Comparison of LiDAR Operation Methods for Forest Inventory in Korean Pine Forests

Precise forest inventory is the key to sustainable forest management. LiDAR technology is widely applied to tree attribute extraction. Therefore, this study compared DBH and tree height derived from Handheld Mobile Laser Scanning (HMLS), Airborne Laser Scanning (ALS), and Integrated ALS and HMLS and determined the applicability of integrating HMLS and ALS scanning methods to estimate individual tree attributes such as diameter at breast height (DBH) and tree height in pine forests of South Korea. There were strong correlations for DBH at the individual tree level (r > 0.95; p < 0.001). HMLS and Integrated ALS-HMLS achieved high accuracy for DBH estimations, showing Root Mean Squared Error (RMSE) of 1.46 cm (rRMSE 3.7%) and 1.38 cm (rRMSE 3.5%), respectively. In contrast, tree height obtained from HMLS was lower than expected, showing an RMSE of 2.85 m (12.74%) along with a bias of −2.34 m. ALS data enhanced the precision of tree height estimations, achieving a RMSE of 1.81 m and a bias of −1.24 m. However, integrating ALS and HMLS data resulted in the most precise tree height estimations resulted in a reduced RMSE to 1.43 m and biases to −0.3 m. Integrated ALS and HMLS and its advantages are a beneficial solution for accurate forest inventory, which in turn supports forest management and planning.