Spatially Explicit Erosion Severity as a Proxy for Landslide Susceptibility in a Mountainous Watershed

Spatial assessment of soil erosion through the application of the RUSLE model and geospatial technologies in the upper and middle reaches of the Tafna watershed (northwestern Algeria)

Assessment of annual soil loss and soil erosion susceptibility in a tropical sub-basin of the Western Ghats, Southern India using RUSLE and AHP

The Western Ghats (WG) of southern India represent a geomorphologically sensitive terrain where soil erosion and landslides constitute major geohazards. The Chittar sub-basin (CSB) of the Achankovil River Basin (ARB) was selected for investigation owing to its steep slopes, high-intensity rainfall, and increasing anthropogenic pressure, which together accentuate erosional processes. This study employs remote sensing and GIS techniques in conjunction with the Analytical Hierarchy Process (AHP) and the Revised Universal Soil Loss Equation (RUSLE) to quantify annual soil loss and delineate erosion susceptibility zones. High erosion affects 684.11 ha (24.8%) of the basin, whereas low erosion is observed across 780.03 ha (28.3%). Multicriteria evaluation indicates that vegetation cover (NDVI), rainfall, soil texture, soil depth, slope, aspect, and elevation exert the greatest influence on erosion susceptibility. Spatial analysis reveals that the western sector of the basin is most vulnerable, the central region exhibits moderate susceptibility, and the northwestern part remains comparatively stable. The results provide a process-based understanding of erosion dynamics in tropical high-relief basins and furnish a decision-support framework for prioritizing soil and water conservation strategies in the WG.

Landslide Susceptibility Mapping of the Beshisahar-Chame Mountain Road Segment, Nepal Using Geographic Information System and the Analytical Hierarchy Process

Landslides represent an incessant natural hazard in the mountainous topography of Nepal, where steep slopes, complex geology, intense monsoonal rainfall, and speedily expanding road infrastructure interact to magnify slope instability. This study evaluates anticipated landslide susceptibility along the Beshisahar-Chame mountainous road segment in central Nepal using a geographic information system (GIS) based multi-criteria decision analysis framework. Ten crucial landslide conditioning factors were considered, including slope, aspect, curvature, geology, soil type, land use and land cover, rainfall, drainage density, distance to drainage, and distance to road. The Analytical Hierarchy Process (AHP) was employed to derive relative weights for each factor through pairwise comparisons, confirming consistency of expert judgment. Separate thematic layers were generated from digital elevation models, satellite imagery, published datasets, and landslide inventory in the field, and successively integrated using weighted overlay analysis within a GIS surroundings. The resulting landslide susceptibility map categorises the study area into five zones which are very low, low, moderate, high, and very high susceptibility. Results point that 12.32% of the segment falls within very high susceptibility zones, while 29.23% and 33.55% are considered as high and moderate susceptibility, respectively. Highly susceptible areas are primarily associated with steep, south-facing slopes, weak and fractured lithologies, proximity to roads and drainage networks, and zones of extreme rainfall. The findings spotlight the dominant role of geology, slope gradient, and anthropogenic interventions, particularly unplanned road construction, in prompting landslides. The susceptibility map provides a powerful spatial structure for disaster risk reduction, infrastructure planning, and slope management, aiding informed decision-making for safer road development and sustainable land-use planning in the Himalaya territory.

Application of GIS techniques for landslide susceptibility mapping in the mountainous regions of Fetakgomo-Tubatse Municipality

Landslides constitute a significant geohazard in mountainous regions where complex interactions between terrain morphology, geological structures, hydrological conditions, and anthropogenic activities contribute to slope instability. Despite increasing concerns regarding slope failures along critical transportation corridors in South Africa, regional-scale comparative landslide susceptibility assessments remain limited, particularly within the structurally complex terrains of the Bushveld Igneous Complex. This study presents a GIS-based landslide susceptibility assessment of the Fetakgomo-Tubatse Municipality, Limpopo Province, South Africa, with particular emphasis on engineered slopes along the R37 and R555 transport corridors. Ten landslide conditioning factors, including slope angle, aspect, elevation, lithology, soil type, rainfall, land use/land cover, and proximity to roads, rivers, and geological lineaments, were integrated using three susceptibility modelling approaches: the Analytical Hierarchy Process (AHP), Fuzzy Logic, and the Extreme Gradient Boosting (XGBoost) machine-learning algorithm. The AHP model employed expert-derived pairwise comparisons and achieved an acceptable consistency ratio of 0.074, while the Fuzzy Logic model incorporated membership functions and a gamma operator to represent environmental uncertainty and gradual susceptibility transitions. Model performance was evaluated using Receiver Operating Characteristic Area Under the Curve (ROC-AUC), confusion matrix analysis, overall accuracy, balanced accuracy, McNemar’s test, and no-information rate statistics. The results indicate that the Fuzzy Logic model achieved the highest predictive performance (ROC-AUC = 0.769), followed by the AHP model (ROC-AUC = 0.757) and the XGBoost model (ROC-AUC = 0.739). Confusion matrix evaluation further confirmed the superior classification performance of the Fuzzy Logic model through higher overall and balanced accuracy values. High-susceptibility zones were concentrated along steep slopes, weathered lithological units, structurally controlled terrains, and road-cut sections where geological discontinuities interact with anthropogenic slope modifications. The findings demonstrate that uncertainty-based susceptibility modelling provides improved predictive capability within geologically heterogeneous terrains and highlight the value of integrating expert knowledge, spatial analysis, and machine-learning techniques for landslide hazard assessment. This study contributes one of the first comparative susceptibility assessments for the Eastern Limb of the Bushveld Igneous Complex and provides a transferable framework for infrastructure planning, disaster risk reduction, and slope management in mountainous environments worldwide.

Soil erosion risk assessment using RUSLE and geospatial techniques in Peruvamba sub-watershed, Kerala, India

Soil erosion is a major environmental challenge in the humid tropical landscapes of the Western Ghats, where intense monsoonal rainfall, steep terrain, and land-use changes accelerate land degradation. This study evaluates soil erosion risk in the Peruvamba sub-watershed of Kerala, India, using the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information System (GIS) techniques. Rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and conservation practice (P) factors were derived from multi-year rainfall records (2014–2023), soil survey data, satellite imagery, and digital elevation models. The average rainfall erosivity was estimated at 2710.67 MJ mm ha⁻¹ h⁻¹ yr⁻¹, while the LS-factor reached a maximum value of 28.88 in steep upland areas, indicating a strong topographic influence on erosion processes. The RUSLE-based assessment estimated a total annual soil loss of approximately 72,500 t yr⁻¹ and a mean soil loss of 17.4 t ha⁻¹ yr⁻¹, exceeding the commonly accepted soil-loss tolerance threshold for tropical regions. Forested and wetland areas exhibited low erosion rates, whereas agricultural lands, plantations, and scrub-dominated areas experienced moderate to severe erosion. Approximately 5.3% of the watershed was classified as very high erosion risk, while 14.7% of the area fell within high to very high erosion categories, representing priority zones for conservation intervention. The results demonstrate that topography and vegetation cover are the dominant controls on soil-loss variability and highlight the importance of targeted soil and water conservation measures, including contour bunding, terracing, and vegetation restoration. Comparison with published erosion estimates from comparable Western Ghats watersheds and sensitivity analysis indicated good agreement and supported the reliability of the model outputs. The integration of RUSLE and GIS provides a robust and scalable framework for erosion-risk assessment and supports sustainable watershed management in the Western Ghats and other tropical environments.

Prioritizing soil erosion susceptible region of Kempty watershed-based on morphometric, hypsometry, forest, and soil using multi-criteria analysis

Soil erosion is the main driving force of several devastating natural hazards in the complex mountainous terrain of the Himalayas. The significant impact of soil erosion includes poor soil productivity and soil type, degraded water quality, land degradation, sedimentation, siltation and ultimately biodiversity losses. Hence, it is necessary to assess soil erosion and prioritize the susceptibility regions for effective control measures to serve as baseline data. The present study attempted to analyze the various morphometric, forest, and soil type parameters through Geographical Information System (GIS) and Multi-Criteria Analysis (MCA) techniques for the identification of soil erosion susceptible regions in forest-imparted Kempty watershed, Mussoorie (Dehradun, Uttarakhand, India). The watershed has been divided into four sub-watersheds (SWS1, SWS2, SWS3, and SWS4) using topographic maps (1:50,000 scale and 20 m contours) and ASTER DEM (30 m resolution). Ranking sub-watersheds that significantly influence erosion has been done based on the integrated analysis of morphometric parameters with forest cover, slope, soil, and hypsometric parameters using MCA. Ranks were integrated with MCA to give in four major classes, i.e., classified from 1 to 4 in which, rank 1 indicates the highest priority; while 4 indicates the lowest priority. Notably, MCA priority ranks depicted that SWS4 has the lowest Cp value (2.27) followed by SWS1 (2.36) suggesting very high priority; whereas, the highest Cp value (2.73) of SWS2 and SWS3 (2.52) showed low priority and medium priority, respectively. The prioritization findings identified as a soil erosion-susceptible area can be recommended for adequate control measures for soil erosion and to reduce surface runoff.