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USEReST - Naples 2008 Terrain Deformation Monitoring with PSInSAR TM Marco Bianchi Sensing the planet Marco Basilico

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USEReST - Naples 2008 The PS Technique TM R1R1 R2R2 PS ΔRΔR By using: temporal series of SAR data identification of: coherent radar targets: the Permanent Scatterers (PS) PS where atmospheric effects can be estimated and removed

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USEReST - Naples 2008 Piton de la Fournaise (Isle de la Reunion) Madagascar LA REUNION Piton de la Fournaise

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USEReST - Naples 2008 Piton SAR Datasets 4 datasets processed : 33 scenes Envisat S2 Ascending track scenes Envisat S2 Descending track scenes Envisat S4 Ascending track scenes Envisat S4 Descending track 320

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USEReST - Naples 2008 Ascending Datasets Multi-Image Reflectivity azimuth range S2 S4

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USEReST - Naples 2008 Descending Datasets, some interferograms S2 Descending: Bn=105 [m]; Bt=-167 [days] S2 Descending: Bn=-303 [m]; Bt=35 [days] S2 Descending T363 Multi-Image Reflectivity S4 Descending: Bn=342 [m]; Bt=-105 [days] S4 Descending: Bn=1.8 [m]; Bt=35 [days] S4 Descending T320 Multi-Image Reflectivity Foreshortening

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USEReST - Naples 2008 Descending Datasets, considerations Most of the interferograms computed starting from descending-mode acquisitions don’t show a coherent signal Just a few inteferograms show coherent signal (see previous examples) where fringes are visible at least over some portions of the volcanic camera Even in case of coherent signal and small normal baseline (see previous examples), strong foreshortening effects prevent from the use of such interferometric pairs for time series displacement estimation. Coherent parts of the volcano resulting from the analysis of descending data don’t overlap with results gathered from ascending data Descending data will be temporarily discarded The analysis of ad-hoc permanent scatterers over Piton will be performed with S2 and S4 ascending data only

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USEReST - Naples 2008 Differential Interferograms: examples S2 Ascending T84 π -π-π radians Master: ; Slave: Bt=70 [days] Bn=29 [m] wrapped unwrapped wrapped unwrapped wrapped unwrapped

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USEReST - Naples 2008 Differential Interferograms: examples S4 Ascending T127 π -π-π radians Master: ; Slave: Bt=105 [days] Bn=175 [m] wrapped unwrapped wrapped unwrapped wrapped unwrapped

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USEReST - Naples 2008 Difficulties in phase unwrapping How to solve very fast motion and abrupt changes in the crater area? Differentials present very fast fringes: phase unwrapping has to be solved in a non conventional way The adopted solution consists in: Use of baselines smaller than 650 meters and 210 days Goldstein filtering 3D unwrapping (2D spatial + time) Master: ; Slave: Bt=70 [days] Bn=29 [m]

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USEReST - Naples 2008 Displacement rate and time series Conventionally, PSInSAR TM uses all the images of the dataset and selects those pixels that show a trend in time consistent with the applied model for velocity: average displacement rate and time series for such pixels are computed. In the Piton case the application of this approach would cause the loss of a great part of the pixels in the area: many pixels don’t obey to the model for the whole period of observation, because of eruptions or seismic events, that yields to strong changes and subsequently to the loss of coherence. Two cases are therefore possible: CASE 2 If it is not possible to find an entire coherent time interval, the whole time span is divided in two or more independent subsets, represented in the aside image in different colors. Each temporal cluster has its own master (instant of reference with displacement equal to zero). The displacement rate is computed as the linear trend of the longest temporal cluster. Each pixel will have its own number and duration of sub-clusters. “Holes” (that is, not used images) are possible. Cluster 1 Cluster 2 Cluster 3 Master 1 Master 2 Master 3 CASE 1 A unique coherent temporal cluster of images is found: in this case a unique time series is given for the analyzed pixel. The displacement rate is computed as the linear trend of the time series Master

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USEReST - Naples 2008 S2 Ascending T84 Velocity Field Velocity along Line of Sight is computed as the linear trend of the longest temporal cluster

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USEReST - Naples 2008 S2 Ascending T84 Time Series: examples master

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USEReST - Naples 2008 S2 Ascending T84 Time Series: examples Each color represents a coherent temporal cluster for the pixel in analysis. Connection among different temporal clusters is NOT significant: no information about what happens among the two is detectable from the data master

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USEReST - Naples 2008 S4 Ascending T127 Velocity Field Velocity along Line of Sight is computed as the linear trend of the longest temporal cluster

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USEReST - Naples 2008 S4 Ascending T127 Time Series: examples master

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USEReST - Naples 2008 S4 Ascending T127 Time Series: examples Each color represents a coherent temporal cluster for the pixel in analysis. Connection among different temporal clusters is NOT significant: no information about what happens among the two is detectable from the data master

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USEReST - Naples 2008 Piton results Visualization Web tool for results visualization, As support to validation activity done by IPGP. Velocity field Time Series No GIS software require for a quick results browsing Powered on Google Maps TM

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USEReST - Naples 2008 Piton, Conclusions 131 Envisat scenes successfully processed (4 datasets: Ascending S2 and S4; descending S2 and S4) Descending results temporarily discarded due to geometrical decorrelation and foreshortening problems: no chance of extraction of time series and average deformation trends. Because of fast motion and abrupt changes, an advanced approach of PSInSAR tailored over the specific situation of the Piton volcano has been carried out, requiring interaction with skilled personnel From ascending interferograms (both S2 and S4 datasets) deformation rate and time series of displacement have been extracted over the crater area In order to extract all the available information, different temporal cluster have been exploited, in order to provide time series also for those pixel that don’t show coherence for the whole period of observation Ad-hoc visualization procedures should be developed: each pixel can be associated to many time series. A clear methodology for data archiving and data representation should be pointed in cooperation with the final user.

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USEReST - Naples 2008 Vulcano and Stromboli datasets 37 scenes (revisiting time=35 days) Time range of investigation: January 11 st 2003 – November 7 th 2007 Master acquisition (temporal reference): January 11 st processed scenes (revisiting time=35 days) Time range of investigation: July 7 th 2003 – October 10 th 2007 Master acquisition (temporal reference): September 19 th 2005 θ ≈ 22.3 [deg] α ≈ 12.2 [deg] θ ≈ 24.0 [deg] α ≈ 12.0 [deg] ASCENDING Track 129 S2 DESCENDING Track 494 S2

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USEReST - Naples 2008 Eolie Islands Stromboli

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USEReST - Naples 2008 Stromboli Geocoded Multi- Image Reflectivity layover DescendingAscending

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USEReST - Naples 2008 Stromboli Ascending dataset Phase Stability Index and Clusters The Phase Stability Index shows areas where phase information is candidate to be coherent Three “coherent” areas (bright) are detectable, separated by large non-coherent areas (dark) Despite different attempts of connecting this three areas, none of them has been considered reliable after quality check evaluation Phase Stabilty Index (a-priori coherence) Clusters of PS candidates Cluster 1 Cluster 6 Cluster 15 az rg Three separated clusters are formed to estimate and remove atmospheric signal; from now on, PS analysis will continue independently for the three regions

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USEReST - Naples 2008 Stromboli Descending dataset: differentials IMPORTANT: as shown by interferograms given as example, fast motion and non-linear behavior is present in the area. Time series extraction is a challenging task that requires further analysis, to solve unwrapping problems related with non linear displacements, above all in the “Sciara del Fuoco” area.

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USEReST - Naples 2008 Stromboli Velocity field, along LOS Descending Velocity field, along LOS Ascending Velocity field, along LOS

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USEReST - Naples 2008 Stromboli Results, summary Results divided into 3 clusters because of coherence problems 1363 PS detected (with Time Series) over the island (~13 Kmq) Geometrical distortions prevent from PS detection on the western slope of the volcano IMPORTANT: as shown by the interferograms reported previously, fast motion and non-linear behavior is present in the area. Unfortunately, the uneven temporal sampling of the ENVISAT data-set as well as the presence of fast temporal decorrelation phenomena do not allow the reconstruction of reliable time series of the displacement field. 698 PS detected (with Time Series) over the island (~13 Kmq) Geometrical distortions prevent from PS detection on the eastern slope of the volcano ==> To get better InSAR results it is recommended to use SAR data with a shorter repeat cycle and – given the extent of the AOI – higher spatial resolution ASCENDING DESCENDING

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USEReST - Naples 2008 Eolie Islands Vulcano

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USEReST - Naples 2008 Vulcano, Geocoded Multi-Image Reflectivity DescendingAscending

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USEReST - Naples 2008 Vulcano Results Velocity field, along LOS

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USEReST - Naples 2008 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

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USEReST - Naples 2008 Vulcano Results, summary 2299 PS detected (with Time Series) over the island (~21 Kmq) Even though geometrical distortions prevent from PS detection in some parts of the volcano, results have a good distribution and coverage 2323 PS detected (with Time Series) over the island (~21 Kmq) Even though some area of the island are not covered by PS because of perspective distortions, coverage and density of measurements are fine ASCENDING DESCENDING

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USEReST - Naples 2008 Thank you

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