Identifying ecologically fragile areas by assessing ecosystem vulnerability is an essential task in environmental conservation and management. Benin is considered a vulnerable area, and its coastal zone, which is subject to erosion and flooding effects, is particularly vulnerable. This study assessed terrestrial ecosystems in Benin by establishing a hybrid ecological vulnerability index (EVI) for 2016 that combined a composite model based on principal component analysis (PCA) with an additive model based on exposure, sensitivity and adaptation. Using inverse distance weighted (IDW) interpolation, point data were spatially distributed by their geographic significance. The results revealed that the composite system identified more stable and vulnerable areas than the additive system; the two systems identified 48,600 km2 and 36,450 km2 of stable areas, respectively, for a difference of 12,150 km2, and 3,729 km2 and 3,007 km2 of vulnerable areas, for a difference of 722 km2. Using Moran’s I and automatic linear modeling, we improved the accuracy of the established systems. In the composite system, increases of 11,669 km2 in the potentially vulnerable area and 1,083 km2 in the highly vulnerable area were noted in addition to a decrease of 4331 km2 in the potential area; while in the additive system, an increase of 3,970 km2 in the highly vulnerable area was observed. Finally, southern Benin was identified as vulnerable in the composite system, and both northern and southern Benin were identified as vulnerable in the additive system. However, regardless of the system, Littoral Province in southern Benin, was consistently identified as vulnerable, while Donga Province was stable.

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A systematic method, incorporating the revised universal soil loss equation model (RUSLE), remote sensing, and the geographic information system (GIS), was used to estimate soil erosion potential and potential area in the Maradi region of south-central Niger. The spatial trend of seasonal soil erosion was obtained by integrating remote sensing environmental variables into a grid-based GIS method. RUSLE is the most commonly used method for estimating soil erosion, and its input variables, such as rainfall erosivity, soil erodibility, slope length and steepness, cover management, and conservation practices, vary greatly over space. These factors were calculated to determine their influence on average soil erosion in the region. An estimated potential mean annual soil loss of 472.4 t/ac/year, based on RUSLE, was determined for the study area. The potential erosion rates varied from 14.8 to 944.9 t/ac/year. The most eroded areas were identified in central and west-southern areas, with erosion rates ranging from 237.1 to 944.9 t/ac/year. The spatial erosion maps can serve as a useful reference for deriving land planning and management strategies and provide the opportunity to develop a decision plan for soil erosion prevention and control in south-central Niger.

Rapid population growth is the main driver of the accelerating urban sprawl into agricultural lands in Egypt. This is particularly obvious in governorates where there is no desert backyard (e.g., Gharbia) for urban expansion. This work presents an overview of machine learning-based and state-of-the-art remote sensing products and methodologies to address the issue of random urban expansion, which negatively impacts environmental sustainability. The study aims (1) to investigate the land-use/land-cover (LULC) changes over the past 27 years, and to simulate the future LULC dynamics over Gharbia; and (2) to produce an Urbanization Risk Map in order for the decision-makers to be informed of the districts with priority for sustainable planning. Time-series Landsat images were utilized to analyze the historical LULC change between 1991 and 2018, and to predict the LULC change by 2033 and 2048 based on a logistic regression–Markov chain model. The results show that there is a rapid urbanization trend corresponding to a diminution of the agricultural land. The agricultural sector represented 91.2% of the total land area in 1991, which was reduced to 83.7% in 2018. The built-up area exhibited a similar (but reversed) pattern. The results further reveal that the observed LULC dynamics will continue in a like manner in the future, confirming a remarkable urban sprawl over the agricultural land from 2018 to 2048. The cultivated land changes have a strong negative correlation with the built-up cover changes (the R2 were 0.73 in 1991–2003, and 0.99 in 2003–2018, respectively). Based on the Fuzzy TOPSIS technique, Mahalla Kubra and Tanta are the districts which were most susceptible to the undesirable environmental and socioeconomic impacts of the persistent urbanization. Such an unplanned loss of the fertile agricultural lands of the Nile Delta could negatively influence the production of premium agricultural crops for the local market and export. This study is substantial for the understanding of future trends of LULC changes, and for the proposal of alternative policies to reduce urban sprawl on fertile agricultural lands.