Neural network-based aboveground biomass estimation in Honghe National Natural Reserve using TM data

Author

Li, Shuang ; Zhang, Zulu ; Zhou, Demin

Author_Institution

Coll. of Population, Resources & Environ., Shandong Normal Univ., Jinan, China

Volume

4

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

1741

Lastpage

1745

Abstract

In order to estimate the wetland vegetation aboveground biomass, the neural network models (BP, RBF) were established based on the Remote Sensing (RS) image of Honghe National Natural Reserve (HNNR) and 29 samples of biomass data. Through training, simulation, and comparing with the measured biomass data, the results show that the accuracy of the biomass estimation by neural network is relatively high. Furthermore the accuracy of the model of dry biomass is higher than that of humid biomass. By comparison between BP network and RBF network, it is found that the RBF network is the better method for estimating the wetland vegetation aboveground biomass with RS information. With the method of RBF, the mean relative error (MRE) of estimated dry biomass was 2.795% and the MRE of estimated humid biomass was 3.366%.

Keywords

backpropagation; geophysical image processing; radial basis function networks; remote sensing; Honghe national natural reserve; aboveground biomass estimation; backpropagation model; dry biomass estimation; mean relative error; neural network; radial basis function model; remote sensing image; wetland vegetation aboveground biomass; Artificial neural networks; Biological system modeling; Biomass; Correlation; Estimation; Radial basis function networks; Vegetation mapping; RS information; biomass; neural network; samples;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5584356

Filename

5584356