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Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the Mount Tai Area Using the RBF ANN Algorithm

Liang Wanga,b, Jiping Liua,b, Shenghua Xub, Jinjin Dongc, Yi Yangd

a School of Resource and Environmental Science, Wuhan University, Wuhan Hubei, China;
b Research Center of Government GIS, Chinese Academy of Surveying and Mapping, Beijing, China;
c Shandong Building Materials Institute of Exploration and Survey, Shandong General Brigade of China National Geological Exploration Center of Building Materials Industry, Jinan, China;
d School of Geomatics and Marine Information, Huaihai Institute of Technology, Lianyungang Jiangsu, China

* Corresponding Author: Shenghua Xu,

Intelligent Automation & Soft Computing 2018, 24(2), 391-398.


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.


Cite This Article

L. Wang, . J. Liu, . S. Xu, . J. Dong and . Y. Yang, "Forest above ground biomass estimation from remotely sensed imagery in the mount tai area using the rbf ann algorithm," Intelligent Automation & Soft Computing, vol. 24, no.2, pp. 391–398, 2018.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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