Forest height estimation based on the RVoG inversion model and the PolInSAR decomposition technique

Monitoring the earth's biosphere is an essential task to understand the global dynamics of ecosystems, biodiversity, and management aspects. Forests, as a natural resource, have an important role to control the climate changes and the carbon cycle. For this reason, biomass and consequently forest height are known as the key information for monitoring the forest and its underlying surface. Several studies have shown that Synthetic Aperture Radar (SAR) imaging systems can provide an appropriate solution to estimate the biomass and the forest height. In this framework, Polarimetric SAR Interferometry (PolInSAR) technique is an effective tool for forest height estimation, due to its sensitivity to location and vertical distribution of the forest structural components. From one point of view, the employed methods are either based on model-based decomposition techniques or inversion models. In this paper, a method based on the combination of two categories has been proposed. Indeed, introducing a new way of combining the two categories for forest height estimation is the novel contribution of this study. The main motivation is to find directly and simultaneity the volume only and ground only complex coherences using the PolInSAR decomposition technique without the need to any a priori information for improving the forest height estimation procedure in the inversion models such as Random Volume over Ground (RVoG) model. The efficiency of the proposed approach was demonstrated by the E-SAR L-band single baseline PolInSAR data over the Remningstorp test site, in southern Sweden. Moreover, Light Detection and Ranging (LiDAR) data were used to evaluate the results. The experimental results showed that the proposed method improved the forest height estimation by 6.86 m.