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.

Landslides are becoming more frequent and severe due to increasing summer heavy rainfall and typhoons caused by climate change. As a result, the importance of research to predict and detect landslides is also growing. In this study, suitable vegetation indices for detecting and predicting landslides in South Korea were identified based on 1,500 landslides, provided by the National Disaster Safety Institute from 2011 to 2017. Various vegetation indices listed in the Index Data Base (IDB) of the Institute for Crop Science and Resource Conservation (INRES) in Germany were reviewed and selected for construction. Five vegetation indices were constructed using Landsat-7 imagery: Normalized Differential Vegetation Index (NDVI), Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI), Atmospherically Resistant Vegetation Index (ARVI), and Renormalized Differenced Vegetation Index (RDVI). Additionally, the Maxent model was applied to predict landslides using each vegetation index, and the accuracy was measured by comparing the ROC-AUC values. The results showed that the SR vegetation index detected landslides most effectively. Furthermore, the accuracy comparison of the Maxent model revealed that the model using vegetation indices had higher ROC-AUC values compared to the model without vegetation indices, with the SR-based Maxent model yielding the most accurate results. This study provides remote sensing data necessary for creating landslide prediction maps. However, future research should consider factors influencing vegetation indices other than landslides and reflect the extent and magnitude of landslide damage.

This study aims to classify and analyze forest ecosystem recovery patterns in the regions of Busan and Gyeongsangnam-do, South Korea, that were affected by landslides caused by Typhoon Sanba in 2012. To evaluate both the structural and functional recovery of the forest ecosystems, we employed two complementary indicators: the Vegetation Recovery Rate (VRR), derived from time-series NDVI (Normalized Differenced Vegetation Index) data, and the Shannon Diversity Index, calculated from the 1st and 2nd national forest health management surveys. VRR quantified the recovery of vegetation cover based on NDVI changes before and after the disturbance, while the change in the Shannon Index represented functional recovery based on Shannon Index variation. Each indicator was interpreted using the median value as a relative threshold (VRR: 100.26%, Shannon Index difference: 0.1431), allowing spatial classification into four recovery types (A ~ D). Type A (high VRR and high Shannon Index difference) indicates full structural and functional recovery, while Type D (low VRR and low Shannon Index difference) signifies areas requiring priority ecological restoration. Spatial distribution patterns of recovery types revealed that Type A areas were concentrated in Ulsan and Miryang, whereas Type D areas were predominantly found in western Gyeongnam such as Hadong, Geochang, and Sancheong. This classification framework provides a basis for developing region-specific forest restoration strategies and highlights the necessity of integrating both remote sensing and field-based ecological indicators for comprehensive post-disaster assessment

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.