This study aimed to develop a thinning tree selection system by applying genetic algorithms based on precisely estimated tree-level forest structural parameters derived from LiDAR data. Conventional thinning tree selection methods have limitations due to their dependence on subjective judgement and field experience of operators, resulting in inconsistency and variations according to skill levels. To address these issues, tree positions, diameters at breast height (DBH), and tree heights were extracted by integrating terrestrial laser scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (ULS) data, forming a Multi-Platform LiDAR dataset. The derived DBH and Hegyi competition index were utilized as indicators for thinning tree selection. Optimization of tree selection was performed using a genetic algorithm, with an objective function designed to maximize the average DBH and minimize the average competition index of the remaining trees, and the system’s performance was compared with results obtained by forestry experts. The results showed that tree detection accuracy exceeded 99%, DBH estimation exhibited an RMSE of 0.74 cm, and tree height estimation showed an RMSE of approximately 2 m, demonstrating the construction of precise forest structural parameters. Compared to expert driven selection, the Genetic Algorithm-based thinning system produced a higher average DBH (30.06 cm vs. 29.26 cm) and a lower Hegyi competition index (1.31 vs. 1.41) under Scenario 3. This indicates superior performance in competition alleviation and growing space allocation among individual trees. Spatial statistical analysis revealed that while expert selection maintained the existing spatial clustering pattern of stand structure (Global Moran’s I = 0.16), the machine learning system achieved an almost random distribution (Global Moran’s I = −0.04) under Scenario 3. This study demonstrates the potential of overcoming the limitations of conventional thinning practices dependent on subjective judgement by introducing an objective, consistent, data-driven quantitative decision support system for precision forest management.

Natural forest regeneration offers economic, ecological, and environmental advantages over artificial regeneration; however, its application is often constrained by uncertainties in stand development and management outcomes. Pre-commercial thinning (PCT), a key assisted natural regeneration practice, is widely used to regulate stand density and improve early stand development. Nevertheless, empirical evidence remains limited regarding how post-thinning residual density influences both tree growth and operational performance in high-density naturally regenerated Pinus densiflora stands. This study evaluated three residual density treatments (RD2000, RD3000, and RD5000) following PCT in naturally regenerated pine stands with an initial density of approximately 30,000 stems ha⁻¹. Diameter at breast height, tree height, and crown area were monitored annually over three years, while thinning productivity and operational costs were quantified during treatment implementation. Residual density significantly affected both biological and operational outcomes. The intermediate residual density (RD3000) showed the most consistent growth responses, whereas the lowest residual density (RD2000) resulted in suppressed growth. The highest residual density (RD5000) achieved the highest productivity and lowest operational costs despite moderate growth performance. These results indicate a trade-off between growth performance and operational efficiency and suggest that an intermediate residual density may provide a balanced strategy for managing naturally regenerated pine stands.

Thinning effects vary depending on thinning type, intensity, and timing, making it important to compare alternative scenarios before field implementation. We aimed to provide a fundamental basis for selecting appropriate thinning operations by analyzing changes in stand structure and competition release effects under different thinning regimes using LiDAR-based thinning simulations. Specifically, we quantitatively evaluated how the competition releases effect of row thinning varied with the spatial position of residual trees. Among the stand structural variables, significant differences among thinning types were observed only for tree height. Quantitative thinning resulted in a relatively uniform structure dominated by upperstory trees, whereas row thinning retained trees across a relatively wide range of height classes. The reduction in the mean competition index of residual trees ranged from approximately 14.9% to 67.3% depending on thinning type. Quantitative thinning showed greater competition-reduction efficiency relative to the tree removal rate, whereas some row thinning types achieved competition reduction and total harvest volume comparable to those of quantitative thinning. However, the competition-reducing effects of row thinning were concentrated among residual trees adjacent to cutting rows, indicating that the spatial positions of residual trees should be considered when evaluating row thinning effects. Overall, thinning methods should be selected by considering not only predicted long-term growth and volume changes but also stand-management objectives and the operational efficiency associated with each thinning approach.

This research aimed to provide fundamental growth data to support efficient afforestation and forest management, analysing the early growth of Korean red pine (Pinus densiflora Siebold & Zucc.) and Japanese larch (Larix kaempferi (Lamb.) Carr.) at varying planting densities in Korean slope plantation forests (30%–35% slope). Therefore, P. densiflora and L. kaempferi (both 2-year-old (1 + 1) seedlings) were planted in the Gwangneung Experimental Forest, Korea in April 2020, according to three different densities (i.e., P. densiflora: 2500, 3000 and 5000 trees ha⁻¹; L. kaempferi: 2000, 2500 and 3000 trees ha⁻¹). The highest net diameter at breast height (DBH) growth and DBH class (8 cm) of P. densiflora were observed at 2500 and 3000 trees ha⁻¹ groups, while the highest net DBH growth and DBH class (>5 cm) of L. kaempferi trees were observed at 2000 trees ha⁻¹ group. Meanwhile, the planting density of 3000 trees ha⁻¹ did not affect the reduction in the net growth of DBH and stem volume in P. densiflora. Conversely, the net growth of stem volume in L. kaempferi was the lowest in the 2500 trees ha⁻¹ group rather than the 3000 trees ha⁻¹ group. This study indicates that the lowest or highest density does not always equate to the highest or lowest stem volume obtainment and provides useful information for initial setup and optimal density when planting two different species, P. densiflora and L. kaempferi afforestation. Results can be utilized to support the planning of post-reforestation management.