Forest Parameters Inversion by Mean Coherence Set from Single-baseline PolInSAR Data

Abstract

Abstract The effectiveness of the forest height inversion methods using polarimetric SAR interferometry data is affected by the estimation accuracy of ground phase, volume only coherence and model prediction for forest canopy layer. Finding the forest model that considers the effects of the signal penetration in the forest medium as well as optimum volume coherence, which is an important purpose of the forest inversion method. This paper suggests a novel inversion algorithm to increase the accuracy of forest height estimation as well as ground phase estimation based on mean of coherence set. For this purpose, an adaptive total least square line fit is proposed to estimate the ground phase and coherence line. Then, the combination between the VE-RVoG model and an optimization volume only coherence algorithm based on mean coherence set and polarization signature is developed to find more accurate forest parameter values. The proposed algorithm applies forest height estimation using L-band PolInSAR data acquired by PolSARProSim software, spaceborne SIR-C and UAVSAR system. The method was further validated by using UAV L-band PolInSARata and the reference data of LiDAR canopy height model over rainforest Lope National Park, Gabon. Results showed that RMSE of results approximate 2.91 m and R2 is 0.909. The obtained results demonstrate the potential of proposed method in forest parameters estimation.