3D point cloud fusion from UAV and TLS to assess temperate managed forest structures

Abstract

Light detection and ranging (LiDAR) technology has become one of the most dominant acquisition methods for modeling forest attributes, such as very accurate tree structure information, which is necessary for qualitative forest management and research activities. In this study, we evaluated the efficacy of standalone unmanned aerial vehicle-laser scanning (UAV-LS) and terrestrial laser scanning (TLS) data to accurately estimate forest tree metrics under differing management types. Furthermore, we combined the UAV-LS and TLS data to test whether fusion can improve the mapping of the three-dimensional (3D) structure of individual trees to favor accurate estimates of tree metrics. We initially calculated the percentage of point density per square meter aboveground in each height class at intervals of 1 m for the UAV-LS, TLS, and fusion datasets. This helped illustrate the vertical point density distribution that reflects the structural complexity between broadleaf and conifer trees. We then used tree-level point clouds to assess several tree metrics, such as diameter at breast height (DBH), total tree height (HT), crown projection area (PAC), crown width (WC), crown length (LC), 3D crown surface (SC), and 3D crown volume (VC). Our results indicated that LiDAR fusion can increase the estimation accuracy of DBH and HT, especially in broadleaves (97.8% accuracy). In addition, fusion significantly reshaped the modeled crown structures in both plots, which led to improved estimates for all crown metrics. The results show empirical evidence that LiDAR fusion can also have a determining role in supporting ecosystem services. For example, detailed 3D mapping of tree crowns can be used to assess the mitigation of rainfall`s kinetic energy by tree crowns concerning soil erosion and sedimentation near habitable zones.