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. LiDAR with capability of canopy penetration yields such high density sampling that detailed terrain and canopy surface models can be derived. Recent success in forest classification using LiDAR derived products including terrain and canopy surface models has been reported in many studies. However, there is still considerable scope for further improvement in classification accuracy by taking maximum advantage of the information extracted from LiDAR data and by employing more efficient classifiers such as support vector machines (SVMs). This study aims to use LiDAR data to generate digital terrain and canopy surface models to identify the location and crown size of individual trees for the species classification of Australian cool temperate rainforest dominated by the Myrtle Beech (Nothofagus cunninghamii) and neighbouring Silver Wattle (Acacia dealbata). The tree species classification was achieved by employing LiDAR-derived structure and intensity variables via linear discriminant analysis (LDA) and SVMs. 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. It demonstrated that the SVMs have significant advantages over the traditional classification methods such as the LDA methods in terms of classification accuracy.
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.