Linear and nonlinear terrain deformation maps from a reduced set of interferometric sar images

Mora, Oscar; Mallorquí, J.J.; Broquetas, A.

doi:10.1109/tgrs.2003.814657

Cited by 661 publications

(471 citation statements)

References 24 publications

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“…Finally, once the N + 1 (wrapped) phase values are estimated, they are paired to retrieve the sequence of noise-filtered DInSAR interferograms, which can be subsequently used to generate surface deformation products through advanced DInSAR approaches (Sandwell and Price 1998, Berardino et al 2002, Mora et al 2003. We also underline that, even though the proposed algorithm is intrinsically small baselineoriented, at this stage, not only SB interferograms but, in principle, any interferogram can be reconstructed.…”

Section: Interferogram Noise-filtering Algorithmmentioning

confidence: 98%

A simple solution to mitigate noise effects in time-redundant sequences of small baseline multi-look DInSAR interferograms

Yang

Pepe

Manzo

et al. 2013

Remote Sensing Letters

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Letters, 2013 Vol. 4, No. 6, 609-618, http://dx.doi.org/10.1080/2150704X.2013 A simple solution to mitigate noise effects in time-redundant sequences of small baseline multi-look DInSAR interferograms We present a simple and effective filtering algorithm to mitigate noise effects in a time-redundant sequence of multi-look small baseline (SB) differential synthetic aperture radar (SAR) interferograms by exploiting the temporal relationships among the selected interferometric data pairs. The proposed method relies on the estimation of the (wrapped) filtered phase terms associated to each SAR acquisition; this result is achieved via a non-linear minimization procedure which is applied to the phase signal of conventional multi-look interferograms without any pixel selection process, and with no a-priori information on the statistics of the involved complex-valued SAR images. Following their estimation, the phase images are paired to reconstruct a new sequence of filtered SB differential interferograms, which are used to generate surface deformation products, such as deformation velocity maps and displacement time-series. The filtering algorithm effectiveness is demonstrated by analysing a set of SAR images acquired by the ENVISAT sensor from 2003 to 2010 over the city of Shanghai, China.

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Section: Interferogram Noise-filtering Algorithmmentioning

confidence: 98%

A simple solution to mitigate noise effects in time-redundant sequences of small baseline multi-look DInSAR interferograms

Yang

Pepe

Manzo

et al. 2013

Remote Sensing Letters

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“…A LOS rate map is calculated from the interferograms corrected for both atmospheric and orbital errors using the InSAR TS + AEM package developed at the University of Glasgow based on the SBAS algorithm [59][60][61] as the flow chart in Figure 4. The RMS between LOS deformation derived from the InSAR TS + AEM package and GPS measurements is smaller than 0.5 mm/year, which was validated independently by Li et al (Abstract G13B-07 presented at Fall Meeting.…”

Section: Rate Mapmentioning

confidence: 99%

Interseismic Deformation of the Altyn Tagh Fault Determined by Interferometric Synthetic Aperture Radar (InSAR) Measurements

Zhu

Wen

et al. 2016

Remote Sensing

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Abstract:The Altyn Tagh Fault (ATF) is one of the major left-lateral strike-slip faults in the northeastern area of the Tibetan Plateau. In this study, the interseismic deformation across the ATF at 85˝E was measured using 216 interferograms from 33 ENVISAT advanced synthetic aperture radar images on a descending track acquired from 2003 to 2010, and 66 interferograms from 15 advanced synthetic aperture radar images on an ascending track acquired from 2005 to 2010. To retrieve the pattern of interseismic strain accumulation, a global atmospheric model (ERA-Interim) provided by the European Center for Medium Range Weather Forecast and a global network orbital correction approach were applied to remove atmospheric effects and the long-wavelength orbital errors in the interferograms. Then, the interferometric synthetic aperture radar (InSAR) time series with atmospheric estimation model was used to obtain a deformation rate map for the ATF. Based on the InSAR velocity map, the regional strain rates field was calculated for the first time using the multi-scale wavelet method. The strain accumulation is strongly focused on the ATF with the maximum strain rate of 12.4ˆ10´8/year. We also show that high-resolution 2-D strain rates field can be calculated from InSAR alone, even without GPS data. Using a simple half-space elastic screw dislocation model, the slip-rate and locking depth were estimated with both ascending and descending surface velocity measurements. The joint inversion results are consistent with a left-lateral slip rate of 8.0˘0.7 mm/year on the ATF and a locking depth of 14.5˘3 km, which is in agreement with previous results from GPS surveys and ERS InSAR results. Our results support the dynamic models of Asian deformation requiring low fault slip rate.

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“…Doris Software for Interferometric SAR processing from Delft University of Technology was used to perform the D-InSAR processing. Subsequently, the method by Mora et al [16], and the Matlab program by Yang et al [17] were used for PS-InSAR processing. The PS-InSAR data processing is briefly described here: (1) subset the study area from the image dataset; (2) compute the baselines of every pair; (3) select a pair that exhibits a minimal temporal baseline to generate an interferogram of the topographic effects; (4) select a minimal temporal baseline as master image and set all other images as slave images, and then select the image pairs for further processing; (5) generate deformation interferograms and corresponding coherence maps for all paired images; (6) generate topographic interferograms for all paired images; (7) compute the difference between deformation and topographic interferograms for all paired images, (8) select the permanent scatterer candidate (PSC) points based on the coherence maps.…”

Section: Ps-insarmentioning

confidence: 99%

Observing Land Subsidence and Revealing the Factors That Influence It Using a Multi-Sensor Approach in Yunlin County, Taiwan

Hsu

Chang²,

Chang

et al. 2015

Remote Sensing

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Land subsidence is a worldwide problem that is typically caused by human activities, primarily the removal of groundwater. In Western Taiwan, groundwater has been pumped for industrial, residential, agricultural, and aquacultural uses for over 40 years. In this study, a multisensor monitoring system comprising GPS stations, leveling surveys, monitoring wells, and Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) was employed to monitor land subsidence in Western Taiwan. The results indicate that land subsidence in Yunlin County was mainly affected by the compaction of subsurface soils and over-pumping of groundwater from deep soils. The study area comprised western foothills, characterized by sediments containing predominantly gravel, and coastal areas, where clay was predominant. The subsidence in coastal areas was more severe than that in the western foothills, as a result of groundwater removal. An additional factor affecting subsidence was the compaction of deep layers caused by deep groundwater OPEN ACCESSRemote Sens. 2015, 7 8203 removal and the deep-layer compaction was difficult to recover. Based on multisensor monitoring results, severe subsidence is mainly affected by compaction of subsurface soils, over-pumping of groundwater from deep soils, and deep soil compaction.

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Linear and nonlinear terrain deformation maps from a reduced set of interferometric sar images

Cited by 661 publications

References 24 publications

A simple solution to mitigate noise effects in time-redundant sequences of small baseline multi-look DInSAR interferograms

A simple solution to mitigate noise effects in time-redundant sequences of small baseline multi-look DInSAR interferograms

Interseismic Deformation of the Altyn Tagh Fault Determined by Interferometric Synthetic Aperture Radar (InSAR) Measurements

Observing Land Subsidence and Revealing the Factors That Influence It Using a Multi-Sensor Approach in Yunlin County, Taiwan

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