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

A novel approach using radial basis function artificial neural networks (RBF ANNs) significantly enhances the accuracy of estimating forest above-ground biomass (AGB) from remotely sensed imagery, outperforming traditional methods like multivariate linear regression and other neural network models. This study applied Landsat 8 OLI imagery and field data to model AGB in Mount Tai, a UNESCO World Heritage Site in Shandong Province, China. The RBF ANN demonstrated superior precision compared to k-Nearest Neighbor (KNN) and backpropagation artificial neural networks (BP ANNs), offering a more efficient and reliable tool for assessing forest productivity and carbon cycling dynamics. This advancement supports better-informed forest management and scientific planning.

Wang, L., liu, j., Xu, S., Dong, J., & Yang, Y. (2018). Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the Mount Tai Area Using the RBF ANN Algorithm. Intelligent Automation and Soft Computing, 24(2), 391–398. https://doi.org/10.1080/10798587.2017.1296660