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Monitoring biomass of water hyacinth by using hyperspectral remote sensing

文献类型： 外文期刊

作者： Wang, Jingjing 1 ; Sun, Ling 1 ; Liu, Huazhou 1 ;

作者机构： 1.Jiangsu Acad Agr Sci, Inst Agr Econ & Informat, Nanjing, Jiangsu, Peoples R China

关键词： invasive species;water hyacinth;biomass;hyperspectral;reflectance

期刊名称：2012 FIRST INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS)

ISSN： 2334-3168

年卷期： 2012 年

页码：

收录情况： SCI

摘要： Establishment biomass monitoring methods of invasive species has an important instructive meaning for control and treatment of invasion, which was also beneficial to national ecological safety and social stability. This study researched the estimation methods of biomass of water hyacinth by hyperspectral remote sensing. The spectral reflectance characteristics were analyzed based on field experiments consisting of different biomass levels. Spectral reflectance and hyperspectral parameters were constructed to be correlated with biomass of water hyacinth and the sensitive parameters were chosen to build biomass estimation models. The result showed that reflectance of near-infrared had better correlation with water hyacinth biomass compared with other wavelength but the correlation is not significant at the 99% confidence level. Red edge parameters including the amplitude of red edge (D-r) and the area of red edge (D-r) and the vegetation indexes including the ration vegetation index (R-nir/ R-o) and normalized difference vegetation index ((R-nir - R-o)/ (R-nir + R-o)) had significant correlation with water hyacinth, especially the latter two parameters which had the correlative coefficient value reaching the 99% confidence level. The estimation models built by sensitive hyperspectral parameters had been verified the precision respectively and the linear regression model based by vegetation index (R-nir/ R-o) got the best accuracy with the RMSE as 1.94 and MRE as 4.26%. The result of this study provide theoretical basis and technology approach for monitoring biomass of water hyacinth by hyperspectral satellite remote sensing.

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