Retrieval of forest parameters using a fractal-based coherent scattering model and a genetic algorithm

In this paper, a procedure for retrieval of forest parameters is developed using the recently developed fractal-based coherent scattering model (FCSM) and a stochastic optimization algorithm. Since the fractal scattering model is computationally extensive, first a simplified empirical model with high fidelity for a desired forest stand is constructed using FCSM, Inputs to the empirical model are the influential structural and electrical parameters of the forest stand, such as the tree density, tree height, trunk diameter, branching angle, wood moisture, and soil moisture. Other finer structural features are embedded in the fractal model. The model outputs are the polarimetric and interferometric response of the forest as a function of the incidence angle. In this study, a genetic algorithm (GA) is employed as a global search routine to characterize the input parameters of a forest stand from a set of measured polarimetric/interferometric backscatter responses of the stand. The success of the inversion algorithm is demonstrated using a set of measured single-polarized interferometric synthetic aperture radar (SAR) data and several FCSM simulation results.