LiDAR-Based Thinning Simulation for Changes in Competition Index and Predicted Growth in a Pinus koraiensis Plantation

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.