South Korea has abundant forest resources capable of supplying the domestic wood demand. Despite the extensive forest resources, there is continued uncertainty about the nature, quantity, and quality of the timber contained in any particular forested area. Additionally, some technical, logistic, and economic challenges act as barriers to the expansion of domestic timber utilization. To overcome these limitations and to enhance the domestic timber utilization in South Korea, this study investigated the optimal location of potential forest industry clusters. The potential forest availability was estimated based on localized allometric equations. The integration of the analytical hierarchy process and GIS modeling, including a supply chain that minimizes transportation costs, allowed the identification of optimal forest industry clusters locations that balanced the economic, environmental, and social dimensions within the forest industry supply chain. The study reveals that the estimated potential forest resources availability presented approximately 1 billion m³, including sawlog (474 million m³) and pulpwood grade (541 million m³). Additionally, 45 percent of the sawlogs and 48 percent of the pup grade wood were produced from the Gangwon and Gyeongsangbuk-do regions. Furthermore, the logistic analysis indicates that ten potential forest industry clusters are best aligned with the optimal socio-economic impacts with minimized timber transportation costs. To identify the optimal size and number of potential forest industry clusters, further studies that consider fixed and variable costs for maintaining the forest industry clusters are required.

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.

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 (LSs) are a natural phenomenon, typically triggered by heavy rainfall and earthquakes, and pose a global danger to human lives and property. LSs frequently occur on steep slopes and in loose soil conditions, and damaged sites can be hard to reach thereafter. Thus, remote approaches would be relevant and practical for investigations, and the use of an Unmanned Aerial Vehicle (UAV) for imaging, along with robust methods to identify and quantify LS-affected areas, is recommended. Therefore, this study proposed a multi-teacher–student knowledge distillation (KD) deep learning framework for landslide semantic segmentation. The lightweight LeMoRe model is selected for the learner model (student), and SegNet, U-Net, DeepLabV3+, and LinkNet are used as the teacher networks. To establish the UAV training dataset, we conducted UAV imaging for four LS sites, including Mogo, Buri, Oejeong, and Sangneung (testing site) villages, in Sancheong County, South Korea, in 2025. First, all plain networks are trained on the training dataset, and then KD is applied for single-teacher and multi-teacher KD. Subsequently, ablation studies are implemented to identify the optimal KD component set and the best student–teacher combination (with equal weights) for subsequent experiments. In addition, the equal-weight combination is tuned for the optimal teacher weight proportion to maximize the teacher knowledge transfer to the student. The best model weight is saved for inference with the testing site of Sangneung to map the LS-damaged areas. The experiment results reveal an improvement in accuracy (6.3% F1, 8.2% IoU with equal weight and 12.3% F1 and around 16.8% IoU with the tuned weight). The inference results for the Sangneung testing site were qualitatively and quantitatively evaluated through visual comparison with the original UAV imagery with three zoom-in areas presented for detailed examination. Finally, efficiency, the trade-off between accuracy and efficiency, limitations, and practical KD are discussed.

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.