Feasibility and Efficiency of Smart Thinning Operation: A Novel Method of Tree Designation and Localization

Conventional thinning operations rely heavily on manual decision-making and field-flagging conducted by an experienced forest manager, which is both labor-intensive and prone to human error. The shortage of skilled forestry workers further highlights the need for technological innovations in forest management. This study evaluated the feasibility and efficiency of an ICT-assisted smart thinning system compared to conventional thinning practices. By integrating LiDAR-based forest inventory, machine learning-driven tree selection, and GNSS-RTK navigation, the smart thinning system is anticipated to achieve effective thinning operations with reduced labor requirements. Field simulations were conducted in Pinus koraiensis (Korean pine) and Larix kaempferi (Japanese larch) stands at Kangwon National University experimental forest in South Korea. The smart thinning system achieved localization accuracies of 80.3% in Korean pine stands and 97.0% in Japanese larch stands using the GNSS-RTK solution during felling operations. Productivity analysis showed a 54% reduction in overall working time compared to conventional methods, with mechanized tree selection requiring only 12% of the time needed for manual selection. Although felling operation in the smart thinning system took 1.9 times longer due to GNSS-RTK system requirements, the overall productivity increased by 2.2 times. These results demonstrate the potential of the smart thinning system as a practical approach to modernizing forest operations, delivering significant improvements in both efficiency and precision.