Support vector machines for tree species identification using LiDAR-derived structure and intensity variables
Article
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. https://doi.org/10.1080/10106049.2012.710653
| Article Title | Support vector machines for tree species identification using LiDAR-derived structure and intensity variables |
|---|---|
| ERA Journal ID | 2010 |
| Article Category | Article |
| Authors | Zhang, Zhenyu (Author) and Liu, Xiaoye (Author) |
| Journal Title | Geocarto International |
| Journal Citation | 28 (4), pp. 364-378 |
| Number of Pages | 15 |
| Year | 2013 |
| Place of Publication | Abingdon, Oxon. United Kingdom |
| ISSN | 1010-6049 |
| 1752-0762 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1080/10106049.2012.710653 |
| Abstract | 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. |
| Keywords | cool temperate rainforest; LiDAR intensity; support vector machines; SVM; tree species identification |
| ANZSRC Field of Research 2020 | 300707. Forestry management and environment |
| 401304. Photogrammetry and remote sensing | |
| 410404. Environmental management | |
| Public Notes | © 2013 Copyright Taylor and Francis Group, LLC. Published version deposited in accordance with the copyright policy of the publisher. |
| Byline Affiliations | Australian Centre for Sustainable Catchments |
| Institution of Origin | University of Southern Queensland |
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