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

This paper investigates the potential of the time-frequency optimization on the basis of the sublook decomposition for forest height estimation. The optimization is deemed to be capable of extracting a relatively accurate volume contribution when P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) systems are adopted to observe forest-covered areas. The highest and the lowest phase centers acquired by the time-frequency optimization modify the conventional three-stage inversion process. This paper presents, for the first time, a performance assessment of the time-frequency optimization on P-band Pol-InSAR data over boreal forests. Simultaneously, to alleviate the model inversion errors caused by topographic fluctuations, forest height is estimated based on the sloped Random Volume over Ground (S-RVoG) model in which the incidence angle is corrected with the terrain slope. The E-SAR P-band Pol-InSAR data acquired during the BIOSAR 2008 campaign in Northern Sweden is utilized to evaluate the performance of the proposed method. From the results of the forest height estimation preprocessed with time-frequency optimization, the root mean square error (RMSE) of Random Volume over Ground (RVoG) and S-RVoG model on negative slope are 5.09 m and 4.71 m, respectively. It is concluded that the time-frequency processing and negative terrain slope compensation improve the inversion performance by 41 . 49 % and 11 . 96 % , respectively.

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Forest Height Estimation Based on P-Band Pol-InSAR Modeling and Multi-Baseline Inversion

Remote Sensing ◽

10.3390/rs12081319 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1319

Author(s):

Xiaofan Sun ◽

Bingnan Wang ◽

Maosheng Xiang ◽

Liangjiang Zhou ◽

Shuai Jiang

Keyword(s):

Standard Deviation ◽

Vertical Structure ◽

Incidence Angle ◽

Three Dimensional ◽

Solution Space ◽

Model Complexity ◽

Two Dimensional ◽

Dimensional Parameter ◽

Model Inversion ◽

Forest Height

The Gaussian vertical backscatter (GVB) model has a pivotal role in describing the forest vertical structure more accurately, which is reflected by P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) with strong penetrability. The model uses a three-dimensional parameter space (forest height, Gaussian mean representing the strongest backscattered power elevation, and the corresponding standard deviation) to interpret the forest vertical structure. This paper establishes a two-dimensional GVB model by simplifying the three-dimensional one. Specifically, the two-dimensional GVB model includes the following three cases: the Gaussian mean is located at the bottom of the canopy, the Gaussian mean is located at the top of the canopy, as well as a constant volume profile. In the first two cases, only the forest height and the Gaussian standard deviation are variable. The above approximation operation generates a two-dimensional volume only coherence solution space on the complex plane. Based on the established two-dimensional GVB model, the three-baseline inversion is achieved without the null ground-to-volume ratio assumption. The proposed method improves the performance by 18.62% compared to the three-baseline Random Volume over Ground (RVoG) model inversion. In particular, in the area where the radar incidence angle is less than 0.6 rad, the proposed method improves the inversion accuracy by 34.71%. It suggests that the two-dimensional GVB model reduces the GVB model complexity while maintaining a strong description ability.

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Four-Stage Inversion Algorithm for Forest Height Estimation Using Repeat Pass Polarimetric SAR Interferometry Data

Remote Sensing ◽

10.3390/rs10081174 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1174 ◽

Cited By ~ 3

Author(s):

Tayebe Managhebi ◽

Yasser Maghsoudi ◽

Mohammad Valadan Zoej

Keyword(s):

Extinction Coefficient ◽

European Space Agency ◽

Sar Interferometry ◽

Experimental Result ◽

Single Frequency ◽

Height Estimation ◽

Suggested Algorithm ◽

Forest Height ◽

Temporal Decorrelation ◽

The Mean

This paper proposes a new method for forest height estimation using single-baseline single frequency polarimetric synthetic aperture radar interferometry (PolInSAR) data. The new algorithm estimates the forest height based on the random volume over the ground with a volume temporal decorrelation (RVoG+VTD) model. We approach the problem using a four-stage geometrical method without the need for any prior information. In order to decrease the number of unknown parameters in the RVoG+VTD model, the mean extinction coefficient is estimated in an independent procedure. In this respect, the suggested algorithm estimates the mean extinction coefficient as a function of a geometrical index based on the signal penetration in the volume layer. As a result, the proposed four-stage algorithm can be used for forest height estimation using the repeat pass PolInSAR data, affected by temporal decorrelation, without the need for any auxiliary data. The suggested algorithm was applied to the PolInSAR data of the European Space Agency (ESA), BioSAR 2007 campaign. For the performance analysis of the proposed approach, repeat pass experimental SAR (ESAR) L-band data, acquired over the Remningstorp test site in Southern Sweden, is employed. The experimental result shows that the four-stage method estimates the volume height with an average root mean square error (RMSE) of 2.47 m against LiDAR heights. It presents a significant improvement of forest height accuracy, i.e., 5.42 m, compared to the three-stage method result, which ignores the temporal decorrelation effect.

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Boreal forest height estimation with SAR interferometry and laser measurements

2009 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2009.5417669 ◽

2009 ◽

Cited By ~ 1

Author(s):

Jaan Praks ◽

Martti Hallikainen ◽

Jaakko Seppanen ◽

Juha Hyyppa

Keyword(s):

Boreal Forest ◽

Sar Interferometry ◽

Height Estimation ◽

Laser Measurements ◽

Forest Height

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

Remote Sensing ◽

10.3390/rs11010042 ◽

2018 ◽

Vol 11 (1) ◽

pp. 42 ◽

Cited By ~ 4

Author(s):

Xiaofan Sun ◽

Bingnan Wang ◽

Maosheng Xiang ◽

Shuai Jiang ◽

Xikai Fu

Keyword(s):

Least Squares ◽

Incidence Angle ◽

Nonlinear Least Squares ◽

Estimation Accuracy ◽

Height Estimation ◽

Low Frequencies ◽

Function Fitting ◽

Forest Height ◽

Vertical Heterogeneity ◽

Least Squares Optimization

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.

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Forest Height Estimation Using a Single-Pass Airborne L-Band Polarimetric and Interferometric SAR System and Tomographic Techniques

Remote Sensing ◽

10.3390/rs13030487 ◽

2021 ◽

Vol 13 (3) ◽

pp. 487

Author(s):

Yue Huang ◽

Qiaoping Zhang ◽

Laurent Ferro-Famil

Keyword(s):

Boreal Forests ◽

Test Site ◽

Forest Monitoring ◽

Height Estimation ◽

Pass System ◽

Single Pass ◽

Forest Height ◽

Ping Pong ◽

L Band ◽

Interferometric Sar

This paper addresses forest height estimation for boreal forests at the test site of Edson in Alberta, Canada, using dual-baseline PolInSAR dataset measured by Intermap’s single-pass system. This particular dataset is acquired by using both ping-pong and non-ping-pong modes, which permit forming a dual-baseline TomoSAR configuration, i.e., an extreme configuration for tomographic processing. A tomographic approach, based on polarimetric Capon and MUSIC estimators, is proposed to estimate the elevation of tree top and of underlying ground, and hence forest height is estimated. The resulting forest DTM and DSM over the test site are validated against LiDAR-derived estimates, demonstrating the undeniable capability of the single-pass L-band PolInSAR system for forest monitoring.

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