Climate change has prolonged droughts and intensified heatwaves worldwide, increasing the frequency, intensity, and spatial extent of forest fires. In South Korea, an increasing incidence of large forest fires has created a need for rapid, accurate forest fire-damaged area boundary delineation to support post-fire recovery and restoration planning. This is particularly important in South Korea, where the forest-fire season is followed by the summer rainy season, increasing the risk of post-fire soil erosion and slope instability and thereby increasing the need for rapid stabilization and restoration measures. However, previous studies have reported varying delineation performance depending on the input configuration, suggesting the need to examine how different spectral inputs and their integration affect boundary delineation. To address this limitation, we propose a hierarchical ensemble U-Net framework for forest fire-damaged area boundary delineation using Sentinel-2 imagery. To design the input data structure, differences between burned and unburned areas were examined using the Blue, Green, Red, RedEdge, and NIR bands and the NDVI and dNDVI indices, and four three-channel combinations (Blue–Green–Red, Blue–Green–RedEdge, Green–Red–NIR, and Green–RedEdge–NIR) and two single indices (NDVI and dNDVI) were chosen as inputs. Three U-Net models were then evaluated: a single-channel dNDVI model, a three-channel NIR-RE-G model, and a hierarchical ensemble model, which integrated the outputs of six independently trained U-Nets. Among the three models, the single-channel dNDVI model showed higher overall accuracy (84.1%), producer’s accuracy (89.0%), and F1-score (0.80) than the three-channel NIR-RE-G model (82.9%, 70.7%, and 0.77, respectively), whereas the NIR-RE-G model achieved higher user accuracy (91.8%); the hierarchical ensemble model yielded the best overall performance, with the highest overall accuracy (85.7%), producer’s accuracy (92.2%), and F1-score (0.83). This suggests that integrating complementary spectral responses can mitigate the limitations of single-input models and support more robust boundary delineation across the six tested wildfire cases in South Korea.

Pine wilt disease (PWD), a major forest disease, has spread across the Republic of Korea since its first reported occurrence in 1988. Control measures are implemented only once damage is confirmed, and they are planned within administrative boundaries, even though the disease may spread across regions. Consequently, both national and regional distribution patterns must be examined. This study was conducted to investigate the spatiotemporal dynamics of PWD between 2021 and 2024 using nationwide inspection records. Spatial clustering was assessed using Global Moran’s I, local concentrations were identified through Hot Spot Analysis, and the movement and orientation of distribution centers were tracked using Mean Center and Standard Deviational Ellipse analyses. Across all years, Moran’s I values ranged from 0.32 to 0.41, indicating significant spatial clustering. Hot spot areas continued to expand until 2024. The Daegu–Gyeongbuk region consistently exhibited the highest proportion of hot spots, while the Daejeon–Sejong–Chungnam region showed notable increases in later years. The center of the detected distribution shifted from a northeastward to a northwestward direction, and regional distribution patterns varied in terms of both contraction and expansion. These findings demonstrate that the detected distribution of PWD does not follow a uniform national pattern but develops heterogeneously across regions, providing baseline information for early monitoring and region-specific control strategies.

Landslides triggered by heavy rainfall have become increasingly frequent and severe, creating a need for the rapid and accurate detection of damaged areas for post-disaster response and recovery planning. This study developed a ChangeFormer-based landslide damage detection model using single-channel differenced Normalized Difference Vegetation Index (dNDVI) imagery derived from pre- and post-event Sentinel-2 data. Landslide reference data were used to construct a patch-based training dataset, and model generalization was evaluated in Sancheong-gun and Hapcheon-gun, Gyeongsangnam-do, Republic of Korea, where landslide damage was reported following heavy rainfall in 2025. As available reference data differed between these regions, region-specific validation strategies were applied. In Sancheong-gun, polygon and point reference data were used for quantitative validation. The model achieved a producer’s accuracy of 100% in the polygon-based validation, detecting all 12 reference damaged sites, and point-based validation showed increasing accuracy with buffer radius, reaching 87.9% accuracy at 80–100 m. In Hapcheon-gun, where quantitative reference data were unavailable, qualitative assessment based on drone imagery indicated that the detected areas were generally consistent with locations believed to represent actual landslide damage. These results suggest that the proposed framework is effective for the post-disaster spatial assessment of landslide-damaged areas.

Landslides triggered by heavy rainfall have become increasingly frequent and severe, creating a need for the rapid and accurate detection of damaged areas for post-disaster response and recovery planning. This study developed a ChangeFormer-based landslide damage detection model using single-channel differenced Normalized Difference Vegetation Index (dNDVI) imagery derived from pre- and post-event Sentinel-2 data. Landslide reference data were used to construct a patch-based training dataset, and model generalization was evaluated in Sancheong-gun and Hapcheon-gun, Gyeongsangnam-do, Republic of Korea, where landslide damage was reported following heavy rainfall in 2025. As available reference data differed between these regions, region-specific validation strategies were applied. In Sancheong-gun, polygon and point reference data were used for quantitative validation. All 12 reference-defined damaged sites were intersected by the model predictions, corresponding to a site-level detection rate of 100%. Point-based assessment showed an increasing distance-based detection rate with increasing positional tolerance, reaching 87.9% within the 80–100 m tolerance range. This result was interpreted as positional agreement between the reference points and predicted damaged areas rather than as overall accuracy. In Hapcheon-gun, where official polygon- and point-based reference data were unavailable, qualitative external validation using drone imagery indicated that the predicted areas were generally consistent with locations interpreted as landslide damage. These results suggest that the proposed framework is effective for the post-disaster spatial assessment of landslide-damaged areas.