Estimation of Pine Forest Height and Underlying DEM Using Multi-Baseline P-Band PolInSAR Data

Fu, Haiqiang; Wang, Changcheng; Zhu, Jianjun; Xie, Qinghua; Zhang, Bing

doi:10.3390/rs8100820

Cited by 35 publications

(36 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the unknown parameters Z can be acquired from Equation (8). In this paper, the sum of the squares of the residuals of the complex observations is used as the cost function of the optimization [21], which is a complex least squares problem. The corresponding expression iŝ…”

Section: Establishment Of Vector Observation Modelmentioning

confidence: 99%

“…However, all the three methods rely heavily on data characteristics, indicating the limitation of application in other forest environments. In addition, H. Fu et al have employed GVB model for direct inversion [21], yet high complexity and large amounts of computation are comprised in this method.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the effect of the incidence angle on the BVF of the GVB model is taken into consideration, when it comes to constructing the CGVB model. The iterative weighted complex least squares (IWCLS) [2,10,[21][22][23][24] method is employed to estimate volume coherence. In IWCLS, in order to curb the influence of noise, the truncated singular value decomposition [25] was applied to cut off the small singular values of the observation matrix.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Section: Establishment Of Vector Observation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Before implementing the PCT algorithm, we need to estimate the priori information of vegetation height h v and topographic phase φ 0 . We exploit the widely used random volume over ground (RVOG) model and three-stage inversion method to obtain forest height and topographic phase [41,62]. In addition, we also note that the PCT algorithm involves two polarization modes: the dominant volume scattering and the dominant ground surface scattering.…”

Section: Polarization Coherence Tomographymentioning

confidence: 99%

Forest Above-Ground Biomass Estimation Using Single-Baseline Polarization Coherence Tomography with P-Band PolInSAR Data

Zhang

Wang

Zhu

et al. 2018

Forests

Self Cite

View full text Add to dashboard Cite

Abstract:Forest above ground biomass (AGB) extraction using Synthetic Aperture Radar (SAR) images has been widely used in global carbon cycle research. Classical AGB inversion methods using SAR images are mainly based on backscattering coefficients. The polarization coherence tomography (PCT) technology which can generate vertical profiles of forest relative reflectivity, has the potential to improve the accuracy of biomass inversion. The relationship between vertical profiles and forest AGB is modeled by some parameters defined based on geometric characteristics of the relative reflectivity distribution curve. But these parameters are defined without physical characteristics. Among these parameters, tomographic height (TomoH) is considered as the most important one. However, TomoH only corresponds to the highest volume relative reflectivity, which is lower than the actual forest height, affecting the accuracy of forest height and AGB inversion. In this paper, we introduce a new parameter, the canopy height (H ac ), for AGB inversion by analyzing the vertical backscatter power loss. Then, we construct an inversion model based on the combination of the new parameter (H ac ) and other parameters from the tomographic profile. The P-band polarimetric SAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign acquired over Krycklan Catchment are selected for the verification experiment at two different flight directions. The results show that H ac performs better in estimating forest height and AGB than TomoH does. The inversion root mean square error (RMSE) of the proposed method is 18.325 t ha −1 , and the result of using TomoH is 21.126 t ha −1 .

show abstract

“…Digital elevation model (DEM) extraction in areas without forest or with sparse trees is the advantage of optical stereo pairs (Alrousan et al 1997, D'Angelo et al 2008, InSAR (Zhang and Yue 2017, Geymen 2014, Gao et al 2017)and Pol-InSAR (Fu et al 2016, Fu et al 2017, Shimada, Natsuaki and Hirose 2018. While, DEM beneath forest canopy is difficult to obtain with optical stereo pairs, InSAR and Pol-InSAR technique as the surface is covered with forest and the images hardly include terrain information.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Airborne L- Band Multi-Baseline Pol-Insar for Dem Extraction Beneath Forest Canopy

Chen

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:DEM beneath forest canopy is difficult to extract with optical stereo pairs, InSAR and Pol-InSAR techniques. Tomographic SAR (TomoSAR) based on different penetration and view angles could reflect vertical structure and ground structure. This paper aims at evaluating the possibility of TomoSAR for underlying DEM extraction. Airborne L-band repeat-pass Pol-InSAR collected in BioSAR 2008 campaign was applied to reconstruct the 3D structure of forest. And sum of kronecker product and algebraic synthesis algorithm were used to extract ground structure, and phase linking algorithm was applied to estimate ground phase. Then Goldstein cut-branch approach was used to unwrap the phases and then estimated underlying DEM. The average difference between the extracted underlying DEM and Lidar DEM is about 3.39 m in our test site. And the result indicates that it is possible for underlying DEM estimation with airborne L-band repeat-pass TomoSAR technique.

show abstract

Estimation of Pine Forest Height and Underlying DEM Using Multi-Baseline P-Band PolInSAR Data

Cited by 35 publications

References 44 publications

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

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

Forest Above-Ground Biomass Estimation Using Single-Baseline Polarization Coherence Tomography with P-Band PolInSAR Data

Evaluation of Airborne L- Band Multi-Baseline Pol-Insar for Dem Extraction Beneath Forest Canopy

Contact Info

Product

Resources

About