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首页> 外文期刊>Canadian Journal of Remote Sensing >Assessing branching structure for biomass and wood quality estimation using terrestrial laser scanning point clouds
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Assessing branching structure for biomass and wood quality estimation using terrestrial laser scanning point clouds

机译:利用地面激光扫描点云评估生物量和木材质量评估的分支结构

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摘要

Terrestrial laser scanning (TLS) accompanied by quantitative tree-modeling algorithms can potentially acquire branching data non-destructively from a forest environment and aid the development and calibration of allometric crown biomass and wood quality equations for species and geographical regions with inadequate models. However, TLS's coverage in capturing individual branches still lacks evaluation. We acquired TLS data from 158 Scots pine (Pinus sylvestris L.) trees and investigated the performance of a quantitative branch detection and modeling approach for extracting key branching parameters, namely the number of branches, branch diameter (b(d)) and branch insertion angle (b) in various crown sections. We used manual point cloud measurements as references. The accuracy of quantitative branch detections decreased significantly above the live crown base height, principally due to the increasing scanner distance as opposed to occlusion effects caused by the foliage. b(d) was generally underestimated, when comparing to the manual reference, while b was estimated accurately: tree-specific biases were 0.89cm and 1.98 degrees, respectively. Our results indicate that full branching structure remains challenging to capture by TLS alone. Nevertheless, the retrievable branching parameters are potential inputs into allometric biomass and wood quality equations.
机译:陆地激光扫描(TLS)与定量树模型算法一起使用,可以潜在地从森林环境中无损获取分支数据,并通过不足的模型来帮助开发和校准物种和地理区域的异位冠生物量和木材质量方程。但是,TLS在捕获单个分支机构方面的覆盖范围仍然缺乏评估。我们从158棵苏格兰松(Pinus sylvestris L.)树中获取了TLS数据,并研究了定量分支检测和建模方法的性能,该方法用于提取关键分支参数,即分支数,分支直径(b(d))和分支插入各个牙冠部分的角度(b)。我们使用手动点云测量作为参考。定量树枝检测的准确性在活冠基部高度以上明显降低,这主要是由于扫描仪距离的增加,而不是树叶引起的遮挡效应。与手册参考相比,b(d)通常被低估了,而b被准确地估计了:树木特定的偏差分别为0.89cm和1.98度。我们的结果表明,完整的分支结构仍然很难通过TLS单独捕获。但是,可检索的分支参数是异速生物量和木材质量方程式的潜在输入。

著录项

  • 来源
    《Canadian Journal of Remote Sensing》 |2018年第5期|462-475|共14页
  • 作者单位

    Univ Helsinki, Dept Forest Sci, FI-00014 Helsinki, Finland|Finnish Geospatial Res Inst, Dept Remote Sensing & Photogrammetry, FI-02431 Masala, Finland|Finnish Geospatial Res Inst, Ctr Excellence Laser Scanning Res, FI-02431 Masala, Finland;

    Finnish Geospatial Res Inst, Dept Remote Sensing & Photogrammetry, FI-02431 Masala, Finland|Finnish Geospatial Res Inst, Ctr Excellence Laser Scanning Res, FI-02431 Masala, Finland;

    Univ Helsinki, Dept Forest Sci, FI-00014 Helsinki, Finland|Finnish Geospatial Res Inst, Ctr Excellence Laser Scanning Res, FI-02431 Masala, Finland;

    Univ Helsinki, Dept Forest Sci, FI-00014 Helsinki, Finland|Finnish Geospatial Res Inst, Ctr Excellence Laser Scanning Res, FI-02431 Masala, Finland;

    Finnish Geospatial Res Inst, Dept Remote Sensing & Photogrammetry, FI-02431 Masala, Finland|Finnish Geospatial Res Inst, Ctr Excellence Laser Scanning Res, FI-02431 Masala, Finland;

    Univ Helsinki, Dept Forest Sci, FI-00014 Helsinki, Finland|Finnish Geospatial Res Inst, Ctr Excellence Laser Scanning Res, FI-02431 Masala, Finland;

    Finnish Geospatial Res Inst, Dept Remote Sensing & Photogrammetry, FI-02431 Masala, Finland|Finnish Geospatial Res Inst, Ctr Excellence Laser Scanning Res, FI-02431 Masala, Finland;

    Univ Helsinki, Dept Forest Sci, FI-00014 Helsinki, Finland|Finnish Geospatial Res Inst, Ctr Excellence Laser Scanning Res, FI-02431 Masala, Finland|Univ Eastern Finland, Sch Forest Sci, FI-80101 Joensuu, Finland;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Forestry; LiDAR; Modeling; Point clouds; Scots pine;

    机译:林业;激光雷达;建模;点云;苏格兰松树;

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