An approach to conifer species classification based on crown structure modeling in high density airborne LiDAR data

Abstract

The knowledge about the species of trees is essential for precision forest management practices. Modern high density airborne Light Detection and Ranging (LiDAR) systems have the ability to acquire large number of LiDAR points, allowing a very detailed characterization of the forest at the individual tree level. In this context, it is possible to use LiDAR data for accurate classification of the tree species. In this paper, we consider the specific problem of species classification of trees belonging to the conifer class. This is particularly challenging when only the external geometric information is considered. To address the problem we propose a novel approach that model the internal crown structure of the conifers. The internal structure is identified by using 3D region growing and Principal Component Analysis (PCA) and is used for defining a set of novel Internal Crown Geometric features (IGFs). Some state-of-the-art External Crown Geometric Features (EGFs) were also used to improve the classification accuracy. Sparse Support Vector Machines (SSVM) was used for classification and to quantify the feature relevances.