Active 3D Imaging of Vegetation based on Multi-Wavelength Fluorescence LiDAR

Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Ac...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-02, Vol.20 (3), p.935
Main Authors: Zhao, Xingmin, Shi, Shuo, Yang, Jian, Gong, Wei, Sun, Jia, Chen, Biwu, Guo, Kuanghui, Chen, Bowen
Format: Article
Language:English
Subjects:
Classification
classification discrimination
fluorescence lidar
Laboratories
Laser induced fluorescence
Lasers
Light
Photosynthesis
Physiology
Radiation
Remote monitoring
Remote sensing
Systems design
Three dimensional imaging
Vegetation
vegetation monitoring
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Summary:Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system's performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20030935