Polarization Coherence Tomography Research Articles

A Dual-Baseline PolInSAR Method for Forest Height and Vertical Profile Function Inversion Based on the Polarization Coherence Tomography Technique

Forest height and vertical structure profile functions can be estimated using polarimetric interferometric synthetic aperture radar (PolInSAR) data based on the random volume over ground (RVoG) model and polarization coherence tomography (PCT) theory, respectively. For each resolution cell, considering different forest vertical scattering structure functions to solve the corresponding forest height, the accuracy of PolInSAR forest height inversion will be improved. In this study, a forest vertical structure profile function and forest height inversion algorithm based on PCT technology was developed by using dual-baseline PolInSAR data. Then the deviation of forest height was corrected according to the inverted forest vertical structure. Finally, the LiDAR and PolInSAR data were employed to verify the proposed method. The experimental results show that the accuracy of the proposed method (tropical forest: RMSE = 5.96 m, boreal forest: RMSE = 3.11 m) is 25.5% and 30.43% higher than that of the dual-baseline RVoG model algorithm (tropical forest: RMSE = 8 m, boreal forest: RMSE = 4.47 m).

First Demonstration of Space-Borne Polarization Coherence Tomography for Characterizing Hyrcanian Forest Structural Diversity

Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height (σdbh) and the number of trees (N), were predicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space-borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height hv and ground phase φ0. The Fourier–Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of σdbh and N, thirteen geometrical and physical parameters were defined (P1−P13). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting σdbh and N. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R2* of 0.25 for σdbh, whereas only 22% of the variation in N was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies.

A Multibaseline PolInSAR Forest Height Inversion Model Based on Fourier–Legendre Polynomials

In this letter, we propose a forest height inversion model based on three-order Fourier-Legendre (FL) polynomials from the multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) data. The proposed model expresses the vertical structure of the volume layer as three-order FL polynomials. Meanwhile, the forest height is treated as an unknown parameter, rather than a priori information, as adopted in polarization coherence tomography technology. On the other hand, to be more realistic, the proposed model uses multipolarization PolInSAR data and considers that the synthetic aperture radar (SAR) signals in different polarizations describe the forest vertical structure in different ways so that we can obtain a more comprehensive forest vertical structure. Airborne P-band PolInSAR data acquired over the boreal and tropical forest areas were selected for testing the forest height inversion method. The results show that, compared to random volume over ground (RVoG) model-based inversion, the accuracy of the proposed model is improved by 28.20% and 17.30%, respectively, for the boreal and tropical forest scenes.

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

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 (Hac), for AGB inversion by analyzing the vertical backscatter power loss. Then, we construct an inversion model based on the combination of the new parameter (Hac) 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 Hac 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.

Forest aboveground biomass estimation using polarization coherence tomography and PolSAR segmentation

Forest aboveground biomass (AGB) is essential for monitoring the carbon cycle budget and climate change. This study proposes a method for the estimation of forest AGB based on polarization coherence tomography (PCT) and polarimetric synthetic aperture radar (PolSAR) segmentation. The data used are the single-baseline polarimetric SAR interferometry data acquired by the German Aerospace Center’s E-SAR sensor at the L-band over the city of Traunstein and its vicinity in 2003. First, vertical structure tomographic profiles (relative reflectivity distribution in vertical direction) were produced by PCT for each pixel and then averaged within each field-surveyed forest stand to obtain the mean tomographic profile. Next, the mean vertical tomographic profiles were parameterized by 10 shape parameters. Several models for biomass estimation were designed based on the relationships between the definition parameters and the in situ measurements using backward step-wise regression and the M-fold (10-fold) cross-validation method. The best model was chosen by evaluation criteria such as R, root mean square error (RMSE), R2, etc. Forest polygons (objects) were produced by image segmentation using forest heights, (produced by coherence optimization representing double-bounce scattering mechanism), (produced by coherence optimization representing the volume scattering mechanism), and the volume scattering mechanism class (produced by Freeman–Durden decomposition and Whishart classification). Finally, the selected model was used to estimate the AGB of each forest polygon and was validated using ground-measured biomass with R2 of 0.883 and RMSE of 39.98 tons ha−1. The results show that the proposed method works well for the estimation of forest AGB. No saturation phenomena have been observed even for the forest stands with AGB larger than 500 tons ha−1.

A ROBUST PCT METHOD BASED ON COMPLEX LEAST SQUARES ADJUSTMENT METHOD

Abstract. Polarization Coherence Tomography (PCT) method has the good performance in deriving the vegetation vertical structure. However, Errors caused by temporal decorrelation and vegetation height and ground phase always propagate to the data analysis and contaminate the results. In order to overcome this disadvantage, we exploit Complex Least Squares Adjustment Method to compute vegetation height and ground phase based on Random Volume over Ground and Volume Temporal Decorrelation (RVoG + VTD) model. By the fusion of different polarimetric InSAR data, we can use more observations to obtain more robust estimations of temporal decorrelation and vegetation height, and then, we introduce them into PCT to acquire more accurate vegetation vertical structure. Finally the new approach is validated on E-SAR data of Oberpfaffenhofen, Germany. The results demonstrate that the robust method can greatly improve accusation of vegetation vertical structure.

A New Function Expansion for Polarization Coherence Tomography

In this letter, we investigate the polarization coherence tomography technique and propose a new function expansion to reconstruct the vertical profile function. Instead of generating profile in Fourier-Legendre series, we deduce orthogonal functions on [-1, 1] by weight of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , which can increase the highest polynomial order and decrease the condition number of the inversion matrix, indicating that the inversion in the new expansion is more stable and less susceptible to noise. Finally, we apply the technique to simulated dual-baseline data and Chinese X-band single-baseline polarimetric synthetic aperture radar interferometry data to demonstrate its validity and robustness.

Forest above ground biomass estimation methodology based on polarization coherence tomography

Forest above ground biomass estimation methodology based on polarization coherence tomography

Analysis of forest backscattering characteristics based on polarization coherence tomography

It is difficult to make an inventory of vertical profiles of forest structure parameters in field measurements. However, analysis and understanding of forest backscattering characteristics contribute to estimation and detection of forest vertical structure because of the close relationships between backscattering characteristics and structure parameters. The vertical structure function in the complex interferometric coherence definition, which represents the vertical variation of microwave scattering with the penetration depth at a point in the 2-D radar image and can be used to analyze the forest backscattering characteristics, can be reconstructed from polarization coherence tomography (PCT). Based on PCT, the paper analyzes the forest backscattering characteristics and explores the inherent relationship between the result of PCT and the forest structure parameters from numerical simulation of Random Volume over Ground model (RVoG), Polarimetrie SAR interferometry (PolInSAR) simulation of forest scene and PolInSAR data at L-band of the test site Traunstein. Firstly, the effects of the extinction coefficient and surface-to-volume scattering ratio in RVoG model on vertical backscattering characteristics are analyzed by means of numerical simulation. Secondly, by applying PCT to L-band POLInSAR simulations of forest scene, different variations of vertical backscattering due to different extinction coefficients and the ratios of surface-to-volume scattering resulting from different polarizations, forest types and densities are displayed and analyzed. Then a concept of relative average backscattering intensity is presented, and the factors which affect its vertical distribution are also discussed. Preliminary results show that there is high sensitivity of the vertical distribution of forest relative average backscattering intensity to the polarization, forest type and density. Finally, based on repeat pass DLR E-SAR L-band airborne POLInSAR data, the capability of PCT technology for detection of forest layer, type and density is discussed.

Polarization coherence tomography

In this paper we introduce a new radar‐imaging technique, called polarization coherence tomography (PCT), which employs variation of the interferometric coherence with polarization to reconstruct a vertical profile function in penetrable volume scattering. We first show how this profile function can be efficiently represented as a Fourier‐Legendre series, with tomographic reconstruction reducing to estimation of the unknown coefficients of this series from coherence data. We then show that we can linearize this inversion by using a priori knowledge of two parameters, namely, volume depth and topographic phase. We further propose a new algorithm based on polarimetric interferometry to estimate these two from the data itself. To assess stability, we investigate both the single‐ and dual‐baseline conditioning of the associated matrix inversion and then concentrate on the single‐baseline case to demonstrate that for sufficient multilooking (around 50), stable retrievals of profiles can be obtained in the presence of coherence noise. Finally, we apply the technique to simulated L band coherent radar data to demonstrate its potential for new applications in radar remote sensing.