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Estimating fractional vegetation cover using the hand-held laser range finder: method and validation

机译:使用手持式激光测距仪估算植被覆盖率:方法和验证

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摘要

The fractional vegetation cover is an important parameter of the earth surface system. Its ground measurement is the basis in the remotely sensed data-based vegetation inversion modelling. At present, the ground measurement methods include mainly the human ocular estimation method, the sampling method and the photographic method. However the ocular estimation method has the issue of low accuracy, and the sampling method needs to conduct some complicated operations while the photographic method is restricted by the height at which the camera can be placed. This article proposes a method using a hand-held laser range finder to make quick observation on fractional vegetation cover of low shrub vegetation. Using binomial distribution, this model established a probability distribution model about the measurement errors to calculate the fractional vegetation cover with various sampling numbers. Two experiments and one simulation using a computer were done in order to validate the method. The result shows that the fractional vegetation cover obtained by using the hand-held laser range finder can meet the precision requirements. In addition to its high precision, this method is simple in operation and calculation if compared with the traditional ground measurement methods.
机译:植被覆盖率是地表系统的重要参数。它的地面测量是基于遥感数据的植被反演建模的基础。当前,地面测量方法主要包括人眼估计方法,采样方法和摄影方法。然而,眼估计方法存在精度低的问题,并且采样方法需要进行一些复杂的操作,而照相方法受到可以放置相机的高度的限制。本文提出了一种使用手持式激光测距仪对低灌木植被的部分植被覆盖进行快速观测的方法。该模型使用二项分布,建立了一个关于测量误差的概率分布模型,以计算具有不同采样数的植被覆盖度分数。为了验证该方法,进行了两次实验和一次计算机模拟。结果表明,使用手持式激光测距仪获得的植被覆盖度可以满足精度要求。与传统的地面测量方法相比,该方法不仅精度高,而且操作和计算简单。

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  • 来源
    《Remote sensing letters》 |2015年第3期|20-28|共9页
  • 作者单位

    Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China|Beijing Normal Univ & Inst Remote Sensing Applica, State Key Lab Remote Sensing Sci, Beijing, Peoples R China;

    Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China|Beijing Normal Univ & Inst Remote Sensing Applica, State Key Lab Remote Sensing Sci, Beijing, Peoples R China;

    Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China|Beijing Normal Univ & Inst Remote Sensing Applica, State Key Lab Remote Sensing Sci, Beijing, Peoples R China;

    Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China|Beijing Normal Univ & Inst Remote Sensing Applica, State Key Lab Remote Sensing Sci, Beijing, Peoples R China;

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