Individual Tree Identification and Segmentation in Pinus spp. Stands through Portable LiDAR

Abstract

Forest inventories are essential for sustainable forest management. In inventories at the tree level, all the information is linked to individuals: species, diameter, height, or spatial distribution, for example. Currently, the implementation of Portable LiDAR (PLS) is being studied, aiming to digitalize forest environments and increase the reliability of forest observations. Performing automatic individual tree identification (ITD) and segmentation (ITS) is essential for the operational implementation of PLS in forestry. Multiple algorithms have been developed for performing these tasks in LiDAR point clouds. Their performance varies according to the LiDAR system and the characteristics of the stand. In this study, the performance of several ITD and ITS algorithms is analyzed in very high-density PLS point clouds in Pinus species stands with a varying presence of understory, shrubs, and branches. The results showed that ITD methods based on finding trunks are more suitable for tree identification in regular stands with no understory. In the ITS process, the methods evaluated are highly conditioned by the presence of understory and branches. The results of this comparison help to identify the most suitable algorithm to be applied to these types of stands, and hence, they might enhance the operability of PLS systems.