Evaluating Landslide Detection and Prediction Potential Using Satellite-Derived Vegetation Indices in South Korea

This study assessed the effectiveness of vegetation index change metrics (ΔVI = Post − Pre) derived from Sentinel-2 imagery for detecting landslide-affected areas and evaluating their relationship with rainfall intensity, thereby enhancing the early-warning potential. The analysis focused on Sancheong-gun, Gyeongsangnam-do, South Korea, where intense rainfall in July 2025 triggered multiple landslides. Pre- and post-event Sentinel-2 Level-2A images (10 m spatial resolution) were used to compute changes in the Normalized Difference Vegetation Index (ΔNDVI), Soil-Adjusted Vegetation Index (ΔSAVI), Modified Soil-Adjusted Vegetation Index (ΔMSAVI), Normalized Difference Moisture Index (ΔNDMI), and Global Vegetation Moisture Index (ΔGVMI) over the landslide-affected post-disaster (PD) and non-damaged (ND) areas. Sensitivity was assessed based on the differences in mean ΔVI between the PD and ND areas, Welch’s t-statistics, and Cohen’s d values. All indices exhibited significant differences between the PD and ND areas (p < 0.001), with ΔMSAVI showing the highest sensitivity (MSAVI > GVMI ≈ SAVI > NDVI > NDMI). Correlation analysis revealed that ΔMSAVI had the strongest positive association with rainfall accumulation (72 h: r = 0.54; 7 days: r = 0.49), indicating that greater rainfall corresponded to stronger vegetation degradation signals. These findings highlight ΔMSAVI as a robust and responsive indicator of rainfall-triggered landslides, supporting its integration into satellite-based early-warning and rapid damage detection systems for improved landslide monitoring and response.