TY - EJOU AU - Wang, Liang AU - Liu, Jiping AU - Xu, Shenghua AU - Dong, Jinjin AU - Yang, Yi TI - Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the Mount Tai Area Using the RBF ANN Algorithm T2 - Intelligent Automation \& Soft Computing PY - 2018 VL - 24 IS - 2 SN - 2326-005X AB - 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. KW - Remote-sensing-based estimation; AGB; RBF ANN; MIV DO - 10.1080/10798587.2017.1296660