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  1. 1. Time-series InSAR with DESDynI:Lessons from ALOS PALSAR<br />Piyush Agrama, Mark Simonsa<br />and Howard Zebkerb<br />aSeismological Laboratory, California Institute of Technology<br />bDepts of EE and Geophysics, Stanford University<br />
  2. 2. Motivation<br />InSAR time-series techniques crucial for many of DESDynI’s stated objectives - Geohazards, Hydrology and subsurface reservoirs<br />Why ALOS PALSAR?<br />L-band mission similar to DESDynI.<br />Lifetime similar to DESDynI.<br />ALOS PALSAR products – a good proxy for DESDynI products.<br />
  3. 3. Overview<br />Noise levels at L-band vs C-band.<br />Topography related artifacts<br />PS-InSAR at L-band<br />Novel time-series techniques: MInTS.<br />
  4. 4. Comparison of Noise Levels<br />Typical resolution of interest – 100m x 100m.<br />Analysis of filtered interferograms with shortest time span.<br />ERS vs ALOS PALSAR.<br />Experiments conducted over the San Francisco Bay Area.<br />
  5. 5. L-band 46 day correlation similar to C-band at 1 day<br />ERS Looks = 80. ALOS Looks = 336.<br />Factor of 2 gain.<br />Factor of 2 observed in InSAR data.<br />L-band Decorrelation at 45 days ~ C-band decorrelation at 1 day<br />Areal coverage similar.<br />ALOS coherence threshold = 0.7 .<br />ERS coherence threshold = 0.7.<br />
  6. 6. L-band 46 day correlation 2x C-band at 35 days<br /><ul><li>ERS Looks = 80.</li></ul> ALOS Looks = 336.<br /><ul><li>Factor of 2 gain.
  7. 7. Factor of 2 gain in phase noise due to coherence threshold.
  8. 8. Temporal decorrelation at L-band is significantly lower.</li></ul>Areal coverage similar.<br />ALOS coherence threshold = 0.7 .<br />ERS coherence threshold = 0.4.<br />
  9. 9. L-band vs C-band<br />Temporal correlation<br />Phase noise (mm)<br />C-band<br /> L-band<br /> L-band<br />C-band<br /><ul><li> Decorrelation noise higher at C-band for longer temporal baselines.
  10. 10. Other noise sources - atmosphere etc. are assumed to be on the same order at both C and L bands</li></li></ul><li>Implications for DESDynI<br />Lower temporal decorrelation for many interferograms favors L-band.<br />More redundant IFG networks for time-series.<br />More coherent IFGs with longer time spans than C-band<br />Reduced temporal decorrelation improves the spatial coverage significantly (for same coherence threshold). <br />Improved coherence => Better unwrapping.<br />Overall: Comparable sensitivity to C-band time-series InSAR products but with greater spatial coverage for rapid interferograms, much better for longer time spans.<br />
  11. 11. ALOS and topo-related errors<br />Parkfield, CA<br />Due to orbit drift, correlation between Bperp and temporal baseline is 0.7.<br />DEM error cannot be distinguished easily from deformation features (SBAS).<br />
  12. 12. PS-InSAR at L-band with ALOS<br />Not as straight-forward as at C-band due to sensor management.<br />ALOS PALSAR – Need to combine different imaging modes.<br />Different noise characteristics of FBD and FBS modes. <br />Need appropriate weighting of the modes when selecting PS.<br />Does work: example over Long Valley Caldera, CA.<br />
  13. 13. Long Valley Caldera<br />PS pixel mask for ALOS PALSAR<br />C-band image from Hooper et al (2004)<br />23 ALOS PALSAR images with baselines < 4 Km.<br />PS density is similar to C-band.<br />Fine tuning needed for handling different modes.<br />Velocities heavily contaminated by topo-related errors.<br />
  14. 14. Implications for DESDynI<br />Plan no systematic relationship between temporal and spatial baselines.<br />L-band allows us to implement simple SBAS/ linear inversion approach more reliably due to better coverage.<br />Other topo-related errors - like tropospheric delay - at same level as ALOS PALSAR.<br />Traditional time-series approaches like SBAS and PS-InSAR should work better for DESDynI than ALOS.<br />
  15. 15. Novel time-series techniques will improve over current methods<br />In many situations, deformation estimates at 500m x 500m suffices to model geophysical phenomenon.<br />Can exploit the spatially correlated nature of deformation at these spatial scales.<br />Can decompose the data into independent components at various spatial scales- e.g, wavelets.<br />Multiscale InSAR time-series (MInTS) developed by Hetland and Simons.<br />
  16. 16. Multiscale InSAR Time Series (MInTS)<br />Create Interferograms <br />Unwrapped phase<br />Coherence<br />Time-series products<br />Reconstruct data using inverted coefficients <br />Create data mask for each IFG and interpolate holes<br />Compute wavelet coefficients and Weights for each IFG<br />Invert wavelet coefficients using temporal model (similar to GPS) <br />
  17. 17. MInTS results at Parkfield<br />Parkfield, CA<br /><ul><li> Resolution of 200m x 200m.
  18. 18. Same stack of 84 IFGs used for SBAS and MInTS.
  19. 19. Linear velocity and sinusoidal seasonal terms estimated. </li></li></ul><li>Conclusions<br /><ul><li> Shorter repeat period and acquisitions in a consistent imaging mode over targets make DESDynI superior to ALOS PALSAR.
  20. 20. Better orbital control and plan significantly decreases uncertainties in deformation estimates due to topo-related errors.
  21. 21. Uncertainty in deformation time-series will match current C-band products but yield much greater spatial coverage.
  22. 22. Novel time-series techniques like MInTS can significantly improve deformation estimates over regions where traditional techniques like SBAS and PS fail.</li>