Abstract
Abstract. In recent years, a lot of studies have shown that polarimetric synthetic aperture radar interferometry (PolInSAR) is a powerful technique for forest height mapping and monitoring. However, few researches address the problem of terrain slope effect, which will be one of the major limitations for forest height inversion in mountain forest area. In this paper, we present a novel forest height retrieval algorithm by integration of dual-baseline PolInSAR data and external DEM data. For the first time, we successfully expand the S-RVoG (Sloped-Random Volume over Ground) model for forest parameters inversion into the case of dual-baseline PolInSAR configuration. In this case, the proposed method not only corrects terrain slope variation effect efficiently, but also involves more observations to improve the accuracy of parameters inversion. In order to demonstrate the performance of the inversion algorithm, a set of quad-pol images acquired at the P-band in interferometric repeat-pass mode by the German Aerospace Center (DLR) with the Experimental SAR (E-SAR) system, in the frame of the BioSAR2008 campaign, has been used for the retrieval of forest height over Krycklan boreal forest in northern Sweden. At the same time, a high accuracy external DEM in the experimental area has been collected for computing terrain slope information, which subsequently is used as an inputting parameter in the S-RVoG model. Finally, in-situ ground truth heights in stand-level have been collected to validate the inversion result. The preliminary results show that the proposed inversion algorithm promises to provide much more accurate estimation of forest height than traditional dualbaseline inversion algorithms.
Highlights
Forest height, as an important forest biophysical parameter, is useful for forest management, biomass inversion and ecosystem modelling (Houghton et al, 2009)
In the past few decades, amount of studies have shown that Polarimetric synthetic aperture radar interferometry (PolInSAR) is a promising new technology for forest height inversion from region scale to global scale using Random volume over ground (RVoG) model(Papathanassiou and Cloude, 2001; Cloude and Papathanassiou, 2003)
Lu et al (2013) propose a slope random volume over ground (S-RVoG) model, which accounts for range terrain slope and successfully extend the RVoG model by aligning the reference frame
Summary
As an important forest biophysical parameter, is useful for forest management, biomass inversion and ecosystem modelling (Houghton et al, 2009). The results have shown this inversion model can effectively improve the inversion performance in forest area with relatively steep terrain condition One limitation of this model is it still exist the assumption about minimum ground to volume scattering ratio in one of the observed polarisation channels. Another limitation is that it need input slope information into the inversion model, which can be acquired from external DEM or derived DEM by InSAR technology itself. We adopt the robust parameters estimation algorithm based on dual baseline PolInSAR data proposed by (Cloude, 2002), which can automatically skip the assumption about the minimum ground to volume scattering ratio and provide more observation information.
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