Hyperspectral inversion of Suaeda salsa biomass under different types of human activity in Liaohe Estuary wetland in north-eastern China
Zhiguo Dou A B C , Youzhi Li D , Lijuan Cui A B C , Xu Pan A B C , Qiongfang Ma E , Yilan Huang A B C , Yinru Lei A B C , Jing Li A B C , Xinsheng Zhao A B C and Wei Li A B C F
+ Author Affiliations
- Author Affiliations
A Institute of Wetland Research, Chinese Academy of Forestry, Xiangshan Road, Haidian District, Beijing, 100091, PR China.
B Beijing Key Laboratory of Wetland Services and Restoration, Xiangshan Road, Haidian District, Beijing, 100091, PR China.
C Beijing Hanshiqiao National Wetland, Ecosystem Research Station, Muyan Road, Shunyi District, Beijing, 101399, PR China.
D College of Bioscience and Biotechnology, Hunan Agricultural University, Nongda Road, Furong District, Changsha, Hunan, 410000, PR China.
E Jilin Provincial Academy of Forestry Science, Linhe Street, Erdao District, Changchun, Jilin, 130000, PR China.
F Corresponding author. Email: wetlands207@163.com
Marine and Freshwater Research 71(4) 482-492 https://doi.org/10.1071/MF19030
Submitted: 30 January 2019 Accepted: 4 June 2019 Published: 10 September 2019
Abstract
Human activities alter the growth of coastal wetland vegetation. In the present study, we used a spectrometer and hyperspectral data to determine and compare the biomass of Suaeda salsa in a coastal wetland under protective and destructive activities. Using typical discriminants, the hyperspectral data of Suaeda salsa were distinguished under the influence of two kinds of human activity, and the accuracy of the inversion model of biomass was established following improved differentiation of the data under the influence of human activities. The original spectral reflectance and vegetation index were selected, and the biomass-inversion model was established by linear regression and partial least-squares regression. The model established by partial least-squares regression had a good precision (R2 > 0.85, RMSE% < 5.6%). Hyperspectral technology can accurately show plant biomass and the indirect effects of interference by human activities of different intensity on coastal wetlands. The accuracy of the models can be improved by distinguishing the vegetation patterns under the influence of different types of human activity, and then constructing the biomass models. This study provides technical support for the use of quantitative remote sensing-based methods to monitor the fragile ecology of coastal wetlands under the influence of human activities.
Additional keywords: biomass inversion, coastal wetland, destructive, protective, vegetation index, partial least-squares.
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