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SBAS-DInSAR GRID processing on-demand: a case study PowerPoint Presentation
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SBAS-DInSAR GRID processing on-demand: a case study

SBAS-DInSAR GRID processing on-demand: a case study

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SBAS-DInSAR GRID processing on-demand: a case study

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  1. SBAS-DInSAR GRID processing on-demand: a case study M. Manunta1,2,F. Casu1,2, R. Cossu3, L. Fusco3, S. Guarino1,R. Lanari1, G. Mazzarella2, E. Sansosti1 (1) Istituto per il Rilevamento Elettromagnetico dell’Ambiente, IREA Consiglio Nazionale delle Ricerche (CNR) Via Diocleziano 328, 80124 Napoli, Italia (2) Dipartimento di Ingegneria Elettrica ed Elettronica Università degli Studi di Cagliari Piazza d’Armi, I-09123 Cagliari, Italia (3) European Space Agency, via Galileo Galilei, 00044 Frascati, Italy.

  2. Standard SBAS algorithm: key idea To produce deformation time-series from a SAR data sequence by: • using DInSAR interferograms characterized by a “small baseline” (smaller than the critical one) in order to mitigate noise (decorrelation) phenomena; Perpendicular Baseline Subset 1 Subset 2 Time

  3. Napoli Bay (ERS Multi-look image) ERS/1_01/05/1996 - ERS/2_15/08/1996 baseline=950 m ERS/2_31/08/1995 - ERS/2_ 15/08/1996 baseline=10 m Why small baseline interferograms? Small baseline DInSAR interferograms are less affected by noise effects (decorrelation), are easier to process (registration/unwrapping steps) and spatial filtering (multilooking) can be effectively carried out.

  4. Standard SBAS algorithm: key idea To produce time-series deformation from a SAR data sequence by: • using DInSAR interferograms characterized by a “small baseline” (smaller than the critical one) in order to mitigate noise (decorrelation) phenomena; • properly “linking” interferometric SAR data subset separated by large baselines. This is done by searching for an LS solution with a velocity minimum norm constraint, easily achieved by applying the SVD method. Perpendicular Baseline Subset 1 Subset 2 Time

  5. Standard SBAS algorithm: key idea 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).

  6. >5 mm/year <-5 ≈ 4.6 mm ascending orbits Mean Standard deviation:≈ 4.8 mm descending orbits ≈ 5 mm Napoli bay: DInSAR analysis and validation ERS1/2 Descending data (1992-2003)

  7. σ ≈ 7.2 mm σ ≈ 8.5 mm σ ≈ 6.8mm σ ≈ 6.9 mm σ ≈ 5.9 mm σ ≈ 8.2 mm Los Angeles area : SAR vs GPS * GPS D SAR [mm/yr] <-10 > 10 Average standard deviation value:≈ 6 mm

  8. Why SBAS-GRID integration? The overall SBAS processing chain: • is very easy to be understood and implemented by “InSAR people” but not necessarly easy enough for end-users only exploiting (not developing) the InSAR technology; • has been successfully tested and validated on more than 100 different sites and has demonstrated to be an effective tool for monitoring volcanoes and seismogenic areas. • typically needs high computing capability (very often hundred interferograms must be produced and inverted). An effective way to overcome these limitations is to combine the high computing capability of a GRID system with the robustness of the SBAS-DInSAR algorithm.

  9. G-POD (Grid Processing-on-demand) Key Idea Move processors close to the data in a flexible and controlled way, thus leaving the data wherever they are archived and reducing dissemination costs and effort.

  10. Computing Elements Over 200 Working Nodes Middleware: Globus (+ LCG 2.6, gLite) Link to external CE and SE (e.g. CNR) Storage Over 150 TB of EO data online Data Interfaces GS products Rolling Archives (ENVISAT, MSG) MODIS NRT products over Europe Access to AMS SatStore Some accesses to NASA and other external data providers Network Gbit LAN 64-192 Mbps to GARR HiSEEN WAN (e.g. to PACs/PDHS) MEGALAB (soon 20-40 Gbps in Frascati area) The ESA G-POD Environment at present • Security User certification • Software resources on-line • IDL, Matlab, BEAT, BEAM, BEST, CQFD, Compilers, public domain image processing utilities • Catalogue queries and data provision functions • Data viewers

  11. SBAS/G-POD Web Interface Geographical coordinates of the selected area SAR data available in the ESA archive Acquisition time interval of interest DInSAR processing parameters Area of interest

  12. SBAS/G-POD Web Interface

  13. SBAS/G-POD Web Interface

  14. SBAS/G-POD Web Interface The processing chain have been successfully tested up to the interferogram generation. In this moment the phase unwrapping step is running!!! We have focused and co-registered 40 ASAR-ENVISAT images (ascending orbits on the Napoli Bay area) and produced 114 interferograms in less than 1.5 days! Our aim is to complete the overall SBAS-DInSAR processing in less than 3 days!

  15. Conclusions The SBAS-DInSAR tool has been implemented on the ESA G-POD system. It has been successfully tested up to the interferometric pair generation. ASAR_08/09/2004-ASAR_25/07/2007 Baseline= 20 m ASAR_25/07/2007-ASAR_29/08/2207 Baseline= 390 m

  16. Conclusions The implemented SBAS-GRID system has demonstrated to be an effective solution for large scale DInSAR data processing, and has been developed to be compatible with the G-POD environment. In particular, the exploited possibility to move the processing tools close to the SAR data and the availability of large computing resources have shown a very high impact for what concerns deformation time series generation from long sequences of SAR acquisitions. Accordingly, it is evident the importance that solutions like the one proposed in this study may have in the forecasting of DInSAR scenarios. The proposed strategy may be applied to different SAR systems, for instance, Cosmo/Sky-MED, Radarsat-1/2, Terrasar-X, in order to improve the DInSAR technology exploitation for monitoring volcanoes and seismogenic areas.