文献类型：期刊文献

英文题名：A clustering-based automatic registration of UAV and terrestrial LiDAR forest point clouds

作者：Chen, Junhua[1,2] Zhao, Dan[1,2] Zheng, Zhaoju[1] Xu, Cong[1,2] Pang, Yong[3,4] Zeng, Yuan[1,2]

第一作者：Chen, Junhua

通信作者：Zeng, Y[1];Zeng, Y[2]

机构：[1]Chinese Acad Sci, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[4]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China

年份：2024

卷号：217

外文期刊名：COMPUTERS AND ELECTRONICS IN AGRICULTURE

收录：;EI(收录号：20240415425660);Scopus(收录号：2-s2.0-85182731811);WOS:【SCI-EXPANDED(收录号:WOS:001170206300001)】；

基金：This study was supported by the National Key Research and Devel- opment Program of China (2020YFE0200800 and 2022YFF1302100) and the National Natural Science Foundation of China (No. 42071344) .

语种：英文

外文关键词：Terrestrial laser scanning; Unmanned aerial vehicle; Registration; LiDAR point clouds; Hierarchical clustering; Forest vertical structure

摘要：Unmanned aerial vehicle laser scanning (ULS) and terrestrial laser scanning (TLS) provide complementary, nondestructive approaches to acquire three-dimensional forest structure information. Registration of their point clouds enables the reconstruction of complete vertical structure of forests. Current registration methods are primarily designed to register different TLS scans and thus are not applicable to ULS-TLS registration directly. In this study, the proposed method first generated multi-layer tree maps from ULS and TLS data using hierarchical clustering, then extracted Fast Point Feature Histograms (FPFH) features for each cluster point based on spatial relationships in the tree maps. After that, a point-to-point matching strategy was used to obtain the transformation matrix of each layer between ULS and TLS trunk point clouds and the best matrix from all layers was finally selected for fine registration. The algorithm was validated in 40 sample plots in Guangxi province of China. Our findings indicated that both high-density ULS and TLS data generate accurate tree maps compared to manually counted tree number, with a Concordance Correlation Coefficient (CCC) of 0.961 and 0.973, respectively. The proposed method performed well in registration accuracy and time efficiency, and achieved a higher matching score (0.945 > 0.928) and lower RMSE (0.144 < 0.151) than manual registration. The average registration time per sample plot of 600 m2 was 48.9 s, with 19.4 s dedicated to coarse registration. This research highlights the potential of clustering-based registration methods for effectively aligning ULS-TLS point cloud data in forests, laying the foundation for further technological advancements in forest vertical structure reconstruction.