Estimation of Vegetation Biomass in an Alpine Marsh Using Multi-angle Hyperspectral Data CHRIS
Authors: 
Wei Wei, Li Wei-na, Zhang Huai-qingAuthors Info & Claims
Wei Wei, Li Wei-na, Zhang Huai-qingAuthors Info & ClaimsPages 22 - 28
Abstract
In this paper, it describes the estimation of vegetation biomass in an alpine marsh based on remote sensing data CHRIS. Vegetation biomass is an important index to evaluate the structure, function and health status of wetland ecosystems, directly reflecting the growth status of vegetation communities. Taking Longbaotan Wetland Nature Reserve as the research object, this study was conducted based on the ESA CHRIS/PROBA data. Remote sensing factors, including original spectral reflectance, narrow band Indices, red edge indices, and the newly established vegetation index - VInew, were extracted at the three angles of +36°, 0° and -36° respectively. Correlation between the factors and vegetation biomass in the alpine marsh was analyzed, and sensitivity of the biomass to angle was discussed. The optimal biomass estimation model was established by using the regression analysis method, and then used to estimate the aboveground vegetation biomass in Longbaotan Wetland. The results showed that the exponential model established with VInew (-36°) as the independent variable had the best fitting effect, with a determination coefficient of 0.613, and the inversion accuracy was improved obviously. The accuracy of reverse solving of biomass was obviously improved by using vegetation indices at different angles, which is very important for the optimization of remote sensing parameters used to retrieve wetland vegetation biomass.
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- Estimation of Vegetation Biomass in an Alpine Marsh Using Multi-angle Hyperspectral Data CHRIS
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December 2017
492 pages
ISBN:9781450363518
DOI:10.1145/3176653
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- Kyoto University: Kyoto University
- Nanyang Technological University
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Association for Computing Machinery
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Published: 27 December 2017
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- National Natural Science Foundation of China
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ICIT 2017
ICIT 2017: 2017 International Conference on Information Technology
December 27 - 29, 2017
Singapore, Singapore
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