An Approach to Tree Species Classification Using Voxel Neighborhood Density-Based Subsampling of Multiscan Terrestrial Lidar Data

The knowledge on the species of individual trees is ineluctable for accurate forest parameter estimation and related studies. Terrestrial Laser Scanning (TLS) remote sensing systems acquire a huge number of point samples that contain very accurate and detailed three dimensional (3D) information of tree structures. Every tree species has unique internal and external crown structural characteristics that can be modeled from its TLS data. However, methods in the state of the art show reduced performance due to inaccurate modeling of tree structures such as the crown, and the branch, and poor selection of features. The proposed method leverages on the fine internal and external crown structural information in TLS data to achieve species classification. We remove noise and stem points in TLS data using a novel voxel neighborhood density-based technique. Internal and external crown geometric features derived from the branch level, and the crown level, respectively, are provided to a non linear Support Vector Machines (SVM) to achieve species classification, and evaluate feature relevance. All experiments were conducted on a set of 75 manually delineated trees belonging to the Spruce, the Pine, and the Birch species.