The objective of this study performed in the Abidjan District is to map land cover units using the Google Earth Engine (GEE) platform and Machine Learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), Naive Bayes (NB), and Minimum Distance (MD). The data used include optical Multispectral Sentinel 2A satellite images with a 10-meter resolution, a 12.5-meter Alos Polsar digital terrain model (DTM) resampled to a 10-meter resolution, as well as cartographic data. The implemented methodology starts with the preprocessing and normalization of the composite image. The final composite image is created using eight spectral indices: NDVI, NDWI, MNDWI, VARI, SBI, SAVI, GCI, RGR, along with the first three bands of Principal Component Analysis and slope information. Subsequently, training and validation points are collected and coded based on image reflectance and ground truth data. The different classifiers SVM, RF, CART, MD, and ND are then trained and evaluated using various metrics such as confusion matrix, overall accuracy, producer’s accuracy, consumer’s accuracy (reliability), and Kappa coefficient. The classification performed with the RF algorithm achieved the highest overall accuracy of 83.28%, with a Kappa coefficient of 0.78. The statistics reveal that the Abidjan District is composed of 28.07% urban areas, 25.35% agricultural and other cultivated areas, 12.39% oil palm plantations, 10.05% rubber plantations, 4.66% banana plantations, 2.53% forests, 3.96% mangroves, 3.80% forest plantations (reforestation), and 9.2% water bodies in 2020. This study has led to an improved mapping of the distribution and proportions of land cover classes in the Abidjan District.

This study focuses on the kohodio watershed in north-eastern of Côte d'Ivoire. The objective is to present the seasonal variation of the water status during the wet season and in the mid-dry season, from 1986 to 2018, using remote sensing. This study focuses on the Kohodio watershed in northeastern Côte d'Ivoire. The aim is to present the seasonal variation of the hydrous state of drains from wet season (December-January) to mid-dry season using Landsat multispectral image processing (TM 1986, ETM + 2002 and OLI 2018). The approach combine: (i) the discrimination of wet drains by calculating moisture indices in mid-dry season and color compositions in wet-season; (ii) the automatic extraction of wet units on our composite indices and images; (ii) and the superposition of the global hydrographic network on the extracted layers. The intersection allowed mapping of wet drains in the wet season and in the mid-dry season. The analysis shows that the physical and spatial hydrous state results in the drying of the drains from 1986 to 2018. Total length of wet drains in the basin is 23.80%; 21.7% and 22.4% of its water potential during wet season; and 21.1%; 7.7% and 10.1% in the dry mid-season, respectively in 1986, 2002 and 2018. From the end of the rains until mid-dry season, rate drain drying is 11.2%; 64.6% and 54.8% respectively in 1986, 2002 and 2018. drain drying observed in the mid-dry season was greatly felt from 1986 to 2002, with a percentage equal to -63.70%. This phenomenon is the consequence of climate change and the construction of dams of reservoirs in the area.