Estimating Position, Diameter at Breast Height, and Total Height of Eucalyptus Trees Using Portable Laser Scanning

Forest management planning depends on accurately collecting information on available resources, gathered by forest inventories. However, due to the extent of the planted areas in the world, collecting information traditionally has become challenging. Terrestrial light detection and ranging (LiDAR) has emerged as a promising tool to enhance forest inventory. However, selecting the optimal 3D point cloud density for accurately estimating tree attributes remains an open question. The objective of this study was to evaluate the accuracy of different point densities (points per square meter) in point clouds obtained through portable laser scanning combined with simultaneous localization and mapping (PLS-SLAM). The study aimed to identify tree positions and estimate the diameter at breast height (DBH) and total height (H) of 71 trees in a eucalyptus plantation in Brazil. We also tested a semi-automatic method for estimating total height. Point clouds with densities greater than 100 points/m2 enabled the detection of over 88.7% of individual trees. The root mean square error (RMSE) of the best DBH measurement was 1.6 cm (RMSE = 5.9%) and the best H measurement (semi-automatic method) was 1.2 m (RMSE = 4.2%) for the point cloud with 36,000 points/m2. When measuring the total heights of the largest trees (H > 31.4 m) using LiDAR, the values were always underestimated considering a reference value, and their measurements were significantly different (p-value < 0.05 by the t-test). For point clouds with a density of 36,000 points/m2, the automated DBH and total tree height estimations yielded RMSEs of 5.9% and 14.4%, with biases of 4.8% and −1.4%, respectively. When using point clouds of 10 points/m2, RMSE values increased to 18.8% for DBH and 28.4% for total tree height, while the bias was 6.2% and 18.4%, respectively. Additionally, total tree height estimations obtained via a semi-automatic method resulted in a lower RMSE of 4.2% and a bias of 1.5%. These findings indicate that point clouds acquired through PLS-SLAM with densities exceeding 100 points/m2 are suitable for automated DBH estimation in the studied plantation. Despite the increased processing time required, the semi-automatic method is recommended for total tree height estimation due to its superior accuracy.