Located in the central northwest of Morocco, the Sidi Mohamed Ben Abdellah dam watershed is particularly exposed to soil degradation risk due to a combination of factors such as wide exposure, lithological heterogeneity, and varying climatic conditions. Therefore, the purpose of the conducted study was to create a spatial map of the areas most susceptible to degradation using the MEDALUS (Method for the Evaluation of the Degree of Soil Loss Susceptibility) model to pinpoint the areas that are most vulnerable to the risk of erosion. The MEDALUS model is a commonly used tool for assessing soil degradation and erosion risks. It takes into account physical, climatic, and land use factors to determine the susceptibility of an area to soil loss. To apply the model to the Sidi Mohamed Ben Abdellah dam watershed, the data on factors such as slope, soil type, vegetation cover, and precipitation would be collected, and this information would be used to generate a map of the areas at greatest risk of erosion. This map could then be used to prioritize conservation and management efforts in the watershed and identify the areas that require additional protection or restoration. The map of erosion sensitivity is produced by combining factors that contribute to the phenomenon, such as vegetation cover, climate, relief, pedology, and human intervention. Cross-referencing these factors in a GIS (geographic information system) allows generating an erosion sensitivity map that highlights the most vulnerable areas to this hazard in the region.
By combining geomatics techniques and remote sensing data, this paper gives a thorough investigation of the forest fires that occurred close to Berkane, Morocco, from July 16 to July 18, 2023. The goals of the study included spatiotemporally tracking the propagation of active forest fires during the fire season, and to accurately map the burned area and detect changes in vegetation cover caused by the fire. A detailed fire severity mapping of the impact of the fire on the forest was made by this integrated approach. We used remote sensing data from various sources, including NASA FIRMS data for the fire period and Sentinel-2 satellite imagery acquired two days before and one day after the fire, to accomplish these goals. In terms of estimating the burned area, our study produced important findings. We were able to estimate 3508.12 hectares, 3517.98 hectares, and 3113.63 hectares using satellite imagery with dNBR, dNDVI, and supervised classification, respectively. These results offer considerable potential for directing post-fire management plans and preserving this critically important forest area. The integration of FIRMS data, Sentinel-2 images, and GIS in our research highlights the need of using this coordinated strategy to conduct an accurate and thorough evaluation of forest fires in the area. In addition to improving our understanding of forest fire dynamics, this study emphasizes the value of using cutting-edge geospatial and remote sensing techniques in attempts to manage wildfires and save the environment. The findings of this study will contribute significantly to guiding post-fire management strategies, thus promoting the conservation of the vital forest area.
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