文献类型：期刊文献

英文题名：Individual Tree Classification Using Airborne LiDAR and Hyperspectral Data in a Natural Mixed Forest of Northeast China

作者：Zhao, Dan[1,2] Pang, Yong[2] Liu, Lijuan[3,4] Li, Zengyuan[2]

第一作者：Zhao, Dan

通信作者：Pang, Y[1]

机构：[1]Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100093, Peoples R China;[3]Zhejiang A&F Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Hangzhou 311300, Peoples R China;[4]Zhejiang A&F Univ, Sch Environm & Resource Sci, Hangzhou 311300, Peoples R China

年份：2020

卷号：11

期号：3

外文期刊名：FORESTS

收录：;EI(收录号：20201408370288);Scopus(收录号：2-s2.0-85082464624);WOS:【SCI-EXPANDED(收录号:WOS:000530221500053)】；

基金：This research was funded by the National Natural Science Foundation of China (No. 31570546 & 41771464). Natural Science Foundation of Zhejiang Province (LY18D010002).

语种：英文

外文关键词：individual tree classification; LiDAR; hyperspectral; SVM; natural forest

摘要：This paper proposes a method to classify individual tree species groups based on individual tree segmentation and crown-level spectrum extraction ("crown-based ITC" for abbr.) in a natural mixed forest of Northeast China, and compares with the pixel-based classification and segment summarization results ("pixel-based ITC" for abbr.). Tree species is a basic factor in forest management, and it is traditionally identified by field survey. This paper aims to explore the potential of individual tree classification in a natural, needle-leaved and broadleaved mixed forest. First, individual trees were isolated, and the spectra of individual trees were then extracted. The support vector machine (SVM) and spectrum angle mapper (SAM) classifiers were applied to classify the trees species. The pixel-based classification results from hyperspectral data and LiDAR derived individual tree isolation were compared. The results showed that the crown-based ITC classified broadleaved trees better than pixel-based ITC, while the classes distribution of the crown-based ITC was closer to the survey data. This indicated that crown-based ITC performed better than pixel-based ITC. Crown-based ITC efficiently identified the classes of the dominant and sub-dominant species. Regardless of whether SVM or SAM was used, the identification consistency relative to the field observations for the class of the dominant species was greater than 90%. In contrast, the consistencies of the classes of the sub-dominant species were approximately 60%, and the overall consistency of both the SVM and SAM was greater than 70%.