Support vector machines for tree species identification using LiDAR-derived structure and intensity variables

Zhang, Zhenyu and Liu, Xiaoye (2013) Support vector machines for tree species identification using LiDAR-derived structure and intensity variables. Geocarto International, 28 (4). pp. 364-378. ISSN 1010-6049


Tree species identification and forest type classification are critical for sustainable forest management and native forest conservation. Recent success in forest classification and tree species identification using LiDAR (light detection and ranging)- derived variables has been reported in many studies. However, there is still considerable scope for further improvement in classification accuracy. It has driven research into more efficient classifiers such as support vector machines (SVMs) to take maximum advantage of the information extracted from LiDAR data for potential increases in the accuracy of tree species classification. This study demonstrated the success of the SVMs for the identification of the Myrtle Beech (the dominant species of the Australian cool temperate rainforest in the study area) and adjacent tree species - notably, the Silver Wattle at individual tree level using LiDAR-derived structure and intensity variables. An overall accuracy of 92.8% was achieved from the SVM approach, showing significant advantages of the SVMs over the traditional classification methods such as linear discriminant analysis in terms of classification accuracy.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2013 Copyright Taylor and Francis Group, LLC. Published version deposited in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying
Date Deposited: 01 Sep 2013 23:34
Last Modified: 15 Jul 2014 01:11
Uncontrolled Keywords: cool temperate rainforest; LiDAR intensity; support vector machines; SVM; tree species identification
Fields of Research : 07 Agricultural and Veterinary Sciences > 0705 Forestry Sciences > 070504 Forestry Management and Environment
09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
Socio-Economic Objective: D Environment > 96 Environment > 9606 Environmental and Natural Resource Evaluation > 960602 Eco-Verification (excl. Environmental Lifecycle Assessment)
Identification Number or DOI: 10.1080/10106049.2012.710653

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