Studies assessing the environmental risks related with metal pollution of agricultural soils are lacking in the coastal area of Doukkala, with is one of Morocco’s most agricultural regions. To overcome the shortcomings of such studies, trace element (As, Cd, Pb, and Zn) analyses were carried...

Land surface temperature plays an essential role in estimating radiation budgets, in heat balance studies, as a control for climate dynamics and for soil–vegetation–atmosphere transfer modeling, and in studying the impact of land use/land cover (LULC) changes at the regional level. This study provides a comprehensive evaluation of the relationship between land use and land surface temperature (LST), through a landscape dynamics assessment based on multi-source and multi-sensor remote sensing technologies. In particular, the study was performed using Landsat satellite 5 TM, ETM, and OLI 8 data for three different dates (1985, 2001 and 2017) and aimed to assess the effects of land use/land cover changes on the LST distribution in the region of Taroudant, Morocco. Spatial and statistical analysis and comparison of maps generated from remotely sensed data using GIS indicate the existence of different changes in the Taroudant region between 1985 and 2017. These changes are predominantly characterized by an increase in built-up areas and bare ground and a decrease in natural areas (vegetation, forest, etc.). The average temperatures in 1985, 2001 and 2017 in open forests were 32.74 °C, 34.37 °C and 39.17 °C, respectively. The farming greenhouse temperatures were 24.09 °C, 28.5 °C, 35.58 °C, and barren soils 37.14 °C, 38.38 °C, 40.01 °C. The average land surface temperatures of farming lands were 24.31 °C, 27.87 °C and 28.61 °C, respectively. As a result, soil artificialization and everything associated with it, such as greenhouse gas emissions, and abusive consumption of farming and natural land, are likely to be the origin of environmental problems and climate change marked mainly by these changes in surface temperature, irregular rainfall, and unprecedented periods of drought.

This research aimed to determine the physicochemical characteristics and heavy metal concentrations of agricultural soils used for grape and wheat production in Morocco in the Mohammedia Benslimane area. The organic matter (OM) content ranged from 0.6% to 2.93 %. The degree of total nitrogen was...

In this study, we aim to evaluate urban sprawl dynamics and surface temperature changes between 1985 and 2019, and to predict a future scenario of their evolution in 2027 and 2038 in great El Jadid...

Recent advancements in remote sensing and geospatial processing tools have ushered in a new era of mapping and monitoring landscape changes across various scales. This progress is critical for understanding and anticipating the underlying drivers of environmental change. In particular, large-scale Land Use and Land Cover (LULC) mapping has become an indispensable tool for territorial planning and monitoring. This study aims to map and evaluate LULC changes in the El Jadida region of Morocco between 1985 and 2020. Utilizing multispectral Landsat imagery, we applied and compared three supervised machine learning classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NNET). Model performance was assessed using statistical metrics, including overall accuracy, the Kappa coefficient, and the F1 score. The results indicate that the RF algorithm was the most effective, achieving an overall accuracy of 90.3% and a Kappa coefficient of 0.859, outperforming both NNET (81.3%; Kappa = 0.722) and SVM (80.2%; Kappa = 0.703). Analysis of explanatory variables underscored the decisive contribution of the NDWI, NDBI, and SWIR and thermal bands in discriminating land cover classes. The spatio-temporal analysis reveals significant urban expansion, primarily at the expense of agricultural land, while forested areas and water bodies remained relatively stable. This trend highlights the growing influence of anthropogenic pressure on landscape structure and underscores its implications for sustainable resource management and land use planning. The findings demonstrate the high efficacy of machine learning, particularly the RF algorithm, for accurate LULC mapping and change detection in the El Jadida region. This study provides a critical evidence base for regional planners to address the ongoing loss of agricultural land to urban expansion.