Cool temperate rainforest and adjacent forests classification using airborne LiDAR data

Article


Zhang, Zhenyu, Liu, Xiaoye, Peterson, Jim and Wright, Wendy. 2011. "Cool temperate rainforest and adjacent forests classification using airborne LiDAR data." Area. 43 (4), pp. 438-448. https://doi.org/10.1111/j.1475-4762.2011.01035.x
Article Title

Cool temperate rainforest and adjacent forests classification using airborne LiDAR data

ERA Journal ID5920
Article CategoryArticle
AuthorsZhang, Zhenyu (Author), Liu, Xiaoye (Author), Peterson, Jim (Author) and Wright, Wendy (Author)
Journal TitleArea
Journal Citation43 (4), pp. 438-448
Number of Pages11
Year2011
PublisherJohn Wiley & Sons
Place of PublicationUnited Kingdom
ISSN0004-0894
1475-4762
Digital Object Identifier (DOI)https://doi.org/10.1111/j.1475-4762.2011.01035.x
Web Address (URL)http://onlinelibrary.wiley.com/doi/10.1111/j.1475-4762.2011.01035.x/pdf
Abstract

The traditional methods of forest classification, based on the interpretation of aerial photographs and processing of multi-spectral and/or hyper-spectral remote sensing data are limited in their ability to capture the structural complexity of the forests compared with analysis of airborne LiDAR (light detection and ranging) data. This is because of LiDAR's penetration of forest canopies such that detailed and three-dimensional forest structure descriptions can be derived. This study applied airborne LiDAR data for the classification of cool temperate rainforest and adjacent forests in the Strzelecki Ranges, Victoria, Australia. Using normalised LiDAR point data, the forest vertical structure was stratified into three layers. Variables characterising the height distribution and density of forest components were derived from LiDAR data within each of these layers. The statistical analyses, which included one-way analysis of variance with post hoc tests, identified effective variables for forest-type classifications. The results showed that using linear discriminant analysis, an overall classification accuracy of 91.4% (as verified by the cross-validation) was achieved in the study area.

KeywordsLiDAR; cool temperate rainforest; forest classification; forest structure;statistical analysis; Strzelecki Ranges
ANZSRC Field of Research 2020401304. Photogrammetry and remote sensing
401102. Environmentally sustainable engineering
410404. Environmental management
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Byline AffiliationsAustralian Centre for Sustainable Catchments
Monash University
Institution of OriginUniversity of Southern Queensland
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