Estimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling
Estimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling
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DOI：
10.1016/j.foreco.2019.02.019
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发表时间：
2019-05-01
影响因子：
3.7
通讯作者：
Bentley, Lisa Patrick
中科院分区：
文献类型：
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作者：
Lau, Alvaro;Martius, Christopher;Bentley, Lisa Patrick
树枝的几何结构与树木内部资源运输的机械安全性和效率有关。因此，树形结构的拓扑结构将树内的物理属性联系起来，并影响树与其环境的相互作用。先前的研究表明，存在一些普遍的原则，这些原则支配着跨越物种和生物地理区域的树木建筑模式。特别是，West、Brown和Enquist （WBE, 1997）以及Savage等人（2010）从对称的分支参数推导出缩放指数（分支半径缩放比alpha和分支长度缩放比beta），并由此推导出整棵树的基于架构的代谢缩放率（theta）。有了这个关键的缩放指数，整棵树或一组树的新陈代谢（例如，叶子的数量，呼吸等）可以异速估计。到目前为止，分支参数值都是手工测量的；无论是直立的活树还是采伐的树。这种测量是耗时的，劳动密集型的，而且容易产生主观误差。遥感，特别是地面激光雷达（TLS），是一个很有前途的替代方案，客观，可扩展，并且能够在没有破坏性采样的情况下收集大量数据。在本文中，我们首先使用MS扫描立木，然后将定量结构模型（TreeQSM）模型拟合到圭亚那热带森林中9棵树的三维点云上，计算树枝长度、树枝半径和基于建筑的代谢率缩放指数。为了验证这些tls衍生的尺度指数，我们将其与从所有树枝bbb10 cm的直接现场测量计算的指数进行了比较，这些指数在四个尺度上：分支级、累积分支顺序、树级和地块级。我们发现由于分支结构重建的偏差，对alpha和beta指数的估计存在偏差。尽管TreeQSM标度指数预测的θ与人工测量的指数相似，但这是由于α和β标度指数的组合都有偏差。此外，人工测量的α和β标度指数与WBE的理论指数存在分歧，这表明热带环境中的树木可能不会遵循WBE提出的对称分支几何形状的预测。我们的研究提供了一种替代方法来估计热带森林树木在树枝和树水平的尺度指数，而不需要破坏性采样。虽然这种方法是基于圭亚那9棵树的有限样本，但它可以用于大规模的植物规模评估。这些新数据可能会提高我们目前对代谢尺度的理解，而不需要采伐树木。
The geometric structure of tree branches has been hypothesized to relate to the mechanical safety and efficiency of resource transport within a tree. As such, the topology of tree architecture links physical properties within a tree and influences the interaction of the tree with its environment. Prior work suggests the existence of general principles which govern tree architectural patterns across of species and bio-geographical regions. In particular, West, Brown and Enquist (WBE, 1997) and Savage et al. (2010) derive scaling exponents (branch radius scaling ratio alpha and branch length scaling ratio beta) from symmetrical branch parameters and from these, an architecture-based metabolic scaling rate (theta) for the whole tree. With this key scaling exponent, the metabolism (e.g., number of leaves, respiration, etc.) of a whole tree, or potentially a group of trees, can be estimated allometrically. Until now, branch parameter values have been measured manually; either from standing live trees or from harvested trees. Such measurements are time consuming, labour intensive and susceptible to subjective errors. Remote sensing, and specifically terrestrial LiDAR (TLS), is a promising alternative, being objective, scalable, and able to collect large quantities of data without destructive sampling. In this paper, we calculated branch length, branch radius, and architecture-based metabolic rate scaling exponents by first using MS to scan standing trees and then fitting quantitative structure models (TreeQSM) models to 3D point clouds from nine trees in a tropical forest in Guyana. To validate these TLS-derived scaling exponents, we compared them with exponents calculated from direct field measurements of all branches > 10 cm at four scales: branch-level, cumulative branch order, tree-level and plot-level. We found a bias on the estimations of alpha and beta exponents due to a bias on the reconstruction of the branching architecture. Although TreeQSM scaling exponents predicted similar theta as the manually measured exponents, this was due to the combination of alpha and beta scaling exponents which were both biased. Also, the manually measured alpha and beta scaling exponents diverged from the WBE's theoretical exponents suggesting that trees in tropical environments might not follow the predictions for the symmetrical branching geometry proposed by WBE. Our study provides an alternative method to estimate scaling exponents at both the branch- 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, it can be implemented for large-scale plant scaling assessments. These new data might improve our current understanding of metabolic scaling without harvesting trees.