Contribution of Remote Sensing in The Study of The Spatio-Temporal Dynamics of Classified Forests: Case of The Classified Forest of Irobo (Southern Ivory Coast)

International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5013 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 Contribution of Remote Sensing in The Study of The Spatio-Temporal Dynamics of Classified Forests: Case of The Classified Forest of Irobo (Southern Ivory Coast) ATSÉ Williams Ghislain Haudy Yapi1, DIBI N’Da Hyppolite2,3, NANAN Kouassi Kouman Noël4, GBODJINOU Yéhounko Bruno Buffon5, BOHOUSSOU Crystel Natacha3 1Doctoral School, Sustainable Agriculture Science and Technology of Felix HouphouëtBoigny University, Ivory Coast. 2Center for Research and Application in Remote Sensing (CURAT), Université Félix Houphouët-Boigny, 22 BP 582 Abidjan 22, Côte d’Ivoire 4. 3Laboratory of Natural Environments and Biodiversity Conservation Abidjan, UFR Biosciences, Félix-Houphouët University 22 BP 582 Abidjan Côte d’Ivoire 4Departement of Environmental Sciences, UFR Governance and Sustainable Development of the University of Bondoukou, Ivory Coast, Côte d’Ivoire. 5Laboratory of Geosciences, Environment, and Applications (LaGEA), National University of Science, Technology, Engineering, and Mathematics (UNSTIM) of Abomey, Benin ABSTRACT: This study was carried out in the south of the Ivory Coast as part of our master's thesis. This study aims to highlight the improvement in knowledge on the phenomenon of degradation in the Irobo classified forest and to provide managers with essential elements for the establishment of a sustainable forest management policy. Concretely, it was a question of (1) characterizing the different types of land use of the Irobo classified forest, (2) mapping the vegetation cover of the classified forest of Irobo from the Landsat images of 1988, 2005 and 2020, (3) evaluating the forest dynamics between 1988 and 2020. To this end, the characterization of the types of land use, the mapping of the dynamics and the evaluation of the forest dynamics between 1988 and 2020 were carried out using cartographic methods on the one hand and area calculations on the other. The results indicate that there are nine types of land use. These are forests, reforestation plots, perennial crops, annual crops, fallows, bare soils and habitats. Regarding the assessment of forest dynamics, it appears that forest cover has lost 11,993.3 ha between 1988 and 2020, which means a decrease of 2.07% per year in favour of agricultural holdings (26,838.9 ha in 2020). KEYWORDS: Classified forest, Forest dynamics, Landsat image, Remote sensing, South Cote d'Ivoire, Irobo. I. INTRODUCTION Ensuring the protection of forests, in a context of development backed by the exploitation of natural resources, presents itself as one of the most important challenges of this century. Indeed, despite their importance, forests are continually cut down and degraded. For example, between 1990 and 2015 there was a net loss of approximately 129 million hectares of forest worldwide (FAO, 2015). It is estimated that approximately 7.6 million hectares of forest have disappeared every year since 2010. Furthermore, the greatest losses of forest areas are in the tropics, particularly in South America and Africa (Lewis, 2006). The causes of deforestation are multiple. Shifting cultivation, fuelwood collection, mining, logging, and infrastructure development are the direct causes in tropical areas (Margono et al., 2012). Deforestation and forest degradation in Côte d'Ivoire are very alarming. Since its independence, the country has based its development on agriculture. With the support of the State, «The success of this country is based on agriculture, «thousands of hectares of forest have vanished in favor of agriculture and forestry. Thus, the cultivated areas , which were approximately 3.0 million hectares in 1970, ells increased to 7.5 million hectares in 1990 (SODEFOR, 2000). It is estimated that they are more than 12 million hectares today. Extensive slash-and-burn agriculture and poaching lead to bushfires which have also devastated large areas of forest. Thus, estimated at 16 million hectares at the end of the 19th century, the Ivorian forest is currently estimated at 3.4 million hectares (MINEF, 2018). The forest resources of Côte d'Ivoire are International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5014 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 thus subject to strong anthropic pressure, leading to the reduction of their areas and their fragmentation. Also, the diversity and variability of ecosystems, the quantity and quality of available forest resources which constitute considerable potential for the wellbeing of populations and future generations are reduced every day (Aké - Assi , ; Cissé et al. 2020,1998 ) . Unbridled deforestation, unhindered by any regulation, now constitutes a threat to biological resources and to populations (Serageldin, 1993). In order to promote the sustainability of forest resources and better conserve their biological diversity, Côte d'Ivoire has undertaken, since the colonial period, the creation of a network of protected areas and classified forests (Koné et al., 2012). These areas cover 6,267,730 hectares, or 19% of national territory. These include 234 classified forests, eight (8) national parks and five (5) nature reserves (IUCN/BRAO, 2008). Classified forests represent areas capable of preserving and supplying the forestry industry with the main species, but also of being sources of supply of harvested products for local populations (Zoro Bi and Kouakou, 2004). However, the various forest massifs continue to suffer from anthropogenic attacks. As a result, most of the Ivorian classified forests are now classified only in name, although they now; pall house not only large cocoa plantations, but also camps and villages (Traoré, 2018). Current deforestation hotspots are located in classified forests where the annual deforestation rate was 3 % over the period 1990-2000 and 4.2% over the period 2000-2015 (Koné, 2015). There remained 844,938 hectares of classified forests in 2015 compared to 1,585,626 hectares in 2000 and 2,129,729 hectares in 1990 ( Koné , 2015). The Irobo classified forest is not left out of this deforestation. Unfortunately, reliable scientific data on the spatio-temporal dynamics of classified forests are quite rare. Several studies have been conducted using remote sensing by different authors such as N'Guessan (2018) and N'Guessan and N'Da (2005) but very few studies have been conducted using remote sensing tools in the Irobo classified forest. It is in this context that this study entitled " Contribution of remote sensing in the study of the spatio -temporal dynamics of classified forests: case of the classified forest of Irobo au South of Ivory Coast » was initiated. It aims to improve knowledge on the plant dynamics of the Irobo classified forest in relation to human actions, through the use of satellite images. It specifically involves characterizing and mapping the different types of land use in the Irobo classified forest and evaluating forest dynamics between 1988 and 2020. A. Description of the study area Created by decree No. 996 of September 29, 1962 of the Water and Forests Service and the Ministry of Agriculture, the Irobo massif is made up of the classified forests of Méné, Bakanou, Cosrou and Bandama. Located in the forested south of Ivory Coast , with an area of approximately 42,000 ha ; it is 25 km from Sikensi, 67 km from Dabou , 80 km from Tiassal é and 105 km from Abidjan . The classified forest of Irobo or Irobo-Méné massif is positioned between the following geographical coordinates: 4 ° 40 ' and 4 ° 50' West longitude and 5 ° 25' and 5 ° 48' North latitude (Zobi and Chessel, 2007). It straddles four departments and two administrative regions: Agné by -Tiassa and Grands Ponts (Figure 1). The FCI is located in the Guinean rainforest sector, with a sub-equatorial climate, hot and humid all year round. Average annual temperatures vary between 26 and 27° C. ( Eldin, 1971 ). The Irobo massif is made up of a typical dense evergreen forest. The overall impression is that of a hygrophilous forest. The vegetation which was a virgin forest (Mangenot, 1955) at Diopyros mannii, at Diospyros spp.,and to Eresmospatha macrocarpa (Guillaumet and Adjanohoun, 1971) is today dominated, on the one hand, by plantations of cacaoyers, deoil palms and other cash crops as well as by fields of various food crops and, on the other hand, by secondary forestswith Musanga cecropioïdes ( parasolier) or bushy stands at Chromolaena odorata. According to the 2014 General Population and Housing Census (RGPH, 2014), the Agnéby -Tiassa region has 606,852 inhabitants, divided between 320,713 men and 286,139 women, for an average annual population growth rate estimated at 1.29%. The population is made up of the Abidji, the Abbey, the Agni, the Adioukrou, the Avikam, the Baoulé , the Dida, mixed with populations from neighboring countries (Burkina Faso, Mali, Guinea). Agriculture, the basis of the country's economy, is the main activity of the local population. Cash crops are mainly cocoa (Theobroma cacao), coffee, (Coffea sp.)and oil palm. (Elaeis guineensis), l’hévéa (Hevea brasiliensis) and coconut (Cocos nucifera). Banana, cassava, corn, rice, taro and yam constitute the staple foods of these populations (Atta et al., 2016). International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5015 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 Figure 1. Geographic location of the study area II. MATERIALS AND METHODS B. Material The equipment used in this study consists of technical equipment and remote sensing data. The technical equipment consists mainly of a Garmin GPS receiver (Map64) for recording the coordinates of the different land use types , a digital camera for taking pictures, descriptive sheets for the different land use types, pairs of boots, machetes and a field vehicle . The optical images used in this study cover les images du satellite Landsat quithe 196-56 scene. Les images sont issues des capteurs Landsat Thematic Mapper (TM4) pour l’image from 12/24/1988, Enhanced Thematic Mapper Plus (ETM+7) pour cellefrom 01/05/2005 and Operational Land Imager (OLI 8) pour l’image acquise lefrom 01/07/2020. They are all available free of charge on the United States Geological Survey (USGS) website via the link https://earthexplorer.usgs.gov/. It should be remembered that vector files of the Irobo classified forest which were made available by SODEFOR made it possible to extract the study area and carry out the cartographic drafting. Regarding software, ENVI 5.3 has was used for preprocessing and processing of satellite images , ArcGIS 10.8 for vectorization and geoprocessing and cartographic restitution as well as Microsoft Excel 2016 for calculations (statistical analyses) and creation of graphics. Methods The methodology adopted in this study combined satellite image processing and field data collection techniques. Image pre - processing began with radiometric correction to correct the effects of various artifacts that disrupt radiometric measurement, including sensor defects and atmospheric haze. Then, one atmospheric correction has been applied to improve image readability. To finalize the pre-processing, we extracted our study area using the shapefile of the contour of the classified forest. Image processing involves the calculation of biophysical indices enabling the overall physical and biological characteristics of the vegetation to be based on the indices :highlighted. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5016 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 - The Standardized Vegetation Index or Normalized Difference Vegetation Index (NDVI) NDVI = (PIR – R) / (PIR + R) - The Brightness Index BI = (R 2 + PIR 2) 1/2 - The Normalized Difference Wetness Index NDWI = (PIR – MIR) / (PIR + MIR) These three indices allowed us to discriminate respectively dense vegetation (forest, reforestation) from those which are not very dense (annual agricultural exploitation, and young fallow) and bare soils (agricultural clearing and habitats); to subdivide plant formations according to their level of ground cover, therefore their density, and to separate the most humid plant formations from those which (N’Da et al., 2008).are the least humid Colored compositions using raw strips (OLI 5-6-4 and OLI 5-6-7) made it possible to discriminate between different types of land use. To refine the treatments, a Principal Component Analysis (PCA) 1-3-2) was performed on the raw tapes to improve the visual quality of the raw tapes and to maximize the information on the first 3 tapes. These treatments allowed: - discrimination between different types of land use, - the selection of sites to visit and orientation on the ground, - the choice of training plots for classification . Based on the discriminated land use and the integration of certain data such as localities, tracks, bodies of water, the limits of the Classified Forest, 140 points to visit were identified. The geographic coordinates of the points were taken and integrated into the GPS. The information collected in the field also made it possible to finalize the digital processing. Indeed, Maximum likelihood-guided classification, which involves identifying spectrally similar areas based on known training sites to extrapolate these signatures to unknown areas, was used to produce the maps. For the 2020 image, the classification was validated using confusion matrices to assess overall accuracy and the Kappa coefficient. A 3x3 median filter was then applied to eliminate isolated pixels and reduce heterogeneity. The classification results were converted from raster to vector format using ArcGIS software. This classification is a procedure for identifying spectrally similar areas on an image by identifying “training” sites of known targets and then extrapolating these spectral signatures to other domains of unknown targets. The validation of the 2020 image classification was carried out by the production and analysis of confusion matrices, to evaluate the overall performance level of the processing, but also of the land use classes through the overall precision and the Kappa coefficient. Once the classification is validated by the different performance tests above , a 3x3 median filter makes it possible to reduce intra-class heterogeneity by eliminating isolated pixels . The classification results obtained in raster format are exported to ArcGIS software for conversion to vector format. La même technique a été utilisée pour les images de 1988 et 2005, avec quelques adaptations, à savoir la composition colorée sur les bandes 4/5/7 pour les capteurs TM et ETM+, et l’utilisation sélective des données de terrain de 2020, en ne retenant que les points invariants (les zones restées stables sur toute la période d'étude). The assessment of changes occurring over the entire study period was made by producing and analyzing transition matrices between maps of two dates (Girard and Girard, 1999). This assessment, which was carried out using ArcGIS 10.8 software, was used to analyze different types of land use between 1988 and 2005, between 2005 and 2020 and between 1988 and 2020. The overall rate of change is obtained from the following mathematical formula: Tg = [(S₂ -S₁ ) / S₁ ] x 100 Where: Tg = Overall rate of change (%) S ₁ = Area of the class at date t ₁ (initial date); S ₂ = Area of the class at date t ₂ (final date), and t ₂ ˃ t ₁ International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5017 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 III. RESULTS AND DISCUSSION C. Results Different types of land use in the Irobo classified forest The mission carried out in the Irobo classified forest allowed us to identify nine (9) types of land use which are: areas of deforestation; secondary forests; perennial cocoa crops, annual crop mosaics young cocoa; perennial crops (rubber and oil palm); degraded forest mosaics /old fallow; young fallow; young perennial crops (rubber and oil palm) and bare soils and habitats. Map of vegetation cover in the Irobo classified forest Mapping performance The overall accuracies of the different classifications for the 1988, 2005 and 2020 images are respectively 91.01%; 87.26% and 88.55% (Tables 1, 2 et 3). The most significant confusions, for these classifications, are observed between the classes of relatively well-preserved secondary forests and reforestation. Other confusions, although minor, were observed between the young perennial rubber / oil palm crop class and the young perennial cocoa crop class, then the bare soil / Habitats class and the young perennial cocoa crop, young perennial rubber / oil palm crop and young fallow land classes. Except for these cases, all other land use types are relatively well discriminated. Table 1 : Confusion matrix of 1988 Landsat TM image classification Overall accuracy = 91.0163%; Kappa coefficient = 0.8829 Classes REB FS CPC MCAJC CPHP MFDVJ JJAC JCPHP SNH REB 93.77 0.31 3.00 0.36 2.30 1.80 0.01 0.01 0 FS 0.24 98.81 4,84 0 0 0,14 0 0 0 CPC 2,32 0,69 86,92 2,27 0,15 0,07 0 0,21 0,04 MCAJC 1,13 0,02 0,70 85,24 0,46 0,02 0 0,26 1,04 CPHP 0,01 0 0 0 81,30 0 0 0 0,16 VJAC 2,10 0,16 3,24 0,42 5,80 97,81 0,01 0,08 0,18 JJAC 0,13 0 0,63 0,00 6,12 0,09 89,19 0 3,89 JCPHP 0,28 0 0 10,79 3,66 0,00 0 97,52 0,52 SNH 0.04 0.01 0.68 0.92 0.20 0.07 10.80 1.92 94.18 TOTAL 100 100 100 100 100 100 100 100 100 Table 2 : Confusion matrix of the classification of the 2005 Landsat ETM+ image Overall accuracy = 87.2688% ; Kappa coefficient = 0.8407 Classes REB FS CPC MCAJC CPHP MFDVJ JJAC JCPHP SNH REB 88,52 2,73 0,07 0,18 4,72 0,92 0,09 0,08 0,10 FS 0,98 91,67 0,56 0,17 0,40 2,06 0,01 0,03 0,44 CPC 1,54 0,31 93,25 1,84 0,10 1,36 1,84 1,12 0,50 MCAJC 0,85 0,58 5,29 96,39 3,91 5,85 4,32 12,67 5,27 CPHP 0,90 0,15 0,02 0,02 84,83 0,37 0 0,01 0 MFDVJ 7,13 4,56 0,25 0,61 4,53 88,38 0,00 0,15 0,17 JJAC 0,07 0 0,32 0,43 0,29 0,73 93,28 5,50 4,53 JCPHP 0,00 0 0,24 0,31 0,86 0,32 0,46 77,25 0,82 SNH 0,00 0 0,00 0,06 0,36 0,00 0 3,19 88,17 TOTAL 100 100 100 100 100 100 100 100 100 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5018 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 Table 3 : Confusion matrix of Landsat-8 image classification (2020) Overall accuracy = (801248/904785) 88.5567%; Kappa coefficient = 0.8633 Classes REB FS CPC MCAJC CPHP MFDVJ JJAC JCPHP SNH REB 88.52 2.73 0,07 0,18 4,72 0,92 0,09 0,08 0,10 FS 0,98 91,67 0,56 0,17 0,40 2,06 0,01 0,03 0,44 CPC 1,54 0,31 93,25 1,84 0,10 1,36 1,84 1,12 0,50 MCAJC 0,85 0,58 5,29 96,39 3,91 5,85 4,32 12,67 5,27 CPHP 0,90 0,15 0,02 0,02 84,83 0,37 0 0,01 0 MFDVJ 7,13 4,56 0,25 0,61 4,53 88,38 0,00 0,15 0,17 JJAC 0,07 0 0,32 0,43 0,29 0,73 93,28 5,50 4,53 JCPHP 0,00 0 0,24 0,31 0,86 0,32 0,46 77,25 0,82 SNH 0,00 0 0,00 0,06 0,36 0,00 0 3,19 88,17 TOTAL 100 100 100 100 100 100 100 100 100 REB = reforestation; FS = secondary forest; CPC = perennial cocoa crop, MCAJC = mosaic annual young cocoa crop; CPHP = perennial rubber and oil palm crop; MFDVJ = degraded forest mosaic old fallow; JJAC = young fallow; JCPHP = young perennial rubber and oil palm crop and SNH = bare soils and habitats. Updated map (2020) updated map of the study area was produced using the Landsat-8 image from 07-01-2020 . The (figure 2) distribution of these land use units is such that practically the entire southern area of the classified forest is covered with perennial crops, including rubber trees and oil palms. It can be seen that annual crops consisting mainly of cassava (Manihot esculenta) and banana trees (Musa sp.) are very few and are grown in association with cocoa trees. The latter are found almost everywhere in the study area. There has been reforestation almost everywhere in the FCI but this is more visible in the extreme west of the central part of the classified forest. The relatively well - preserved secondary forests are located along the main tracks running through the Irobo classified forest and scattered in the northern part of the latter in the form of forest islands. It is noted that the mosaics of degraded / old fallow forests are concentrated in the northwestern and southwestern parts of the classified forest. The largest areas are occupied by the classes "mosaic annual and young cocoa crops" (22%) and "perennial cocoa crops " (31%) (Tableau 4). Table 4: Areas (in ha) and proportions (in %) of land use classes from the Landsat-8 image (2020) CLASSES AREAS PROPORTIONS REB 2,596.00 6.19 FS 6,044.75 14.41 CPC 12,953.49 30.89 MCAJC 9,188.01 21.91 CPPH 3,958.10 9.44 MFDVJ 5,099.59 12.16 JJAC 1,323.54 3.16 JCPHP 739.30 1.76 SNH 37.08 0.09 41,939.86 100 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5019 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 A-Land use map of Irobo classified forest (2020) Figure 2: Updated land cover map and spatial distribution of FCI biotopes from the 2020 Landsat-8 image Historical maps (1988 and 2005) Historical maps of the study area were produced using Landsat TM images from 24-12-1988 and ETM+ from 05-01-2005. We obtained nine (9) land use classes with the images from 1988 and 2005 (Figure 3). In 1988, the largest areas were occupied by secondary forests and reforestation, with a coverage of 43% and 22% of the Irobo classified forest, respectively. These forests were concentrated in the central part and scattered in some places in the northern part of the classified forest. As for reforestation, they were concentrated in the northern part of the forest. In 2005, the classes "mosaic annual young cocoa crop" and «secondary forest «occupied the largest areas with respectively 45% and 20% of the classified forest. The mosaics of annual young cocoa crops occupied the southern zone and the extreme north of the classified forest. The secondary forests, for their part, were located in the central part of the FCI (Tables 5 and 6). PCC 31% MACYC 22% PCRP 9% FS 14% 6% YFL 3% REF BSH 0% YPCRP 2% MFDOF 12% REF= reforestation ; FS= secondary forest ; PCC= perennial cocoa crop, MACYC= mosaic annual crop young cocoa; PCRP= perennial crops: rubber trees and oil palms; MFDOF = mosaic forest degraded old fallow land ; YFL= young fallow land ; YPCRP= young perennial crops: rubber trees and oil palms ; BSH= bare soils and habitats. A-Land use categories by area (2020) International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5020 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 Table 5: Spatial distribution of the areas of OCS classes in 1988 CLASSES AREAS (in ha ) PROPORTIONS (in %) REB 9017.14 21.50 FS 18038.05 43.01 CPC 2899.11 6.9 1 MCAJC 3964.13 9.45 CPPH 2590.22 6, 19 VJAC 2866.55 6.8 3 JJAC 553.95 1, 32 JCPHP 1761.73 4.2 SNH 248.98 0.59 TOTAL 41,939.86 100 Table 6: Spatial distribution of OCS class areas in 2005 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5021 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 Figure 3: FCI land cover maps based on 2005 Landsat-ETM+ and 1988 Landsat TM imagery Mapping the spatiotemporal dynamics of land use in the Irobo classified forest using Landsat images from 1988, 2005 and 2020 The map showing the dynamics of land use types in the Irobo classified forest, taking into account the years 1988, 2005, and 2020, is the result of juxtaposing the land use maps for each of these years. From 1988 to 2020, we have an interval of thirty-two (32) years and a year step of at least 15 years between two dates. From 1988 to 2020, we have an interval of thirty-two (32) years and a gap of at least 15 years between two dates. The evolution of the different types of land use from 1988 to 2020 is recorded in Table II and summarized in the graph in Figure 15. Analysis of Figure 15 shows that the evolution of land use types is very uneven, and we observe two trends in their evolution from 1988 to 2020: an increase and a decrease in area. The increase in area is notable in the categories “perennial cocoa cultivation,” “mosaic of young annual cocoa cultivation,” “perennial rubber and oil palm cultivation,” “mosaic of degraded forest/old fallow land,” and “young fallow land.” The “perennial cocoa cultivation” category, which covered an area of 2,899.11 ha in 1988, increased to 12,953.49 ha in 2020. The class "mosaic " that annual culture young cocoa" goes from 3964.13 ha to 9188.01 ha with an area which was 18807.49 ha in 2005. The third class, which concerns rubber and oil palm crops, has gone from 2590.22 ha to 3958.1 ha. The “degraded forest mosaic / old fallow” class presents 5099.59 ha in 2020 while it covered 2866.55 ha in 1988. As for the last class of this te trend, the "young fallow " class which covered 559.95 ha, increases to 1323.54 ha today. The second trend includes the following classes: "reforestation", " secondary forest ", "young perennial rubber and oil palm cultivation " and " bare soils and habitats". With areas respective areas of 9017.14 ha, 18038.05 ha, 1761.73 ha and 248.98 ha in 1988, they cover respectively in 2020; 2596 ha, 6044.75 ha 739.3 ha and 37.08 ha. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5028 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 Anthropogenic pressures from industrial and family farms are the primary causes of the significant deforestation of the study site. While industrial farming remained confined to the south of the classified forest, family cocoa farms spread rapidly throughout the site from the 2000s onwards. In view of this study, we envisage: - Using drones fordiscriminer la classe forêt dégradée et la classe vieille jachère; - Test the ability of drone data to discriminate between the different reforestations in this classified forest, - Take inventory of the flora of the 6,000 hectares of forest relics that still exist in this area. REFERENCES 1. Abrou NEJ, 2019. Anthropogenic activities, floristic diversity and dynamics of the vegetation of the Tanoé-Ehy Marshes (FMTE) forest area, South-East of Côte d'Ivoire. Single doctoral thesis, UFR Biosciences, Félix Houphouet-Boigny University, Côte d'Ivoire, 185p. 2. Adou Yao CY, 2005. Peasant practices and dynamics of biodiversity in the Monogaga classified forest (Ivory Coast). Single doctoral thesis, Department of Human Nature and Society, MNHN University, Paris, France, 233 p. 3. Aké-Assi L., 1998. Impact of forestry and agricultural development on the conservation of biological diversity in Côte d'Ivoire. LE FLAMBOYANT , 48 : 8587. 4. Alexandre DY, 1979. From natural regeneration to silviculture in tropical forests. Multigr. ORSTOM Adiopodoumé, 12 p. 5. Atta AC J, O. Soulemane, Yao KA, Kasse KB & Yaokokoré-Béibro KH, 2016. Characterization of human-elephant conflicts in the department of Sikensi (southeast Ivory Coast). African Agronomy 28 (3): 30 - 41 6. Avenard JM, Eldin M., Girad G., Sircoulon J., Touchebeuf P., Guillaumet JL, Adjanohoum E. & Perraud A., 1971. The natural environment of the Ivory Coast . ORSTOM memoir n ° 50 , Paris, France, 392 p. 7. Barima YSS, 2009. Dynamics, fragmentation and plant diversity of forest landscapes in forest-savannah transition environments in the Department of Tanda (Ivory Coast). Doctoral thesis, Université libre de Bruxelles, Belgium, 178p. 8. Bonn F & Rochon G., 1992. Summary of Remote Sensing, Vol I. Principles and Methods, University of Quebec Press. AUPELF, 485 p. 9. Cisse A., Ouattara M., N’guessan E. A. et Abrou J. E. N. (2020). Diversité végétale et usages des plantes dans une zone de savane soudanienne : Cas de la localité de Ferkessédougou (Nord, Côte d’Ivoire). International Journal of Biological and Chemical Sciences, vol. 14, n° 8, pp. 2807‑2825. https://doi.org/10.4314/ijbcs.v14i8.13 10. Congalton GR, 1991 . A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment , 46 : 35-37. 11. Eldin J., 1971. The Climate . In: The natural environment of the Ivory Coast. ORSTOM memoir , 50 : 157-263. 12. FAO, 2015. Global Forest Resources Assessment 2015: How are the world's forests changing? FAO Rome, 56 p. 13. Girard MC & Girard CM, 1999. Processing of remote sensing data. Dunod, Paris, 529 p.+ CD-ROM. 14. Guillaumet J. L & Adjanohoun E., 1971. The vegetation of Ivory Coast. in - The natural environment of Ivory Coast , ORSTOM Memoir , 50 Paris, pp 157-263. 15. Jensen J.R., 1995. Introductory Digital Image Processing: A remonte Sensing Perspective, Second Edition, Prentice-Hall Press, Englewood Cliffs, New Jersey, 316 p. 16. Koffi KA, 2016. Vegetation dynamics and conservation values of formerly cultivated areas of Azagny National Park (southern Ivory Coast ’) . Doctoral thesis , UFR Biosciences, Félix Houphou ët - Boigny University , Abidjan, Ivory Coast’, 185p. 17. Koné I., Kouadio AS, Zadou DA, Gleanou KE & Ibo GJ, 2012. Regular upheavals in the legal and institutional frameworks of forest management in Côte d'Ivoire: when it is not enough to legislate and establish institutions. In: Forests and humans: a community of destiny: Governance: the tree that hides the forest (eds. C. villeneuve). Chair in eco-consulting at the University of Quebec in Chicoutimi, pp. 90-97. 18. Koné M., 2015. Evolution of dense forest cover and impact of deforestation on the migration of the cocoa loop in Ivory Coast. Doctoral thesis, Nangui Abrogoua University, UFR Sciences de la Nature, 185 p. 19. Kpangui KB, 2015. Dynamics , plant diversity and ecological values of cocoa -based agroforests in the Kokumbo sub - International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5029 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 prefecture ( Central Ivory Coast ) . Doctoral thesis , UFR Biosciences, University of Cocody-Abidjan, Ivory Coast , 206 p. 20. Lewis S. L., 2006. Tropical forests and the changing earth system. Philosophical Transactions of the Royal Society, 361 (1465) : 195-210. 21. Mangenot G., 1955. Études sur les forêts des plaines et plateaux de la Côte d'Ivoire. Etudes éburnéenes, IFAN, IV – pp 561. 22. Margono B. A., Turubanova S., Zhuravleva I., Popapov P., Tyukavina A., Baccini A., Goetz S., & Hansen M. C., 2012. Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010. Environmental Research Letters, 7 (3) : 17 p. 23. MINEF, 2019. National Strategy for the Preservation, Rehabilitation and Extension of Forests in Côte d’Ivoire. 3 p. 24. N'Da DH, 2007. Study and monitoring by remote sensing and geographic information system of a protected area subject to pressures . Single doctoral thesis. Cocody University - Abidjan . 174 p . 25. N'Da HD, N'Guessan EK, Egnankou MW, & Affian K., 2008 . Contribution of remote sensing to monitoring deforestation in the Marahoué National Park ( Ivory Coast ) . Teledetection , Editions des Archives Contemporaines / Editions scientifiques GB / Gordon and Breach Scientific Publishers, 2008, 8 ( 1 ), pp.1734. 26. N'Guessan AE, 2018. Vegetation dynamics and biomass reconstitution factors in secondary forests: the case of the Agbo 1 classified forest (South-East of Côte d'Ivoire). Doctoral thesis, Botany Laboratory, UFR Biosciences, Félix HouphouetBoigny University, Côte d'Ivoire, 149p. 27. N'Guessan KE & Conde A., 1989. Use of high-resolution satellite data (HRV from SPOT and TM from Landsat) for the study of anthropogenic pressures on the Korhogo classified forest in the North of Ivory Coast. Bulletin of the French Society of Photogrammetry and Remote Sensing , 116: 18-26. 28. N'Guessan KE & N'Da HD, 2005. Characterization and mapping by satellite remote sensing of the vegetation of the Bouaflé classified forest (Ivory Coast). Ivorian Review of Sciences and Technology ., 06, p. 161-172. 29. N'Guessan KE, N'Da HD, Bellan , M.-F. & Blasco F., 2006. Anthropogenic pressure on a forest reserve in Côte d' Ivoire : contribution of remote sensing . Remote sensing , 5 ( 4 ) : pp.307 – 323. 30. N'Go YA, Goné DL, Savané I. & Gobl é MM, 2005. Groundwater potential of fissured aquifers in the Agboville region ( SouthEast of Côte d'Ivoire): Hydroclimatic and physical characteristics. Afrique Science , 01 (1): 127-124. 31. Oldeman RAA, 1990. Forests:elements of sylvology. Springer-Verlag , Berlin, 624 p. 32. Prossy C., 1999. Contribution of synthetic aperture radar data to the study of forest system dynamics, Doctoral thesis from Paul Sabatier University Toulouse III, France, 96. 33. Quattrochi D. & Pelletier R., 1990. Remote sensing for analysis of landscapes: an introduction. In Quantitative methods in landscape ecoloy . Edited by M. Turner and R. Gardner. Springer-Verlag, Nex York, 536 p. 34. Serageldin I., 1993. Protecting Africa's Tropical Rainforests Environment and Sustainable Development World Bank Second Edition, Washington, DC, 60 p. 35. Skupinski G., Ti DBT & Weber C., 2009. Multi-date Spot satellite images and spatial metrics in the study of urban and suburban change - The case of the lower Bruche valley (Bas-Rhin, France). Cybergeo: European journal of geography, UMR 8504 Géographie - cités . 36. Sarr MA, 2009. “Mapping of land use changes between 1990 and 2002 in northern Senegal (Ferlo) using Landsat images”, Cybergeo: European Journal of Geography, Environment, Nature, Landscape, document 472p. 37. SODEFOR, 2000. Action Plan for the implementation of the new Forest Policy – Rep. of Côte d’Ivoire, Ministry of Construction and the Environment – Forest Development Company; September 2000 38. Tamia AJ, Moreau R., Fortier M., 1996. Morphological modifications of a ferrallitic soil from Ivory Coast under different cultivation methods. In: Structure and fertility of tropical soils: third meeting of the thematic group, Montpellier, September 13, 1995. Montpellier: ORSTOM, 24-34. 39. Tonye E., Akono A. & Ndi NA, 1999. Processing of remote sensing images by example University of Youndé 1. National Polytechnic School Yaoundé. Laboratory and signal processing, 250 p. 40. Traoré K., 2018. Forest cover in Côte d'Ivoire: a critical analysis of the forest management situation (classified, parks and reserves). The International Journal of Social Sciences and Humanities Invention 5 (02): 4387-4397. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-15, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5030 *Corresponding Author: ATSÉ Williams Ghislain Haudy Yapi Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5013-5030 41. IUCN/BRAO, 2008. Evaluation of the effectiveness of protected area management: parks and reserves in Côte d'Ivoire. IUCN, Gland, Switzerland and Cambridge, United Kingdom, 43 p. 42. Wolff E., 2005. Report of a mapping mission to Kisantu (DR Congo). SLCD, Brussels, Belgium, 9 p. 43. Yossi H., 1995. Dynamics of post-cultural vegetation in the Sudanian zone of Mali. Thesis of the Higher Institute of Training and Applied Research (Isfra), Bamako, Mali, 170p. 44. Zobi CI & Chessel D., 2007. Analysis of the process of appearance and disappearance of commercial species in two types of humid forests of Ivory Coast. African Agronomy 19 (3): 301-322 45. Zoro Bi. IA & Kouakou LK, 2004. Study of the rattan sector in the district of Abidjan (Southern Ivory Coast). BASE , 8 (3): 199 - 209. Cite this Article: Williams Ghislain Haudy Yapi, A., Hyppolite, D., Kouassi Kouman Noël, N., Yéhounko Bruno Buffon, G., Crystel Natacha, B. (2025). Culture and Governance: Promoting sustainable social change, case study: Cámara Popular del jirón Amazonas in the city of Lima-2024. International Journal of Current Science Research and Review, 8(10), pp. 5013-5030. DOI: https://doi.org/10.47191/ijcsrr/V8-i10-15