earticle

영어

This study aimed to develop a hybrid stem taper and volume estimation model using multi-platform LiDAR data for Pinus koraiensis (PK) and Larix kaempferi (LK) in a forest located in Gangwon Province, Republic of Korea. The research employed Terrestrial Laser Scanning (TLS) to capture detailed point cloud data of tree stems and Airborne Laser Scanning (ALS) for precise height measurements. By integrating the stem profiles derived from TLS with Kozak’s stem taper model, a hybrid estimation model was constructed to improve the accuracy of calculating individual tree taper curves and stem volumes. The proposed model was tested against traditional volume estimation methods, specifically the Korea Forest Service's standard volume table, to assess its accuracy. The standard volume table method exhibited root mean square error (RMSE) values of 0.12 m³ for PK and 0.13 m³ for LK. In contrast, the hybrid model showed significantly lower RMSE values of 0.07 m³ for PK and 0.05 m³ for LK, representing an accuracy improvement of approximately 42% for PK and 62% for LK. Additionally, the study estimated log production based on individual tree profiles generated from the hybrid model, accounting for factors such as stem length, diameter, and curvature. The increased accuracy of the hybrid model highlights its potential for providing more precise stem volume estimations and improving the reliability of forest inventories. The results suggest that this hybrid approach, leveraging the strengths of both TLS and ALS, offers a more efficient and accurate alternative to traditional volume estimation methods. The precise taper curves and volume estimates derived from this method can significantly contribute to sustainable forest management practices, particularly in assessing timber production and monitoring forest growth. By eliminating the need for destructive sampling, this method provides a non-invasive solution for estimating tree volumes. In conclusion, the multi-platform LiDAR-based hybrid model offers a promising solution for more accurate stem volume estimation, supporting forest inventory development and enhancing decision-making in forest management and timber production.