PolInSAR tomography for vertical profile retrieval of forest vegetation using spaceborne SAR data

Sushil Kumar Joshi; Shashi Kumar; Shefali Agrawal; Ho Tong Minh Dinh

doi:10.1117/12.2228068

11 May 2016 PolInSAR tomography for vertical profile retrieval of forest vegetation using spaceborne SAR data

Sushil Kumar Joshi, Shashi Kumar, Shefali Agrawal, Ho Tong Minh Dinh

Proceedings Volume 9877, Land Surface and Cryosphere Remote Sensing III; 987709 (2016) https://doi.org/10.1117/12.2228068
Event: SPIE Asia-Pacific Remote Sensing, 2016, New Delhi, India

Abstract

Forest height plays a crucial role to investigate the bio-physical parameters of forest and the terrestrial carbon. PolInSAR based inversion modeling has been successfully implemented on airborne and space-borne SAR data. SAR tomography, which is an extension of cross-track interferometric processing is a recent approach to separate scatterers in cross range direction, thus generates its vertical profile. This study highlighted the potential of tomographic processing of fully polarimetric Radarsat-2 SAR system to retrieve backscatter power at different height levels. Teak forest in Haldwani forest division of Uttarakhand state of India was chosen as the test site. Since SAR tomography is a spectral estimation problem, Fourier transform and beamforming based spectral estimations were applied on the dataset to obtain their vertical profiles. Fourier severely suffered from high side lobes which was drastically reduced by implementing beam-forming by taking into account the multi-looking effect at the expense of radiometric accuracy. Backscattered power values were found to be different at different height levels of the forest vegetation. Vertical profile for Fourier as well as beam-forming were also retrieved.

Citation Download Citation

Sushil Kumar Joshi, Shashi Kumar, Shefali Agrawal, and Ho Tong Minh Dinh "PolInSAR tomography for vertical profile retrieval of forest vegetation using spaceborne SAR data", Proc. SPIE 9877, Land Surface and Cryosphere Remote Sensing III, 987709 (11 May 2016); https://doi.org/10.1117/12.2228068

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