Soil scientists can aid in an essential part of ecological conservation and rehabilitation by quantifying soil properties, such as soil organic carbon (SOC), and is stock (SOCs) SOC is crucial for providing ecosystem services, and, through effective C-sequestration, the effects of climate change can be mitigated. In remote mountainous areas with complex terrain, such as the northern Maloti-Drakensberg in South Africa and Lesotho, direct quantification of stocks or even obtaining sufficient data to construct predictive Digital Soil Mapping (DSM) models is a tedious and expensive task. Extrapolation of DSM model and algorithms from a relatively accessible area to remote areas could overcome these challenges. The aim of this study was to determine if calibrated DSM models for one headwater catchment (Tugela) can be extrapolated without re-training to other catchments in the Maloti-Drakensberg region with acceptable accuracy. The selected models were extrapolated to four different headwater catchments, which included three near the Motete River (M1, M2, and M3) in Lesotho and one in the Vemvane catchment adjacent to the Tugela. Predictions were compared to measured stocks from the soil sampling sites (n = 98) in the various catchments. Results showed that based on the mean results from Universal Kriging (R² = 0.66, NRMSE = 0.200, and ρ_c = 0.72), least absolute shrinkage and selection operator or LASSO (R² = 0.67, NRMSE = 0.191, and ρ_c = 0.73) and Regression Kriging with cubist models (R² = 0.61, NRMSE = 0.184, and ρ_c = 0.65) had the most satisfactory outcome, whereas the soil-land inference models (SoLIM) struggled to predict stocks accurately. Models in the Vemvane performed the worst of all, showing that that close proximity does not necessarily equal good similarity. The study concluded that a model calibrated in one catchment can be extrapolated. However, the catchment selected for calibration should be a good representation of the greater area, otherwise a model might over- or under-predict SOCs. Successfully extrapolating models to remote areas will allow scientists to make predictions to aid in rehabilitation and conservation efforts of vulnerable areas.

Landslide mapping is essential for hazard assessment and mitigation efforts, with remote sensing techniques offering valuable tools for detecting and monitoring changes in land cover associated with landslide events. In this study, a pixel-based change detection approach with fuzzy membership functions was employed to map landslide scars in both the Pinetown and Port St. Johns areas of South Africa. Covariates including the Normalized Difference Vegetation Index (NDVI), slope, and Vertical Distance to Channel Network (VDCN) were selected to detect changes in land cover indicative of landslide occurrence. High-resolution satellite imagery from PlanetScope (3 m) was utilized to capture small-scale landslide features, enhancing the accuracy of the mapping process. Results from the calibration area (Pinetown) have a Kappa value of 0.60, F1 score of 0.93, and an overall accuracy of 0.88, whereas extrapolation areas (Port St. Johns) resulted in a Kappa value of 0.70, F1 score of 0.95, and an overall accuracy of 0.92%. Challenges such as spatial incoherence and over-detection were encountered, highlighting the need for careful interpretation and refinement of the mapping approach. Extrapolation of the pixel-based change detection method from the Pinetown to the Port St. Johns area revealed limitations associated with differences in land use practices, surface cover, and availability of satellite imagery. Despite these challenges, the approach detected additional landslide occurrences in both study areas. Overall, this study demonstrates the effectiveness of pixel-based change detection with fuzzy membership functions for mapping landslide scars in diverse settings.

Landslides are a major natural hazard capable of causing severe damage to infrastructure, ecosystems, and human life. They result from complex interactions of geological, hydrological, and environmental factors, with soil properties playing a crucial role by influencing the mechanical behavior and moisture dynamics of slope materials that drive initiation and progression. In South Africa, few studies have examined soil influences on landslide susceptibility, and none have been conducted in the Eastern Cape Province. This study investigated the role of soil physical and chemical properties in landslide susceptibility by comparing profiles from landslide scars and stable sites in the Port St. Johns and Lusikisiki region. Samples from topsoil and subsoil horizons were analyzed for soil organic matter (SOM), cation exchange capacity (CEC), saturated hydraulic conductivity (Ksat), exchangeable sodium adsorption ratio (SARexc), and texture. Statistical analyses included the Shapiro–Wilk test to evaluate data normality. For inter-profile comparisons, Welch’s t-test was applied to normally distributed data, while the Mann–Whitney U test was used for non-normal distributions. Intra-profile differences across more than two groups were assessed using the Kruskal–Wallis test for the non-normally distributed data. Results showed that landslide-prone soils had higher SOM, CEC, and Ksat in topsoil, promoting moisture retention and rapid infiltration, which favor pore pressure build-up and slope failure. Non-landslide soils displayed higher sodium-related indices and finer textures, suggesting more uniform water retention and resilience. Vertical variation in landslide soils indicated hydraulic discontinuities, fostering perched saturation zones. Findings highlight landslide initiation as a product of interactions between hydromechanical gradients and chemical dynamics.

Rain-induced landslides pose a growing threat to communities in South Africa’s high-rainfall regions, particularly in steep, densely settled and hydrologically complex catchments. The aim of this study was to assess the spatio-temporal response of slope stability to rainfall events using a coupled hydrological–slope stability model. The April 2022 floods in the Durban/Pinetown area (catchment U60F) served as a case study for evaluating the predictive capability of the factor of safety (Fs) under different thresholds. The calculated Fs values reflected the impact of rising soil moisture on slope instability, with model outputs highlighting increasing landslide susceptibility in response to cumulative rainfall. The model’s performance was assessed using confusion matrices, receiver operator characteristic curves and area under the curve values. While the traditional Fs < 1 threshold yielded a moderate specificity (0.67) but low sensitivity (0.38), an optimised threshold of Fs = 1.43 improved sensitivity to 0.78, although with a trade-off in precision and specificity. The Fs-based model captures broad spatial patterns of landslide susceptibility, but its predictive power remains moderate (area under the curve = 0.63) and is limited by the absence of infrastructural and anthropogenic triggers, such as road culverts, terracing and retaining walls, in the modelling framework. Nonetheless, when calibrated appropriately and integrated into early warning systems, Fs mapping provides valuable insights into landslide risk during extreme rainfall events.