Paper Info
Title
Neural network-based aboveground biomass estimation in Honghe National Natural Reserve using TM data
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%.
Year
DOI
Venue
2010
10.1109/ICNC.2010.5584356
ICNC
Keywords
Field
DocType
neural network,remote sensing,radial basis function networks,mean relative error,samples,remote sensing image,rs information,biomass,honghe national natural reserve,dry biomass estimation,backpropagation,wetland vegetation aboveground biomass,geophysical image processing,radial basis function model,backpropagation model,aboveground biomass estimation,artificial neural networks,correlation,relative error,neural network model,estimation,nature reserve
Soil science,Biomass,Vegetation,Computer science,Wetland,Artificial intelligence,Backpropagation,Artificial neural network,Nature reserve,Machine learning,Approximation error
Conference
Volume
ISBN
Citations 
4
978-1-4244-5958-2
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Shuang Li100.34
Zulu Zhang200.34
Demin Zhou313.42