Semi-Automatic Identification and Pre-Screening of Geological–Geotechnical Deformational Processes Using Persistent Scatterer Interferometry Datasets

Remote Sensing - Tập 11 Số 14 - Trang 1675
Roberto Tomás1, José Ignacio Pagán1, José A. Navarro2, Miguel Cano1, José Luis Pastor1, Adrián Riquelme1, María Cuevas-González2, Michele Crosetto2, Anna Barra2, Oriol Monserrat2, Juan M. López‐Sánchez3, Alfredo Ramón Morte4, Salvador Ivorra1, Matteo Del Soldato5, Lorenzo Solari5, Silvia Bianchini5, Federico Raspini5, F. Novali6, A. Ferretti6, E. Costantini7, Francesco Trillo7, Gerardo Herrera8,9, Nicola Casagli5
1Departamento de Ingeniería Civil, Universidad de Alicante, P.O. Box 99, 03080 Alicante, Spain
2Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Geomatics Division, Av. Gauss, 7 08860 Castelldefels, Spain
3Instituto Universitario de Investigación Informática, Universidad de Alicante, P.O. Box 99, 03080 Alicante, Spain
4Sistema de Información Geográfica de la Universidad de Alicante (SIGUA), Universidad de Alicante, P.O. Box 99, 03080 Alicante, Spain
5Earth Sciences Department, University of Florence, Via La Pira, 4, 50121 Firenze, Italy
6TRE-Altamira, Via di Ripa Ticinese, 79, 20143 Milan, Italy
7e-GEOS—An Italian Space Agency/Telespazio Company, Via Tiburtina, 965, 00156 Rome, Italy
8Earth Observation and Geohazards Expert Group (EOEG), EuroGeoSurveys, the Geological Surveys of Europe, Rue Joseph II, 36–38, 1000 Brussels, Belgium
9Geohazards InSAR Laboratory and Modeling Group, Instituto Geológico y Minero de España (IGME), C/. Alenza 1, 28003 Madrid, Spain

Tóm tắt

This work describes a new procedure aimed to semi-automatically identify clusters of active persistent scatterers and preliminarily associate them with different potential types of deformational processes over wide areas. This procedure consists of three main modules: (i) ADAfinder, aimed at the detection of Active Deformation Areas (ADA) using Persistent Scatterer Interferometry (PSI) data; (ii) LOS2HV, focused on the decomposition of Line Of Sight (LOS) displacements from ascending and descending PSI datasets into vertical and east-west components; iii) ADAclassifier, that semi-automatically categorizes each ADA into potential deformational processes using the outputs derived from (i) and (ii), as well as ancillary external information. The proposed procedure enables infrastructures management authorities to identify, classify, monitor and categorize the most critical deformations measured by PSI techniques in order to provide the capacity for implementing prevention and mitigation actions over wide areas against geological threats. Zeri, Campiglia Marittima–Suvereto and Abbadia San Salvatore (Tuscany, central Italy) are used as case studies for illustrating the developed methodology. Three PSI datasets derived from the Sentinel-1 constellation have been used, jointly with the geological map of Italy (scale 1:50,000), the updated Italian landslide and land subsidence maps (scale 1:25,000), a 25 m grid Digital Elevation Model, and a cadastral vector map (scale 1:5000). The application to these cases of the proposed workflow demonstrates its capability to quickly process wide areas in very short times and a high compatibility with Geographical Information System (GIS) environments for data visualization and representation. The derived products are of key interest for infrastructures and land management as well as decision-making at a regional scale.

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Tài liệu tham khảo

Pepe, A., and Calò, F. (2017). A Review of Interferometric Synthetic Aperture RADAR (InSAR) Multi-Track Approaches for the Retrieval of Earth’s Surface Displacements. Appl. Sci., 7.

Shanker, 2011, Comparison of Persistent Scatterers and Small Baseline Time-Series InSAR Results: A Case Study of the San Francisco Bay Area, IEEE Geosci. Remote Sens. Lett., 8, 592, 10.1109/LGRS.2010.2095829

Crosetto, 2016, Persistent Scatterer Interferometry: A review, ISPRS J. Photogramm. Remote Sens., 115, 78, 10.1016/j.isprsjprs.2015.10.011

Ferretti, 2001, Permanent scatterers in SAR interferometry, IEEE Trans. Geosci. Remote Sens., 39, 8, 10.1109/36.898661

Ferretti, 2000, Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry, IEEE Trans. Geosci. Remote Sens., 38, 2202, 10.1109/36.868878

Berardino, 2002, A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms, IEEE Trans. Geosci. Remote Sens., 40, 2375, 10.1109/TGRS.2002.803792

Casu, 2006, A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data, Remote Sens. Environ., 102, 195, 10.1016/j.rse.2006.01.023

Wolf, D., and Fernández, J. (2007). An Overview of the Small BAseline Subset Algorithm: A DInSAR Technique for Surface Deformation Analysis. Deformation and Gravity Change: Indicators of Isostasy, Tectonics, Volcanism, and Climate Change, Birkhäuser.

Tomás, R., and Li, Z. (2017). Earth Observations for Geohazards: Present and Future Challenges. Remote Sens., 9.

Bianchini, 2018, From Picture to Movie: Twenty Years of Ground Deformation Recording Over Tuscany Region (Italy) with Satellite InSAR, Front. Earth Sci., 6, 177, 10.3389/feart.2018.00177

Samsonov, 2014, Rapidly accelerating subsidence in the Greater Vancouver region from two decades of ERS-ENVISAT-RADARSAT-2 DInSAR measurements, Remote Sens. Environ., 143, 180, 10.1016/j.rse.2013.12.017

Herrera, 2013, Multi-sensor advanced DInSAR monitoring of very slow landslides: The Tena Valley case study (Central Spanish Pyrenees), Remote Sens. Environ., 128, 31, 10.1016/j.rse.2012.09.020

Cignetti, M., Manconi, A., Manunta, M., Giordan, D., De Luca, C., Allasia, P., and Ardizzone, F. (2016). Taking Advantage of the ESA G-POD Service to Study Ground Deformation Processes in High Mountain Areas: A Valle d’Aosta Case Study, Northern Italy. Remote Sens., 8.

Raucoules, 2007, Use of SAR interferometry for detecting and assessing ground subsidence, C. R. Geosci., 339, 289, 10.1016/j.crte.2007.02.002

Costantini, 2017, Analysis of surface deformations over the whole Italian territory by interferometric processing of ERS, Envisat and COSMO-SkyMed radar data, Remote Sens. Environ., 202, 250, 10.1016/j.rse.2017.07.017

Romero, 2014, Radar interferometry techniques for the study of ground subsidence phenomena: A review of practical issues through cases in Spain, Environ. Earth Sci., 71, 163, 10.1007/s12665-013-2422-z

Barra, A., Solari, L., Béjar-Pizarro, M., Monserrat, O., Bianchini, S., Herrera, G., Crosetto, M., Sarro, R., González-Alonso, E., and Mateos, R. (2017). A Methodology to Detect and Update Active Deformation Areas Based on Sentinel-1 SAR Images. Remote Sens., 9.

Meisina, 2008, Geological Interpretation of PSInSAR Data at Regional Scale, Sensors, 8, 7469, 10.3390/s8117469

Bianchini, 2012, Landslide HotSpot Mapping by means of Persistent Scatterer Interferometry, Environ. Earth Sci., 67, 1155, 10.1007/s12665-012-1559-5

Zhao, 2012, Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA, Remote Sens. Environ., 124, 348, 10.1016/j.rse.2012.05.025

Raspini, 2018, Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites, Sci. Rep., 8, 7253, 10.1038/s41598-018-25369-w

Solari, 2018, Fast detection of ground motions on vulnerable elements using Sentinel-1 InSAR data, Geomat. Nat. Hazards Risk, 9, 152, 10.1080/19475705.2017.1413013

Ardizzone, 2014, Enhanced landslide investigations through advanced DInSAR techniques: The Ivancich case study, Assisi, Italy, Remote Sens. Environ., 142, 69, 10.1016/j.rse.2013.11.003

Navarro, J.A., Cuevas, M., Tomás, R., Barra, A., and Crosetto, M. (2019, January 3–5). A toolset to detect and classify Active Deformation Areas using interferometric SAR data. Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management, Heraklion, Crete, Greece.

Navarro, J.A., Cuevas, M., Barra, A., and Crosetto, M. (2018, January 24–27). Detection of Active Deformation Areas based on Sentinel-1 imagery: An efficient, fast and flexible implementation. Proceedings of the 18th International Scientific and Technical Conference, Crete, Greece.

Notti, 2014, A methodology for improving landslide PSI data analysis, Int. J. Remote Sens., 35, 2186, 10.1080/01431161.2014.889864

He, 2015, Mapping Two-Dimensional Deformation Field Time-Series of Large Slope by Coupling DInSAR-SBAS with MAI-SBAS, Remote Sens., 7, 12440, 10.3390/rs70912440

Turner, A.K., and Schuster, R.L. (1996). Landslide types and processes. Landslides: Investigation and Mitigation, Academy Press. National Research Council, Transportation and Research Board Special Report.

Lee, 2004, Determination and application of the weights for landslide susceptibility mapping using an artificial neural network, Eng. Geol., 71, 289, 10.1016/S0013-7952(03)00142-X

Ayalew, 2005, The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan, Geomorphology, 65, 15, 10.1016/j.geomorph.2004.06.010

Rosi, 2018, The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: Geomorphological features and landslide distribution, Landslides, 15, 5, 10.1007/s10346-017-0861-4

Rosi, 2016, Subsidence mapping at regional scale using persistent scatters interferometry (PSI): The case of Tuscany region (Italy), Int. J. Appl. Earth Obs. Geoinf., 52, 328

Stucchi, 2017, SH-wave seismic reflection at a landslide (Patigno, NW Italy) integrated with P-wave, J. Appl. Geophys., 146, 188, 10.1016/j.jappgeo.2017.09.011

Del Soldato, M., Solari, L., Poggi, F., Raspini, F., Tomás, R., Fanti, R., and Casagli, N. (2019). Landslide-Induced Damage Probability Estimation Coupling InSAR and Field Survey Data by Fragility Curves. Remote Sens., 11.

Barazzuoli, 1999, Olocenic alluvial aquifer of the River Cornia coastal plain (southern Tuscany, Italy): Database design for groundwater management, Environ. Geol., 39, 123, 10.1007/s002540050443

Coltorti, 2011, The sagging deep-seated gravitational movements on the eastern side of Mt. Amiata (Tuscany, Italy), Nat. Hazards, 59, 191, 10.1007/s11069-011-9746-3

Ferretti, 2011, A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR, IEEE Trans. Geosci. Remote Sens., 49, 3460, 10.1109/TGRS.2011.2124465

Del Soldato, M., Farolfi, G., Rosi, A., Raspini, F., and Casagli, N. (2018). Subsidence Evolution of the Firenze–Prato–Pistoia Plain (Central Italy) Combining PSI and GNSS Data. Remote Sens., 10.

Tuscany Region (2019, April 02). CORINE Land Cover. Available online: http://www502.regione.toscana.it/geoscopio/usocoperturasuolo.html.