Open AccessDeriving backscatter reflective factors from 32-channel full-waveform LiDAR data for the estimation of leaf biochemical contents
Author(s) -
Wang Li,
Zheng Niu,
Gang Sun,
Shuai Gao,
Mingquan Wu
Publication year - 2016
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.24.004771
Subject(s) - remote sensing , hyperspectral imaging , backscatter (email) , ranging , lidar , vegetation (pathology) , environmental science , mean squared error , partial least squares regression , wavelength , canopy , optics , computer science , mathematics , geology , physics , statistics , botany , telecommunications , biology , medicine , pathology , wireless
Hyperspectral light detection and ranging (HSL) is a newly developed active remote sensing technique. In this study, we firstly presented an improved hyperspectral full-waveform LiDAR system with 32 detection channels. Then, the quality of the data collected from two types of leaves by this system was evaluated using signal to noise ratio. Two different reflective factors that can describe the backscatter capability of detected targets were developed based on the HSL data. Hundreds of vegetation indices (VIs) were calculated through a full search for the possible combination of the reflective factors at near-infrared and visible wavelengths. Finally, the high-dimensional VIs (n = 998) were used to estimate three leaf biochemical contents using principle component regression (PCR) models with cross validation. Results showed that high correlations were found between leaf biochemical contents and the HSL-derived VIs at shorter visible wavelengths. The prediction of biochemical contents obtained satisfactory results with a root mean squared error of 0.45% for nitrogen content (R 2 = 0.71), 1.41 mgg -1 for chlorophylla/b content (R 2 = 0.83), and 0.38 mgg -1 for carotenoid content (R 2 = 0.77), respectively. To conclude, the improved HSL system showed great potential for the remote estimation of vegetation biochemical contents, which will significantly extend the scope of quantitative remote sensing with vegetation.