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A User-Oriented Methodology for DInSAR Time Series Analysis and Interpretation: Landslides and Subsidence Case Studies

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Abstract

Recent advances in multi-temporal Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) have greatly improved our capability to monitor geological processes. Ground motion studies using DInSAR require both the availability of good quality input data and rigorous approaches to exploit the retrieved Time Series (TS) at their full potential. In this work we present a methodology for DInSAR TS analysis, with particular focus on landslides and subsidence phenomena. The proposed methodology consists of three main steps: (1) pre-processing, i.e., assessment of a SAR Dataset Quality Index (SDQI) (2) post-processing, i.e., application of empirical/stochastic methods to improve the TS quality, and (3) trend analysis, i.e., comparative implementation of methodologies for automatic TS analysis. Tests were carried out on TS datasets retrieved from processing of SAR imagery acquired by different radar sensors (i.e., ERS-1/2 SAR, RADARSAT-1, ENVISAT ASAR, ALOS PALSAR, TerraSAR-X, COSMO-SkyMed) using advanced DInSAR techniques (i.e., SqueeSAR™, PSInSAR™, SPN and SBAS). The obtained values of SDQI are discussed against the technical parameters of each data stack (e.g., radar band, number of SAR scenes, temporal coverage, revisiting time), the retrieved coverage of the DInSAR results, and the constraints related to the characterization of the investigated geological processes. Empirical and stochastic approaches were used to demonstrate how the quality of the TS can be improved after the SAR processing, and examples are discussed to mitigate phase unwrapping errors, and remove regional trends, noise and anomalies. Performance assessment of recently developed methods of trend analysis (i.e., PS-Time, Deviation Index and velocity TS) was conducted on two selected study areas in Northern Italy affected by land subsidence and landslides. Results show that the automatic detection of motion trends enhances the interpretation of DInSAR data, since it provides an objective picture of the deformation behaviour recorded through TS and therefore contributes to the understanding of the on-going geological processes.

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Abbreviations

SDQI:

SAR dataset quality index

TS:

Time series

NI:

Number of images Index

TI:

Time Index

MTBI:

Mean temporal baseline index

MSBI:

Mean spatial baseline index

SRI:

Spatial resolution index

References

  • Arnaud, A., Adam, N., Hanssen, R., Inglada, J., Duro, J., Closa, J. and Eineder, M. (2003), ASAR ERS interferometric phase continuity. Geoscience and Remote Sensing Symposium, 2003. IGARSS ‘03. Proceedings. 2003 IEEE International, 2, pp. 1133–1135, 21–25 July 2003.

  • Berardino, P., Fornaro, G., Lanari, R. and Sansosti, E. (2002), A new algorithm for surface deformation monitoring based on small baseline differential interferograms. Geoscience and Remote Sensing, IEEE Transactions, 40, no. 11, pp. 2375–2383.

  • Berti, M., Corsini, A., Franceschini, S., and Iannacone, J. P. (2013), Automated classification of Persistent Scatterers Interferometry time series, Nat. Hazards Earth Syst. Sci., 13, 1945–1958, doi:10.5194/nhess-13-1945-2013.

  • Bianchini, S., Cigna, F., Righini, G., Proietti, C., and Casagli, N. (2012), Landslide hotspot mapping by means of persistent scatterer interferometry. Environmental Earth Sciences, 67, pp 1155–1172.

  • Blanco-Sanchez, P., Mallorquí, J.J., Duque, S. and Monells, D. (2008), The Coherent Pixels Technique CPT): An Advanced DInSAR Technique for Nonlinear Deformation Monitoring, Pure Appl. Geophys., 165, pp. 1167–1194.

  • Bonano, M., Manunta, M., Marsella, M., and Lanari, R. (2012). Long-term ERS/ENVISAT deformation time-series generation at full spatial resolution via the extended SBAS technique. International Journal of Remote Sensing, 33(15), 4756–4783.

  • Bovenga, F., Wasowski, J., Nitti, D. O., Nutricato, R., and Chiaradia, M. T. (2012). Using COSMO/SkyMed X-band and ENVISAT C-band SAR interferometry for landslides analysis. Remote Sensing of Environment, 119, 272–285.

  • Calò, F., Ardizzone, F., Castaldo, R., Lollino, P., Tizzani, P., Guzzetti, F., Lanari, R., Angeli M.-G., Pontoni, F. and Manunta, M. (2014). Enhanced landslide investigations through advanced DInSAR techniques: The Ivancich case study, Assisi, Italy. Remote Sensing of Environment, 142, 69–82.

  • Casu, F., Manzo, M., and Lanari, R. (2006). A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data. Remote Sensing of Environment, 102(3–4), 195–210. doi:10.1016/j.rse.2006.01.023.

  • Chen, F., Lin, H., Yeung, K., and Cheng, S. (2010), Detection of slope instability in Hong Kong based on multi-baseline differential SAR interferometry using ALOS PALSAR data, GIScience Remote Sens., 47, 208–220.

  • Cigna, F., Tapete, D., and Casagli, N., (2012): Semi-automated extraction of Deviation Indexes (DI) from satellite Persistent Scatterers time series: tests on sedimentary volcanism and tectonically-induced motions, Nonlin. Processes Geophys., 19, 643–655.

  • Colesanti, C. and Wasowsky, J. (2006) Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry, Eng Geol., 88, 173–199.

  • Crosetto, M and Cuevas, M. (2011). Enabling Access to Geological Information in Support of GME. EUROPEAN COMMISSION Seventh Framework Programme D3.6 PSI Validation Version 1 17th November 2011.

  • Crosetto, M., Monserrat, O., Iglesias, R., and Crippa, B. (2010). Persistent Scatterer Interferometry: Potential, limits and initial C- and X-band comparison. Photogrammetric Engineering and Remote Sensing, 76 (9), 1061–1069.

  • Devanthéry, N., Crosetto, M., Monserrat, O., Cuevas-González, M., and Crippa, B. (2014). An Approach to Persistent Scatterer Interferometry. Remote Sensing , 6(7), 6662–6679.

  • Farina, P., Colombo, D., Fumagalli, A., Marks, F. and Moretti S. (2006) Permanent Scatterers for landslide investigations: outcomes from the ESA-SLAM project Engineering Geology, 88 (3–4), pp. 200–217.

  • Ferretti, A., Prati, C. and Rocca, F., (2001) Permanent Scatterers in SAR interferometry. Geoscience and Remote Sensing, IEEE Transactions, 39(1).pp 8–20.

  • Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F. and Rucci, A., (2011) A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR. IEEE T Geo-science and Remote Sensing, IEEE Transactions, 49 (9), pp.3460–3470.

  • Franceschetti, G., and Lanari, R. (1999) Synthetic aperture radar processing. CRC press.

  • González, P. J., and Fernández, J. (2011). Error estimation in multitemporal InSAR deformation time series, with application to Lanzarote, Canary Islands. Journal Of Geophysical Research: solid earth (1978–2012), 116(b10).

  • Hanssen, R. (2001), Radar Interferometry: Data Interpretation and ErrorAnalysis, 308 pp., Kluwer Acad., Dordrecht, Netherlands.

  • Herrera, G., Gutiérrez, F., García-Davalillo, J. C., Guerrero, J., Notti, D., Galve, J. P., and Cooksley, G. (2013). Multi-sensor advanced DInSAR monitoring of very slow landslides: the Tena valley case study (central Spanish Pyrenees). Remote Sensing of Environment, 128, 31–43.

  • Hooper, A. (2008). A multi-temporal Insar method incorporating both persistent scatterer and small baseline approaches. Geophysical Research Letters, 35(16), L16302. doi:10.1029/2008GL034654.

  • Marinkovic, P. S., van Leijen, F., Ketelaar, G., and Hanssen, R. F. (2005). Recursive data processing and data volume minimization for PS-InSAR. In International geoscience and remote sensing symposium (Vol. 4, p. 2697).

  • Meisina, C., Zucca, F., Notti, D., Colombo, A., Cucchi, A., Savio, G., Giannico, C. and Bianchi M. (2008), Geological interpretation of PSInSAR data at regional scale. Sensors 8, 7469–7492.

  • Metternicht, G., Hurni, L., and Gogu, R. (2005), Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sensing of Environment 98, 284–303.

  • Milone, G. and Scepi, G. (2011) A clustering approach for studying ground deformation trends in Campania Region through the use of PS-InSAR time series analysis, J. Appl. Sci. Res., 11, 610–620.

  • Raspini, F., Loupasakis, C., Rozos, D., Adam, N. and Moretti, S., (2014) - Ground subsidence phenomena in the Delta municipality region (Northern Greece): Geotechnical modelling and validation with Persistent Scatterer Interferometry. International Journal of Applied Earth Observation and Geoinformation, Vol. 28, pp 78–89.

  • Rocca, F. (2007). Modeling interferogram stacks. Geoscience and Remote Sensing, IEEE Transactions on, 45(10), 3289–3299.

  • Sousa, J. J., Ruiz, A. M., Hanssen, R. F., Bastos, L., Gil, A. J., Galindo-Zaldívar, J., and Sanz de Galdeano, C. (2010). PS-InSAR processing methodologies in the detection of field surface deformation—Study of the Granada basin (Central Betic Cordilleras, southern Spain). Journal of Geodynamics, 49(3), 181–189.

  • Sowter, A., Bateson, L., Strange, P., Ambrose, K., and Fifik Syafiudin M. (2013) DInSAR estimation of land motion using intermittent coherence with application to the South Derbyshire and Leicestershire coalfields. Remote Sensing Letters 4(10): 979–987.

  • Teatini, P., Tosi, L., Strozzi, T., Carbognin, L., Cecconi, G., Rosselli, R., and Libardo, S. (2012). Resolving land subsidence within the Venice lagoon by persistent scatterer SAR interferometry. Physics and Chemistry of the Earth, Parts A/B/C, 40, 72–79.

  • Tapete, D., and Casagli, N. (2013), Testing Computational Methods to Identify Deformation Trends in RADARSAT Persistent Scatterers Time Series for Structural Assessment of Archaeological Heritage. In B. Murgante et al., (eds.), Computational Science and Its Applications—ICCSA 2013 (pp. 693–707): Springer Berlin Heidelberg.

  • Tomas, R., Herrera. G., Lopez-Sanchez, J.M., et al. (2010). Study of the land subsidence in the Orihuela city (SE Spain) using PSI data: distribution, evolution and correlation with conditioning and triggering factors. Engineering Geology, 115, 105–121.

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Acknowledgments

The SAR data for Piemonte and Liguria regions in NW Italy were obtained through the project “Risk Nat” with Regione Liguria and ARPA Piemonte. The data over Oltrepò Pavese were derived from collaboration with Regione Lombardia. Both these data were processed by Tele-Rilevamento Europa - T.R.E. s.r.l. -. Datasets over Valle de Tena were derived from the SUDOE project in collaboration with IGME (Instituto Geologico Y Minero de España), Madrid, Spain, and processed by Altamira Information. SAR datasets over Umbria region, central Italy, were processed in the framework of the EC FP7 DORIS project (contract n. 242212). SAR datasets over Daunia Apennines, Southern Italy, were processed in the framework of contract with Italian Department of Civil Protection (contract n. 622).

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Authors and Affiliations

  1. Earth and Environmental Science Department, University of Pavia, Via Ferrata, 1, 27100, Pavia, Italy

    Davide Notti, Claudia Meisina & Francesco Zucca

  2. CNR, IREA, via Diocleziano 328, 80124, Naples, Italy

    Fabiana Calò & Michele Manunta

  3. British Geological Survey (BGS), Natural Environment Research Council (NERC), Nicker Hill, Keyworth, NG12 5GG, Nottinghamshire, UK

    Francesca Cigna

  4. Geohazards InSAR Laboratory and Modeling Group (InSARlab), Geoscience Research Department, Geological Survey of Spain (IGME), Alenza 1, E-28003, Madrid, Spain

    Davide Notti & Gerardo Herrera

  5. Dipartimento di Scienze Biologiche, Geologiche e Ambientali, Università di Bologna, Via Zamboni 67, 40126, Bologna, Italy

    Matteo Berti

  6. Department of Geography, Institute of Hazard, Risk and Resilience (IHRR), Durham University, Durham, UK

    Deodato Tapete

  7. Unidad Asociada de investigación IGME-UA de movimientos del terreno mediante interferometría radar (UNIRAD), Universidad de Alicante, P.O. Box 99, 03080, Alicante, Spain

    Gerardo Herrera

  8. Earth Observation and Geohazards Expert Group (EOEG), EuroGeoSurveys, The Geological Surveys of Europe, 36-38, Rue Joseph II, 1000, Brussels, Belgium

    Gerardo Herrera

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  1. Davide Notti
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  2. Fabiana Calò
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  3. Francesca Cigna
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  4. Michele Manunta
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  7. Claudia Meisina
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  8. Deodato Tapete
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  9. Francesco Zucca
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Correspondence to Davide Notti.

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Notti, D., Calò, F., Cigna, F. et al. A User-Oriented Methodology for DInSAR Time Series Analysis and Interpretation: Landslides and Subsidence Case Studies. Pure Appl. Geophys. 172, 3081–3105 (2015). https://doi.org/10.1007/s00024-015-1071-4

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