An Optimal Eigenvalue-Based Decomposition Approach for Estimating Forest Parameters Over Forest Mountain Areas

This paper aims to provide a new method for retrieving forest parameters in forest mountain areas by using L-band polarimetric interferometry synthetic aperture radar (PolInSAR) data. Applying the model-based (ground, double-bounce, and volume scattering) decomposition techniques to PolInSAR data has opened a new way for vegetation parameters estimation. However, the modeling of the vegetation backscattering mechanisms is complicated due to the influences of the topographic slope variation and assumptions about the volume scattering component. In order to overcome these limitations, an eigenvalue-based decomposition technique is proposed. In which, a simple volume scattering model introduced by Neumann is used. The proposed method has improved 1.2664 m height of forest trees compared to the three-state inverse approach. In addition, evaluation results with simulated data generated from PolSARProSim software and PolInSAR data over the Kalimantan areas, Indonesia from ALOS/PALSAR L-band spaceborne radar system show that the proposed method produces reasonable and outstanding physical results in comparison with traditional decomposition methods. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.