Shreya Chandola

Bio: Shreya Chandola is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 40 citations.

Polarimetric SAR interferometry

Abstract: In this paper, we examine the role of polarimetry in synthetic aperture radar (SAR) interferometry. We first propose a general formulation for vector wave interferometry that includes conventional scalar interferometry as a special case. Then, we show how polarimetric basis transformations can be introduced into SAR interferometry and applied to form interferograms between all possible linear combinations of polarization states. This allows us to reveal the strong polarization dependency of the interferometric coherence. We then solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms. A simplified stochastic scattering model for an elevated forest canopy is introduced to demonstrate the effectiveness of the proposed algorithms. In this way, we demonstrate the importance of wave polarization for the physical interpretation of SAR interferograms. We investigate the potential of polarimetric SAR interferometry using results from the evaluation of fully polarimetric interferometric shuttle imaging radar (SIR)-C/X-SAR data collected during October 8-9, 1994, over the SE Baikal Lake Selenga delta region of Buriatia, Southeast Siberia, Russia.

Single-baseline polarimetric SAR interferometry

Abstract: The objective of this paper is to examine the application of single-baseline polarimetric SAR interferometry to the remote sensing and measurement of structure over forested terrain. For this, a polarimetric coherent scattering model for vegetation cover suitable for the estimation of forest parameters from interferometric observables is introduced, discussed and validated. Based on this model, an inversion algorithm which allows the estimation of forest parameters such as tree height, average extinction, and underlying topography from single-baseline fully polarimetric interferometric data is addressed. The performance of the inversion algorithm is demonstrated using fully polarimetric single baseline experimental data acquired by DLR's E-SAR system at L-band.

SAR-Based Estimation of Above-Ground Biomass and Its Changes in Tropical Forests of Kalimantan Using L- and C-Band

Abstract: Kalimantan poses one of the highest carbon emissions worldwide since its landscape is strongly endangered by deforestation and degradation and, thus, carbon release. The goal of this study is to conduct large-scale monitoring of above-ground biomass (AGB) from space and create more accurate biomass maps of Kalimantan than currently available. AGB was estimated for 2007, 2009, and 2016 in order to give an overview of ongoing forest loss and to estimate changes between the three time steps in a more precise manner. Extensive field inventory and LiDAR data were used as reference AGB. A multivariate linear regression model (MLR) based on backscatter values, ratios, and Haralick textures derived from Sentinel-1 (C-band), ALOS PALSAR (Advanced Land Observing Satellite’s Phased Array-type L-band Synthetic Aperture Radar), and ALOS-2 PALSAR-2 polarizations was used to estimate AGB across the country. The selection of the most suitable model parameters was accomplished considering VIF (variable inflation factor), p-value, R2, and RMSE (root mean square error). The final AGB maps were validated by calculating bias, RMSE, R2, and NSE (Nash-Sutcliffe efficiency). The results show a correlation (R2) between the reference biomass and the estimated biomass varying from 0.69 in 2016 to 0.77 in 2007, and a model performance (NSE) in a range of 0.70 in 2016 to 0.76 in 2007. Modelling three different years with a consistent method allows a more accurate estimation of the change than using available biomass maps based on different models. All final biomass products have a resolution of 100 m, which is much finer than other existing maps of this region (>500 m). These high-resolution maps enable identification of even small-scaled biomass variability and changes and can be used for more precise carbon modelling, as well as forest monitoring or risk managing systems under REDD+ (Reducing Emissions from Deforestation, forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks) and other programs, protecting forests and analyzing carbon release.

Potential of Space-Borne PolInSAR for Forest Canopy Height Estimation Over India—A Case Study Using Fully Polarimetric L -, C -, and X -Band SAR Data

Abstract: This paper aims to demonstrate the potential of space-borne datasets for forest height estimation over Indian tropical forests. Fully polarimetric space-borne SAR interferometry (PolInSAR) data is acquired at the L -, C -, and X -band frequencies. The X -band data are acquired in two modes—single pass and repeat pass. The datasets are compensated for decorrelation due to SNR and varying spatial baselines. The remaining major decorrelation is the volumetric decorrelation, which is modeled using random volume over ground model to estimate the PolInSAR height for all the three frequencies. The modified Three-stage inversion algorithm is utilized for forest stand height estimation. The effect of forest biomass on height inversion accuracy is assessed. Furthermore, a first-order estimate of the absolute temporal decorrelation is demonstrated for the repeat-pass space-borne PolInSAR datasets. Extensive field validation campaigns are carried out in the tropical forest ranges. The forest height is inverted using data at all the three frequencies and validated with field measured values. The zero-temporal baseline bistatic TerraSAR-X/TanDEM-X and the L -band ALOS-2/PALSAR-2 result in a good height inversion with ${\boldsymbol{r}^2}$ and RMSE of 0.77 and 1.86 m ( X -band) and 0.75 and 1.94 m ( L -band), respectively. Furthermore, a comparison of estimated height of ALOS-2/PALSAR-2, TerraSAR-X, and RadarSAT-2 has been done with respect to the estimated height of TerraSAR-X/TanDEM-X. It is observed that RMSE of height inversion with respect to TerraSAR-X/TanDEM-X height is found to be 5.4 m, 7.6 m, and 12.8 m for ALOS-2/PALSAR-2, RadarSAT-2, and TerraSAR-X, respectively.

Spaceborne PolSAR Tomography for Forest Height Retrieval

Abstract: Synthetic aperture radar (SAR) data with polarization diversity and multibaseline acquisition has the capability to resolve the structural information of the target. Three-dimensional (3-D) imaging using SAR tomography is an advance and recent technology that focuses on backscattered echoes not only in the 2-D plane defined by the azimuth and range coordinates, but also in cross-range direction. Forests act as a semitransparent media for radar wavelengths, and thus allow us to gather the vertical distribution of this volumetric scatterer. This paper investigated the potential of polarimetric SAR tomography (PolTomSAR) to retrieve the scatterer distribution of forest area using C-band Radarsat-2 spaceborne SAR dataset. Teak patch of Haldwani forest was chosen as the test site to validate the robustness of TomoSAR algorithms. Fourier, beamforming, Capon, Capon like, and polarimetric capon were implemented in this study and backscatter power variations with their corresponding vertical profile for a particular azimuth bin were derived and analyzed. It was observed that Fourier suffered from high sidelobes that were reduced by beamforming and Capon. Limitations of beamformer were highlighted and well resolved by Capon. However, both beamforming and Capon yielded low backscattering power at different height levels. The Capon-like algorithm substantially improved the backscatter intensity at different forest height levels. Polarimetric capon surpassed the Capon algorithm and was able to estimate better heights by incorporating various polarization channels. Height map was generated for fully polarimetric Capon and validated with the ground truth data.