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The Saka region and its environs are situated in the northeastern part of Morocco. This study aimed to optimize automated lineament extraction based on the comparing of Landsat-8 optical satellite data with Sentinel-2B for enhanced analysis. The research delved into the structural lineaments within the Saka region, with the objective of advancing the understanding of lineament extraction techniques. Remote sensing techniques were employed to extract and map these lineaments Furthermore, the study sought to elucidate the distribution and genesis of volcanism in the Saka region and its surroundings in the context of geodynamics. The availability of optical and multispectral remote sensing datasets, including those from Landsat-8 OLI and Sentinel-2B, characterized by medium and high spatial resolutions, enhances the efficiency and simplicity of lineament mapping – an essential component of any structural geological investigation. However, due to the differences in spatial resolution and sensitivity to land cover, the outcomes from these diverse data sources were derived with varying resolutions display variability. The spatial resolution of the images significantly influences the precision and clarity of the retrieved lineaments. The findings underscore a strong correlation between lineament directions (primarily NE-SW, E-W, NW-SE) and faults, i.e., correspond to the distribution of volcanic outcrops in the Saka area and its vicinity. For validation purposes, the lineaments extracted through directional filtering were compared to the manually obtained lineaments, alongside lineaments digitized from the pre-existing neotectonic map (faults) as well as satellite images depicting lineaments in the study area. Density analysis was employed to investigate the correlation between the concentration of lineaments and the distribution of pre-existing faults. Additionally, the geological map was utilized to refine the correlation between density distribution and the spatial orientations of volcanic rock formations in the study area.
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