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A new approach for estimating living vegetation volume based on terrestrial point cloud data

机译：一种基于地面点云数据的生物量估算方法

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摘要

Living vegetation volume (LVV), one of the most difficult tree parameters to calculate, is among the most important factors that indicates the biological characteristics and ecological functions of the crown. Obtaining precise LVV estimates is, however, challenging task because the irregularities of many crown shapes are difficult to capture using standard forestry field equipment. Terrestrial light detection and ranging (T-LiDAR) can be used to record the three-dimensional structures of trees. The primary branches of Larix olgensis and Quercus mongolica in the Qingyuan Experimental Station of Forest Ecology at the Chinese Academy of Sciences (CAS) were taken as the research objects. A new rapid LVV estimation method called the filling method was proposed in this paper based on a T-LiDAR point cloud. In the proposed method, the branch point clouds are divided into leaf points and wood points. We used RiSCAN PRO 1.64 to manually separate the leaf points and wood points under careful visual inspection, and calculated that leaf points and wood points accounted for 91% and 9% of the number of the point clouds of branches. Then, the equation LVV = V1N (where N is the number of leaf points, and V1 is cube size) is used to calculate LVV. When the laser transmission frequency is 300,000 points/second and the point cloud is diluted to 30% using the octree method, the point cloud can be replaced by a cube (V1) of 6.11 cm3 to fill the branch space. The results showed that good performance for this approach, the measuring accuracy for L. olgensis and Q. mongolica at the levels of α = 0.05 and α = 0.01, respectively (94.35%, 90.01% and 91.99%, 85.63%, respectively). The results suggest that the proposed method can be conveniently used to calculate the LVV of coniferous and broad-leaf species under specific scanning settings. This work is explorative because hypotheses or a theoretical framework have not been previously defined. Rather, we would like to contribute to the formation of hypotheses as a background for further studies.
机译：活植物体积（LVV）是最难计算的树木参数之一，是表明树冠生物学特性和生态功能的最重要因素之一。但是，获得精确的LVV估计值是一项艰巨的任务，因为许多冠状形状的不规则性很难用标准的林业野外设备捕获。地面光检测和测距（T-LiDAR）可用于记录树木的三维结构。以中国科学院清远森林生态实验站的落叶松和蒙古栎的主要分支为研究对象。基于T-LiDAR点云，提出了一种新的快速LVV估计方法，称为填充法。在该方法中，将分支点云分为叶点和木点。我们使用RiSCAN PRO 1.64在仔细的目视检查下手动分离叶点和木点，并计算出叶点和木点分别占分支点云数量的91％和9％。然后，使用公式LVV = V1N（其中N是叶点数，V1是立方体大小）来计算LVV。当激光传输频率为300,000点/秒并且使用八叉树法将点云稀释到30％时，可以用6.11 cm 3 的立方体（V1）替换点云以填充分支空间。结果表明，该方法具有良好的性能，分别在α= 0.05和α= 0.01时（分别为94.35％，90.01％和91.99％，85.63％）对ol。olgensis和Q. mongolica进行测量。结果表明，该方法可方便地用于在特定扫描条件下计算针叶和阔叶树种的LVV。这项工作是探索性的，因为假设或理论框架尚未预先定义。相反，我们希望为假设的形成做出贡献，以作为进一步研究的背景。

著录项

  • 期刊名称 PLoS Clinical Trials
  • 作者

    Le Li; Changfu Liu;

  • 作者单位
  • 年(卷),期 2012(14),8
  • 年度 2012
  • 页码 e0221734
  • 总页数 22
  • 原文格式 PDF
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