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Open AccessArticle

Object-Based Tree Species Classification Using Airborne Hyperspectral Images and LiDAR Data

by Yanshuang Wu 1,2,3 and Xiaoli Zhang 1,2,*
1
Beijing Key Laboratory of Precision Forestry, Forestry College, Beijing Forestry University, Beijing 100083, China
2
Key Laboratory for Forest Silviculture and Conservation of the Ministry of Education, Beijing Forestry University, Beijing 100083, China
3
Heilongjiang Institute of Geomatics Engineering, Harbin 150081, China
*
Author to whom correspondence should be addressed.
Forests 2020, 11(1), 32; https://doi.org/10.3390/f11010032
Received: 10 November 2019 / Revised: 19 December 2019 / Accepted: 21 December 2019 / Published: 24 December 2019
The identification of tree species is one of the most basic and key indicators in forest resource monitoring with great significance in the actual forest resource survey and it can comprehensively improve the efficiency of forest resource monitoring. The related research has mainly focused on single tree species without considering multiple tree species, and therefore the ability to classify forest tree species in complex stand is not clear, especially in the subtropical monsoon climate region of southern China. This study combined airborne hyperspectral data with simultaneously acquired LiDAR data, to evaluate the capability of feature combinations and k-nearest neighbor (KNN) and support vector machine (SVM) classifiers to identify tree species, in southern China. First, the stratified classification method was used to remove non-forest land. Second, the feature variables were extracted from airborne hyperspectral image and LiDAR data, including independent component analysis (ICA) transformation images, spectral indices, texture features, and canopy height model (CHM). Third, random forest and recursion feature elimination methods were adopted for feature selection. Finally, we selected different feature combinations and used KNN and SVM classifiers to classify tree species. The results showed that the SVM classifier has a higher classification accuracy as compared with KNN classifier, with the highest classification accuracy of 94.68% and a Kappa coefficient of 0.937. Through feature elimination, the classification accuracy and performance of SVM classifier was further improved. Recursive feature elimination method based on SVM is better than random forest. In the spectral indices, the new constructed slope spectral index, SL2, has a certain effect on improving the classification accuracy of tree species. Texture features and CHM height information can effectively distinguish tree species with similar spectral features. The height information plays an important role in improving the classification accuracy of other broad-leaved species. In general, the combination of different features can improve the classification accuracy, and the proposed strategies and methods are effective for the identification of tree species at complex forest type in southern China. View Full-Text
Keywords: tree species classification; hyperspectral images; LiDAR; feature combination; k-nearest neighbor; support vector machine tree species classification; hyperspectral images; LiDAR; feature combination; k-nearest neighbor; support vector machine
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Wu, Y.; Zhang, X. Object-Based Tree Species Classification Using Airborne Hyperspectral Images and LiDAR Data. Forests 2020, 11, 32.

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