Estimating Position, Diameter at Breast Height, and Total Height of Eucalyptus Trees Using a PLS-SLAM

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 Brazil, collecting information traditionally has become challenging. Based on the factors mentioned above, 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. The main findings indicate that denser point clouds (> 100 points.m-2) provided a more accurate representation of tree stems, successfully segmenting over 88.7% of the trees. The root mean square error (RMSE) of the best DBH measurement was 1.6 cm (5.9%) and of the best H measurement was 1.2 m (4.2%) for the point cloud with 36,000 returns.m-2. 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). Point cloud degradation tended to reduce the accuracy of the DBH estimations, which was more evident in smaller trees (DBH ≤ 27.3 cm). In general, the degradation of the point cloud reduced the accuracy of the H estimates, which was more evident with larger trees (H > 31.4 m). Despite the reduction in accuracy in the conditions described above, we highlight the potential of PLS-SLAM to identify individuals in the plantation and estimate their main attributes.