Estimation of forest biomass using Support Vector machines from comprehensive remote sensing data

As forest biomass estimation depends on the various remote sensing factors, multiple regression model may not fully capture the complex relationship among the variable. Support Vector machines have already proven their ability in solving the nonlinear and multi-dimensional problems. This paper proposed to use Support Vector machines to improve the accuracy of forest biomass retrival with LiDAR and SPOT5 and adopted the leave-one-out method to validate the model accuracy. Results showed that (i) Support Vector machines had the best performance on the present data set as compared to the Back Propogation Networks,Radius Basis Function Networks and K nearest neighbor algorithm; (ii) compared to the single data source, the cooperative utilization of LiDAR and SPOT5 had the better result and this conclusion was suitable for the four using nonparametric methods; (iii) as the number of the input data dimension increasing, Support Vector machines was immune to the multi-dimension affection and performed better than other three schemes.