Worldwide urbanization drives rural-urban transformation (RUT) which has major consequences in many countries of the Global South where there is an urgent need to better understand and manage the underlying processes and consequences for ecosystem services. To fill existing knowledge gaps on the extent and time course of RUT in Morocco, this study focused on (i) analyzing the spatial patterns of rural-urban transformation in the Rabat-Sale-Kenitra (RSK) region from 1972 to 2020, (ii) identifying key mechanisms of change, and (iii) defining the main driving forces behind the spatial transformation patterns. To this end, we processed data of the Landsat free archive, historical grayscale Corona images, and nighttime lights datasets on Google Earth Engine (GEE) using machine learning classifiers and LandTrendr spectral-temporal segmentation algorithms. With an overall accuracy (OA) ranging from 88–95%, the results revealed that during the study period the RSK region experienced a 473% growth of horizontal built-up reflected in an area increase from 63.4 km²to 299.9 km². The main changes occurred along the Kenitra-Rabat-Temara axis and in central cities connected to the main road network. The horizontal expansion of large and medium-sized cities led to the formation of a Rural-Urban Interface (RUI) on the outskirts. The urban sprawl of some cities has affected the surrounding rural lands within the RUI. Environmental, social, economic, and political forces have interacted in shaping the changes in rural-urban landscapes.

Understanding and governing human settlement patterns is a major challenge of the urban age. While rural settlements emerge as parts of agricultural landscapes, cities typically evolve in economically strategic locations, and over time form hierarchical systems of cities. Purposeful planning and the collective, self-organized behavior of the inhabitants interact in the development of regional settlement patterns. Since self-organizing systems often produce fractal patterns in nature, this study combines approaches of land use science, city ranking, and urban planning under a fractal theory framework, to analyze the settlement system of the Indian Punjab. Scaling levels were defined by discontinuities in the size distribution of built-up areas (Global Urban Footprint), which correlated to population-based classifications (r = 0.9591). Self-similarity across scales was supported by geo-statistical similarity (p < 0.05) of distances and angles between settlements of successive classes, and the overall fractal dimension of D_B = 1.95. When compared to a modeled Sierpinski Carpet, more than 50% of the settlements met the fractal geometry rules at larger scales. The spatial distribution of small villages, however, deviated, indicating a scale-related shift in organizing principles. Explicitly acknowledging cross-scale relations and self-organisation in regional planning policies may lead to more sustainable settlement structures that are in harmony with natural system properties.

Since the 1990s, Morocco’s agriculture has been characterized by the co-existence and transformation of both modern and traditional smallholder systems. In the Atlas Mountains, the effects of rural–urban transformation have led to intensified irrigated agriculture in some agricultural areas, while others were abandoned. To better understand these effects, this study aimed at (1) analyzing the land use and land cover (LULC) changes, (2) assessing the structure and dynamics of vegetation, and (3) comparing a Support Vector Machine (SVM) classification approach with a seasonal rules-based approach. We, therefore, employed a semi-automatic supervised classification of LULC using Landsat data from the 1990s to the 2020s to distinguish between Open Canopy Vegetation, Bareland, Forest, and Water. Overall accuracies achieved ranged from 88% to 90% in 1990, 2000, 2010, and 2020. SVM results indicated the share of Bareland as >80% of the landscape in all periods. With the seasonal rules-based approach, 10% less Bareland was detected than with the SVM approach. Our findings indicate the limitation of detecting vegetation reflectance in semi-arid mountainous regions such as that prevailing in Morocco using a single machine learning method.

Background: Insufficient knowledge of the genetic and phenotypic diversity in the local Beninese goat population combined with the lack of understanding of its adaptive capacity to ongoing environmental and societal changes hampers the development of strategies for better management and genetic improvement. The objective of this study was to establish the current geographical distribution of goats in Benin based on their morphology and model the potential habitat suitability of the three known main goat phenotypes (i.e., Djallonké goat or Type I, Sahelian goat or Type II, and their Crossbreeds or Type III) under climate change scenarios. Ten qualitative and 26 linear body measurements were taken on 2114 adult female goats sampled across the three vegetation zones of the country. Fifteen ratios were generated from the quantitative variables. The data were analyzed using generalized linear model procedures followed by multiple comparisons of least-squares means and multivariate analytical methods, including canonical discrimination analysis and hierarchical ascendant classification. Each goat was then assigned to one of the three aforementioned main goat phenotypes following its morphological characteristics and according to the a priori cluster membership defined in the previous step. The Maximum Entropy algorithm was used to model the current and future distribution of the three goat phenotypes under climate change scenario using the Representative Conservation Pathways 4.5 and 8.5. Results: All linear body measurements varied among vegetation zones. In the discriminant function analysis, 71% of the measured individuals were correctly classified in their vegetation zone of origin by seven measured variables and three ratios. The cluster procedure analysis revealed two groups of goats subdivided into the three main phenotypes. The modeling results showed that the currently highly favorable habitats were distributed in the South for Type I, in the North for Type II, and both South and North for Type III. However, under climate change scenarios, the favorable habitats for Type I decreased while those of Types II and III increased. Conclusions: The results of this study confirm the spatial variation of the goat population in Benin. The habitat suitability model can be used to support decision-making toward better management of goat genetic diversity in Benin.

With the increasing frequency of floods in recent decades, particularly in West Africa, many regions have faced unusual and recurrent flooding events. Communities in flood-prone areas experience heightened insecurity, loss of property, and, in some cases, serious injuries or fatalities. Consequently, flood risk assessment and mitigation have become essential. This comparative study between Niamey and Lokoja employs Geographic Information Systems (GIS) and the Analytical Hierarchy Process (AHP) to delineate flood susceptibility, vulnerability, and risk zones. The study utilized a comprehensive range of thematic layers, with weight percentages assigned to each parameter as follows: 29% for elevation, 24% for slope, 15% for the Topographic Wetness Index (TWI), 9% for drainage density, 9% for distance from rivers, 4% for both precipitation and the Normalized Difference Water Index (NDWI), and 2% each for the Normalized Difference Vegetation Index (NDVI) and soil type. To validate these weightings, a consistency ratio was calculated, ensuring it remained below 10%. The findings reveal that 32% of the Niamey study area is at risk of flooding, compared to approximately 15% in Lokoja. The results highlight a very high flood potential, particularly in areas near the Niger River, with this potential decreasing as elevation increases. Given the current prevalence of extreme weather events in West Africa, it is crucial to employ effective tools to mitigate their adverse impacts. This research will assist decision-makers in quantifying the spatial vulnerability of flood-prone areas and developing effective flood risk assessment and mitigation strategies in the region.