CLASSIFYING TREE SPECIES USING STRUCTURE AND SPECTRAL DATA FROM LiDAR

Two airborne laser scanning datasets with leaf-on and leaf-off conditions were used to compare parameters derived from crown structure metrics and intensity data. Five deciduous species and six coniferous species were collected at the Washington Park Arboretum, Seattle, Washington, USA. Linear (LDA) and quadratic (QDA) discriminate functions were used to classify these selected species groups. Overall, classification accuracy was highest when using intensity variables with the leaf-off data in both LDA (98.9%) and QDA (99.0%). In terms of structure variables, leaf-on variables showed higher accuracy (74.9 %) than leaf-off variables (50.2 %) while in terms of intensity variables, leaf-off variables showed higher accuracy (97.1 %) than leaf-on variables (63.0 %) in LDA. QDA showed higher classification accuracy than LDA for all cases. The overall result indicates that parameters computed from LiDAR-based crown structures and intensity data can be used to differentiate species groups and also implies that tree species classification depends on the collected LiDAR datasets and the derived parameters.