Statistical analysis of LiDAR-derived structure and intensity variables for tree species identification

Paper

Zhang, Zhenyu, Liu, Xiaoye and Wright, Wendy. 2013. "Statistical analysis of LiDAR-derived structure and intensity variables for tree species identification." Denys, P., Strack, M., Moore, A. B. and Whingham, P. (ed.) 2013 GIS and Remote Sensing Research Conference (SIRC NZ 2013) . Otago, New Zealand 29 - 30 Aug 2013 Wellington, New Zealand.
Paper/Presentation Title

Statistical analysis of LiDAR-derived structure and intensity variables for tree species identification

Presentation TypePaper
AuthorsZhang, Zhenyu (Author), Liu, Xiaoye (Author) and Wright, Wendy (Author)
EditorsDenys, P., Strack, M., Moore, A. B. and Whingham, P.
Journal or Proceedings TitleProceedings of the 125th NZIS Annual Conference, and SIRC NZ 2013: GIS and Remote Sensing Research Conference
Number of Pages4
Year2013
Place of PublicationWellington, New Zealand
ISBN9780473247560
9780473247577
Web Address (URL) of Paperhttps://www.otago.ac.nz/surveying/news/seminars/otago043532.html
Conference/Event2013 GIS and Remote Sensing Research Conference (SIRC NZ 2013)
Event Details
2013 GIS and Remote Sensing Research Conference (SIRC NZ 2013)
Event Date
29 to end of 30 Aug 2013
Event Location
Otago, New Zealand
Abstract

Tree species identification is critical for sustainable forest management and native forest conservation. It has been recognised that airborne LiDAR (light detection and ranging) offers advantages over the interpretation of aerial photographs and processing of multi-spectral and/or hyper-spectral remote sensing data in forest classification. However, as shown by our previous studies of forest communities of the Strzelecki Ranges, Victoria, Australia, the only use of LiDAR-derived structure variables may not offer unequivocal distinction between all forest types, such as cool temperate rainforest dominated by the Myrtle Beech (Nothofagus cunninghamii) and adjacent Silver Wattle (Acacia dealbata) forest. This paper reports the results of deploying both structure and intensity variables derived from small-footprint, high-density discrete airborne LiDAR data for the classification of the Myrtle Beech and the Silver Wattle at individual tree level in the Strzeleckis. The tree species classification was achieved via linear discriminant analysis with cross-validation, the accuracy having been assessed by an error matrix. The results showed that the inclusion of LiDAR-derived intensity variables improved the accuracy of the classification of the Myrtle Beech and the Silver Wattle species in the study area. An overall classification accuracy of 86.4% was achieved using both structure and intensity variables.

Keywordscool temperate rainforest, LiDAR, LiDAR intensity, statistical analysis, Strzelecki Ranges, tree species identification
ANZSRC Field of Research 2020401304. Photogrammetry and remote sensing
Public Notes

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Byline AffiliationsSchool of Civil Engineering and Surveying
Monash University
Institution of OriginUniversity of Southern Queensland
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