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Open AccessArticle

Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction

1
Center of Excellence of Laser Scanning Research, Finnish Geospatial Research Institute, Masala FI-02430, Finland
2
Key Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy of Sciences, Beijing 100094, China
3
Electronic Information School, Wuhan University, Wuhan 430079, China
4
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
5
Department of Electronics Engineering, Anhui Jianzhu University, Hefei 230601, China
6
Department of Communication and Information Engineering, Shanghai Polytechnic University, Shanghai 200216, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 2007; https://doi.org/10.3390/rs11172007
Received: 3 July 2019 / Revised: 18 August 2019 / Accepted: 22 August 2019 / Published: 26 August 2019
(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)
Non-contact and active vegetation or plant parameters extraction using hyperspectral information is a prospective research direction among the remote sensing community. Hyperspectral LiDAR (HSL) is an instrument capable of acquiring spectral and spatial information actively, which could mitigate the environmental illumination influence on the spectral information collection. However, HSL usually has limited spectral resolution and coverage, which is vital for vegetation parameter extraction. In this paper, to broaden the HSL spectral range and increase the spectral resolution, an Acousto-optical Tunable Filter based Hyperspectral LiDAR (AOTF-HSL) with 10 nm spectral resolution, consecutively covering from 500–1000 nm, was designed. The AOTF-HSL was employed and evaluated for vegetation parameters extraction. “Red Edge” parameters of four different plants with green and yellow leaves were extracted in the lab experiments for evaluating the HSL vegetation parameter extraction capacity. The experiments were composed of two parts. Firstly, the first-order derivative of the spectral reflectance was employed to extract the “Red Edge” position (REP), “Red Edge” slope (RES) and “Red Edge” area (REA) of these green and yellow leaves. The results were compared with the referenced value from a standard SVC© HR-1024 spectrometer for validation. Green leaf parameter differences between HSL and SVC results were minor, which supported that notion the HSL was practical for extracting the employed parameter as an active method. Secondly, another two different REP extraction methods, Linear Four-point Interpolation technology (LFPIT) and Linear Extrapolation technology (LET), were utilized for further evaluation of using the AOTF-HSL spectral profile to determine the REP value. The differences between the plant green leaves’ REP results extracted using the three methods were all below 10%, and the some of them were below 1%, which further demonstrated that the spectral data collected from HSL with this spectral range and resolution settings was applicable for “Red Edge” parameters extraction. View Full-Text
Keywords: hyperspectral LiDAR; Red Edge; AOTF; vegetation parameters hyperspectral LiDAR; Red Edge; AOTF; vegetation parameters
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MDPI and ACS Style

Jiang, C.; Chen, Y.; Wu, H.; Li, W.; Zhou, H.; Bo, Y.; Shao, H.; Song, S.; Puttonen, E.; Hyyppä, J. Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction. Remote Sens. 2019, 11, 2007. https://doi.org/10.3390/rs11172007

AMA Style

Jiang C, Chen Y, Wu H, Li W, Zhou H, Bo Y, Shao H, Song S, Puttonen E, Hyyppä J. Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction. Remote Sensing. 2019; 11(17):2007. https://doi.org/10.3390/rs11172007

Chicago/Turabian Style

Jiang, Changhui; Chen, Yuwei; Wu, Haohao; Li, Wei; Zhou, Hui; Bo, Yuming; Shao, Hui; Song, Shaojing; Puttonen, Eetu; Hyyppä, Juha. 2019. "Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction" Remote Sens. 11, no. 17: 2007. https://doi.org/10.3390/rs11172007

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