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首页> 外文期刊>International journal of remote sensing >Modelling lidar-derived boreal forest canopy cover with SPOT 4 HRVIR data
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Modelling lidar-derived boreal forest canopy cover with SPOT 4 HRVIR data

机译：使用SPOT 4 HRVIR数据模拟激光雷达衍生的北方森林冠层覆盖

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摘要

Forest canopy cover (Q is needed in forest area monitoring and for many ecological models. Airborne scanning lidar sensors can produce fairly accurate C estimates even without field training data. However, optical satellite images are more cost-efficient for large area inventories. Our objective was to use airborne lidar data to obtain accurate estimates of C for a set of sample plots in a boreal forest and to generalize C for a large area using a satellite image. The normalized difference vegetation index (NDVI) and reduced simple ratio (RSR) were calculated from the satellite image and used as predictors in the regressions. RSR, which combines information from the red, near-infrared, and shortwave infrared bands, provided the best performance in terms of absolute root mean square error (RMSE) (7.3%) in the training data. NDVI produced a markedly larger RMSE (10.0%). However, in an independent validation data set, RMSE increased (13.0-17.1%) because the systematic sample of validation plots contained more variation than the training plots. Our results are better than those reported earlier, which is probably explained by more consistent C estimates derived from the lidar. Our approach provides an efficient method for creating C maps for large areas.
机译：森林冠层覆盖（在森林面积监测和许多生态模型中都需要Q。即使没有野外训练数据，机载扫描激光雷达传感器也可以产生相当准确的C估计值。但是，光学卫星图像对于大面积清单更具成本效益。我们的目标将使用机载激光雷达数据获取一组北方森林样地中C的准确估计值，并使用卫星图像对大面积的C进行归一化归一化差异植被指数（NDVI）和简化简单比率（RSR） RSR结合了红色，近红外和短波红外波段的信息，在绝对均方根误差（RMSE）方面表现最佳（7.3％） ）中的NDVI产生了显着更大的RMSE（10.0％），但是，在独立的验证数据集中，RMSE增加了（13.0-17.1％），因为系统地进行了验证情节包含比训练情节更多的变化。我们的结果比之前报道的要好，这可能是由激光雷达得出的更一致的C估计值所解释的。我们的方法为创建大面积C地图提供了一种有效的方法。

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  • 来源
    《International journal of remote sensing》 |2013年第22期|8172-8181|共10页
  • 作者

    Lauri Korhonen; Janne Heiskanen; Ilkka Korpela;

  • 作者单位

    School of Forest Sciences, University of Eastern Finland, 80101 Joensuu, Finland;

    Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland;

    Department of Forest Sciences, University of Helsinki, Helsinki, Finland;

  • 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
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