 2021-2030 - Maintenir une croissance élevée, inclusive et résiliente après la COVID–19: Une contribution du Groupe de la Banque mondiale à la Stratégie de developpement à l’horizon 2030. © World Bank. http://hdl.handle.net/10986/36454 License: CC BY 3.0 IGO.” 34. Yao N., O., (2019). Dynamics and ecological value of vegetation in the sub-Sudanese sector; case of the department. Doctoral thesis, Laboratory of Botany, Félix Houphouët-Boigny University, Cocody - Abidjan, Côte d’Ivoire, 229 p. 35. Yao, F. Z., Reynard, E., Ouattara, I., N'go, Y. A., Fallot, J.-M., & Savané, I. (2018). A new statistical approach to assess climate variability in the White Bandama watershed, Northern Côte d’Ivoire. Atmospheric and Climate Sciences, 8(4), 402-423. https://doi.org/10.4236/acs.2018.84027 Cite this Article: KOUASSI N'Zibla Roch-Ghislaine, KOUAKOU Amani Abell Mike, SILUÉ Pagadjovongo Adama, NANAN Kouassi Kouman Noël, YAO N'Guessan Olivier, Bohoussou Cristel Natacha (2025). Influence of Climate Variability on The Dynamics of Land Use Land Cover in the Sub-Soudanian Sector: The Case of the Badenou Classified Forest, Northern Côte D'ivoire. International Journal of Current Science Research and Review, 8(11), pp. 5716-5731. DOI: https://doi.org/10.47191/ijcsrr/V8-i11-28