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  • Modificare date, inserire numero immagini( 53) interferogrammi (102) i inserire orbita : discendente, TRACK: FRAME:
  • Modificare date, inserire numero immagini( 53) interferogrammi (102) i inserire orbita : discendente, TRACK: FRAME:
  • Modificare date, inserire numero immagini( 53) interferogrammi (102) i inserire orbita : discendente, TRACK: FRAME:
  • Modificare date, inserire numero immagini( 53) interferogrammi (102) i inserire orbita : discendente, TRACK: FRAME:
  • Modificare date, inserire numero immagini( 53) interferogrammi (102) i inserire orbita : discendente, TRACK: FRAME:
  • Modificare date, inserire numero immagini( 53) interferogrammi (102) i inserire orbita : discendente, TRACK: FRAME:
  • Modificare date, inserire numero immagini( 53) interferogrammi (102) i inserire orbita : discendente, TRACK: FRAME:
  • Modificare date, inserire numero immagini( 53) interferogrammi (102) i inserire orbita : discendente, TRACK: FRAME:
  • Modificare date, inserire numero immagini( 53) interferogrammi (102) i inserire orbita : discendente, TRACK: FRAME:
  • Modificare date, inserire numero immagini( 53) interferogrammi (102) i inserire orbita : discendente, TRACK: FRAME:
  • From 1992 to 1995 a broad subsidence pattern affects the caldera roof with maximum displacement located along its major axis (Mallard lake dome and Sour Creek dome. The 2003 -2009 time interval is characterized by a spectacular inversion of the caldera floor deformation. More specifically, Mallard lake dome and Sour Creek dome areas, which were affected by a subsidence phenomenon during the 1992–1995 period, are now subject to an uplift event.

Transcript

  • 1.
    • A. PEPE 1 , G. ZENI 1 , P. TIZZANI 1 , F. CASU 1 , M. MANUNTA 1 , R. LANARI 1
    • 1. IREA – CNR, Napoli (Italy)
    Analysis of the 1992-2010 Dynamic Deformation Affecting the Yellowstone Caldera
  • 2.
    • We present the results obtained by applying the Small BAseline Subset (SBAS) DInSAR algorithm to a long sequence of ERS and ENVISAT SAR images acquired from 1992 to 2010 over the Yellowstone Caldera .
    • The deformation field under investigation is very complex.
    • We summarize the key ideas at the base of the SBAS approach, which exploits conventional multi-look differential interferograms with small spatial and temporal baselines.
    • Temporal decorrelation noise severely corrupts the generated interferograms. To limit as much as possible the impact of such effects on the deformation time-series, we identified an “optimal” distribution of the interferometric SAR data pairs .
    • The generated interferograms were unwrapped. The procedure was also properly complemented with a region-growing procedure to expand the unwrapping data in areas with low spatial coherence. No a priori information on the expected deformation was exploited to process the data.
    Summary
  • 3. GEODYMANIC & VOLCANOLOGY
  • 4.  
  • 5. Accelerated uplift of the Yellowstone caldera revealed by GPS and InSAR data (2004-2007) modified from Chang et al. (2007). (a) Map view of the uplift with GPS vertical and horizontal vectors and background showing line of sight (nearly vertical) deformation in 28 mmdisplacement bands. (b) Cross section of modeled 10° SE-dipping sill that is interpreted to be inflating at 0.1 km3 per year, consistent with the modeled rate of inflation from the heat flow and geochemical data. Color contours are Coulomb stress increase (red) or decrease (blue) caused by inflation of the sill.
  • 6. SBAS-DInSAR algorithm: key ideas To produce deformation times-series from a SAR dataset, the SBAS approach:
    • properly “links” the interferometric SAR data subset (if present) separated by large baselines (the SVD method is applied).
    • exploits interferograms characterized by a “small baseline” in order to mitigate decorrelation phenomena;
    • “ merging” SAR data acquired by different sensors with the same illumination geometry, as for the case of ERS-1/2 and ENVISAT IS2 .
    • Achieved accuracies:
    • ≈ 1 - 2 mm/year on the mean deformation velocity
    • ≈ 5 - 10 mm on the single displacement
    ENV Δ Δ ERS
  • 7. MONTANA WYOMING WYOMING IDAHO
  • 8. Time Span: 1992-2010 Track 41, Frame 2709 31 ERS SAR images 22 ENVISAT acquisitions MONTANA WYOMING WYOMING IDAHO
  • 9. Yellowstone SAR dataset Time [year] Perpendicular Baseline [m] ERS ENVISAT
  • 10. Yellowstone SAR dataset: Temporal Decorrelation Effects Time [year] Perpendicular Baseline [m] ERS ENVISAT 10/12/2006 06/09/2005
  • 11. Time [year] Perpendicular Baseline [m] ERS ENVISAT Yellowstone SAR dataset: Temporal Decorrelation Effects 08/30/1995 07/12/1992
  • 12. SAR data pair Selection To produce long-term deformation times-series we look for a sequence of differential interferograms that are less affected, as much as possible, by decorrelation noise effects. Among the possible interferometric distribution, we search for DInSAR data pairs that form a triangle in the temporal/perpendicular baseline domain. In particular, we identify the triangulation Tr maximizing the average spatial coherence of the interferograms:
  • 13. SAR data pair selection To achieve this task, separately for the ERS and ENVISAT SAR data sets, we started with an empty set and we added, at each step, the edges characterized by the highest spatial coherence values, with that does not cross any of the previously added edges. The greedy triangulation for a set of N acquisitions in the temporal/perpendicular baseline domain is obtained by
  • 14. ERS1-2 Interferometric Distribution Time [year] Perpendicular Baseline [m] ERS ENVISAT
  • 15. ERS1-2 Interferometric Distribution Time [year] Perpendicular Baseline [m] ERS ENVISAT
  • 16. ERS1-2 Interferometric Distribution Time [year] Perpendicular Baseline [m] ERS ENVISAT
  • 17. ERS1-2 Interferometric Distribution Time [year] Perpendicular Baseline [m] ERS ENVISAT
  • 18. ERS1-2 Interferometric Distribution Time [year] Perpendicular Baseline [m] ERS ENVISAT
  • 19. ERS1-2 Interferometric Distribution Time [year] Perpendicular Baseline [m] ERS ENVISAT
  • 20. … and ENVISAT Time [year] Perpendicular Baseline [m] ERS ENVISAT ERS ENVISAT
  • 21. Optimal Interferometric Distribution Time [year] Perpendicular Baseline [m] Finally, we also cut from the triangulations the triangles involving large baseline interferograms ERS ENVISAT ERS ENVISAT
  • 22. Optimal Pseudo-Triangulations: Some Considerations and Further Developments Some preliminarly results will be presented at the Fringe 2011 meeting !!! The spatial coherence is a biased estimator Accordingly, to make the selection more robust, we are developing a different approach, which is not based on the use of the spatial coherence but exploits the temporal consistency of the multilook phases over triangular loops. Indeed, for distributed targets: gives us an indirect measure of the noise corrupting the three interferograms. We may take into account this information to identify the most appropriate data pair distribution A C B
  • 23. Mean Deformation Velocity Map Standard Devation Map A very complex deformation scenario is revealed SBAS-DInSAR Results
  • 24. SBAS-DInSAR/GPS Comparison (1)
  • 25. hvwy, 0.59 lkwy, 1.04 nrwy, 0.36 ofwy, 0.18 p680, 0.33 p686, 0.24 p709, 0.61 p711, 0.29 p712, 0.24 p713, 0.39 p716, 0.41 wlwy, 0.86 GPS/SBAS Standard Deviation [cm] SBAS-DInSAR/GPS Comparison (2)
  • 26. SBAS-DInSAR Results Analysis
  • 27. SBAS-DInSAR Results Analysis
  • 28. SBAS-DInSAR Results Analysis
  • 29. SBAS-DInSAR Results Analysis
  • 30. We benefited of the availability of the large amount of data acquired by the ERS-1/2 and ENVISAT sensors during the 1992-2010 time period in order to investigate long term surface deformation of Yellowstone caldera. The retrieved DInSAR time-series have revealed a complex scenario characterized by strongly non-linear trends of the spatial and temporal behaviour of the deformation field. Conclusion