Application of neural networks for the retrieval of forest woody volume from SAR multifrequency data at l and C bands

This work aims at investigating the potential of L (ALOS/PALSAR) and C (ENVISAT/ASAR) band SAR images in forest biomass monitoring and setting up a retrieval algorithm, based on Artificial Neural Networks (ANN), for estimating the Woody Volume (WV, in m3/ha) from combined satellite acquisitions. The investigation was carried out on two test areas in central Italy, where ground WV measurements were available. An innovative retrieval algorithm based on ANN was developed for estimating WV from L and C bands SAR data. The novelty consists of an accurate training of the ANN with several thousands of data, which allowed the implementation of a very robust algorithm. The RMSE values found on San Rossore area were ?40 m3/ha (L band data only), and 25-30 m3/ha (L with C band). On Molise, by using combined data at L and C bands, RMSE<30m3/ha was obtained. Keywords: ANN; backscattering; Woody Volume; LiDAR; ALOS/PALSAR; ENVISAT/ASAR.