The Ourika sub-watershed is composed of about twenty different watersheds with diverse lithology, slope, and structural organization. In order to better characterize the basin, we inventoried and extensively assessed the different types of thresholds implemented in each micro-watershed. The present study focused on the area located between Meltsen and Sidi Ali Oufarés faults, which includes several micro-watersheds that have been modified by the installation of structures. We selected 12 micro-watersheds from the main tributaries draining this zone, based on the level of risk: four micro-watersheds on the right bank from upstream to downstream (Wigrane and Walighane, Tachmacht, and Touggalkhir), and eight micro-watersheds on the left bank from upstream to downstream (Imintaddarte, Oussane, Tikhfert, Tighazrit, Igri Foudene, Asni, Taljarft, and Tarzaza). The results of our study allowed us to detect and inventory 545 erosion protection structures made of masonry, gabions, and dry stone. However, the majority of these structures were damaged in several micro-watersheds due to steep slopes, torrential rainfall, and especially the solid sediment load resulting from the erosion of easily erodible old alluvial cones. This study serves as a warning to various stakeholders and decision-makers to ensure proper management in this mountainous system. The distribution of these thresholds is as follows: 62 masonry thresholds, accounting for 13.37%; 247 gabion thresholds, accounting for 45.32%; and 236 dry stone thresholds, accounting for 43.30%. The assessment of these structures revealed anomalies such as the loss of 17.43% of embankment structures and the destruction of certain thresholds.
Geological mapping undoubtedly plays an important role in several studies and remote sensing data are of great significance in geological mapping, particularly in poorly mapped areas situated in inaccessible regions. In the present study, Principal Component Analysis (PCA), Band Rationing (BR) and Minimum Noise Fraction (MNF) algorithms are applied to map lithological units and extract lineaments in the Amezri-Amassine area, by using multispectral ASTER image and global digital elevation model (GDEM) data for the first time. Following preprocessing of ASTER images, advanced image algorithms such as PCA, BR and MNF analyses are applied to the 9ASTER bands. Validation of the resultant maps has relied on matching lithological boundaries and faults in the study area and on the basis of pre-existing geological maps. In addition to the PCA image, a new band-ratio image, 4/6–5/8–4/5, as adopted in the present work, provides high accuracy in discriminating lithological units. The MNF transformation reveals improvement over previous enhancement techniques, in detailing most rock units in the area. Hence, results derived from the enhancement techniques show a good correlation with the existing litho-structural map of the study area. In addition, the present results have allowed to update this map by identifying new lithological units and structural lineaments. Consequently, the methodology followed here has provided satisfactory results and has demonstrated the high potential of multispectral ASTER data for improving lithological discrimination and lineament extraction.
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