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  • Si veda la dipendenza con topo (ETNA)
  • Rocca.ppt

    1. 1. F Rocca Dipartimento di Elettronica e Informazione Politecnico di Milano SAR interferometry for sub millimeter land motion studies
    2. 2. ERS -1 - 1991 35 days revisit cycle
    3. 3. Terrain Deformation Monitoring: The ERS – Envisat era
    4. 4. Decennial Etna motion: vertical
    5. 5. Decennial Etna motion: E-W
    6. 6. Piton de la Fournaise (Isle de la Reunion) Piton de la Fournaise Madagascar LA REUNION
    7. 7. Differential Interferograms: examples (1) S2 Ascending T84 π - π 15 -74 radians Master : 20031130; Slave: 20030921 Bt=70 [days] Bn=29 [m] wrapped unwrapped wrapped unwrapped wrapped unwrapped
    8. 8. S2 Ascending T84 Velocity Field
    9. 9. S4 Ascending T127 Velocity Field
    10. 10. S2 Ascending T84 Time Series: examples (1) 1 2 3 1 2 3 master master master
    11. 11. S4 Ascending T127 Time Series: examples (1) 1 2 3 1 2 3 master master master
    12. 12. Vulcano, Eolie Islands (Italy) Descending track 494 30 scenes Envisat S2 Ascending track 129 37 scenes Envisat S2
    13. 13. Velocity field, along LOS
    14. 14. Velocity field, along LOS
    15. 15. Decomposition in East and Vertical velocities Vertical velocity field Easting velocity field east west up down Ascending and descending results both cover the crater area and other parts of the island: wherever the two data are simultaneously available, a decomposition from ascending and descending displacement to easting and vertical components is possible, on a grid of 100x100 meters resolution
    16. 16. Displacement Time Series, examples
    17. 17. Displacement Time Series, examples
    18. 18. Landslides detection and monitoring Identifying landslides: Piedmont Piedmont landslides
    19. 23. Sentinel 1 A/B 22 years later 12 days revisit cycle
    20. 24. <ul><li>SAR interferometry 20 years later </li></ul><ul><li>(1991 – 2011) </li></ul><ul><li>How about ground motion recovery? </li></ul><ul><li>Target selection for coherence optimization </li></ul><ul><li>Absolute geometries </li></ul><ul><li>Shorter revisit times with constellations </li></ul><ul><li>Nowadays: </li></ul><ul><li>The path delay measurement is reliable to </li></ul><ul><li>the mm, for a reasonable spatial resolution </li></ul>
    21. 25. Densifying the set of reference targets (Persistent Scatterers)
    22. 26. Covariance matrices of multipass SAR Rows and columns correspond to progressive times Examples for: Persistent Scatterers (that terminate) Progressive decorrelation Seasonal effects
    23. 28. SqueeSAR uses nearly all interferograms, weighted with their coherence (see above). Phase linking is then carried out, estimating the sequence of the N-1 phases using the N(N-1)/2 interferograms. seasonal Markov
    24. 30. PS
    25. 31. Squeezing all the information: Squeesar
    26. 32. An example: the InSalah Case (Algeria) <ul><li>The InSalah Gas storage project is the first CO 2 sequestration effort in an active reservoir. </li></ul><ul><li>1 million CO 2 tons are reinjected into the subsurface each year. </li></ul>PSInSAR estimated volume and pressure changes, and finally the permeability within the reservoir.
    27. 34. Elastic Model Definition <ul><li>Apertures (dislocations) and volumetric variations along a fault plane </li></ul><ul><li>Inversion using the Poisson ratio in the overburden </li></ul><ul><li>Inverted parameters: </li></ul><ul><li>Fault plane position </li></ul><ul><li>Center of fault </li></ul><ul><li>Reject </li></ul>
    28. 35. Inversion results UD component Measures Model
    29. 36. Inversion results EW component Measures Model
    30. 38. Arrival time and Trajectories <ul><li>Pressure changes -> propagating fronts </li></ul><ul><li>We use the time derivative of the pressure </li></ul><ul><li>From the time arrivals -> permeability </li></ul><ul><li>A. Rucci, D. W. Vasco, Fluid pressure arrival time tomography, SEG 2009, Houston </li></ul>Arrival time Trajectories
    31. 39. The reject across the fault
    32. 40. Cosmo Skymed data: Total change: 1.8mm 28kt/mm
    33. 41. We can map surface deformations into permeability changes; the InSalah story is condensed in 1 cm surface motion 1 mm sensitivity is achieved today with reduced resolution, but can improve with numerical atmospheric models. The revisit time is paramount
    34. 42. <ul><li>Effects of motion direction </li></ul><ul><li>The UD component is practically LOS </li></ul><ul><li>The SNR of the EW component is just a few dB down </li></ul><ul><li>The NS component can only be retrieved with speckle correlation. The dispersion σ d , referred to the azimuthal resolution is, for N looks: </li></ul>
    35. 43. MAI: Multiple Aperture Interferometry <ul><li>However, if the wide Doppler range of Sentinel - 1 (±0.7 0 ) is fully used say for SPOT instead than TOPS scan, then the equivalent azimut resolution is δ eq : </li></ul>With say 1500 looks (4500m 2 ), the dispersion of the ground motion estimates along the NS direction could be of the order of 1 cm. Multiple Aperture Interferometry proposes to use wide Doppler ranges to achieve a very high equivalent azimuth resolution, even if not completely filling the entire Doppler band (pass band speckle correlation [1]). [1] De Zan F., 2011, Coherent Shift Estimation for Stacks of SAR Images, GRSL to appear
    36. 44. We still have to solve efficiently the problem of the Atmospheric Phase Screen that biases the outcomes
    37. 45. APS estimation procedure Model Parameters DEM
    38. 46. Mathematical modeling of the APS Characterized by a variogram K,A,B,C LMS from the data Z from a DEM 4-parameter model
    39. 47. Typical example of atmospheric data
    40. 48. APS from Ground Based RADAR GBSAR measures APS fluctuations from ms to months (range = 4 km). The t a power law (Kolmogorov) has been verified on variograms from t > 1 s APS has considerably less power during night time APS in short time APS in long time σ φ =1 rad 2hours ~ 40km mm 2 mm 2
    41. 49. K values: synoptic view Ascending Descending
    42. 50. 6am
    43. 51. 6pm
    44. 52. What we can do today with good control of the Atmospheric Phase Screen
    45. 53. The CESI experiment Estimated displacement (Radarsat 1 data) Vertical displacement (ground truth) Vertical displacement (estimated) Horizontal displacement (ground truth) Horizontal displacement (estimated) Rmse = 0.58mm (h!); 0.75mm (v)
    46. 54. A Ground Based SAR at the XX dam The future: UAV, geosynchronous, both? IBIS-L
    47. 55. Corner 2 Corner 1 Corner 4 Corner 3
    48. 56. Permanent Scatterers analysis : displacement series
    49. 57. Permanent Scatterers analysis : displacement series
    50. 58. Conclusions for the Ground Based SAR (15 h. observation) The data show coherence > 0.8 The atmospheric effect is very low for the good meteorological conditions The rms noise is of the order of 0.1mm
    51. 59. What happens if we are far away from PS? we have to estimate the APS from other sources: <ul><li>Numerical Weather Predictions </li></ul><ul><li>Meris </li></ul><ul><li>GPS </li></ul>
    52. 60. InSAR vs NWP T351, desce, 10UTC 30 images 10 std IWV maps T172, asce, 21UTC 41 images 20 std IWV maps The Rome dataset
    53. 61. InSAR vs NWP Spectral decorrelation Long spatial wavelengths have a few hours correlation, short spatial wavelengths decorrelate in less than 1 hour
    54. 62. InSAR vs NWP Analysis and separation of the stationary (layered) delay Layered delay Differential delay
    55. 63. MM5 vs InSAR, Rome T172 9pm asce Prediction of the change with height
    56. 64. InSAR vs NWP MM5: Positive in retrieving the change with height Very low correlation of turbulent terms Strong dependence on the starting time WRF performs better than MM5
    57. 65. Meris vs InSAR Cloud coverage can be a problem Rome T351, morning passes
    58. 66. Meris vs InSAR T351, Meris vs APS power spectra Similar frequency content after removing the stationary component
    59. 67. Meris vs InSAR And the accuracy is not enough… Meris IWV [mm] InSAR IWV [mm]
    60. 68. <ul><li>In desert areas Meris shows good performances (Li et al) </li></ul><ul><li>Meris has higher resolution and spectral content than MM5 </li></ul><ul><li>But in Rome the correlation with InSAR APS is too low </li></ul>Meris vs InSAR
    61. 69. GPS vs InSAR The Como test-site 480 descending, 10am (28 images) 487 ascending, 9pm (38 images)
    62. 70. GPS vs InSAR Delay, Como experiment, descending track Different ways for estimating the stationary term, for different stations (color)
    63. 71. GPS vs InSAR Best performances, ~ 50% success Ascending Descending GPS std InSAR std diff std corr coeff GPS std InSAR std diff std corr coeff   3.69 3.37 3.43 0.53 3.43 1.72 2.85 0.56   2.34 4.27 4.27 0.28 2.73 5.26 3.52 0.79   3.61 5.00 1.98 0.95 0.63 3.35 3.03 0.59   2.94 3.36 1.06 0.95 5.36 6.07 2.79 0.89   2.18 1.15 1.27 0.89 1.67 1.83 2.05 0.32  
    64. 72. Closer to any PS, the estimate of the APS improves: in a circle of PS with diameter D (m), the mean square APS reduces q times, q is lower than D q max q
    65. 73. In the case of interferogram chaining, if there is a limited coherence γ from one take to the next, the measured phase is: L = number of looks (> 4); N = number of takes in the observation time σ atm = dispersion of the APS; w1, w2 noises with unit variance Signal and noise grow with the number of takes Even if γ =0.3, just 6 looks are equivalent to a PS, as the coherence is limited only by APS and not by decorrelation.
    66. 74. MM5 has a strong “random” component and a more stable stratification estimation. WRF performs better, further work is needed. Meris has shown positive results in flat deserted areas, but not in our case studies. GPS has a success rate of 50%. The connection between InSAR and the other methodologies lies in the stationary term estimation. Permanent Scatterers (or interferogram chaining) are still the best way to accurately estimate the APS.
    67. 75. The competition: Optical and GPS levelling: Approximate results
    68. 76. A recent survey comparison for an accelerator design in Japan
    69. 81. Photon-counting detector with an accuracy of 20 ps (3.3 mm two way) Max point rate ~ 1000 pts/sec. Low atmospheric effects Photon counting devices
    70. 82. <ul><li>The future? </li></ul>
    71. 83. <ul><li>Recent, forthcoming , and proposed satellites: </li></ul><ul><li>- X band: 4xCSK, 2xTSX, Kompsat , TSX, 2xCSK2 </li></ul><ul><li>- C band: Rsat2 , Sentinel - 1 A/B; 3xRCM </li></ul><ul><li>- L band: 2xSAOCOM, Palsar 2, Desdyni, TSL </li></ul><ul><li>- The proposal for a geosynchronous SAR (EOPUS) </li></ul>
    72. 84. <ul><li>C band data have shown their effectiveness </li></ul><ul><li>X band data are good for urban applications </li></ul><ul><li>L band data are useful for forest studies </li></ul><ul><li>P band data, only, reach ground (tomography) </li></ul>Remarks
    73. 85. GEO synchronous S AR for A tmosphere and T errain observation Prof. A . Broquetas – UPC Univesitat Politècnica de Catalunya, Electromagnetic and Photonic Eng. Group Dr. D. D’Aria - ARESYS Prof. N. Casagli, G. Righini – Università degli studi di Firenze, Dept. of Earth Sciences Prof. S . Hobbs – Cranfield University, Cranfield Space Research Centre Prof. A. Monti Guarnieri, Prof. F. Rocca – Politecnico di Milano, Dept. of Electronic and Inf. Science R. Ferretti – University of L’Aquila, CETEMPS Prof. M. Nazzareno Pierdicca – Sapienza University of Roma, Dept. of Electronic Engineering   Prof. G. Wadge – University of Reading, Environmental Systems Science Centre Prof. Dr. H Rott – University of Innsbruck Dr. C. Svara, Dr. A. Torre – Thales Alenia Space Italia Earth Explorer EE8 proposal: COM3/EE8/32 GEOSAT
    74. 86. Geosynchronous satellite <ul><li>The geosynchronous concept is as old as SAR [ K. Tomiyasu, 1978] ; however, it has never been realized. </li></ul><ul><li>To make it feasible we might use: </li></ul><ul><li>The 2.4 kW - TWT developed for CoreH 2 0 </li></ul><ul><li>Long integration times (up to 12 hours), stable targets, PS </li></ul><ul><li>Quick looks every 20’ to measure and compensate the APS </li></ul>
    75. 87. The problem: water vapor fluctuations <ul><li>Water vapor changes defocus the scene , unless controlled and compensated. Examples and statistical analysis made by: </li></ul><ul><li>Terrasar-X (PSINSAR), GPS, Ground based radar, MM5 </li></ul>
    76. 88. GEOSAR for Atmosphere Medi terranean Hurric ane ECMWF medium-scale maps <ul><li>Yielding high spatial (200 x 10 m) and temporal (20’) resolution water-vapor maps for: </li></ul><ul><li>Weather forecast </li></ul><ul><li>Correction of tropospheric delays for GPS and SAR </li></ul>
    77. 89. GEOSAT: The Wide Beam GEOSAT may be a guest payload on Italian Space Agency (ASI) SIGMA missions, placed appox 9 o longitude . Look direction would then be close to SN for Europe. Backgound mission wide beam over central Europe (2000 km). NESZ = -19 dB, 0.5x 0.5km resolution, 20’ revisit. Fine resolution 10 x 10 m (twice daily revisit). Applications: WV maps, glaciers, urban.
    78. 90. GEOSAT coverage: the SPOT beam Geoseismic risk in Europe Number of Lanslides in Europe <ul><li>A 700 km SPOT beam centered on Naples, would be used for monitoring: </li></ul><ul><li>MEDIterranean hurriCANE and Water vapor (thus extending southward the wide beam), </li></ul><ul><li>Many many landlsides </li></ul><ul><li>Active volcanoes </li></ul>
    79. 91. Any TV antenna becomes a good reflector 80 cm antenna  SNR = 21 dB (12 hours); SNR=2 dB (10 mins) 47 Millions of users parabolas in Europe (2002) Number of home satellite antennas 1999 millions 2002 millions 1999-2002 Increase % total population % millions % millions and Pacific 19.5 17.5 -11 -2.1 1 1 and US 13.7 20.1 47 6.4 4 6 EUROPE 33.8 43.6 26 8.7 4 5 Latin America and the 1.6 2.7 62 1.0 0 1 North Africa and the 8.9 11.9 29 2.6 3 4 0.0 0.0 177 0.0 0.0 0 Sub Saharan 0.4 1.2 83 0.3 0 0 World 77.9 96.8 22 16.8 1 2
    80. 93. <ul><li>GEOSAT could provide short time (20 min to hours) monitoring for: </li></ul><ul><ul><li>atmospheric and hydrogeological effects volcanoes and glaciers </li></ul></ul><ul><ul><li>It would have: </li></ul></ul><ul><li>wide-swath and SPOT beams </li></ul><ul><li>complementarity to LEO for revisit, look angle </li></ul><ul><li>compatibility with Telecom, as a guest payload. </li></ul>
    81. 94. <ul><li>Conclusions </li></ul><ul><li>For ground motion applications, spatial resolution is not essential, but short revisit time and good vertical precision are paramount </li></ul><ul><li>With a dense and long set of PS (desert areas), we are already much below the millimeter error. In the future, NWP and GPS may help. </li></ul><ul><li>The geosynchronous satellite could help to obtain continuous observation. </li></ul><ul><li>Optical and GPS systems, at the moment, do not offer either spatial continuity or sufficient precision. </li></ul>
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