A Method for Quantifying Understory Leaf Area Index in a Temperate Forest through Combining Small Footprint Full-Waveform and Point Cloud LiDAR Data
A Method for Quantifying Understory Leaf Area Index in a Temperate Forest through Combining Small Footprint Full-Waveform and Point Cloud LiDAR Data
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结合小足迹全波形和点云 LiDAR 数据量化温带森林林下叶面积指数的方法
DOI：
10.3390/rs13153036
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发表时间：
2021-08
期刊：
影响因子：
5
通讯作者：
Lihong Yu
中科院分区：
文献类型：
--
作者：
Jinling Song;Xiao Zhu;Jianbo Qi;Yong Pang;Lei Yang;Lihong Yu
林下植被在森林生态系统的结构和功能中发挥着重要作用。光探测和测距 (LiDAR) 可以以点云或全波形数据的形式提供林下信息。点云数据具有出色的表示植被三维结构的能力，而全波形数据包含有关激光脉冲与植被之间相互作用的更详细信息；这两种类型已广泛用于估计各种森林冠层结构参数，包括叶面积指数（LAI）。在这里，我们结合两种类型 LiDAR 数据的优点，提出了一种量化温带森林林下 LAI 的新方法。为了实现这一目标，我们首先使用点云数据估计间隙概率的垂直分布，以自动确定地块级别上层和林下植被之间的高度边界。然后，我们对全波形数据进行反卷积，消除系统脉冲引起的模糊效应，恢复激光雷达系统的垂直分辨率。随后，我们分解了去卷积数据并积分了地块级边界高度，以区分从上层、下层和土层返回的波形分量。最后，我们修改了基本 LiDAR 方程，引入林下树叶光谱信息来量化林下 LAI。我们的结果经过地面测量验证，表明新方法可以很好地估计林下 LAI，R2 为 0.54，均方根误差 (RMSE) 为 0.21。我们的研究表明，通过点云和全波形 LiDAR 数据的结合使用，可以成功量化林下 LAI。
Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection and ranging (LiDAR) can provide understory information in the form of either point cloud or full-waveform data. Point cloud data have a remarkable ability to represent the three-dimensional structures of vegetation, while full-waveform data contain more detailed information on the interactions between laser pulses and vegetation; both types have been widely used to estimate various forest canopy structural parameters, including leaf area index (LAI). Here, we present a new method for quantifying understory LAI in a temperate forest by combining the advantages of both types of LiDAR data. To achieve this, we first estimated the vertical distribution of the gap probability using point cloud data to automatically determine the height boundary between overstory and understory vegetation at the plot level. We then deconvolved the full-waveform data to remove the blurring effect caused by the system pulse to restore the vertical resolution of the LiDAR system. Subsequently, we decomposed the deconvolved data and integrated the plot-level boundary height to differentiate the waveform components returned from the overstory, understory, and soil layers. Finally, we modified the basic LiDAR equations introducing understory leaf spectral information to quantify the understory LAI. Our results, which were validated against ground-based measurements, show that the new method produced a good estimation of the understory LAI with an R2 of 0.54 and a root-mean-square error (RMSE) of 0.21. Our study demonstrates that the understory LAI can be successfully quantified through the combined use of point cloud and full-waveform LiDAR data.
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DOI：
10.3390/rs10020262
发表时间：
2018-02
期刊：
Remote. Sens.
影响因子：
--
作者：
Qiaoli Wu;C. Song;Jinling Song;Jindi Wang;Shaoyuan Chen;Bo Yu
通讯作者：
Qiaoli Wu;C. Song;Jinling Song;Jindi Wang;Shaoyuan Chen;Bo Yu
影响因子：
8.2
作者：
Jiaying Wu;J. Aardt;G. Asner
通讯作者：
Jiaying Wu;J. Aardt;G. Asner
影响因子：
1.4
作者：
B. Eskelson;L. Madsen;J. Hagar;H. Temesgen
通讯作者：
B. Eskelson;L. Madsen;J. Hagar;H. Temesgen
影响因子：
5
作者：
Evans, Jeffrey S.;Hudak, Andrew T.;Smith, Alistair M. S.
通讯作者：
Smith, Alistair M. S.
影响因子：
13.5
作者：
B. Wing;M. Ritchie;K. Boston;W. Cohen;A. Gitelman;M. Olsen
通讯作者：
B. Wing;M. Ritchie;K. Boston;W. Cohen;A. Gitelman;M. Olsen