Advanced geospatial modeling and assessment of land degradation severity zones in India’s semi-arid regions

Abstract

Land degradation poses significant challenges to sustainable development, particularly in semi-arid regions where ecosystems are highly vulnerable. This study employs a cutting-edge geospatial and multi-criteria decision-making (MCDM) approach to delineate land degradation severity zones (LDSZs) in Anantapur district, India—a region characterized by persistent environmental stress. Utilizing thematic layers such as geology, geomorphology, soil properties, slope, and remote sensing indices (NDVI, MNDWI, NDSI, and LST), the study integrates high-resolution Landsat 8 OLI/TIRS (2023) and DEM datasets with local meteorological data for precise spatial analysis. The LDSZ classification identified critical degradation patterns, with river/stream/waterbody areas occupying 3.06% of the landscape and varying severity zones covering the remaining areas: very low (4.58%), low (20.56%), moderate (31.09%), high (27.62%), and very high (13.08%). Validation using the receiver operating characteristic (ROC) curve resulted in an area under the curve (AUC) value of 0.825, demonstrating the model’s reliability. By synthesizing geospatial data and MCDM, this research offers a dynamic framework for mapping and quantifying land degradation. It underscores the pressing need for context-specific land management practices to mitigate severe degradation while paving the way for broader applications in other semi-arid regions. This approach represents a significant leap in assessing and addressing land degradation, providing a robust scientific basis for future interventions and policy development.