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A comparison of UAV laser scanning, photogrammetry and airborne laser scanning for precision inventory of small-forest properties

机译:UAV激光扫描,摄影测量和空气载入激光扫描的比较,用于小林产精度清点

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摘要

This study addresses the use of multiple sources of auxiliary data from unmanned aerial vehicles (UAVs) and airborne laser scanning (ALS) data for inference on key biophysical parameters in small forest properties (5-300 ha). We compared the precision of the estimates using plot data alone under a design-based inference with model-based estimates that include plot data and the following four types of auxiliary data: (1) terrain-independent variables from UAV photogrammetric data (UAV-SfM); (2) variables obtained from UAV photogrammetric data normalized using external terrain data (UAV-SfM(DTM)); (3) UAV-LS and (4) ALS data. The inclusion of remotely sensed data increased the precision of DB estimates by factors of 1.5-2.2. The optimal data sources for top height, stem density, basal area and total stem volume were: UAV-LS, UAV-SfM, UAV-SfM(DTM) and UAV-SfM(DTM). We conclude that the use of UAV data can increase the precision of stand-level estimates even under intensive field sampling conditions.
机译:本研究解决了来自无人机(无人机)和空气传播的辅助数据的多种辅助数据来源,用于推断小森林特性(5-300公顷)的关键生物物理参数推断。我们将估计的精度与基于设计的推断仅在基于模型的估计下使用绘图数据,包括包含绘图数据和以下四种类型的辅助数据:(1)从UAV摄影测量数据(UAV-SFM)的地形无关的变量); (2)从使用外部地形数据归一化的UAV摄影测量数据(UAV-SFM(DTM))获得的变量; (3)UAV-LS和(4)ALS数据。包含远程感测的数据通过1.5-2.2的因素增加了DB估计的精度。顶部高度,阀杆密度,基础区域和总杆体积的最佳数据源是:UAV-LS,UAV-SFM,UAV-SFM(DTM)和UAV-SFM(DTM)。我们得出结论,即使在密集的场采样条件下,使用UAV数据的使用也可以提高支架估计的精度。

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  • 来源
    《Forestry》 |2020年第1期|共13页
  • 作者单位

    Norwegian Inst Bioecon Res NIBIO Div Forest &

    Forest Resources Hgsk Veien 8 N-1433 As Norway;

    Scion Dept Forest Informat Sala St Private Bag 3020 Rotorua 3046 New Zealand;

    Scion Dept Forest Informat 10 Kyle St Christchurch 8011 New Zealand;

    Norwegian Inst Bioecon Res NIBIO Div Forest &

    Forest Resources Hgsk Veien 8 N-1433 As Norway;

    Scion Dept Forest Informat Sala St Private Bag 3020 Rotorua 3046 New Zealand;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 林业;
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