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5 March 2019 Quantitative Estimation of Biomass of Alpine Grasslands Using Hyperspectral Remote Sensing
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In order to promote the application of hyperspectral remote sensing in the quantification of grassland areas' physiological and biochemical parameters, based on the spectral characteristics of ground measurements, the dry AGB and multisensor satellite remote sensing data, including such methods as correlation analysis, scaling up, and regression analysis, were used to establish a multiscale remote sensing inversion model for the alpine grassland biomass. The feasibility and effectiveness of the modelwere verified by the remote sensing estimation of a time-space sequence biomass of a plateau grassland in northern Tibet. The results showed that, in the ground spectral characteristic parameters of the grassland's biomass, the original wave bands of 550, 680, 860, and 900 nm, as well as their combination form, had a good correlation with biomass. Also, the remote sensing biomass estimationmodel established on the basis of the two spectral characteristics (VI2 and Normalized Difference Vegetation Index [NDVI]) had a high inversion accuracy andwas easy to realize, with a fitting R2 of 0.869 and an F test value of 92.6. The biomass remote sensing estimate after scale transformation had a standard deviation of 53.9 kg/ha from the fitting model established by MODIS NDVI, and the estimation accuracy was 89%. Therefore, it displayed the ability to realize the estimation of large-scale and long-time sequence remote sensing biomass. The verification of themodel's accuracy, comparison of the existing research results of predecessors, and analysis of the regional development background demonstrated the effectiveness and feasibility of this method.

© 2018 The Society for Range Management. Published by Elsevier Inc. All rights reserved.
Bo Kong, Huan Yu, Rongxiang Du, and Qing Wang "Quantitative Estimation of Biomass of Alpine Grasslands Using Hyperspectral Remote Sensing," Rangeland Ecology and Management 72(2), 336-346, (5 March 2019).
Received: 4 July 2017; Accepted: 17 October 2018; Published: 5 March 2019

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