Land degradation poses a critical threat to global ecosystems, food security, and sustainable development. Identifying priority areas for environmental restoration is essential for optimizing limited conservation resources. This study presents a comprehensive geospatial framework for assessing land degradation and delineating restoration priorities by integrating multi-source remote sensing data, GIS analytics, and machine learning algorithms. We analysed key indicators of degradation, including vegetation cover (NDVI), soil erosion risk (RUSLE model), land surface temperature (LST), and land use/land cover change, across a degraded watershed over a 20-year period (2003-2023). The analytical hierarchy process (AHP) was employed to weight these indicators based on their relative importance in driving degradation processes. Our results revealed that 35% of the study area experiences severe to very severe degradation, primarily in agricultural marginal lands and deforested slopes. The implementation of a random forest classifier achieved 88% accuracy in mapping degradation hotspots. By overlaying degradation severity with conservation feasibility factors (slope, proximity to settlements, land tenure), we identified three priority tiers for restoration intervention. High-priority zones (18% of the degraded area) represent critical locations where restoration would yield maximum ecological and socio-economic benefits. Medium and low-priority zones were also delineated to guide phased implementation strategies. Validation using field data and historical restoration outcomes confirmed the robustness of our priority mapping approach. This study demonstrates that geospatial analysis provides a scientifically rigorous, cost-effective, and scalable methodology for targeting restoration efforts. The developed framework offers land managers and policymakers a practical decision-support tool for optimizing environmental restoration investments and combating land degradation effectively.

land degradation, geospatial, environment, analysis, importance, restauration

environmental engineering

Presentation: poster