Reda Fekry;Wei Yao;Lin Cao;Xin She

Co-Innovation Center for Sustainable Forestry, Nanjing Forestry University, 159 Longpan Road, Nanjing, postcode =210037, China;Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong; The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China;Co-Innovation Center for Sustainable Forestry, Nanjing Forestry University, 159 Longpan Road, Nanjing, postcode =210037, Chin

Ground;aerial view mobile LiDAR;Point cloud;Co-registration;Fusion;QSM;Tree parameter retrieval;POINT CLOUDS;TERRESTRIAL;BIOMASS;TLS;COREGISTRATION;REGISTRATION;EXTRACTION;AIRBORNE;DENSITY;VOLUM

Light detection and ranging (LiDAR) has contributed immensely to forest mapping and 3D tree modelling. From the perspective of data acquisition, the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels. This research develops a general framework to integrate ground-based and UAV-LiDAR (ULS) data to better estimate tree parameters based on quantitative structure modelling (QSM). This is accomplished in three sequential steps. First, the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy. Next, redundancy and noise were removed for the ground-based/ULS LiDAR data fusion. Finally, tree modeling and biophysical parameter retrieval were based on QSM. Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest, including poplar and dawn redwood species. Generally, ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data. The fusion-derived tree height, tree volume, and crown volume significantly improved by up to 9.01%, 5.28%, and 18.61%, respectively, in terms of rRMSE. By contrast, the diameter at breast height (DBH) is the parameter that has the least benefits from fusion, and rRMSE remains approximately the same, because stems are already well sampled from ground data. Additionally, particularly for dense forests, the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR. Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests, whereby the improvement owing to fusion is not significant.