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Terrain Deformation Monitoring with PSInSARTM

Marco Bianchimarco.bianchi@treuropa.com

Sensing the planet

Marco Basilicomarco.basilico@treuropa.com

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The PS TechniqueTM

R1

R2

PS

ΔR

By using:• temporal series of SAR dataidentification of:• coherent radar targets:

the Permanent Scatterers (PS)

PSPS

PS

where atmospheric effects can beestimated and removed

noiser 4

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Piton de la Fournaise(Isle de la Reunion)

Mad

agas

car

LA REUNION

Piton de la Fournaise

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Piton SAR Datasets

4 datasets processed:

33 scenes Envisat S2 Ascending track 8439 scenes Envisat S2 Descending track 36332 scenes Envisat S4 Ascending track 12727 scenes Envisat S4 Descending track 320

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Ascending Datasets Multi-Image Reflectivity

azimuth

rang

e

S2 S4

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Descending Datasets, some interferograms

S2 Descending: 20040403-20040717 Bn=105 [m]; Bt=-167 [days]

S2 Descending: 20031011-20031115 Bn=-303 [m]; Bt=35 [days]

S2 Descending T363 Multi-Image Reflectivity

S4 Descending: 20040331-20040714 Bn=342 [m]; Bt=-105 [days]

S4 Descending: 20051221-20060125Bn=1.8 [m]; Bt=35 [days]

S4 Descending T320 Multi-Image Reflectivity

Foreshortening

Foreshortening

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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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Differential Interferograms: examples S2 Ascending T84

π

-π15

-74

radians

Master: 20031130; Slave: 20030921 Bt=70 [days] Bn=29 [m]

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unwrapped

wrapped

unwrapped

wrapped

unwrapped

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Differential Interferograms: examples S4 Ascending T127

π

-π-41

76

radians

Master: 20060322; Slave: 20051207 Bt=105 [days] Bn=175 [m]

wrapped

unwrapped

wrapped

unwrapped

wrapped

unwrapped

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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: 20031130; Slave: 20030921 Bt=70 [days] Bn=29 [m]

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Displacement rate and time series

Conventionally, PSInSARTM 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 2If 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 ofsub-clusters. “Holes” (that is, not used images) are possible.

Cluster 1Cluster 2

Cluster 3

Master 1Master 2

Master 3

CASE 1A 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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S2 Ascending T84 Velocity Field

Velocity along Line of Sight is computed as the lineartrend of the longest temporal cluster

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S2 Ascending T84 Time Series: examples

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2 3

1

2

3

master

mastermaster

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S2 Ascending T84 Time Series: examples

Each color represents a coherent temporal clusterfor the pixel in analysis.Connection among different temporal clusters is NOTsignificant: no information about what happens amongthe two is detectable from the data.

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8 9

78

9

master master master

mastermaster master

master master master

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S4 Ascending T127 Velocity Field

Velocity along Line of Sight is computed as the lineartrend of the longest temporal cluster

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S4 Ascending T127 Time Series: examples

1

2 3

1

2

3

master

mastermaster

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S4 Ascending T127 Time Series: examples

7

8 9

Each color represents a coherent temporal clusterfor the pixel in analysis.Connection among different temporal clusters is NOTsignificant: no information about what happens amongthe two is detectable from the data.

78 9

master master mastermaster

master master

mastermaster

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Piton results Visualization

http://maps.treuropa.com

Web tool for results visualization,As support to validation activitydone by IPGP.

Velocity field

Time Series

No GIS software require for a quick results browsing

Powered on Google MapsTM

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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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Vulcano and Stromboli datasets

• 37 scenes (revisiting time=35 days)• Time range of investigation: January 11st 2003 – November 7th 2007• Master acquisition (temporal reference): January 11st 2006

• 30 processed scenes (revisiting time=35 days)• Time range of investigation: July 7th 2003 – October 10th 2007• Master acquisition (temporal reference): September 19th 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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Eolie IslandsStromboli

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Stromboli Geocoded Multi-Image Reflectivity

layover

DescendingAscending

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Stromboli Ascending datasetPhase 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 1Cluster 6Cluster 15

az

rg

Three separated clusters are formedto estimate and remove atmospheric signal; from now on, PS analysis willcontinue independently for the threeregions

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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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Stromboli Velocity field, along LOS

Descending Velocity field, along LOSAscending Velocity field, along LOS

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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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Eolie IslandsVulcano

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Vulcano, Geocoded Multi-Image Reflectivity

DescendingAscending

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Vulcano Results

Velocity field, along LOS

Velocity field, along LOS

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Decomposition in East and Vertical velocities

Vertical velocity fieldEasting velocity field

eastwestupdown

Ascending and descending results both cover the crater area and other parts of the island: wherever the two data are simultaneously available, adecomposition from ascending and descending displacement to easting and vertical components is possible, on a grid of 100x100 meters resolution

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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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Thank you