文献类型：期刊文献

英文题名：Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features

作者：Ma, Zhenyu[1,2] Pang, Yong[1] Wang, Di[3] Liang, Xiaojun[1] Chen, Bowei[1,4] Lu, Hao[1,5] Weinacker, Holger[2] Koch, Barbara[2]

第一作者：Ma, Zhenyu

通信作者：Pang, Y[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Univ Freiburg, Dept Environm & Nat Resource, D-79104 Freiburg, Germany;[3]Aalto Univ, Dept Built Environm, Aalto 00076, Finland;[4]Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;[5]Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China

年份：2020

卷号：12

期号：7

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20201908635596);Scopus(收录号：2-s2.0-85084257428);WOS:【SCI-EXPANDED(收录号:WOS:000537709600028)】；

基金：This research was funded by National Key Research and Development Program (2017YFD0600404) and CAF Foundation (CAFYBB2016ZD004)

语种：英文

外文关键词：light detection and ranging (LiDAR); airborne laser scanning (ALS); individual tree crown (ITC) segmentation; larch plantation; region growing; canopy morphology

摘要：The detection of individual trees in a larch plantation could improve the management efficiency and production prediction. This study introduced a two-stage individual tree crown (ITC) segmentation method for airborne light detection and ranging (LiDAR) point clouds, focusing on larch plantation forests with different stem densities. The two-stage segmentation method consists of the region growing and morphology segmentation, which combines advantages of the region growing characteristics and the detailed morphology structures of tree crowns. The framework comprises five steps: (1) determination of the initial dominant segments using a region growing algorithm, (2) identification of segments to be redefined based on the 2D hull convex area of each segment, (3) establishment and selection of profiles based on the tree structures, (4) determination of the number of trees using the correlation coefficient of residuals between Gaussian fitting and the tree canopy shape described in each profile, and (5) k-means segmentation to obtain the point cloud of a single tree. The accuracy was evaluated in terms of correct matching, recall, precision, and F-score in eight plots with different stem densities. Results showed that the proposed method significantly increased ITC detections compared with that of using only the region growing algorithm, where the correct matching rate increased from 73.5% to 86.1%, and the recall value increased from 0.78 to 0.89.