• Spectroscopy and Spectral Analysis
  • Vol. 37, Issue 6, 1809 (2017)
FENG Ming-bo1、2、*, NIU Zheng1, and SUN Gang1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 103964/jissn1000-0593(2017)06-1809-05 Cite this Article
    FENG Ming-bo, NIU Zheng, SUN Gang. The Analysis of Vegetation Spectra Based on Multi-Band Lidar[J]. Spectroscopy and Spectral Analysis, 2017, 37(6): 1809 Copy Citation Text show less

    Abstract

    Multi-band Lidar is a new means to obtain spectra. It is different from the traditional spectrometer which can only get spectra in the two-dimensional level. The multi-band Lidar based on the super-continuum spectrum laser light, uses the penetrability and high concentration of laser and gets the spectra of vegetation in vertical direction, relying on the grating instrument system. With it the spectral distribution of vegetation in the three-dimensional level can be obtained. In this paper, 32-band Lidar is applied. Firstly, the spectra obtained with 32-band Lidar and ASD spectrometer in a darkroom are compared, which proves that they have the same shape, accurately indicating the reflection characteristics of vegetation from 458 to 865 nm. Then with the analysis of the correlations of spectral index and chlorophyll content, it is found that the fitting relations between the spectral indices and chlorophyll content acquire nice effect and there is highly significant correlation between the Modified Simple Ratio (MSR) based on multi-band Lidar spectra and chlorophyll content, the determination coefficient (R2) is 0780 2 and the RMSE is only 0508 1. Finally, with multi-band Lidar, the spectra in the vertical direction and the three-dimensional point cloud distribution of spectra are obtained via multiple scanning. Based on the optimal fitting equation between MSR and chlorophyll content, three-dimensional distribution of chlorophyll content is gotten.
    FENG Ming-bo, NIU Zheng, SUN Gang. The Analysis of Vegetation Spectra Based on Multi-Band Lidar[J]. Spectroscopy and Spectral Analysis, 2017, 37(6): 1809
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