YellowIGARSS.ppt
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A. PEPE A. PEPE 1 1 , , G. ZENI G. ZENI 1 , , P. TIZZANI 1 , F. CASU 1 , M. MANUNTA 1 , R. LANARI 1 1. IREA – CNR, Napoli (Italy) Analysis of the 1992-2010 Dynamic Deformation Affecting the Yellowstone Calde
Transcript of YellowIGARSS.ppt
A. PEPEA. PEPE11, , G. ZENIG. ZENI11, , P. TIZZANI11, F. CASU1 , M. MANUNTA11 , R. LANARI11
1. IREA – CNR, Napoli (Italy)
Analysis of the 1992-2010 Dynamic Deformation
Affecting the Yellowstone Caldera
We present the results obtained by applying the We present the results obtained by applying the Small BAseline Subset Small BAseline Subset (SBAS) (SBAS) DInSAR algorithmDInSAR algorithm to a long sequence of ERS and ENVISAT SAR images acquired to a long sequence of ERS and ENVISAT SAR images acquired from 1992 to 2010 over the from 1992 to 2010 over the Yellowstone CalderaYellowstone Caldera. .
The deformation field under investigation is very complex.The deformation field under investigation is very complex.
We summarize the key ideas at the base of the SBAS approach, which exploits We summarize the key ideas at the base of the SBAS approach, which exploits conventional multi-look differential interferograms with small spatial and temporal conventional multi-look differential interferograms with small spatial and temporal baselines.baselines.
Temporal decorrelation noise severely corrupts the generated interferograms. To Temporal decorrelation noise severely corrupts the generated interferograms. To limit as much as possible the impact of such effects on the deformation time-series, limit as much as possible the impact of such effects on the deformation time-series, we identified we identified an “optimal” distribution of the interferometric SAR data pairsan “optimal” distribution of the interferometric SAR data pairs. .
The generated interferograms were unwrapped. The procedure was also properly The generated interferograms were unwrapped. The procedure was also properly complemented with a region-growing procedure to expand the unwrapping data in complemented with a region-growing procedure to expand the unwrapping data in areas with low spatial coherence. areas with low spatial coherence. No a priori information on the expected No a priori information on the expected deformation was exploited to process the data.deformation was exploited to process the data.
Summary
SBAS-DInSAR algorithm: key ideasTo produce deformation times-series from a SAR dataset, the SBAS approach:
properly “links” the interferometric SAR data subset (if present) separated by large baselines (the SVD method is applied).
exploits interferograms characterized by a “small baseline” in order to mitigate decorrelation phenomena;
ENVΔ
ΔERS
“merging” SAR data acquired by different sensors with the same illumination geometry, as for the case of ERS-1/2 and ENVISAT IS2.
Achieved accuracies:
• ≈ 1 - 2 mm/year on the mean deformation velocity
• ≈ 5 - 10 mm on the single displacement
MONTANA
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Time Span: 1992-2010
Track 41, Frame 2709
31 ERS SAR images
22 ENVISAT acquisitions
Yellowstone SAR dataset
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Yellowstone SAR dataset: Temporal Decorrelation Effects
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Yellowstone SAR dataset: Temporal Decorrelation Effects
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SAR data pair Selection
To produce long-term deformation times-series we look for a sequence of differential interferograms that are less affected, as much as possible, by decorrelation noise effects.
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Among the possible interferometric distribution, we search for DInSAR data pairs that form a triangle in the temporal/perpendicular baseline domain.
In particular, we identify the triangulation Tr maximizing the average spatial coherence of the interferograms:
ERS1-2 Interferometric Distribution
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ERS1-2 Interferometric Distribution
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ERS1-2 Interferometric Distribution
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ERS1-2 Interferometric Distribution
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ERS1-2 Interferometric Distribution
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ERS1-2 Interferometric Distribution
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… and ENVISAT
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ERSENVISAT
Optimal Interferometric Distribution
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Finally, we also cut from the triangulations the triangles involving large baseline interferograms
Mean Deformation Velocity Map Standard Devation Map
A very complex deformation scenario is revealed
SBAS-DInSAR Results
SBAS-DInSAR/GPS Comparison (1)
hvwy, 0.59lkwy, 1.04nrwy, 0.36ofwy, 0.18p680, 0.33p686, 0.24p709, 0.61p711, 0.29p712, 0.24p713, 0.39p716, 0.41wlwy, 0.86
GPS/SBAS Standard Deviation [cm]
SBAS-DInSAR/GPS Comparison (2)
SBAS-DInSAR Results Analysis
SBAS-DInSAR Results Analysis
SBAS-DInSAR Results Analysis
SBAS-DInSAR Results Analysis
We benefited of the availability of the large amount of data acquired by the ERS-1/2 and ENVISAT sensors during the 1992-2010 time period in order to investigate long term surface deformation of Yellowstone caldera.
The retrieved DInSAR time-series have revealed a complex scenario characterized by strongly non-linear trends of the spatial and temporal behaviour of the deformation field.
Conclusion
