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Open AccessArticle

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

by Dan Zhao 1,2, Yong Pang 2,*, Lijuan Liu 3,4 and Zengyuan Li 2
1
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100093, China
3
Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Zhejiang A&F University, Hangzhou 311300, China
4
School of Environmental and Resource Science, Zhejiang A&F University, Hangzhou 311300, China
*
Author to whom correspondence should be addressed.
Forests 2020, 11(3), 303; https://doi.org/10.3390/f11030303
Received: 2 January 2020 / Revised: 27 February 2020 / Accepted: 5 March 2020 / Published: 10 March 2020
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
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%. View Full-Text
Keywords: individual tree classification; LiDAR; hyperspectral; SVM; natural forest individual tree classification; LiDAR; hyperspectral; SVM; natural forest
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Zhao, D.; Pang, Y.; Liu, L.; Li, Z. Individual Tree Classification Using Airborne LiDAR and Hyperspectral Data in a Natural Mixed Forest of Northeast China. Forests 2020, 11, 303.

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