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 (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.

Rapid and reliable detection of landslides is essential for disaster response, hazard assessment, and environmental monitoring, particularly in environments where timely information is critical. Recognizing the need, this study proposes a unified framework that integrates pixel-based LULC classification with landslide detection using multispectral satellite imagery. In this framework, landslides are identified as vegetation-to-bare land transitions constrained by temporal, terrain, and morphological conditions, making detection dependent on accurate classification of vegetation and bare land. An optimal classifier configuration was selected through a statistical scheme based on three-way analysis of variance (ANOVA) to ensure reliable classification and applied to pre- and post-event imagery to derive landslide candidate areas. To improve detection reliability, terrain and morphological filters were applied, including a slope constraint selected through sensitivity analysis and a length-to-width (L/W) ratio to reduce false detections. The classification accuracies achieved across the two study sites ranged from 0.85 to 0.89, supporting the detection of approximately 70% of the total landslide area, with precision up to 0.93 and F1-scores of 0.77–0.79. Lower slope thresholds improved detection completeness, while higher thresholds increased omission errors. These findings suggest that the proposed integration of optimized LULC classification and constrained change detection may provide a practical and potentially transferable framework for improving landslide detection performance under varying environmental conditions.

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.