Weighing trees with lasers: advances, challenges and opportunities.

Weighing trees with lasers: advances, challenges and opportunities.
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DOI:
10.1098/rsfs.2017.0048
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发表时间:
2018-04-06
期刊:
影响因子:
4.4
通讯作者:
Wilkes P
Wilkes P
中科院分区:
生物学2区
文献类型:
--
作者:
Disney MI;Boni Vicari M;Burt A;Calders K;Lewis SL;Raumonen P;Wilkes P

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地面激光扫描(TLS)为量化树木和森林结构,特别是地上生物量(AGB)提供了令人兴奋的新方法。我们展示了TLS如何解决目前基于经验异速生长标度方程(ASES)估计AGB的方法的一些关键不确定性和局限性,这些方程是所有大规模AGB估计的基础。TLS提供了非常详细的非破坏性测量树木的形式独立于树木的大小和形状。我们展示了来自各种热带和温带森林的三维(3D)TLS测量的示例,并描述了如何使用所得到的TLS点云来生成分支和树干大小、形状和分布的定量3D模型。这些模型可以大大提高AGB的估计,提供新的,改进的大规模ASE,并提供一系列与结构相关的基本树属性的见解。对单个三维树结构的大量详细测量也有可能在一些领域开辟新的和令人兴奋的研究途径,在这些领域,测量的困难迄今为止阻止了检测和理解缩放、形式和功能的潜在模式的统计方法。我们讨论了这些机会和一些仍然需要克服的挑战,以使TLS方法得到更广泛的采用。
Terrestrial laser scanning (TLS) is providing exciting new ways to quantify tree and forest structure, particularly above-ground biomass (AGB). We show how TLS can address some of the key uncertainties and limitations of current approaches to estimating AGB based on empirical allometric scaling equations (ASEs) that underpin all large-scale estimates of AGB. TLS provides extremely detailed non-destructive measurements of tree form independent of tree size and shape. We show examples of three-dimensional (3D) TLS measurements from various tropical and temperate forests and describe how the resulting TLS point clouds can be used to produce quantitative 3D models of branch and trunk size, shape and distribution. These models can drastically improve estimates of AGB, provide new, improved large-scale ASEs, and deliver insights into a range of fundamental tree properties related to structure. Large quantities of detailed measurements of individual 3D tree structure also have the potential to open new and exciting avenues of research in areas where difficulties of measurement have until now prevented statistical approaches to detecting and understanding underlying patterns of scaling, form and function. We discuss these opportunities and some of the challenges that remain to be overcome to enable wider adoption of TLS methods.
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