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Vertical distribution inversion of biochemical parameters using hyperspectral LiDAR

  • role： First author第一作者
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：gaoshuai@radi.ac.cn
  • Introduction：高帅，1983年生，男，博士研究生，研究方向为植被定量遥感、激光雷达遥感、机器学习与空间数据挖掘。E-mail：gaoshuai@radi.ac.cn
Shuai GAO1,  
  • role： Corresponding author通信作者
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：niuzheng@radi.ac.cn
  • Introduction：牛铮，1965年生，男，研究员，研究方向为植被定量遥感、全球变化遥感。E-mail：niuzheng@radi.ac.cn
Zheng NIU12*,  
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Gang SUN1,  
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Yuchu QIN1,  
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Wang LI1,  
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

Haifeng TIAN12

реферат

A hyperspectral Light Detection And Ranging (LiDAR) can obtain high spectral properties of the observed object and provides a new method for detecting a three dimesional distribution of vegetation structure and biochemical characteristics. In this study, a data process flow and a biochemical characteristics method were proposed. Laboratory experiments were conducted on the basis of this instrument, and a vertical distribution extraction method of vegetation biochemical components was provided. First, we proposed the hyperspectral LiDAR waveform data processing method in accordance with the characteristics of the instrument. Second, an indoor Kniphofia scanning experiment was utilized, and the LiDAR point cloud data with high spectral properties were obtained. Finally, chlorophyll and carotene vertical distributions were extracted on the basis of the relationship between the vegetation index and biochemical components. Results show that the biochemical content of a red leaf at the top of vegetation is low, which is generally lower than 0.5 mg/g, and carotene is less than 0.2 mg/g. However, the biochemical component content in the middle of the green leaves was evidently high. This study showed that the instrument has a considerable application prospect in the field of quantitative remote sensing.

ключеви́че слова́

hyperspectral LiDAR;biochemical parameter;vertical distribution;full waveform;point cloud

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