The Ourika watershed, located in the North-West of Moroccan High Atlas, has undergone several spatio-temporal changes and accelerated land use dynamics as a result of the interaction of climatic, topographic and anthropogenic factors. The objective of this study is to monitor the evolution of land use in the study area over the past 33 years. Landsat satellite imagery has been chosen for land cover mapping, providing a sufficient detail to identify land cover characteristics while providing more or less complete coverage of the area of action. Landsat 5 Thematic Mapper satellite images from 1987 and Landsat 8 Operational Land Imager from 2019 were used, with a spatial resolution of 30m. The images were treated and classified using Support Vector Machine algorithm (SVM) implemented on QGIS Geographic Information System software. The classification evaluation shows a Kappa coefficient of 85% and 84% and an overall accuracy of 95% and 94% for 1987 and 2019 respectively. Furthermore, the results showed a 10% decrease in the forest as well as a significant increase in the pasture, arboriculture, bare land and buildings with a respective percentage of 5.99%, 1.67%, 1.48% and 1.37% accordingly.
Climate change, which is expected to continue in the future, is increasingly becoming a major concern affecting many components of the biodiversity and human society. Understanding its impacts on forest ecosystems is essential for undertaking long-term management and conservation strategies. This study was focused on modeling the potential distribution of Quercus suber in the Maamora Forest, the world’s largest lowland cork oak forest, under actual and future climate conditions and identifying the environmental factors associated with this distribution. Maximum Entropy approach was used to train a Species Distribution Model and future predictions were based on different greenhouse gas emission scenarios (Representative Concentration Pathway RCPs). The results showed that the trained model was highly reliable and reflected the actual and future distributions of Maamora’s cork oak. It showed that the precipitation of the coldest and wettest quarter and the annual temperature range are the environmental factors that provide the most useful information for Q. suber distribution in the study area. The computed results of cork oak’s habitat suitability showed that predicted suitable areas are site-specific and seem to be highly dependent on climate change. The predicted changes are significant and expected to vary (decline of habitat suitability) in the future under the different emissions pathways. It indicates that climate change may reduce the suitable area for Q. suber under all the climate scenarios and the severity of projected impacts is closely linked to the magnitude of the climate change. The percent variation in habitat suitability indicates negative values for all the scenarios, ranging –23% to –100%. These regressions are projected to be more important under pessimist scenario RCP8.5. Given these results, we recommend including the future climate scenarios in the existing management strategies and highlight the usefulness of the produced predictive suitability maps under actual and future climate for the protection of this sensitive forest and its key species – cork oak, as well as for other forest species.
This article aims to shed light on the process of known degradation of the forest area of Benslimane province during the period 1990–2020 and to specify the most important human causes which contributed to it (quarries, extension of the built-up area, the impact of agricultural activities, grazing and collection of firewood), by using remote sensing techniques (spatial images for the years 1990–2000–2010–2020) to produce Land Cover maps. The following satellite images were used, Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI, with a spatial precision of 30 m, the Semi-Automatic Classification Plugin (SCP) in QGIS was used for atmospheric correction, and the Spectral Angle Mapping algorithm for the images’ classification. The rating evaluation of the Kappa coefficient shows the following ratios for the years 1990–2000–2010–2020 respectively ; 0.89–0.90–0.90–0.93. The results showed that the forest area of Benslimane province has declined by 11.4% or about 6,027.7 ha between 1990–2020 at the rate of 200 ha/year, which has been turned into matorral land or bare land. This forest also lost 35.2% of its vegetative density and has become much sparser, while the original grazing areas surrounding it have been reduced by 50.4%. Moreover, the area of quarries increased by 1,097.4%, the percentage of built-up area increased by 328.2%, and the agricultural area expanded by 32.7%. These results can be used as preliminary data for future studies and can help policymakers focus on the real drivers of forest degradation, in order to develop interventions to ensure the sustainability of natural resources.
In Morocco, the phenomena of water erosion cause significant economic losses mainly linked to the silting up of dams, the degradation of equipment and socio-economic infrastructures, the loss of soil productivity and the insecurity of the population. The SWAT (Soil and Water Assessment Tool) model was used to estimate the quantities of sediments generated by the various erosive processes at the level of the Ourika watershed. The SWAT modeling, which is done with daily time steps, used as basic data; a Digital Elevation Model GDEM-ASTER (Global Digital Elevation-Advanced Space borne Thermal Emission and Reflection Radiometer) with 30 m of resolution, a land cover map developed from the Landsat 8 OLI (Operational Land Imager) satellite image of 2017 with 30 m of resolution and a soil map published by FAO (Harmonized World Soil Database). Also, daily meteorological data from the Tensift Water Basin Agency over a period from 1992 to 2001 were used. The results obtained showed that soil losses due to water erosion in the Ourika watershed reached an average of 9.18 t.ha-1.year-1. The model was calibrated and validated using the SWAT-CUP (SWAT Calibration and Uncertainty Procedures) software SUFI-2 (Sequential Uncertainty Fitting) and after several simulations and iterations a determination coefficient R2 of 0.76 was obtained.
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