Articles | Volume 22, issue 5
https://doi.org/10.5194/nhess-22-1609-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-1609-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Earthquake-induced landslide monitoring and survey by means of InSAR
Department of Civil Engineering, University of Blida 1, Blida City, Algeria
Mohamed Abed
Department of Civil Engineering, University of Blida 1, Blida City, Algeria
Ahmed Mebarki
CORRESPONDING AUTHOR
University Gustave Eiffel, UPEC, CNRS, Laboratory Multi Scale and Simulation (MSME/UMR 8208), 5 blvd Descartes, 77454 Marne-la-Vallée, France
Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, Nanjing Tech University, 5 New Mofan Rd, Gulou, Nanjing, 211816, Jiangsu, China
Milan Lazecky
IT4Innovations, VSB-TU Ostrava, 17, Listopadu 15, 70833 Ostrava-Poruba, Czech Republic
School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK
Related authors
No articles found.
Zhuyu Yang, Bruno Barroca, Ahmed Mebarki, Katia Laffréchine, Hélène Dolidon, and Lionel Lilas
Nat. Hazards Earth Syst. Sci., 24, 3723–3753, https://doi.org/10.5194/nhess-24-3723-2024, https://doi.org/10.5194/nhess-24-3723-2024, 2024
Short summary
Short summary
To integrate resilience assessment into practical management, this study designs a step-by-step guide that enables managers of critical infrastructure (CI) to create specific indicator systems tailored to real cases. This guide considers the consequences of hazards to CI and the cost–benefit analysis and side effects of implementable actions. The assessment results assist managers, as they are based on a multi-criterion framework that addresses several factors valued in practical management.
C. Scott Watson, John R. Elliott, Susanna K. Ebmeier, Juliet Biggs, Fabien Albino, Sarah K. Brown, Helen Burns, Andrew Hooper, Milan Lazecky, Yasser Maghsoudi, Richard Rigby, and Tim J. Wright
Geosci. Commun., 6, 75–96, https://doi.org/10.5194/gc-6-75-2023, https://doi.org/10.5194/gc-6-75-2023, 2023
Short summary
Short summary
We evaluate the communication and open data processing of satellite Interferometric Synthetic Aperture Radar (InSAR) data, which measures ground deformation. We discuss the unique interpretation challenges and the use of automatic data processing and web tools to broaden accessibility. We link these tools with an analysis of InSAR communication through Twitter in which applications to earthquakes and volcanoes prevailed. We discuss future integration with disaster risk-reduction strategies.
Cited articles
ASF DAAC: Alaska Satellite Facility,
https://search.asf.alaska.edu/#/, last access: 26 June 2021.
ASF DAAC: Sentinel-1, https://search.asf.alaska.edu/, last access: 10 May 2022.
Bakon, M., Perissin, D., Lazecky, M., and Papco, J.: Infrastructure Non-linear Deformation Monitoring Via Satellite Radar Interferometry,
Procedia Technol., 16, 294–300, https://doi.org/10.1016/j.protcy.2014.10.095, 2014.
Braun, A.: Radar satellite imagery for humanitarian response: Bridging the
gap between technology and application,
https://publikationen.uni-tuebingen.de/xmlui/bitstream/handle/10900/91317/Braun2019 Radar satellite imagery for humanitarian response UB.pdf?sequence=1 (last access: 8 May 2022), 2019.
Canaslan Çomut, F., Gürboğa, Ş., and Smail, T.: Estimation of
co-seismic land deformation due to Mw 7.3 2017 earthquake in Iran (12 November 2017) using Sentinel-1 DInSAR, Bull. Miner. Res. Explor.,
162, 11–30, https://doi.org/10.19111/bulletinofmre.604026, 2020.
Cascini, L., Peduto, D., Pisciotta, G., Arena, L., Ferlisi, S., and Fornaro,
G.: The combination of DInSAR and facility damage data for the updating of
slow-moving landslide inventory maps at medium scale, Nat. Hazards Earth
Syst. Sci., 13, 1527–1549, https://doi.org/10.5194/nhess-13-1527-2013, 2013.
COMET: COMET-LiCS Sentinel-1 InSAR portal, COMET [data set], https://comet.nerc.ac.uk/COMET-LiCS-portal/, last access: 10 May 2022.
Congedo, L.: Semi-Automatic Classification Plugin: A Python tool for the
download and processing of remote sensing images in QGIS, J. Open Source
Softw., 6, 3172, https://doi.org/10.21105/joss.03172, 2021.
ESA: ESA's radar observatory mission for GMES operational services, https://sentinel.esa.int/documents/247904/349449/S1_SP-1322_1.pdf (last access: 8 May 2022), 2012.
ESA: Resolution and Swath – Sentinel-1 – Missions – Sentinel Online –
Sentinel, https://sentinel.esa.int/web/sentinel/missions/sentinel-1/instrument-payload/resolution-swath
(last access: 26 June 2021), 2021a.
ESA: SAR Instrument – Sentinel-1 SAR Technical Guide – Sentinel Online –
Sentinel, https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar/sar-instrument
(last access: 26 June 2021), 2021b.
ESA Copernicus: Copernicus Open Access Hub, https://scihub.copernicus.eu/dhus/#/home, last access: 10 May 2022.
Frizon de Lamotte, D., de Lamotte, D. F., Bezar, B. Saint, Bracène, R.,
and Mercier, E.: The two main steps of the Atlas building and geodynamics of
the western Mediterranean, Tectonics, 19, 740–761, 2000.
Galve, J. P., Castañeda, C., and Gutiérrez, F.: Railway deformation
detected by DInSAR over active sinkholes in the Ebro Valley evaporite karst,
Spain, Nat. Hazards Earth Syst. Sci., 15, 2439–2448,
https://doi.org/10.5194/nhess-15-2439-2015, 2015.
Goudarzi, M. A.: Detection and measurement of land deformations caused by
seismic events using InSAR, Sub-pixel correlation, and Inversion techniques,
p. 7, https://webapps.itc.utwente.nl/librarywww/papers_2010/msc/gem/goudarzi.pdf, (last access: 8 May 2022), 2010.
Herrera, G., Fernández, J. A., Tomás, R., Cooksley, G., and Mulas, J.: Advanced interpretation of subsidence in Murcia (SE Spain) using A-DInSAR data – Modelling and validation, Nat. Hazards Earth Syst. Sci., 9, 647–661, https://doi.org/10.5194/nhess-9-647-2009, 2009.
Hooper, A., Zebker, H., Segall, P., and Kampes, B.: A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers, Geophys. Res. Lett., 31, 1–5, https://doi.org/10.1029/2004GL021737, 2004.
Jacquemart, M. and Tiampo, K.: Leveraging time series analysis of radar
coherence and normalized difference vegetation index ratios to characterize
pre-failure activity of the Mud Creek landslide, California, Nat. Hazards
Earth Syst. Sci., 21, 629–642, https://doi.org/10.5194/nhess-21-629-2021, 2021.
Jia, H., Zhang, H., Liu, L., and Liu, G.: Landslide deformation monitoring by
adaptive distributed scatterer interferometric synthetic aperture radar,
Remote Sens., 11, 1–18, https://doi.org/10.3390/rs11192273, 2019.
Jung, J. and Yun, S. H.: Evaluation of coherent and incoherent landslide
detection methods based on synthetic aperture radar for rapid response: A case study for the 2018 Hokkaido landslides, Remote Sens., 12, 1–26,
https://doi.org/10.3390/rs12020265, 2020.
Kim, J. W.: Applications of Synthetic Aperture Radar (SAR)/SAR Interferometry (InSAR) for Monitoring of Wetland Water Level and Land Subsidence, Ohio State Univ., 1–111, https://core.ac.uk/download/pdf/159596183.pdf (last access: 8 May 2022), 2013.
Laneve, G., Bruno, M., Mukherjee, A., Messineo, V., Giuseppetti, R., De Pace, R., Magurano, F., and Ugo, E. D.: Remote Sensing Detection of Algal Blooms in a Lake Impacted by Petroleum Hydrocarbons, Remote Sens., 14, 121, https://doi.org/10.3390/RS14010121, 2021.
Lazeckỳ, M., Hatton, E., González, P. J., Hlaváčová, I.,
Jiránková, E., Dvořák, F., Šustr, Z., and Martinovič,
J.: Displacements monitoring over Czechia by IT4S1 system for automatised
interferometric measurements using Sentinel-1 data, Remote Sens., 12, 1–21, https://doi.org/10.3390/RS12182960, 2020a.
Lazeckỳ, M., Spaans, K., González, P. J., Maghsoudi, Y., Morishita, Y., Albino, F., Elliott, J., Greenall, N., Hatton, E., Hooper, A., Juncu, D.,
McDougall, A., Walters, R. J., Watson, C. S., Weiss, J. R., and Wright, T.
J.: LiCSAR: An automatic InSAR tool for measuring and monitoring tectonic
and volcanic activity, Remote Sens., 12, 2430, https://doi.org/10.3390/RS12152430, 2020b.
LNHC: Laboratoire National de l'Habitat et de la Construction, LNHC,
http://lnhc-dz.com/, last access: 26 June 2021.
Mazzanti, P., Rocca, A., Bozzano, F., Cossu, R., and Floris, M.: Landslides
Forecasting Analysis By Displacement Time Series Derived From Satellite
INSAR Data: Preliminary Results, undefined, http://www.nhazca.it/pdf/Mazzanti_et_al_2011.pdf (last access: 8 May 2022), 2012.
Meng, Q., Confuorto, P., Peng, Y., Raspini, F., Bianchini, S., Han, S., Liu,
H., and Casagli, N.: Regional recognition and classification of active loess
landslides using two-dimensional deformation derived from sentinel-1
interferometric radar data, Remote Sens., 12, 1541, https://doi.org/10.3390/rs12101541, 2020.
Merghadi, A., Abderrahmane, B., and Tien Bui, D.: Landslide susceptibility
assessment at Mila basin (Algeria): A comparative assessment of prediction
capability of advanced machine learning methods, ISPRS Int. J. Geo-Inform., 7, 268, https://doi.org/10.3390/ijgi7070268, 2018.
Moretto, S., Bozzano, F., and Mazzanti, P.: The role of satellite insar for
landslide forecasting: Limitations and openings, Remote Sens., 13, 1–31, https://doi.org/10.3390/rs13183735, 2021.
Morishita, Y.: Nationwide urban ground deformation monitoring in Japan using
Sentinel-1 LiCSAR products and LiCSBAS, Prog. Earth Planet. Sci., 8, 6, https://doi.org/10.1186/s40645-020-00402-7, 2021.
Morishita, Y.: yumorishita/LiCSBAS, GitHub [code], https://github.com/yumorishita/LiCSBAS, last access: 10 May 2022.
Morishita, Y., Lazecky, M., Wright, T. J., Weiss, J. R., Elliott, J. R. and Hooper, A.: LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor, Remote Sens., 12, 424, https://doi.org/10.3390/rs12030424, 2020.
Mouloud, H. and Badreddine, S.: Probabilistic seismic hazard assessment in
the Constantine region, Northeast of Algeria, Arab. J. Geosci., 10, 156,
https://doi.org/10.1007/s12517-017-2876-5, 2017.
Netzband, M., Stefanov, W. L., and Redman, C.: Applied remote sensing for
urban planning, governance and sustainability, Springer, https://doi.org/10.1007/978-3-540-68009-3, 2007.
Pawluszek-Filipiak, K. and Borkowski, A.: Integration of DInSAR and SBAS
techniques to determine mining-related deformations using Sentinel-1 data: The case study of rydultowy mine in Poland, Remote Sens., 12, 242, https://doi.org/10.3390/rs12020242, 2020.
Peláez Montilla, J. A., Hamdache, M., and Casado, C. L.: Seismic hazard in Northern Algeria using spatially smoothed seismicity. Results for peak ground acceleration, Tectonophysics, 372, 105–119,
https://doi.org/10.1016/S0040-1951(03)00234-8, 2003.
Rapant, P., Struhár, J., and Lazeck?, M.: Radar interferometry as a
comprehensive tool for monitoring the fault activity in the vicinity of
underground gas storage facilities, Remote Sens., 12, 271, https://doi.org/10.3390/rs12020271, 2020.
Roque, D., Perissin, D., Falcão, A. P., Fonseca, A. M., and Maria, J.:
Dams regional safety warning using time-series insar techniques, in: Second
Internatinal Dam World Conf., 21–24, https://www.researchgate.net/publication/317620924_DAM_REGIONAL_SAFETY_WARNING_USING_TIME-SERIES_INSAR_TECHNIQUES (last access: 8 May 2022), 2015.
Sanabria, M. P., Guardiola-Albert, C., Tomás, R., Herrera, G., Prieto, A., Sánchez, H., and Tessitore, S.: Subsidence activity maps derived from
DInSAR data: Orihuela case study, Nat. Hazards Earth Syst. Sci., 14, 1341–1360, https://doi.org/10.5194/nhess-14-1341-2014, 2014.
Tampuu, T., Praks, J., Uiboupin, R., and Kull, A.: Long term interferometric
temporal coherence and DInSAR phase in Northern Peatlands, Remote Sens., 12, 7–9, https://doi.org/10.3390/rs12101566, 2020.
Tzouvaras, M., Danezis, C., and Hadjimitsis, D. G.: Small scale landslide
detection using Sentinel-1 interferometric SAR coherence, Remote Sens., 12, 1560, https://doi.org/10.3390/rs12101560, 2020.
USGS: M 5.0 – 3 km NNE of Sidi Mérouane, Algeria, https://earthquake.usgs.gov/earthquakes/eventpage/us6000bag6/executive
(last access: 26 June 2021), 2021a.
USGS: USGS Earthquake Hazards Program, https://earthquake.usgs.gov/ (last access: 26 June 2021), 2021b.
Wang, Z., Li, Z., and Mills, J.: A new approach to selecting coherent pixels
for ground-based SAR deformation monitoring, ISPRS J. Photogram. Remote
Sens., 144, 412–422, https://doi.org/10.1016/j.isprsjprs.2018.08.008, 2018.
Wempen, J. M.: International Journal of Mining Science and Technology
Application of DInSAR for short period monitoring of initial subsidence due to longwall mining in the mountain west United States, Int. J. Min. Sci.
Technol., 30, 33–37, https://doi.org/10.1016/j.ijmst.2019.12.011, 2020.
WWO: Mila, Mila, Algeria Historical Weather Almanac, https://www.worldweatheronline.com/mila-weather-history/mila/dz.aspx /, last access: 26 June 2021.
Short summary
The Sentinel-1 SAR datasets and Sentinel-2 data are used in this study to investigate the impact of natural hazards (earthquakes and landslides) on struck areas. In InSAR processing, the use of DInSAR, CCD methods, and the LiCSBAS tool permit generation of time-series analysis of ground changes. Three land failures were detected in the study area. CCD is suitable to map landslides that may remain undetected using DInSAR. In Grarem, the failure rim is clear in coherence and phase maps.
The Sentinel-1 SAR datasets and Sentinel-2 data are used in this study to investigate the impact...
Altmetrics
Final-revised paper
Preprint