High basin of Oum Er Rbia River, which is located in Middle Atlas Mountain region, is prone to landslide problems due to the geological features combined with the climate change and human activities. The present work including inventory mapping was conducted to establish landslide susceptibility map using GIS-based spatial multicriteria approach. Eight landslide-related factors, including land cover, lithology, distance to road, distance to fault, distance to drainage network, elevation, aspect and slope gradient, were selected for the present assessment. Weight for each factor is assigned using Analytic Hierarchy Process (AHP) depending on its influence on the landslide occurrence. The landslide susceptibility map was derived using weighted overlay method and categorized into five susceptible classes namely, very low (VL), low (L), moderate (M), high (H). The results revealed that 30.16% of the study area is at very low risk, 12.66% at low risk, 25.75% of moderate risk, 22.59% of high risk and 9.11% of very high risk area coverage. The very high landslide vulnerability zones are more common within the river valleys on steep side slopes. Most landslides also involve rocks belonging to the Triassic weathered marl and clay-rich formation. Moreover, human activities namely the construction and the expansion of agricultural lands into forests intervene in inducing landslides through altering the slope stability along the river banks. Lastly, effectiveness of these results was checked by computing the area under ROC curve (AUC) that showed a satisfactory result of 76.7%. The landslide susceptibility map of the Oum Er Rbia high basin provides valuable information about present and future landslides, which makes it viable. Such map may be helpful for planners and decision makers for land-use planning and slope management.

The Ikkour watershed located in the Middle Atlas Mountain (Morocco) has been a subject of serious soil erosion problems. This study aimed to assess the soil erosion susceptibility in this mountainous watershed using Universal Soil Loss Equation (USLE) and spectral indices integrated with Geographic Information System (GIS) environment. The USLE model required the integration of thematic factors’ maps which are rainfall aggressiveness, length and steepness of the slope, vegetation cover, soil erodibility, and erosion control practices. These factors were calculated using remote sensing data and GIS. The USLE-based assessment showed that the estimated total annual potential soil loss was about 70.66 ton ha⁻¹ year⁻¹. This soil loss is favored by the steep slopes and degraded vegetation cover. The spectral index method, offering a qualitative evaluation of water erosion, showed different degrees of soil degradation in the study watershed according to FI, BI, CI, and NDVI. The results of this study displayed an agreement between the USLE model and spectral index approach, and indicated that the predicted soil erosion rate can be due to the most rugged land topography and an increase in agricultural areas. Indeed, these results can further assist the decision makers in implementation of suitable conservation program to reduce soil erosion.