文献类型：期刊文献

英文题名：Improving Estimation of Tree Parameters by Fusing ALS and TLS Point Cloud Data Based on Canopy Gap Shape Feature Points

作者：Zhou, Rong[1,2,3] Sun, Hua[1,2,3] Ma, Kaisen[1,2,3,4] Tang, Jie[1,2,3] Chen, Song[1,2,3] Fu, Liyong[1,5] Liu, Qingwang[5]

第一作者：Zhou, Rong

通信作者：Sun, H[1];Sun, H[2];Sun, H[3]

机构：[1]Cent South Univ & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China;[2]Hunan Prov Key Lab Forestry Remote Sensing Based B, Changsha 410004, Peoples R China;[3]Key Lab Natl Forestry & Grassland Adm Forest Resou, Changsha 410004, Peoples R China;[4]Hunan Univ Sci & Technol, Natl Local Joint Engn Lab Geospatial Informat, Xiangtan 411201, Peoples R China;[5]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2023

卷号：7

期号：8

外文期刊名：DRONES

收录：;EI(收录号：20243416893605);Scopus(收录号：2-s2.0-85169149193);WOS:【SCI-EXPANDED(收录号:WOS:001057256600001)】；

基金：This research is supported by the National Science and Technology Major Project of China's High Resolution Earth Observation System (Project Number: 21-Y20B01-9001-19/22), the Hunan Provincial Natural Science Foundation of China (2022JJ30078), and the Natural Science Foundation of China (31971578).

语种：英文

外文关键词：point cloud registration; canopy gap; terrestrial laser scanning; airborne laser scanning; tree height

摘要：Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) are two ways to obtain forest three-dimensional (3D) spatial information. Due to canopy occlusion and the features of different scanning methods, some of the forest point clouds acquired by a single scanning platform may be missing, resulting in an inaccurate estimation of forest structure parameters. Hence, the registration of ALS and TLS point clouds is an alternative for improving the estimation accuracy of forest structure parameters. Currently, forest point cloud registration is mainly conducted based on individual tree attributes (e.g., location, diameter at breast height, and tree height), but the registration is affected by individual tree segmentation and is inefficient. In this study, we proposed a method to automatically fuse ALS and TLS point clouds by using feature points of canopy gap shapes. First, the ALS and TLS canopy gap boundary vectors were extracted by the canopy point cloud density model, and the turning or feature points were obtained from the canopy gap vectors using the weighted effective area (WEA) algorithm. The feature points were then aligned, the transformation parameters were solved using the coherent point drift (CPD) algorithm, and the TLS point clouds were further aligned using the recovery transformation matrix and refined by utilizing the iterative closest point (ICP) algorithm. Finally, individual tree segmentations were performed to estimate tree parameters using the TLS and fusion point clouds, respectively. The results show that the proposed method achieved more accurate registration of ALS and TLS point clouds in four plots, with the average distance residuals of coarse and fine registration of 194.83 cm and 2.14 cm being much smaller compared with those from the widely used crown feature point-based method. Using the fused point cloud data led to more accurate estimates of tree height than using the TLS point cloud data alone. Thus, the proposed method has the potential to improve the registration of ALS and TLS point cloud data and the accuracy of tree height estimation.