Comparison of LiDAR Operation Methods for Forest Inventory in Korea Pine Forest

Precise forest inventory is the key to sustainable forest management. LiDAR technology is applied to tree attribute extraction widely. 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 Diameter at Breast Height (DBH) and tree height in pine forests of South Korea. There were strong correlations for DBH at 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 root mean square error (RMSE) of 1.81 m and a bias of -1.24 m. However, integrating ALS and HMLS data resulted in the most precise of the tree height estimations with RMSEs reaching 1.43 m and bias of ˗0.3 m. Integrated ALS and HMLS and its advantages is a beneficial solution for accurate forest inventory which in turn supports forest management and planning.