Fracture aquifers are actual major issue in hydrogeology. They are an essential resource for many populations around the world. The present study focuses on major fractures that control groundwater and surface water flow, and play a key role in the productivity of fractured reservoirs. The use of numerical processing techniques (Selective Principal Component Analysis (SPCA), band ratios) and directional filtering by Sobel and Yésou allowed the identification of major fractures in the Black Volta Basin in Côte d’Ivoire. Validation of the latter was done by several field works (geology, hydrogeology and geomorphology) and a comparative analysis. Three groups of NS, NE-SW and E-W lineament directions were identified from these images. The structural field data generally indicate similar directions. Field data and satellite image processing show that most of these lineaments correspond to either shear faults or veins related to Eburnian and post-Eburnian orogenic events. The comparative analysis carried out revealed concordances with previous studies and automatically detected structures in the same area. The final lineament map generated for the study area will help to identify potential areas for drilling for water supply purposes.

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.

Population growth and human activities have negatively affected the vegetation cover in Côte d'Ivoire. The objective of this study is to analyse the land cover dynamics around the Taabo Dam from 1988 to 2017 and from 2017 to 2050. Landsat satellite data from 1988, 2002 and 2017 were used. Supervised maximum likelihood classification and Pontius intensity analysis were applied. It was found that global change was most rapid from 2017 to 2050. Losses and gains of water and bare soil are dormant while those of the other classes are active during the first-time interval. During the second time interval, the gain of crop and fallow and bare soil will be active but their loss will be dormant. Water will have dormant gains and losses while grassy savannah and shrubland will have active gains and losses. The gain from crops and fallows targets bare soil and habitats, shrub and grass savannah but avoids water. However, the gain in bare soil and habitats targeted crop and fallow land and grassy savannah, but avoided water and shrub savannah in both intervals.

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.

The mining industry is a main sector for economic and social development of a country. However, this industry generates many negative impacts on human and biophysical environments. Located south-east of C

This study realized at Bondoukou region in north-eastern of Cote d'Ivoire. It aims to analyze hydrodynamic properties of fissured aquifers with a view to improve drinking water supply at this region. Methodology applied for this study started by mapping of fractures networks from treatment of Landsat 7 satellite images with directional filters. After that, it was established relationship among the parameters of groundwater capture and their relation with fracturing and nature of rock formation. Geostatistical analysis of specific capacity of wells from corrected last step drawdown closed the methodology. Use of remote sensing led to map 5000 fractures responsible of water-bearing-layers inside bedrocks. Productive fractures are met between 30 and 60 meters under the ground with these principals directions: NE-SW, NW-SE and E-W. Great yields are also met at weathering between 15 to 45 meters. Approximately 83, 41% of arrival water occurred in the first 30 meters of the basement. Porphyroid granodiorit produced water more than other rocks formations. Variogram structuring of Log (Q/s) revealed that this hydrodynamic parameter can be considered as a regionalized variable with a range equals to 7,36 km. This is a proof of best connectivity of fractures networks identified. Estimation of specific capacities at the whole studied area was made by kriging. All results obtained through this study contribute to best comprehension of hydrodynamic properties of aquifer at North-east of C

The groundwater's exploitation located in the bedrock is one way indicated to provide water to population. Indeed, the aquifers of the fissured base constitute excellent water tanks. This article aims to determine the sites favorable to the establishment of drillings to good flows in the department of Dabakala. The methodological approach used is the combination of various geophysics methods and remote sensing. A card of morpho-structural alignments was established. This one highlighted two families of fractures hydraulically active of N70

This study aimed at a better knowledge of aquifers of fractures of Bondoukou region (north-east of C