Remote Sensing Applications and Decision Support

Spaceborne PolInSAR tomography for vertical profile retrieval of forest vegetation

[+] Author Affiliations
Sushil Kumar Joshi, Shashi Kumar

Indian Institute of Remote Sensing, Photogrammetry and Remote Sensing Department, Indian Space Research Organization, Dehradun, Uttarakhand, India

J. Appl. Remote Sens. 11(1), 016001 (Jan 02, 2017). doi:10.1117/1.JRS.11.016001
History: Received August 20, 2016; Accepted December 6, 2016
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Abstract.  Forest height plays a crucial role in investigating the biophysical parameters of forests and the terrestrial carbon. PolInSAR-based inversion modeling has been successfully implemented on airborne and spaceborne synthetic aperture radar (SAR) data. SAR tomography is a recent approach to separate scatterers in the cross-range direction and generate its vertical profile. This study highlights the potential of tomographic processing of multibaseline fully polarimetric Radarsat-2 C-band SAR data to estimate radar reflectivity at different forest height levels. A teak patch of Haldwani forest in Uttarakhand state of India was chosen as the test site to perform tomography. Since SAR tomography is a spectral estimation problem, Fourier transform (FT), beamforming (BF), and Capon-based spectral estimators were applied on the dataset to obtain the backscattering power contributions at different forest height levels. Fourier showed high backscatter power retrieval at different forest heights. The radar reflectivities at different heights were significantly reduced by BF followed by Capon. Tomographic profile of FT severely suffered from high sidelobes, which was drastically reduced by implementing BF. Capon further reduced the sidelobes and achieved a substantially improved tomographic profile. The height maps were generated for these algorithms and validated with ground truth data.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Sushil Kumar Joshi and Shashi Kumar
"Spaceborne PolInSAR tomography for vertical profile retrieval of forest vegetation", J. Appl. Remote Sens. 11(1), 016001 (Jan 02, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.016001


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