In artificial forests, regular thinning is required to promote diameter growth for producing high-quality, large-diameter timber. However, selecting trees for thinning often relies on qualitative and subjective assessments by field workers. The purpose of this study was to develop a quantitative method for selecting trees for thinning by combining machine learning algorithms and competition indices. Our study site included the Pinus koraiensis area within a Kangwon National University research forest in the Republic of Korea. Data from a model development site were used for the basic crown classification model for Pinus koraiensis. The model was optimized by adjusting hyperparameters. Different algorithms, including Random Forest, XGBoost, and LightGBM (LGBM), were improved using Random Search. LGBM showed the highest accuracy of 71.6%. LGBM—in combination with the competition indices—was used to classify the crown class in the application site and select trees for thinning. Compared to the combination of Braathe and Martin-EK indices, the combination of LGBM and Hegyi index enabled the even distribution of the residual stand in the entire site after thinning. It lowered the distribution of hot spots, which represent competition. Thus, the combination of LGBM and Hegyi index was the most effective option to improve the spatial distribution of trees after thinning. Our findings can improve forest management by providing a quantitative and objective method for selecting trees for thinning.

This study developed a forest management plan model using reinforcement learning (Q-learning) to optimize both the economic and ecological functions of forests. Management objectives for national forests were established, and forest conditions were analyzed using GIS spatial data and administrative records. A 60-year forest management plan was formulated to predict timber production and management performance across different regions and time periods. Our analysis revealed that Scenario 3 (Carbon Storage Priority) demonstrated the highest economic value, starting at approximately KRW 576.2 billion in the initial period and escalating to KRW 775.7 billion over six 10-year periods, totaling 60 years. In addition to its economic performance, Scenario 3 effectively improved forest age class structure and ensured a stable timber supply, making it the most balanced approach for sustainable forest management. By focusing on carbon storage as a key management goal, this approach highlights the potential for achieving both economic and environmental benefits concurrently. These results suggest that reinforcement learning is a powerful tool for developing long-term forest management strategies that address multiple objectives, including economic viability, ecological sustainability, and resource optimization.

Wildland–urban interface (WUI) wildfires pose escalating threats worldwide due to climate change and urban expansion. Gangneung City, South Korea, accounts for approximately 56% of provincial wildfire damage over the past decade, highlighting the need for systematic WUI susceptibility assessment. This study developed a high-resolution wildfire susceptibility map for forest-adjacent areas in Gangneung City and validated it using historical fire records from 1991 to 2024. Thirteen susceptibility factors in four categories—forest–topographic, anthropogenic, accessibility, and fire-response—were weighted using a literature-based frequency method and mapped at 10 m × 10 m resolution into four grades (Grade 1: very high to Grade 4: low). High-susceptibility zones (Grades 1–2) covered 51.7% of the total area, rising to 91.9% within interior forest-adjacent zones. Of 206 historical fires, 139 (67.5%) occurred within forest-adjacent zones; 93.4% of interior-zone fires fell in high-susceptibility areas. Hikers’ negligence was the leading ignition cause (30.8%). This is the first South Korean study to map WUI wildfire susceptibility distinguishing interior and exterior forest-adjacent zones at 10 m resolution, with spatial validity confirmed through over 30 years of fire records. The map provides a practical decision-support tool for spatially targeted WUI fire prevention and resource deployment at the local government level.