Tree species information is crucial for accurate forest parameter estimation. Small footprint high density multireturn Light Detection and Ranging (LiDAR) data contain a large amount of structural details for modelling and thus distinguishing individual tree species. To fully exploit the potential of these data, we propose a data-driven tree species classification approach based on a volumetric analysis of single-tree-point-cloud that extracts features that are able to characterize both the internal and the external crown structure. The method captures the spatial distribution of the LiDAR points within the crown by generating a feature vector representing the threedimensional (3D) crown information. Each element in the feature vector uniquely corresponds to an Elementary Quantization Volume (EQV) of the crown. Three strategies have been defined to generate unique EQVs that model different representations of the crown components. The classification is performed by using a Support Vector Machines (C-SVM) classifier using the histogram intersection kernel that has the enhanced ability to give maximum preference to the key features in high dimensional feature space. All the experiments were performed on a set of 200 trees belonging to Norway Spruce, European Larch, Swiss Pine, and Silver Fir (i.e., 50 trees per species). The classifier is trained using 120 trees and tested on an independent set of 80 trees. The proposed method outperforms the classification performance of the state-of-the-art method used for comparison.
Individual tree level inventory performed using high density multi-return airborne Light Detection and Ranging (LiDAR) systems provides both internal and external geometric details on individual tree crowns. Among them, the parameters such as, the stem location, and Diameter at Breast Height of the stem (DBH) are very relevant for accurate biomass, and forest growth estimation. However, methods that can accurately estimate these parameters along the vertical canopy are lacking in the state of the art. Thus, we propose a method to locate and model the stem by analyzing the empty volume that appears within the 3D high density LiDAR point cloud of a conifer, due to the stem. In a high LiDAR density data, the points most proximal to the stem location in the upper half of the crown are very likely due to laser reflections from the stem and/or the branch-stem junctions. By locating accurately these points, we can define the lattice of points representing branch-stem junctions and use it to model the empty volume associated to the stem location. We identify these points by using a state-of-the-art internal crown structure modelling technique that models individual conifer branches in a high density LiDAR data. Under the assumption that conifer stem can be closely modelled using a cone shape, we regression fit a geometric shape onto the lattice of branch-stem junction points. The parameters of the geometric shape are used to accurately estimate the diameter at breast height, and height of the tree. The experiments were performed on a set of hundred conifers consisting of trees from six dominant European conifer species, for which the height and the DBH were known. The results prove the method to be accurate.
The knowledge about individual trees in forest is highly beneficial in forest management. High density small foot- print multi-return airborne Light Detection and Ranging (LiDAR) data can provide a very accurate information about the structural properties of individual trees in forests. Every tree species has a unique set of crown structural characteristics that can be used for tree species classification. In this paper, we use both the internal and external crown structural information of a conifer tree crown, derived from a high density small foot-print multi-return LiDAR data acquisition for species classification. Considering the fact that branches are the major building blocks of a conifer tree crown, we obtain the internal crown structural information using a branch level analysis. The structure of each conifer branch is represented using clusters in the LiDAR point cloud. We propose the joint use of the k-means clustering and geometric shape fitting, on the LiDAR data projected onto a novel 3-dimensional space, to identify branch clusters. After mapping the identified clusters back to the original space, six internal geometric features are estimated using a branch-level analysis. The external crown characteristics are modeled by using six least correlated features based on cone fitting and convex hull. Species classification is performed using a sparse Support Vector Machines (sparse SVM) classifier.