New morphological features for urban tree species identification using LiDAR point clouds

Urban tree species identification is the basis for studying the urban-environment coordination mechanism at the species level. Although the gradual maturity of remote sensing data and related research including light detection and ranging (LiDAR) provides a good foundation for the realization of this technology, multiple reasons such as cost, data openness, study scope limitations, and weakness of traditional morphological features make such data still challenging to apply to subtropical urban trees with heterogeneous canopy structures and high biodiversity. To address the problem, we developed two large-scale LiDAR morphological features in this research by, 1) modifying the rotate image method based on the axisymmetric structure to make it easier to use, and 2) developing an innovative adaptive ellipsoid method to extract the canopy features of the non-axisymmetric structure effectively. We evaluated the ability of these two morphological features to describe 12 common subtropical urban tree (SUT) species in Hong Kong growing in urban parks and streets, obtaining an accuracy of 88%. And the advantages of the proposed method are demonstrated by comparison with existing LiDAR morphological features and mean decrease accuracy (MDA) analysis. Our results illustrated that the rotate image feature based on the axisymmetric structure did not perform as well as the adaptive ellipsoid feature based on the non-axisymmetric structure in SUT, and the combined application of these two new morphological features got further accuracy improvement. The method proposed in this study had significant advantages in terms of accuracy, the number of species included, and generalisation capability compared to existing studies on the identification of subtropical urban trees.