Brown Bag Seminar: Matteo Pardini

German Aerospace Center - DLR

 Abstract:

In July 2015 and February 2016, ONERA (SETHI system) and DLR (F-SAR system) acquired P- and L-band fully polarimetric multi-baseline Synthetic Aperture Radar (SAR) data within the ESA-funded AfriSAR campaign, in support of the development of the geophysical algorithms of future long-wavelength spaceborne missions, with BIOMASS as primary objective. Four test sites in Gabon (Lopè, Mondah, Rabi and Mabounie) with different physical forest structure types, biomass levels, growth stages, and levels and kinds of disturbance have been imaged. The acquisitions have been designed in order to enable the inversion of key forest vertical structure-based parameters, like forest height and (high resolution) vertical reflectivity profiles (or a parameterization of them), by means of SAR Polarimetry, Polarimetric Interferometry and Tomography.

 In February and March 2016, NASA GSFC and JPL carried out Lidar and UAVSAR L-band acquisitions, respectively, over the same test sites. In particular, the Lidar data were acquired with the full-waveform Laser Vegetation and Ice Sensor (LVIS) with a 20m footprint. The full-waveform profiles and the related height metrics are available for validation of and comparisons with SAR products. In the meanwhile, since 2015 the DLR TanDEM-X system has been acquiring full-/single-pol single-pass X-band InSAR data from space over the same test sites in order to enable long- and short-wavelength comparisons of interferometric (structure-related) signature. Finally, several ground inventory campaigns took place in 2016.

 The purpose of this presentation is to show first results at P-, L-, and X-band aimed at exploring the AfriSAR data content in terms of forest structure. At P- and L-band (DLR F-SAR data), this analysis could be carried out means of (direct) SAR tomographic imaging and estimation techniques. The key step of this analysis is a non-ambiguous separation between ground and volume contributions, which in turn relies on an accurate estimation of the ground topography under a Random Volume assumption. The estimation of the ground topography is also an indication of penetration. At X-band (TanDEM-X), no tomographic analyses are possible, thus this analysis is limited to the observation space generated by a (single-pass) single-polarization complex coherence. For all frequency, comparisons with the LVIS waveforms have been carried out. Especially at X-band, this comparison is particularly important in view of the Lidar GEDI mission, in order to understand potentials and challenges of a fusion between GEDI waveforms and TanDEM-X coherences over forests towards enhanced products.

  Bio:

Matteo Pardini received the Master degree (cum laude) in Telecommunication Engineering at the University of Pisa in 2006 and the PhD in Information Engineering from the same University in June 2010. In January 2010, he joined the Radar Concepts department of the Microwaves and Radar Institute of the German Aerospace Center (DLR) in Oberpfaffenhofen (Germany), after a visiting research period from August to December 2009. His general interests are in the area of Synthetic Aperture Radar signal processing for information retrieval and application development. In particular, his present scientific research focuses on the coherent processing of polarimetric synthetic aperture radar data for polarimetric interferometry, tomography and differential tomography for 3-D bio-/geophysical information extraction over natural scenarios at multiple frequencies, and mission design. He is member of the DLR TanDEM-X and Tandem-L Science Teams. He has participated to several conferences in the related fields, chairing and organizing invited sessions. Since September 2015, he serves as associate editor for the SPIE Journal of Applied Remote Sensing. He regularly acts as a reviewer for scientific journals and international conferences in the signal processing and remote sensing fields. He is Member of the IEEE since 2002, of EURASIP since 2006, and of AGU since 2016.