M. Manunta1,2, F. Casu1,2, R. Cossu3, L. Fusco3, S. Guarino1, R. Lanari1, G. Mazzarella2, E. Sansosti1

(1) Istituto per il Rilevamento Elettromagnetico dell’Ambiente, IREA Consiglio Nazionale delle Ricerche (CNR)Via Diocleziano 328, 80124 Napoli, Italia

(2) Dipartimento di Ingegneria Elettrica ed ElettronicaUniversità degli Studi di Cagliari

Piazza d’Armi, I-09123 Cagliari, Italia

(3) European Space Agency, via Galileo Galilei, 00044 Frascati, Italy.

SBAS-DInSAR GRID processing on-demand:

a case study

To produce deformation time-series from a SAR data sequence by:

using DInSAR interferograms characterized by a “small baseline” (smaller than the critical one) in order to mitigate noise (decorrelation) phenomena;

Subset 2

Subset 1

Standard SBAS algorithm: key idea

Time

Per

pen

dic

ula

r B

asel

ine

Why small baseline interferograms?Small baseline DInSAR interferograms are less affected by noise effects (decorrelation), are easier Small baseline DInSAR interferograms are less affected by noise effects (decorrelation), are easier to process (registration/unwrapping steps) and spatial filtering (multilooking) can be effectively to process (registration/unwrapping steps) and spatial filtering (multilooking) can be effectively carried out.carried out.

ERS/1_01/05/1996 - ERS/2_15/08/1996 baseline=950 m

ERS/2_31/08/1995 - ERS/2_ 15/08/1996 baseline=10 m

Napoli Bay (ERS Multi-look image)

To produce time-series deformation from a SAR data sequence by:

using DInSAR interferograms characterized by a “small baseline” (smaller than the critical one) in order to mitigate noise (decorrelation) phenomena;

Subset 2

Subset 1

properly “linking” interferometric SAR data subset separated by large baselines. This is done by searching for an LS solution with a velocity minimum norm constraint, easily achieved by applying the SVD method.

Standard SBAS algorithm: key idea

Time

Per

pen

dic

ula

r B

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Standard SBAS algorithm: key idea

For each “coherent pixel” the time series For each “coherent pixel” the time series deformation is computed by searching for an LS deformation is computed by searching for an LS solution with a minimum norm constraint (the solution with a minimum norm constraint (the SVD method is applied).SVD method is applied).

≈ ≈ 4.6 mm ascending orbits4.6 mm ascending orbits

Mean Standard Mean Standard

deviation:deviation: ≈ 4.8 mm descending orbits≈ 4.8 mm descending orbits

≈ ≈ 5 mm5 mm

Napoli bay: DInSAR analysis and validation

>5

<-5

mm

/year

mm

/year

ERS1/2 Descending ERS1/2 Descending datadata

(1992-2003)(1992-2003)

σσ ≈ 7.2 ≈ 7.2 mmmm

σσ ≈ 8.5 ≈ 8.5 mmmm

σσ ≈ 6.9 ≈ 6.9 mmmm

σσ ≈ ≈ 6.8mm6.8mm

σσ ≈ 5.9 ≈ 5.9 mmmm

σσ ≈ 8.2 ≈ 8.2 mmmm

** GPS GPSSARSAR

Los Angeles area : SAR vs GPS

Average standard deviation value:Average standard deviation value: ≈ 6 mm≈ 6 mm

[mm/yr]

> 10<-10

The overall SBAS processing chain:

• is very easy to be understood and implemented by “InSAR people” but not necessarly easy enough for end-users only exploiting (not developing) the InSAR technology;

• has been successfully tested and validated on more than 100 different sites and has demonstrated to be an effective tool for monitoring volcanoes and seismogenic areas.

• typically needs high computing capability (very often hundred interferograms must be produced and inverted).

An effective way to overcome these limitations is to combine the high computing capability of a GRID system with the robustness of the SBAS-DInSAR algorithm.

Why SBAS-GRID integration?

Key Idea

Move processors close to the data in a flexible and controlled way, thus leaving the data wherever they are archived and reducing dissemination costs and effort.

G-POD(Grid Processing-on-demand)

The ESA G-POD Environment at present

• SecurityUser certification

• Software resources on-line– IDL, Matlab, BEAT, BEAM, BEST,

CQFD, Compilers, public domain image processing utilities

– Catalogue queries and data provision functions

– Data viewers

• Computing Elements – Over 200 Working Nodes– Middleware: Globus (+ LCG 2.6, gLite)– Link to external CE and SE (e.g. CNR)

• Storage– Over 150 TB of EO data online

• Data Interfaces – GS products Rolling Archives

(ENVISAT, MSG)– MODIS NRT products over Europe– Access to AMS SatStore– Some accesses to NASA and other

external data providers

• Network – Gbit LAN– 64-192 Mbps to GARR– HiSEEN WAN (e.g. to PACs/PDHS)– MEGALAB (soon 20-40 Gbps in

Frascati area)

SBAS/G-POD Web Interface

Area of interest

Geographical coordinates of the selected area

Acquisition time interval of interest

SAR data available in the ESA archive

DInSAR processing parameters

SBAS/G-POD Web Interface

SBAS/G-POD Web Interface

The processing chain have been successfully tested up to the interferogram

generation. In this moment the phase unwrapping step is running!!!

SBAS/G-POD Web Interface

We have focused and co-registered 40 ASAR-ENVISAT images (ascending orbits on the Napoli Bay

area) and produced 114 interferograms in less than

1.5 days!

Our aim is to complete the overall SBAS-DInSAR processing in less than

3 days!

Conclusions

The SBAS-DInSAR tool has been implemented on the ESA G-POD system. It has been successfully tested up to the interferometric pair generation.

ASAR_08/09/2004-ASAR_25/07/2007

Baseline= 20 m

ASAR_25/07/2007-ASAR_29/08/2207

Baseline= 390 m

Conclusions

The implemented SBAS-GRID system has demonstrated to be an effective solution for large scale DInSAR data processing, and has been developed to be compatible with the G-POD environment.

In particular, the exploited possibility to move the processing tools close to the SAR data and the availability of large computing resources have shown a very high impact for what concerns deformation time series generation from long sequences of SAR acquisitions.

Accordingly, it is evident the importance that solutions like the one proposed in this study may have in the forecasting of DInSAR scenarios.

The proposed strategy may be applied to different SAR systems, for instance, Cosmo/Sky-MED, Radarsat-1/2, Terrasar-X, in order to improve the DInSAR technology exploitation for monitoring volcanoes and seismogenic areas.