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.

This study aims to segment individual tree structures (stem, crown, and ground) from terrestrial LiDAR-derived point cloud data (PCD) and to compare the segmentation accuracy between two models: XGBoost (machine learning) and PointNet++ (deep learning). A total of 17 input features were categorized into spatial coordinates and normals, geometric structure features, and local distribution features. These were combined into four input configurations and evaluated under three downsampling conditions (2048, 4096, and 8192 points), resulting in 12 experimental setups. XGBoost achieved the highest stem segmentation F₁-score of 87.8% using all features with 8192 points, whereas Point-Net++ reached 92.1% using only spatial coordinates and normals with 4096 points. The analysis of missegmentation patterns showed that XGBoost frequently confused structures near stem-to-ground boundaries and around branch junctions, while PointNet++ occasionally missegmented complex regions between stems and crowns. Regarding processing time, XGBoost required 10 to 47 min across all conditions, whereas Point-Net++ required 49 min for the 2048-point condition and up to 168 min for 8192 points. Overall, XGBoost provided advantages in computational efficiency and in generating feature-importance scores, while PointNet++ outperformed XGBoost in segmentation accuracy and the recognition of structurally complex regions.