Exploring parameter selection for carbon monitoring based on Landsat-8 imagery of the aboveground forest biomass on Mount Tai

Forests play a fundamental role in stabilizing the global climate. Aboveground biomass (AGB) is important to the global carbon balance and environmental protection. Remotely sensed features such as single-band information, vegetation indices, texture features and terrain factors have been assessed to accurately estimate the forest biomass. A feature selection SVM-RFE (support vector machine recursive feature elimination) method is proposed to explore the relationship between the biomass and parameters derived from the Mount Tai area Landsat-8 imagery, thereby improving the AGB estimation accuracy. The least significant parameter is recursively removed according to a scoring function to determine the optimal subset of parameters. The performance of the SVM-RFE algorithm biomass feature selection method is then compared with the widely used stepwise regression method. The results show that the SVM-RFE method is superior to the stepwise linear regression method. And it could accurately estimate the AGB compared with field measurements in the context of the recent reducing emissions from forest deforestation and degradation (REDD) mechanism adopted by the United Nations.