Support vector machines for tree species identification using LiDAR-derived structure and intensity variables |
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| Authors: | Zhenyu Zhang |
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| Institution: | 1. Centre for GIS, School of Geography and Environmental Science, Monash University , Clayton , Victoria , 3800 , Australia;2. Faculty of Engineering and Surveying, Australian Centre for Sustainable Catchments, University of Southern Queensland , Toowoomba , 4350 , Queensland , Australia |
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| 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. |
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| Keywords: | support vector machines SVM LiDAR LiDAR intensity cool temperate rainforest tree species identification |
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