文献类型：期刊文献

英文题名：Comparison of Modeling Algorithms for Forest Canopy Structures Based on UAV-LiDAR: A Case Study in Tropical China

作者：Peng, Xi[1,2,3] Zhao, Anjiu[2] Chen, Yongfu[1,3] Chen, Qiao[1,3] Liu, Haodong[1,3] Wang, Juan[1,3,4] Li, Huayu[1,3,4]

第一作者：Peng, Xi

通信作者：Chen, Q[1];Chen, Q[2]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Sichuan Agr Univ, Coll Forestry, Chengdu 611130, Peoples R China;[3]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[4]Southwest Forestry Univ, Coll Forestry, Kunming 650224, Yunnan, Peoples R China

年份：2020

卷号：11

期号：12

起止页码：1-16

外文期刊名：FORESTS

收录：;EI(收录号：20205109667158);Scopus(收录号：2-s2.0-85097820818);WOS:【SCI-EXPANDED(收录号:WOS:000601989300001)】；

基金：This research was funded by the fundamental research funds for the Central Nonprofit Research Institution of Chinese Academy of Forestry (CAF) (grant no. CAFBB2017ZB004), balance funds of Research Institute of Forest Resource Information Techniques (IFRIT), CAF, "Research on the Mechanism of Natural Regeneration barriers of Dacrydium pierrei in Hainan Island" (grant no. 2019JYZJ01).

语种：英文

外文关键词：tropical forests; vertical structure; forest attributes; regression model; recursive feature elimination

摘要：Knowledge of forest structure is vital for sustainable forest management decisions. Terrestrial laser scanning cannot describe the canopy trees in a large area, and it is unclear whether unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the ability to capture the forest canopy structural parameters in tropical forests. In this study, we estimated five forest canopy structures (stand density (N), basic area (G), above-ground biomass (AGB), Lorey's mean height (HL), and under-crown height (hT)) with four modeling algorithms (linear regression (LR), bagged tree (BT), support vector regression (SVR), and random forest (RF)) based on UAV-LiDAR data and 60 sample plot data from tropical forests in Hainan and determined the optimal algorithms for the five canopy structures by comparing the performance of the four algorithms. First, we defined the canopy tree as a tree with a height >= 70% HL. Then, UAV-LiDAR metrics were calculated, and the LiDAR metrics were screened by recursive feature elimination (RFE). Finally, a prediction model of the five forest canopy structural parameters was established by the four algorithms, and the results were compared. The metrics' screening results show that the most important LiDAR indexes for estimating HL, AGB, and hT are the leaf area index and some height metrics, while the most important indexes for estimating N and G are the kurtosis of heights and the coefficient of variation of height. The relative root mean squared error (rRMSE) of five structure parameters showed the following: when modeling HL, the rRMSEs (10.60%-12.05%) obtained by the four algorithms showed little difference; when N was modeled, BT, RF, and SVR had lower rRMSEs (26.76%-27.44%); when G was modeled, the rRMSEs of RF and SVR (15.37%-15.87%) were lower; when hT was modeled, BT, RF, and SVR had lower rRMSEs (10.24%-11.07%); when AGB was modeled, RF had the lowest rRMSE (26.75%). Our results will help facilitate choosing LiDAR indexes and modeling algorithms for tropical forest resource inventories.