Detailed reconstruction of trees from terrestrial laser scans for remote sensing and radiative transfer modelling applications
This study presents a method for three-dimensional (3D) reconstruction of forest tree species that are, for instance, required for simulations of 3D canopies in radiative transfer modelling. We selected three forest species of different architecture: Norway spruce (Picea abies) and European beech (Fagus sylvatica), representatives of European production forests, and white peppermint (Eucalyptus pulchella), a common forest species of Tasmania. Each species has a specific crown structure and foliage distribution. Our algorithm for 3D model construction of a single tree is based on terrestrial laser scanning (TLS) and ancillary field measurements of leaf angle distribution, percentage of current-year and older leaves, and other parameters that could not be derived from TLS data. The algorithm comprises four main steps: (i) segmentation of a TLS tree point cloud separating wooden parts from foliage, (ii) reconstruction of wooden parts (trunks and branches) from TLS data, (iii) biologically genuine distribution of foliage within the tree crown and (iv) separation of foliage into two age categories (for spruce trees only). The reconstructed 3D models of the tree species were used to build virtual forest scenes in the Discrete Anisotropic Radiative Transfer model and to simulate canopy optical signals, specifically: angularly anisotropic top-of-canopy reflectance (for retrieval of leaf biochemical compounds from nadir canopy reflectance signatures captured in airborne imaging spectroscopy data) and solar-induced chlorophyll fluorescence signal (for experimentally unfeasible sensitivity analyses).
Australian Research Council
Publication titleIn Silico Plants
Department/SchoolSchool of Geography, Planning and Spatial Sciences
PublisherOxford University Press
Place of publicationUnited Kingdom
Rights statement© The Author(s) 2021. Published by Oxford University Press on behalf of the Annals of Botany Company. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.