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TH1.L09 - ADVANCES IN THE GENERATION OF DEFORMATION TIME SERIES FROM SAR DATA SEQUENCES IN AREAS AFFECTED BY LARGE DYNAMICS
 

TH1.L09 - ADVANCES IN THE GENERATION OF DEFORMATION TIME SERIES FROM SAR DATA SEQUENCES IN AREAS AFFECTED BY LARGE DYNAMICS

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    TH1.L09 - ADVANCES IN THE GENERATION OF DEFORMATION TIME SERIES FROM SAR DATA SEQUENCES IN AREAS AFFECTED BY LARGE DYNAMICS TH1.L09 - ADVANCES IN THE GENERATION OF DEFORMATION TIME SERIES FROM SAR DATA SEQUENCES IN AREAS AFFECTED BY LARGE DYNAMICS Presentation Transcript

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    • Summary This approach has been tested on an ENVISAT ASAR data archive (Track 61, Frames 7173-7191) related to the Galàpagos Islands, focusing on Sierra Negra caldera (Galapagos Islands) . Results have been finally compared to continuous GPS measurements. In this work, we applied the SBAS strategy to the sequence of the range and azimuth Pixel-Offset estimates , achieved from the same set of small baseline data pairs . Therefore, to exploit conventional InSAR time series algorithms (such as the PSI or the Small BAseline Subset ones), which are phase based, would become extremely difficult. In areas characterized by large and rapid deformation dynamics , deformation can not be easily retrieved by exploiting conventional InSAR, due to high fringe rates (possible PhU problems) and misregistration. A way to overcome this limitation is to exploit the amplitude information.
    • Key idea of the SBAS algorithm (Berardino et al. 2002) For each coherent pixel, the time series deformation is computed by searching for an LS solution with a minimum norm constraint (the SVD method is applied).
    • Mathematical framework of the SBAS algorithm by solving the linear system wherein  is the interferometric phase vector, B the coefficient matrix and M the number of SAR images. To solve the linear system, we apply the SVD-method 1 . Following the unwrapping operation we evaluate (for each pixel) the displacement velocity vector v 1 Berardino P. et alii: “A new Algorithm for Surface Deformation Monitoring based on Small Baseline Differential SAR Interferograms”, IEEE Trans. Geosci. Remote Sensing , Vol. 40, No. 11, pp. 2375-2383, November 2002.
    • Why small baseline data pairs?
      • Small baseline InSAR interferograms are less affected by noise effects (decorrelation) and are easier to process (registration / unwrapping steps).
      ENVISAT Amplitude: Galàpagos Baseline: 1000m Baseline: 40m
    • Further constraints: deformation amount Eruptive event Conventional InSAR Possible PhU problems Co-Registration problems
    • Conventional SBAS-InSAR results: Galàpagos 26 ASAR Envisat scenes, T61 In collaboration with F. Amelung Azimuth ? Sierra Negra Fernandina Isabela October, 21st 2005 <-3 >3 cm/yr
    • Pixel-Offset SBAS (PO-SBAS): key idea We exploit the AMPLITUDE information in order to retrieve deformation in areas characterized by large dynamics. In particular, we apply the well known SBAS approach to the Pixel-Offsets retrieved via cross-correlation of the amplitude (AMPCOR routine) relevant to small baseline SAR image pairs. Note that, we may directly use the already registered SAR image pairs computed to generate the SBAS-InSAR time series.
    • Why (again) small baseline data pairs? Perp. Bas. 40m Perp. Bas. 1000m
    • Pixel-Offset SBAS (PO-SBAS): “good pixels” vs. baseline Quadratic relationship Small Baseline Constraint
    • PO-SBAS results: Galàpagos Conventional SBAS-InSAR mask Pixel-Offset SBAS mask Range Azimuth (mostly North) <-3 >3 cm/yr
    • Validation of RANGE displacements: SAR vs GPS time series GPS data from Chadwick, et al. 2006, Geology GV01 GV03 GV05 GV02 GV06 GV04  SAR - GPS
    • Validation of AZIMUTH displacements: SAR vs GPS time series GPS data from Chadwick, et al. 2006, Geology GV03 GV02 GV04  SAR - GPS
    • GV03 GV04 Validation of displacements: SAR vs GPS baseline change GV06 GPS Estimates , from Geist, et al. 2008, Bull. of Volc.
    • Summary  accuracy ca. 1/20 of pixel
    • Conclusions and further developments We present the results achieved by exploiting the amplitude information in order to retrieve deformation in areas characterized by large dynamics (eg. Volcanic and Seismogenic areas). We show that the well known SBAS approach can be successfully applied also to the Pixel-Offsets retrieved via cross-correlation of the amplitude relevant to small baseline SAR image pairs . We obtain deformation time series relevant to displacements in both range and azimuth direction in areas where no interferometric phase could be exploited ; the achieved accuracy is of about 1/20 of pixel. The exploitation of higher resolution SAR images (TERRASAR-X, COSMO-Skymed) is envisaged. The retrieved offset information can be further used in order to properly register the analyzed data pairs, thus “restoring” the possibility of generating interferogram sequences, following Sang-Ho Yun et al., GRL, 2007.
    • Pixel-Offsets: X-Band case study 12-Jan-2010 Haiti earthquake, Mw 7.0 COSMO-SkyMED, X-band, wavelength ~ 3.1cm 12-Dec-2009 / 21-Jan-2010 Spotlight SAR images 1 meter ground resolution Amplitude Image Azimuth Range
    • COSMO-SkyMED, X-band, wavelength ~ 3.1cm 12-Dec-2009 / 21-Jan-2010 Spotlight SAR images 1 meter ground resolution Pixel-Offsets: X-Band case study Interferometric Phase low coherence 1.56 0 cm Phase Azimuth Range
    • COSMO-SkyMED, X-band, wavelength ~ 3.1cm 12-Dec-2009 / 21-Jan-2010 Spotlight SAR images 1 meter ground resolution Pixel-Offsets: X-Band case study Range Amplitude Pixel-Offset Map low coherence >50 <-50 cm Range Offsets Azimuth Range
    •