Forest Height Estimation Based on Constrained Gaussian Vertical Backscatter Model Using Multi-Baseline P-Band Pol-InSAR Data

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.

S-RVoG Model Inversion Based on Time-Frequency Optimization for P-Band Polarimetric SAR Interferometry

“…In the light of Equation 12, a larger k z leads to the decrease of coherence and the increase of the phase in the random volume solution space, while a smaller k z leads to an opposite effect. That conclusion is also verified by comparing Figures 2 and 4, from which we can also conclude that the solution space corresponding to a larger k z owns a lower inversion sensitivity, whereas that corresponding to a smaller k z owns a higher inversion sensitivity [6,35]. The homologous experimental results are presented in Figure 12.…”

“…Various forest height estimation methods have been proposed for the P-band data of BIOSAR 2008 campaign [6,32,35,48]. Whether it uses Pol-InSAR or TomoSAR to retrieve forest height, the multi-baseline configuration is a very important solution.…”

“…The RVoG model deems that the power peak locates at the top of the canopy, nevertheless, when P-band is utilized to observe the boreal forest, backscatter power is chiefly concentrated in the middle and lower parts of the canopy. The way to cope with this limitation is to adopt some new complicated models [35,[48][49][50][51].…”

“…That is because the NGVR assumption is invalid under this circumstance. Up to now, two main strategies have been adopted to ameliorate the underestimation [3,35]. The first strategy is to increase the observation space with multi-baseline data [3,26,[36][37][38].…”

Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data

“…Other studies found that the incidence angle also influences the canopy height inversion since the vertical wavenumber is estimated based on the incidence angle [61,62]. A smaller angle causes a larger vertical wavenumber relative to the effective baseline [61]. Using X-band and C-band with a weak penetration depth, a larger vertical wavelength can be sensitive to canopy height inversion.…”