Impacts of land use land cover changes in mountainous catchments – a case study of Klaserie Catchment, Mariepskop Mountain

Land use and land cover (LULC) change remains a central driver of ecological transformation, particularly in semi-arid mountainous catchments where terrain complexity, limited data availability, and climate variability intersect with increasing human population and activity. This study examines the drivers, spatial–temporal patterns, and hydrological implications of LULC change in the Klaserie Catchment, located in the semi-arid eastern region of South Africa, over a 34-year period (1990–2024). The study employed a remote sensing–based methodological framework integrating Landsat and Sentinel-2 imagery with machine learning algorithms, specifically Simple Non-Iterative Clustering (SNIC) and the Random Forest classifier, processed within Google Earth Engine (GEE). This approach was used to detect and delineate spatiotemporal changes across four major LULC categories: vegetation, bare land, built-up areas, and water bodies. Classification accuracies averaged (91–97%) with Kappa values of 0.84–0.94. Built-up areas increased by 199%, bare land declined by 30%, vegetation expanded slightly, and water bodies remained stable. The detected shifts in vegetation, bare land, and built-up areas further imply potential disruptions to hydrological processes in this mountainous catchment. Increased open surfaces and vegetation change may alter runoff generation, reduce infiltration, and affect baseflow regulation, with implications for streamflow variability, sediment transport, and water availability downstream. These findings underscore the urgent need for integrated land use planning, sustainable resource management, and climate-resilient policies to balance development pressures with the conservation of ecosystems and water security in semi-arid catchments.

Grantor dc:publisher.institution

Universty of the Western Cape

Year dc:date.issued

2026

Author dc:creator

• Yono, Anothando

• Earth observation
• environmental change
• hydrological implications
• machine learning classification
• semi-arid catchments.

Licence

• https://uwcscholar.uwc.ac.za/bitstreams/b7423399-b82c-4d0e-8c37-309f565746a0/download