基于神经网络的玛纳斯河流域植被地上生物量反演

doi:10.13866/j.azr.2019.04.09

Abstract

Abstract: Aboveground biomass reflects the capability of ecosystems to obtain energy.Analysis on the spatial distribution pattern is of great significance for understanding the structure and function of ecosystems.The accuracy of inverting aboveground biomass with the conventional approach is low due to the lack of samples and the uncertainty of impact factors.In this study,Extreme Learning Machine (ELM) was used to train the remote sensing factors of 105 samples which included seven-band pixel values of TM image and vegetation factors,and the remaining 34 samples were used for verification.The results confirmed that ELM approach could invert vegetable aboveground biomass with a higher accuracy,and its determination coefficient of curve fitting reached 0.89.In addition,the inversion of vegetation aboveground biomass in the Manas River Basin from 2010 to 2015 found that the biomass was relatively stable in the upper area of the Manas River Basin,was in an increase trend in the middle plains and was in a deterioration trend in the downstream desert.

Key words: vegetable, aboveground biomass, neural network model, land use, Manas River Basin, Xinjiang

ZHANG Yuan, WANG Ling, BAO An-ming, LIU Hai-long. Inversion of Vegetable Aboveground Biomass in the Manas River Basin Based on Neural Network[J].Arid Zone Research, 2019, 36(4): 863-869.

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Inversion of Vegetable Aboveground Biomass in the Manas River Basin Based on Neural Network

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