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Operational wide-area stem volume estimation based on airborne laser scanning and national forest inventory data

机译:基于机载激光扫描和国家森林清单数据的广域业务量估计

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摘要

This paper evaluates the performance of a recently developed approach for wide-area stem volume estimations based on airborne laser scanning (ALS) and national forest inventory (NFI) data in the case where data recorded under operational conditions are used as input. This entails that neither ALS data nor NFI samples were collected and optimized for the current study. The approach was tested for the Austrian state of Vorarlberg, which covers an area of 2601 km~2 and encloses about 970 km~2 of forest land. ALS data with point densities varying between 1 and 4 points m~(-2) were acquired in the framework of a commercial state-wide terrain mapping project during several winter- and summer-flight campaigns. The stem volume model was calibrated with all NFI data available for Vorarlberg, whereas additional local forest inventory data were used for independent validation. Moreover, several relevant operational issues were addressed in this study, such as the determination of the optimum area used to calculate the reference laser metrics input to the model, the effect of gridding point cloud data to speed up processing, and the stratification of input data into coniferous and deciduous sample plots.rnWithout tree species stratification and based on the 3D laser heights model, calibration provided a maximum R of 0.79 and a standard deviation (SD) of residuals derived from cross-validation of 107.4 m~3 ha~(-1) (31.5%). Calibrating the model only with coniferous samples increased the achieved R~2 to 0.81 and decreased SD to 104.8 m~3 ha~(-1) (29.7%). As only eight NFI sample plots were available for deciduous forest a robust calibration of a separate model could not be obtained. Calibrating the model with a rasterized canopy height model (CHM) instead of using the 3D laser heights just led to a slight decrease in accuracy (R~2 = 0.75, SD = 120.9 m~3 ha~(-1) (35.5%)) without forest-type stratification and R~2 = 0.78 and SD = 117.2 m~3 ha~(-1) (33.1%) for the coniferous stem volume model). Finally, the stem volume model calibrated with CHM data was adopted to generate a stem volume map of the entire State of Vorarlberg. Validation of this map with the additional local forest inventory data confirmed the accuracies (R~2 = 0.75; SD = 135.6 m~3 ha~(-1) (32.3%)) that were derived during calibration of the stem volume model based on the NFI data. The models and methodsrnpresented in this study are used operationally for forest and environment policy purposes and practical applications in Austria.
机译:本文将在操作条件下记录的数据用作输入的情况下,基于机载激光扫描(ALS)和国家森林清单(NFI)数据,评估了最近开发的广域茎量估计方法的性能。这需要针对当前研究既未收集ALS数据也未收集NFI样本并对其进行了优化。该方法在奥地利的福拉尔贝格州进行了测试,该州占地2601 km〜2,约970 km〜2的林地。在数个冬季和夏季飞行活动中,在一个商业性的全州地形测绘项目的框架下,获取了点密度在1到4点m〜(-2)之间的ALS数据。茎体积模型已使用可用于福拉尔贝格州的所有NFI数据进行校准,而其他本地森林清单数据则用于独立验证。此外,本研究解决了一些相关的操作问题,例如确定用于计算输入到模型的参考激光度量的最佳区域,网格化点云数据对加快处理速度的影响以及输入数据的分层在没有树种分层的情况下,基于3D激光高度模型,校准提供的最大R为0.79,而交叉验证的残差标准偏差(SD)为107.4 m〜3 ha〜(- 1)(31.5%)。仅用针叶样品校准模型可将达到的R〜2提高到0.81,将SD降低到104.8 m〜3 ha〜(-1)(29.7%)。由于只有八个NFI样地可用于落叶林,因此无法获得单独模型的稳健校准。使用栅格化的树冠高度模型(CHM)而不是使用3D激光高度来校准模型只会导致精度略有下降(R〜2 = 0.75,SD = 120.9 m〜3 ha〜(-1)(35.5%) )且没有森林类型的分层,针叶茎体积模型的R〜2 = 0.78和SD = 117.2 m〜3 ha〜(-1)(33.1%)。最后,采用通过CHM数据校准的茎体积模型来生成整个福拉尔贝格州的茎体积图。使用其他本地森林资源清查数据对此地图进行验证,证实了在基于以下标准的茎量模型校准过程中得出的精度(R〜2 = 0.75; SD = 135.6 m〜3 ha〜(-1)(32.3%))。 NFI数据。本研究中提出的模型和方法可用于奥地利的森林和环境政策目的以及实际应用。

著录项

  • 来源
    《International journal of remote sensing》 |2009年第19期|5159-5175|共17页
  • 作者单位

    Christian Doppler Laboratory for 'Spatial Data from Laser Scanning and Remote Sensing' at the Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Gusshausstrasse 27-29, 1040 Vienna, Austria;

    Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Gusshausstrasse 27-29, 1040 Vienna, Austria;

    Christian Doppler Laboratory for 'Spatial Data from Laser Scanning and Remote Sensing' at the Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Gusshausstrasse 27-29, 1040 Vienna, Austria;

    Department of Forest Inventory at the Federal Research and Training Centre for Forests, Natural Hazards and Landscape, Seckendorff-Gudent-Weg, 1130 Vienna, Austria;

    Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Gusshausstrasse 27-29, 1040 Vienna, Austria;

    Stand Montafon Forstfonds, Montafonerstrasse 21, 6780 Schruns, Austria;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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