10.4121/uuid:0120f3c6-cfa6-42a5-84bf-d9e598283c59
A.I. (Alvaro) Lau Sarmiento
A.I. (Alvaro)
Lau Sarmiento
0000-0002-0419-7002
Data supporting the research of: Estimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling
4TU.Centre for Research Data
2019
Dataset
Forestry Sciences
WBE plant scaling exponent
architecture-based metabolic rate
destructive harvesting
quantitative structure models
terrestrial LiDAR
Jackson, T. (Tobias)
0000-0001-8143-6161
Raumonen, P. (Pasi)
0000-0001-5471-0970
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research
2019-02-26
https://doi.org/10.1016/j.foreco.2019.02.019
media types: application/zip, text/csv, text/plain
1
CC BY 4.0
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 TreeQSM and manually measured dataset at branch-level (0:45 and 0.63) and at the tree-level (0.46 and 0.64). The “b” was the exponent which most deviated between TreeQSM and manually measured dataset at branch-level (0.42 and 0.07) and at tree-level (0.41 and 0.05). At tree-level, we found that all estimated averaged exponents deviated significantly from metabolic scaling theory predictions (“a”=1/2 ; “b” =1/3 ; “ö”=3/4 ). Our study provides an alternative method to estimate scaling exponents variation at branch-level and tree-level in tropical forest trees without the need for destructive sampling. Although this approach is based on a limited sample of nine trees in Guyana, can be implemented for large-scale plant scaling assessments. This new data might improve our current understanding of metabolic scaling without harvesting trees.
Guyana
-59.00000
5.00000