A Novel Method for Forest Height Estimation Using PolInSAR Data

Monitoring the earth and its biosphere is an essential task in any scale to achieve a sustainable development. Therefore, forests, as an invaluable natural resource, have an important role to control the climate changes and the carbon cycle. For this reason, biomass and consequently forest height have been known as the key information for monitoring the forest and its underlying surface. Several studies, it has been shown that Synthetic Aperture RADAR (SAR) imaging systems can greatly help to this purpose. In this framework, a novel technique called Polarimetric SAR Interferometry (PolInSAR) is an appropriate and an available tool for forest height estimation, due to its sensitivity to location and vertical distribution of the forest structural components. Based on this, from a view point, the methods employed in this field can be divided into two categories: a) based on Random Volume over Ground (RVoG) inversion model, and b) based on model-based decomposition techniques of PolInSAR data. In this study, in order to improve the forest height estimation, a novel method based on the combination of two mentioned categories has been proposed. The performance and the efficiency of the proposed method were demonstrated by two datasets simulated from the PolSARProSim software in L and P bands. So that, in L band, it obtained 0.69 m, 1.84 m and 3.38 m improvements for height estimation in Digital Elevation Model (DEM) differencing, coherence amplitude and combined (phase and coherence) methods, respectively. As well, in P band, in it obtained 1.53 m and 2.74 m improvements especially in DEM differencing and coherence amplitude methods, respectively.