文献类型：期刊文献

英文题名：Hyperspectral inversion of Suaeda salsa biomass under different types of human activity in Liaohe Estuary wetland in north-eastern China

作者：Dou, Zhiguo[1,2,3] Li, Youzhi[4] Cui, Lijuan[1,2,3] Pan, Xu[1,2,3] Ma, Qiongfang[5] Huang, Yilan[1,2,3] Lei, Yinru[1,2,3] Li, Jing[1,2,3] Zhao, Xinsheng[1,2,3] Li, Wei[1,2,3]

第一作者：Dou, Zhiguo

通信作者：Li, W[1];Li, W[2];Li, W[3]

机构：[1]Chinese Acad Forestry, Inst Wetland Res, Xiangshan Rd, Beijing 100091, Peoples R China;[2]Beijing Key Lab Wetland Serv & Restorat, Xiangshan Rd, Beijing 100091, Peoples R China;[3]Beijing Hanshiqiao Natl Wetland, Ecosyst Res Stn, Muyan Rd, Beijing 101399, Peoples R China;[4]Hunan Agr Univ, Coll Biosci & Biotechnol, Nongda Rd, Changsha 410000, Hunan, Peoples R China;[5]Jilin Prov Acad Forestry Sci, Linhe St, Changchun 130000, Jilin, Peoples R China

年份：2020

卷号：71

期号：4

起止页码：482-492

外文期刊名：MARINE AND FRESHWATER RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000526573200007)】；

基金：This research was financially supported by the National Key R&D Program of China (2017YFC0506200) and the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (CAFYBB2018GB001).

语种：英文

外文关键词：biomass inversion; coastal wetland; destructive; protective; vegetation index; partial least-squares

摘要：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 (R-2 > 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.