Urban Tree Species Mapping Using Airborne LiDAR and Hyperspectral Data

Abstract

In this study the high-spatial resolution (0.5 m) hyperspectral imagery with LiDAR data were fused at tree crown object level to classify 8 common tree species in Anyang, Henan, China. First, vertical forest features were extracted from LiDAR point clouds resulting in a canopy height model (CHM), followed by the acquisition of tree crown object (TCO) information from the CHM using a mean shift algorithm. Then, the CHM was combined with a minimum noise fraction transformation (MNF) and enhanced vegetation index (EVI), which were extracted from hyperspectral images. These combined features were used as the input to the SVM to produce a rough classification scheme for different tree species. Finally, a majority voting method was applied to the TCO to produce the final tree species map. The experiment showed that a combination of CHM-spatial-spectral features to classify tree species led to higher accuracy when compared to using only MNF features in the pixel-wise classification. However, the CHM and EVI features had their own limitations, largely depending on different characteristics of the different tree species.