Yassine Zarhloule

Groundwater resources are depleting worldwide owing to climate change and exponential rise in population. In order to manage groundwater resources, consideration must be given to availability of the groundwater and the functioning of the aquifer system. In this regard, the present study aims to explore groundwater potential zones for Guercif, Morocco for highlighting the importance of groundwater resources management. The groundwater potential zones are investigated using multicriteria decision analysis technique in GIS software from perspectives of slope, drainage density, land use landcover, geology, soil type and groundwater levels. As per results, poor groundwater potential zones in the study area were minimum (about 1 km2) followed by excellent (257 km2), fair (632 km2), moderate (2710 km2) and good (6064 km2) groundwater potential zones, respectively. Watershed of the study area has good groundwater potential zones of about 62.75% whereas only 0.01% falls in poor groundwater potential zones. The current research concluded that estimating the groundwater potential zones using GIS and RS remote sensing techniques could saves time and money as compared to the conventional methods. The study is recommended for decisions making and managing the groundwater resources for betterment of the region in future. Therefore, the study provides critical insights for public and government sectors to understand the potential zones for sustainable groundwater utilization and management.

This study presents a comprehensive analysis of the hydrochemistry of the Tendrara aquifer in the Tendrara region of Morocco. The study encompasses various aspects of the region, including its geography, administration, relief, demography, economy, and climate, to establish a solid understanding...

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region.