GA-SVR Algorithm for Improving Forest Above Ground Biomass Estimation Using SAR Data

Synthetic aperture radar (SAR) features have been demonstrated that they have the potentiality to improve forest above ground biomass (AGB) estimation accuracy, especially including polarimetric information. Genetic algorithms (GAs) have been successfully implemented in optimal feature identification, while support vector regression (SVR) has great robustness in parameter estimation. The use of combined GAs and SVR can improve the accuracy of forest AGB estimation through simultaneously identifying the optimal SAR features and selecting the SVR model parameters. In this article, 14 SAR polarimetric features were extracted from C-band and L-band full-polarization SAR images and worked as input SAR features, respectively. C-band data was acquired on GaoFen-3 mission, we also call it GF-3 image. L-band data was ALOS-2 PALSAR-2 data. Both feature subsets from GF-3 and ALOS-2 PALSAR-2 and SVR hyper parameters used in the forest AGB estimation were optimized by a GA processing, where 8 different settings of 3 kinds of parameters, as 512 kind of different combinations were applied for SVR hyper parameters searching field. The results of GA-SVR performance using the two datasets were presented and compared with two traditional methods: the algorithm of GA feature selection companied with default SVR parameters (GA+ default SVR), and the algorithm of GA feature selection companied with grid searching for SVR parameter selection (GA+Grid SVR). The results showed that the proposed GA-SVR algorithm improved the forest AGB estimation accuracy with cross-validation coefficient of 80.21% for GF-3 and 71.41% for ALOS-2 PALSAR-2 data.

Automated summary

In this study, the potential of synthetic aperture radar (SAR) features in improving forest above ground biomass (AGB) estimation accuracy is demonstrated. By combining genetic algorithms (GAs) and support vector regression (SVR), the study shows an enhanced accuracy in forest AGB estimation, with promising results obtained from GF-3 and ALOS-2 PALSAR-2 datasets.