Modeling the Spatial Distribution of Forest Carbon Storage by Neural Network Based on Radial Basis Function

By means of applying the radialbasisfunctionneuralnetwork (RBFnn) method and integrated with the field survey data from 930 sample plots obtained by the forest inventory and the corresponding TM images, the spatial distribution of the forest carbon storage of Lin′an Municipality was simulated by taking 3 three vegetation indices, i.e., TM4/57, ARVI and KT2 as the input variables. The results showed the radialbasisfunctionneuralnetwork (RBFnn) method could accurately generate the spatial distribution and the variation of forest carbon storage, and there was a very good consistency between the simulated results and the data obtained from the field survey, which provided the predictive studies of forest carbon storage with methodological reference.