Effective number of layers: A new measure for quantifying three-dimensional stand structure based on sampling with terrestrial LiDAR
Effective number of layers: A new measure for quantifying three-dimensional stand structure based on sampling with terrestrial LiDAR
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DOI：
10.1016/j.foreco.2016.09.003
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发表时间：
2016-11-15
影响因子：
3.7
通讯作者：
Seidel, Dominik
中科院分区：
文献类型：
--
作者：
Ehbrecht, Martin;Schall, Peter;Seidel, Dominik
林分结构异质性与生物多样性保护的相关性日益得到认可，并需要有效的测量工具。在这里，我们通过计算不同希尔数（0D，1D，2D）的有效层数（ENL）作为子地块垂直结构的度量来量化森林结构。然后，我们使用抽样技术，以涵盖研究地块内的水平结构变异。ENL描述的垂直结构的基础上占用1米宽的垂直层的树木组件相对于总的空间占用的stin.Space占用量化的体素模型从地面激光扫描（TLS）在德国的150个森林地块。我们使用单次扫描方法，与多次扫描相比，该方法需要更少的现场工作和后处理。单次扫描得出的平均ENL及其变异系数成功地区分了森林结构在很大范围内的均匀年龄，非均匀年龄和未管理的阔叶林和针叶林。ENL与林分的断面积、二次平均直径、树干密度以及林龄等林分综合指标相关。ENL可以用来描述结构异质性，并被证明是有效的评估TLS。(C)© 2016 Elsevier B.V.版权所有。
The relevance of stand structural heterogeneity for biodiversity conservation is increasingly recognized and efficient tools for its measurement are demanded. Here, we quantified forest structure by calculating the effective number of layers (ENL) for different Hill Numbers (0D, 1D, 2D) as a measure of vertical structure of a subplot. We than use sampling techniques to cover the horizontal structural variability within study plots. ENL describes the vertical structure based on the occupation of 1 m wide vertical layers by tree components relative to the total space occupation of a stand. Space occupation was quantified by a voxel-model obtained from terrestrial laser scanning (TLS) on 150 forest plots in Germany. We used a single scan approach, which requires less field work and post-processing compared to multiple-scans. Single-scan derived mean ENL and its coefficient of variation successfully differentiated forest structures over a wide range of even-aged, uneven-aged and unmanaged broadleaved and coniferous stands. ENL was correlated to the stand summary measures basal area, quadratic mean diameter and stem density as well as stand age. ENL can be used to describe structural heterogeneity and proved to be efficiently assessable by TLS. (C) 2016 Elsevier B.V. All rights reserved.