4 October 2017 An approach to conifer stem localization and modeling in high density airborne LiDAR data
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Abstract
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.
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A. Harikumar, F. Bovolo, L. Bruzzone, "An approach to conifer stem localization and modeling in high density airborne LiDAR data", Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270Q (4 October 2017); doi: 10.1117/12.2279526; https://doi.org/10.1117/12.2279526
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