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Wind damage is an important driver of forest structure and dynamics, but it is poorly understood in natural broadleaf forests. This paper presents a new approach in the study of wind damage: combining terrestrial laser scanning (TLS) data and finite element analysis. Recent advances in tree reconstruction from TLS data allowed us to accurately represent the 3D geometry of a tree in a mechanical simulation, without the need for arduous manual mapping or simplifying assumptions about tree shape. We used this simulation to predict the mechanical strains produced on the trunks of 21 trees in Wytham Woods, UK, and validated it using strain data measured on these same trees. For a subset of five trees near the anemometer, the model predicted a five-minute time-series of strain with a mean cross-correlation coefficient of 0.71, when forced by the locally measured wind speed data. Additionally, the maximum strain associated with a 5 ms−1 or 15 ms-1 wind speed was well predicted by the model (N = 17, R2 = 0.81 and R2 = 0.79, respectively). We also predicted the critical wind speed at which the trees will break from both the field data and models and find a good overall agreement (N = 17, R2 = 0.40). Finally, the model predicted the correct trend in the fundamental frequencies of the trees (N = 20, R2 = 0.38) although there was a systematic underprediction, possibly due to the simplified treatment of material properties in the model. The current approach relies on local wind data, so must be combined with wind flow modelling to be applicable at the landscape-scale or over complex terrain. This approach is applicable at the plot level and could also be applied to open-grown trees, such as in cities or parks.

Tree architecture influences physical and ecological processes within the tree. Prior work suggested the existence of general principles which govern these processes. Among these, the West, Brown and Enquist (WBE) theory is prominent; it holds that biological function has its origin in a tree's idealized branching system network; from which scaling exponents can be estimated. The scaling exponents of the WBE theory (branch radius scaling ratio, “a” and branch length scaling ratio “b”) can be derived from branch parameters and from these, metabolic scaling rate “ö” can be derived. Until now, branch parameter values are taken from direct measurements; either from standing trees or from harvested trees. Such measurements are time consuming, labour intensive and susceptible to subjective errors. Terrestrial LiDAR (TLS) is a promising alternative, being both less biased to error, scalable, and being able to collect large quantities of data without the need of destructive sampling the trees. In this thesis we estimated scaling exponents and derived metabolic rate from TLS and quantitative structure models (TreeQSM) models from nine trees in a tropical forest in Guyana. To validate these TLS-derived scaling exponents, we compared them with scaling exponents and derived metabolic rate from field measurements at three levels: branch-level, tree-level and plot-level. For that, we destructive sampled the scanned trees and measured all branches > 10 cm. Our results show that, with some limitations, radius, length scaling exponents and architecture-based metabolic rate can be derived from 3D data of tree point clouds. However, we found that only “ö” converged between our TreeQSM modelled and manually measured dataset at both, branch-level (0.59 and 0.50 for TreeQSM and manually measured exponent, respectively) and at tree-level (0.56 and 0.51). Our results did not support the same conclusion for “a” nor “b”- neither at branch-level nor at tree-level. The “a” diverged between Tre

Allometric equations are currently used to estimate above-ground biomass (AGB) based on the indirect relationship with tree parameters. Terrestrial laser scanning (TLS) can measure the canopy structure in 3D with high detail. In this study, we develop an approach to estimate AGB from TLS data, which does not need any prior information about allometry. We compare these estimates against destructively harvested AGB estimates and AGB derived from allometric equations. We also evaluate tree parameters, diameter at breast height (DBH) and tree height, estimated from traditional field inventory and TLS data. Tree height, DBH and AGB data are collected through traditional forest inventory, TLS and destructive sampling of 65 trees in a native Eucalypt Open Forest in Victoria, Australia. Single trees are extracted from the TLS data and quantitative structure models are used to estimate the tree volume directly from the point cloud data. AGB is inferred from these volumes and basic density information and is then compared with the estimates derived from allometric equations and destructive sampling. AGB estimates derived from TLS show a high agreement with the reference values from destructive sampling, with a concordance correlation coefficient (CCC) of 0·98. The agreement between AGB estimates from allometric equations and the reference is lower (CCC = 0·68–0·78). Our TLS approach shows a total AGB overestimation of 9·68% compared to an underestimation of 36·57–29·85% for the allometric equations. The error for AGB estimates using allometric equations increases exponentially with increasing DBH, whereas the error for AGB estimates from TLS is not dependent on DBH. The TLS method does not rely on indirect relationships with tree parameters or calibration data and shows better agreement with the reference data compared to estimates from allometric equations. Using 3D data also enables us to look at the height distributions of AGB, and we demonstrate that 80% of the AGB at plo

This paper presents a method for reconstructing automatically the quantitative structure model of every tree in a forest plot from terrestrial laser scanner data. A new feature is the automatic extraction of individual trees from the point cloud. The method is tested with a 30-m diameter English oak plot and a 80-m diameter Australian eucalyptus plot. For the oak plot the total biomass was overestimated by about 17 %, when compared to allometry (N = 15), and the modelling time was about 100 min with a laptop. For the eucalyptus plot the total biomass was overestimated by about 8.5 %, when compared to a destructive reference (N = 27), and the modelling time was about 160 min. The method provides accurate and fast tree modelling abilities for, e.g., biomass estimation and ground truth data for airborne measurements at a massive ground scale.

Allometric equations are currently used to estimate above-ground biomass (AGB) based on the indirect relationship with tree parameters. Terrestrial laser scanning (TLS) can measure the canopy structure in 3D with high detail. In this study, we develop an approach to estimate AGB from TLS data, which does not need any prior information about allometry. We compare these estimates against destructively harvested AGB estimates and AGB derived from allometric equations. We also evaluate tree parameters, diameter at breast height (DBH) and tree height, estimated from traditional field inventory and TLS data. Tree height, DBH and AGB data are collected through traditional forest inventory, TLS and destructive sampling of 65 trees in a native Eucalypt Open Forest in Victoria, Australia. Single trees are extracted from the TLS data and quantitative structure models are used to estimate the tree volume directly from the point cloud data. AGB is inferred from these volumes and basic density information and is then compared with the estimates derived from allometric equations and destructive sampling. AGB estimates derived from TLS show a high agreement with the reference values from destructive sampling, with a concordance correlation coefficient (CCC) of 0·98. The agreement between AGB estimates from allometric equations and the reference is lower (CCC = 0·68–0·78). Our TLS approach shows a total AGB overestimation of 9·68% compared to an underestimation of 36·57–29·85% for the allometric equations. The error for AGB estimates using allometric equations increases exponentially with increasing DBH, whereas the error for AGB estimates from TLS is not dependent on DBH. The TLS method does not rely on indirect relationships with tree parameters or calibration data and shows better agreement with the reference data compared to estimates from allometric equations. Using 3D data also enables us to look at the height distributions of AGB, and we demonstrate that 80% of the AGB at plo

Terrestrial laser scanning (TLS) produces 3D data with high detail and accuracy. In this paper we explore the potential of TLS data in combination with a method for reconstruction tree structure to estimate above ground biomass (AGB) in Australian eucalypt forest. Single trees are isolated from the registered TLS point cloud and are used as input for the reconstruction method. We explore the impact of different input parameters on the reconstruction and compare inferred AGB estimates from volume reconstruction and basic density with destructively sampled reference values. Based on a limited number of samples, regression analysis demonstrated R2 of 0.98 to 0.99, with an intercept of 110 kg for unfiltered TLS point clouds and 19.8 kg for filtered point clouds. These initial results demonstrate the potential of tree reconstruction from TLS for rapid, repeatable and robust AGB estimation.