ERS-ENVISAT ENVISAT deformation time-series analysis: a unique opportunity to investigate long term surface deformation of large areas

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1 ERS-ENVISAT ENVISAT deformation time-series analysis: a unique opportunity to investigate long term surface deformation of large areas R. Lanari 1, P. Berardino 1, M. Bonano 1,2, F. Casu 1, A. Manconi 1, M. Manunta 1, M. Manzo 1, A. Pepe 1, S. Pepe 1, E. Sansosti 1, G. Solaro 1,3, P. Tizzani 1,3, G. Zeni 1 1 IREA-CNR, via Diocleziano 328, 80123, Napoli, Italy 2 Università di Roma La Sapienza,, 00185, Roma, Italy 3 INGV-OV, via Diocleziano 328, 80123, Napoli, Italy

2 Outline Briefly introduce the deformation time series scenarios made possible sible due to the availability of the huge archives of ERS and ENVISAT data. Summarize the basic idea of the SBAS algorithm which is the considered approach for generating deformation time-series from ERS and ENVISAT acquisitions. Show some key results achieved by exploiting long sequences of ERS and ENVISAT SAR images. Present some conclusive remarks and possible future developments.

3 ERS and ENVISAT SAR data distribution (Napoli area) ERS data -prf < fd <prf ENVISAT data IS2 swath (VV) Cross-interferometry is limited but these ENVISAT data are geometrically compatible with ERS ones

4 The Small BAseline Subset (SBAS) algorithm The SBAS approach allows to produce long term deformation times-series series by: exploiting interferograms characterized by a small baseline ; properly linking interferometric SAR data subset (if present) separated by large baselines (the SVD method is applied); Perpendicular Baseline Subset 1 Subset 2 Acquisitions Time

5 The Small BAseline Subset (SBAS) algorithm The SBAS approach allows to produce long term deformation times-series series by: exploiting interferograms characterized by a small baseline ; properly linking interferometric SAR data subset (if present) separated by large baselines (the SVD method is applied); merging SAR data acquired by different sensors with the same illumination geometry, as for the case of ERS-1/2 and ENVISAT IS2. Perpendicular Baseline ERS ENVISAT / Acquisitions Time

6 ERS The SBAS algorithm for ERS/ENVISAT ENVISAT analysis ERS ERS ENVISAT ERS ENVISAT ERS For each coherent pixel of the ERS and ENVISAT interferograms the deformation time series is computed. This result is achieved by searching for an LS solution with a minimum norm constraint (the SVD method is applied). Berardino, P., Fornaro, G., Lanari, R., Sansosti, E., A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing 40 (11),

7 Two-scale approach Low spatial resoultion analysis (pixel size about 100 x 100 m) Full spatial resoultion analysis (pixel size about 10 x 10 m)

8 Napoli bay SBAS-DInSAR performances * Δ SAR LEV SAR Mt. Etna volcano Standard deviation: 1-22 mm/year on the mean deformation velocity mm on the single displacement * Δ SAR GPS SAR

9 ERS-ENVISAT deformation time series may play an important role in investigating the temporal evolution of the detected deformation fields in different contexts VOLCANIC SEISMOGENIC URBAN

10 Mt. Etna (Italy) 125 Descending and 147 Ascending orbit SAR data (1992( 2009) Mean deformation velocity year < -2 cm/year > 2 Ascending ASI SRV Project Descending

11 Mt. Etna (Italy) East-West < -2 cm/year > 2 Vertical < -1.5 cm/year > 1.5

12 Mt. Etna (Italy) Eruption Eruption Ascending < -2 cm/year > 2 Δ ERS Δ ENV

13 Istanbul (Turkey) Δ ERS Δ ENV 89 Descending orbit SAR data (1992( 2009) Izmit earthquake Mean deformation velocity <-5 5 >5 mm/year Trend changes due to the Izmit earthquake Collaboration (?) with GFZ

14 Roma (Italy): low resolution analysis Δ ERS Δ ENV > 7.5 Roma City Mean velocity [mm/yr] Colli Albani < Descending orbit ERS/ENVISAT data (1992( 2008) Collaboration with La Sapienza (Metro-C contract)

15 Roma (Italy): full resolution analysis Δ ERS Δ ENV > 7.5 Mean velocity [mm/yr] Viale Giustiniano Imperatore < Descending orbit ERS/ENVISAT data (1992( 2008)

16 Roma (Italy) P1 P2 P3 P1 P2 P3

17 Locone Lake dam (Italy) 110 Descending orbit SAR data (1992( 2007)

18 Some remarks The ERS-ENVISAT deformation time series represent a unique set of observations relevant to the temporal evolution of the detected deformation fields induced by different phenomena. Interrupting the continuity of the ENVISAT (2010) and ERS (2011) represents a big loss! sequences However, some more work is needed to fully exploit the information contained in the retrieved deformation time series. For instance, most of the modeling/inversion attempts are still limited to portions of the deformation time series (mean deformation velocity, some interferograms, etc.). The variable t seems not very relevant. To increase the availability of long term deformation time series is probably a key issue in order to improve the exploitation of these information ion (SUPERSITES).

19 SBAS and GENESI-DR

20 SBAS/GENESI-DR integration: : ENVISAT case study We have completed the overall SBAS-DInSAR processing in less than 2 days! >5 mm/year Visit our poster #12 <-5

21 Thank you!

22 Southern California: T127 T356 > 1.5 Mean velocity [cm/yr] <-1.5

23 Southern California:

24 Accuracy of InSAR measurements

25 34.3 N E Confronto sistematico InSAR GPS: area di Los Angeles Dati Dataset Dati geodetici geodetici: SAR: analizzati: 42 ERS-1/2 più di immagini stazioni discendenti GPS sono presenti ( ) nell area analizzata 33.5 N E

26 Confronto InSAR GPS: area di Los Angeles (2) ERS-1/2 Descending data ( )

27 Confronto InSAR GPS: area di Los Angeles (3) * GPS SAR σ 7.2 mm σ 8.5 mm σ 6.8mm σ 6.9 mm σ 5.9 mm σ 8.2 mm Accuratezza Valore misurato della velocità della deviazione media di deformazione standard tra stimata dati InSAR da dati e GPS InSAR 6-77 mm 1-22 mm/anno

28 Confronto InSAR livellazioni: area di Napoli 41.0 N 14.0 E Campi Flegrei Caldera 0 km 10 Napoli City Vesuvio Volcano Dataset SAR: 58 immagini discendenti ERS-1/2 ( ) 2002) Dataset geodetico: misure di più di 100 caposaldi di livellazione 58 immagini ascendenti ERS-1/2 ( ) 40.7 N 14.7 E

29 Confronto InSAR livellazioni: area di Napoli (2) σ 10.3 mm σ 8.4 mm σ 5.8 mm σ 3.9 mm σ 5.6 mm σ 4.0 mm * Leveling data SAR descending data SAR ascending data

30 Confronto InSAR livellazioni: area di Napoli (3) Deviazione standard: InSAR-livellazioni 4.6 mm per i dati ascendenti 4.8 mm per i dati discendenti 5 mm

31 Napoli Bay (Italy( Italy) ) test case: ERS ENVISAT Ascending Orbit LICO ARFE QUAR IPPO OSVE SANA BKE1 PRET BAIA RITE ACAE AGR1 TERZ Campi Flegrei Mt. Vesuvio ONPI cm/year < -1 > 1

32 Napoli Bay (Italy( Italy) ) test case : : SAR * : GPS LICO ARFE QUAR IPPO OSVE SANA BKE1 PRET BAIA RITE ACAE AGR1 TERZ ONPI = = 0.44 cm

33 Napoli Bay (Italy( Italy) ) test case = = 1.0 cm Unfiltered = = 0.44 cm Filtered

34 Napoli Bay (Italy) test case Ascending Standard Deviation (cm) Descending GPS Filt Unfilt Filt Unfilt ACAE AGR ARFE BAIA IPPO LICO ONPI OSVE PRET QUAR RITE SANA mean EGU 2009

35 Mt. Etna (Italy( Italy) ) test case: : ERS - ENVISAT Descending < -2 cm/year > 2 Ascending < -2 cm/year > 2

36 Mt. Etna (Italy( Italy) ) test case RPN PER EMFN EMAL LAM STP EFAR MIL GIA EMEG NUN PDG EMGL CAP ETDF ENIC ESLN

37 Mt. Etna (Italy( Italy) ) test case = = 1.0 cm Descending Filtered = = 1.3 cm Descending Unfiltered EMEG

38 Mt. Etna (Italy( Italy) ) test case Ascending Filtered = = 1.0 cm Ascending Unfiltered = = 1.35 cm EMEG

39 Mt. Etna (Italy( Italy) ) test case Ascending Standard Deviation (cm) GPS Filt Unfilt EFAR EMAL EMEG EMFN EMGL ENIC ESLN CAP GIA LAM NUN

40 Mt. Etna (Italy( Italy) ) test case

41 Accuratezza dei prodotti InSAR: : caso di studio della faglia di Hayward N W N Baia di San Francisco Hayward LOS < -66 mm/yr > 6 Dati ERS-1/2 ( ) 2000) N W

42 Confronto tra misure InSAR e di alignment array * Alignment array SAR Deviazione standard 1.5 mm

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