Suchergebnisse

In arid and semi-arid environments, producing accurate maps of forest tree cover using optical remote sensing data is essential to understand their spatial distributions and dynamics. In this respect, the current study aimed to explore the effectiveness of support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF)machine learning (ML) models to map the forest tree species of Ait Bouzid region (Central High Atlas, Morocco) by using Sentinel-2A data. The results from all models showed that about 19-28%, 21-27%, 16-24%, 15-18%, and 0,3-0,32% of the area was covered by euphorbia, red juniper, cedar, holm oak, bare ground, and water body, respectively. According to the overall accuracy (OA) and kappa coefficient, the SVM classifier showed the highest OA (73%) and kappa (0.66) values, followed by KNN (OA=70%, kappa=0.62) and RF (OA=67%, kappa=0.59). Regarding LC classes, water, bare soil, and holm oak could be identified with the producer's accuracy attaining 100%, while red juniper and cedar were the most challenging classes to determine for all ML classifiers, with the producer's accuracy of 40-50% and 40-67%. This study revealed the potential of ML approaches coupled with multispectral Sentinel-2A data for forest species cartography in arid areas with high accuracy. Furthermore, it provides crucial information about forest tree species distribution for developing forest management plans.

Morocco watersheds, which provided many ecosystem services necessary for the socio-economic life of rural communities, are experiencing significant change and environmental problems. Therefore, examining potential soil erosion considered a major problem in the Moroccan highlands is very important to prioritize high erosion severity areas. Keeping in view of the above aspects, the present study aimed to evaluate and map areas at risk of water erosion in the upstream Tassaoute watershed (central High Atlas, Morocco), using the Priority Action Program/Regional Activity Center (PAP/RAC) method associated with Geographic Information Systems (GIS) and remote sensing. The PAP/RAC approach consisted of integrating the natural factors that influence water erosion, namely slope, lithology, vegetation cover and land use. This method provided an accurate cartographic product that reflects the reality of the state of soil degradation and the qualitative assessment of erosion. The generated erosion risk map of the study area showed that the phenomenon of erosion threatens this basin, especially in the middle and downstream, such that 40% of the basin surface has significant erosion and the high and very high degree of erosion represented 27% of the total surface of the study area. These results therefore demonstrated the PAP/CAR model reliability in assessing and mapping of water erosion risks in the upstream Tassaoute basin.