@Article{10798587.2017.1296660,
AUTHOR = {Liang Wang, Jiping Liu, Shenghua Xu, Jinjin Dong, Yi Yang},
TITLE = {Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the  Mount Tai Area Using the RBF ANN Algorithm},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {2},
PAGES = {391--398},
URL = {http://www.techscience.com/iasc/v24n2/39765},
ISSN = {2326-005X},
ABSTRACT = {Forest biomass is a significant indicator for substance accumulation and forest succession, and can 
provide valuable information for forest management and scientific planning. Accurate estimations of 
forest biomass at a fine resolution are important for a better understanding of the forest productivity 
and carbon cycling dynamics. In this study, considering the low efficiency and accuracy of the existing 
biomass estimation models for remote sensing data, Landsat 8 OLI imagery and field data cooperated 
with the radial basis function artificial neural network (RBF ANN) approach is used to estimate the 
forest Above Ground Biomass (AGB) in the Mount Tai area, Shandong Province of East China. The 
experimental results show that the RBF model produces a relatively accurate biomass estimation 
compared with multivariate linear regression (MLR), k-Nearest Neighbor (KNN), and backpropagation 
artificial neural network (BP ANN) models.},
DOI = {10.1080/10798587.2017.1296660}
}