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.

This study investigated the linear and nonlinear effects of environmental drivers on burn severity during the 2025 Uiseong wildfire, the largest forest fire recorded in South Korea. Burn severity, quantified using the differenced Normalized Burn Ratio (dNBR), was analyzed with the Generalized Linear Model (GLM) and Generalized Additive Model (GAM). The results identified the Normalized Difference Vegetation Index (NDVI) as the dominant predictor, with approximately eightfold greater importance than any other variable. A clear threshold response showed that the probability of High-severity burns increased sharply when NDVI exceeded approximately 0.25–0.30. Coniferous forests were 3.65 times more likely to experience High-severity burns than broadleaf forests, whereas managed forests exhibited lower burn severity. Topographic and meteorological variables generally had weaker effects, although several showed nonlinear response patterns. Overall, fuel availability and continuity were the primary determinants of burn severity, while topographic and meteorological factors played secondary, modifying roles. However, the model showed limited ability to identify Low-severity areas. These findings provide a scientific basis for improving forest management and wildfire mitigation strategies.