Previous studies on island biogeography theory have limitations in that they are mostly focused on total plant species and the landscape factors of the islands. Our study was conducted to overcome these limitations by dividing the plants into five growth forms and analyzing climate and landscape factors on inhabited islands, uninhabited islands, and overall. This was achieved using plant data from 578 islands of an archipelago in South Korea. To test the relationship between the species richness of each growth form and environmental factors, we performed ordinary least squares regressions and multi-model inference tests. The results showed that the island area had the largest influence on species richness of all growth forms in overall and uninhabited islands. Moreover, climate factors, in addition to island area, significantly affected species richness of all growth forms on inhabited islands. However, the effect and of isolation-related landscape factors (i.e., distance from the mainland and structural connectivity) were different among growth forms and island categories. Our study reveals that there are differences in the effects of environmental factors on the growth forms of plants among island categories. This suggests that biodiversity management and conservation strategies should be applied separately to different growth forms and islands.

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.