Abstract
In the case of low frequencies (e.g., P-band) radar observations, the Gaussian Vertical Backscatter (GVB) model, a model that takes into account the vertical heterogeneity of the wave-canopy interactions, can describe the forest vertical backscatter profile (VBP) more accurately. However, the GVB model is highly complex, seriously reducing the inversion efficiency because of a number of variables. Given that concern, this paper proposes a constrained Gaussian Vertical Backscatter (CGVB) model to reduce the complexity of the GVB model by establishing a constraint relationship between forest height and the backscattering vertical fluctuation (BVF) of the GVB model. The CGVB model takes into account the influence of incidence angle on scattering mechanisms. The BVF of VBP described by the CGVB model is expressed with forest height and a polynomial function of incidence angle. In order to build the CGVB model, this paper proposes the supervised learning based on RANSAC (SLBR). The proposed SLBR method used forest height as a prior knowledge to determine the function of incidence angle in the CGVB model. In this process, the Random Sample Consensus (RANSAC) method is applied to perform function fitting. Before building the CGVB model, iterative weighted complex least squares (IWCLS) is employed to extract the required volume coherence. Based on the CGVB model, forest height estimation was obtained by nonlinear least squares optimization. E-SAR P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) data acquired during the BIOSAR 2008 campaign was used to test the performance of the proposed CGVB model. It can be observed that, compared with Random Volume over Ground (RVoG) model, the proposed CGVB model improves the estimation accuracy of the areas with incidence angle less than 0.8 rad and less than 0.6 rad by 28.57 % and 40.35 % , respectively.
Highlights
Forest height is an important parameter for biomass extraction and forest dynamics research which are significant for studying the environment and climate, but the estimation of structure brings a considerable constraint in the biomass estimation
In order to build the constrained Gaussian Vertical Backscatter (CGVB) model, this paper proposes the supervised learning based on Random Sample Consensus (RANSAC) (SLBR)
The forest height estimation of validation stands in the region where the incidence angle is less than 0.6 rad is shown in Figure 7d,e corresponding to Random Volume over Ground (RVoG) model and the proposed CGVB model, respectively
Summary
Forest height is an important parameter for biomass extraction and forest dynamics research which are significant for studying the environment and climate, but the estimation of structure brings a considerable constraint in the biomass estimation. Based on the second approach, Random Volume over Ground (RVoG) model uses exponential attenuation to describe the shape of VBP [10,11,12], depicting forest canopy as a homogeneous layer with a fixed extinction. The GVB model takes into account predominant contributions in the vegetation layer for forest height inversion [4,5,6], and consists of three parameters, so it can describe more VBP shapes than RVoG model. For P-band Pol-InSAR, the inversion based on either RVoG model or GVB model generally requires quad-polarimetric MB data. Based on RVoG model, one way to still estimate forest height with SB P-band data is to fix the extinction value [14].
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