Application of Hyperspectral LiDAR on 3-D Chlorophyll-Nitrogen Mapping of Rohdea Japonica in Laboratory

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.9667-9679
• Main Authors: Du, Lin, Jin, Zhili, Chen, Bowen, Chen, Biwu, Gao, Wei, Yang, Jian, Shi, Shuo, Song, Shalei, Wang, Mengmeng, Gong, Wei, Wang, Wei
• Format: Article
• Language: English
• Subjects: Artificial neural network (ANN); Artificial neural networks; Broadband; broadband spectral index; Chl-N content mapping; Chlorophyll; Chlorophylls; Convolution; Estimation; hyperspectral LiDAR (HSL); Indexes; Inversion; Laser radar; Lidar; Mapping; Mirrors; Neural networks; Nitrogen; Photosynthesis; Plant cover; Remote sensing; Silicon; Spectra; Spectral sensitivity; Three dimensional models; Three-dimensional displays; Vegetation; Vegetation index; Vegetation mapping
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3111295

Biochemicals, such as chlorophyll (Chl) and nitrogen (N), are closely related to photosynthesis process of vegetation. Their accurate estimation is an important topic in remote sensing of vegetation. Previous studies mainly focused on Chl-N content inversion in leaf and canopy level, and few cared about their 3-D distributions, which was also an important indicator for the growth status of vegetation (GSV). Hyperspectral LiDAR (HSL) is a novel active remote sensing technology, which has target-sensitive band with hyperspectra resolution. Its 3-D point cloud data simultaneously contains rich spectral and precise geometrical characteristics of the target. This work aims to apply HSL data on 3-D Chl-N content mapping in vegetation through constructing HSL-based spectral indices (SIs). Except for following the SI forms of previous works, the normalized differential vegetation index and ratio index (RI) with four broadbands in an HSL spectral space were successively proposed to invert Chl-N content for the whole vegetation based on the artificial neural network (ANN) method. These four broadbands were transformed based on the relative spectral response curve of detector and the feature weights (FWs) of multiwavelength, respectively. Results show that most HSL-based ANN models can accurately invert Chl-N content with a mean R 2 of >0.75, and some that fusing broadband data with convolution transformation, namely the FW-based RI, can even obtain a model R 2 of 0.84 for N content inversion. Thus, HSL can be efficiently applied to 3-D Chl-N content mapping of vegetation and has great potential in GSV monitoring.