3. GEODYMANIC & VOLCANOLOGY

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.

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