Differential synthetic aperture radar (SAR) interferometry for detection land subsidence in Derna City, Libya
-
Heba Basyouni Ibrahim
,
Mahmoud Salah
Abstract
The country of Libya, situated on the Mediterranean fault zone, has a distinctive geodynamic regime due to the interplay between the Eurasian and African plates, which governs its tectonic evolution. In addition to its seismological significance, Libya is characterized by numerous subsidence and slope instabilities in regions with steep terrain. These geological phenomena have significant consequences for the built environment, as they pose an immediate danger to entire towns and essential infrastructure. Furthermore, infrequent weather phenomena, such as intense precipitation and thunderstorms, when coupled with the geological characteristics of some regions and the presence of seismically active terrain, have the potential to trigger landslide and land subsidence, resulting in significant harm to vital infrastructure. The current study utilizes the DInSAR technology to identify distinct subsidence occurrences that were induced by intense precipitation in coastal regions of Libya, specifically in Derna. These areas experienced significant flooding resulting in collapses during September 2023. A total of six pairs of co-event Interferometric Synthetic Aperture Radar (SAR) were utilized to generate displacement maps in the vertical, north-east, and north-west directions for the purpose of analysing the deformations. The aforementioned activities are conducted via Sentinel-1A images, which is freely accessible through the Copernicus program. Additionally, flood-prone zones were defined using Sentinel-1 GRD imagery. The Interferometric processing revealed multiple areas of subsidence. Subsidence rates of up to −14 cm were found in Derna city’s urban cores after flood. The findings suggest that subsidence may have an effect on the flood-proneness of the region of Derna City as Ground subsidence also occurred in the period immediately before the earthquake, at a rate of −14 cm.
Acknowledgments
Many thanks to the European Space Agency for open access images for researchers and SNAP Software, which helped in analyzing images and obtaining accurate and very fast results instead of traditional methods.
-
Research ethics: Not applicable.
-
Author contributions: The first author analyzed and wrote the introduction, methodology, and results, while the second author steered the preparation of the document and validation, The third author implemented the software with the first author and the fourth author The creator of the basic concept oversaw all elements and authored conclusions and recommendations and pursue with corresponding authors to ensure the completeness and verification procedure.
-
Competing interests: All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.
-
Research funding: None declared.
-
Data availability: Not applicable.
References
1. Chen, W, Hong, H, Li, S, Shahabi, H, Wang, Y, Wang, X, et al.. Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. J Hydrol 2019;575:864–73. https://doi.org/10.1016/j.jhydrol.2019.05.089.Suche in Google Scholar
2. Choubin, B, Moradi, E, Golshan, M, Adamowski, J, Sajedi-Hosseini, F, Mosavi, A. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ 2019;651:2087–96. https://doi.org/10.1016/j.scitotenv.2018.10.064.Suche in Google Scholar PubMed
3. Birkholz, S, Muro, M, Jeffrey, P, Smith, HM. Rethinking the relationship between flood risk perception and flood management. Sci Total Environ 2014;478:12–20. https://doi.org/10.1016/j.scitotenv.2014.01.061.Suche in Google Scholar PubMed
4. Shahabi, H, Shirzadi, A, Ghaderi, K, Omidvar, E, Al-Ansari, N, Clague, JJ, et al.. Detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: hybrid intelligence of bagging ensemble based on K-nearest neighbor classifier. Remote Sens 2020;12:266. https://doi.org/10.3390/rs12020266.Suche in Google Scholar
5. Peel, MC, Finlayson, BL, McMahon, TA. Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 2007;11:1633–44. https://doi.org/10.5194/hess-11-1633-2007.Suche in Google Scholar
6. Liu, S, Wang, Z. Choice of surveying methods for landslides monitoring. In: Landslides and engineered slopes. From the past to the future. London, UK: CRC Press, Taylor & Francis Group; 2008:1211–16 pp.10.1201/9780203885284-c160Suche in Google Scholar
7. Ferretti, A, Monti-guarnieri, A, Prati, C, Rocca, F. InSAR principles: guidelines for SAR interferometry processing and interpretation. Paris, France: European Space Agency; 2007.Suche in Google Scholar
8. Eci, L. Sentinel-1 Toolbox – TOPS interferometry tutorial. Toronto, ON, Canada: European Space Agency; Array Systems Computing Inc.; 2016.Suche in Google Scholar
9. Braun, A, Veci, L. Sentinel-1 Toolbox – TOPS interferometry tutorial. Waterloo, ON, Canada: European Space Agency; SkyWatch Space Applications Inc.; 2020.Suche in Google Scholar
10. Zebker, HA, Rosen, PA, Hensley, S. Atmospheric effects in Interferometric synthetic aperture radar surface deformation and topographic maps. J Geophys Res Solid Earth 1997;102:7547–63. https://doi.org/10.1029/96jb03804.Suche in Google Scholar
11. Yu, C, Li, Z, Penna, NT. Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model. Remote Sens Environ 2018;204:109–21. https://doi.org/10.1016/j.rse.2017.10.038.Suche in Google Scholar
12. Hearn, GJ, Duncumb, RW. Using stereo aerial photography and satellite InSAR to help assess slope hazards for a hydropower project in Mountainous Southern Albania. Q J Eng Geol Hydrogeol 2018;51:265–75. https://doi.org/10.1144/qjegh2017-100.Suche in Google Scholar
13. Di Martire, D, Tessitore, S, Brancato, D, Ciminelli, MG, Costabile, S, Costantini, M, et al.. Landslide detection integrated system (LaDIS) based on in-situ and satellite SAR interferometry measurements. CATENA 2016;137:406–21. https://doi.org/10.1016/j.catena.2015.10.002.Suche in Google Scholar
14. Bovenga, F, Pasquariello, G, Pellicani, R, Refice, A, Spilotro, G. Landslide monitoring for risk mitigation by using corner reflector and satellite SAR interferometry: the large landslide of Carlantino (Italy). CATENA 2017;151:49–62. https://doi.org/10.1016/j.catena.2016.12.006.Suche in Google Scholar
15. Strozzi, T, Klimeš, J, Frey, H, Caduff, R, Huggel, C, Wegmüller, U, et al.. Satellite SAR interferometry for the improved assessment of the state of activity of landslides: a case study from the Cordilleras of Peru. Remote Sens Environ 2018;217:111–25. https://doi.org/10.1016/j.rse.2018.08.014.Suche in Google Scholar
16. Corsetti, M, Fossati, F, Manunta, M, Marsella, M. Advanced SBAS-DInSAR technique for controlling large civil infrastructures: an application to the Genzano Di Lucania dam. Sensors 2018;18:2371. https://doi.org/10.3390/s18072371.Suche in Google Scholar PubMed PubMed Central
17. D’Aranno, P, Di Benedetto, A, Fiani, M, Marsella, M. Remote sensing technologies for linear infrastructure monitoring. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci 2019;XLII-2/W11:461–8. https://doi.org/10.5194/isprs-archives-xlii-2-w11-461-2019.Suche in Google Scholar
18. Cuca, B, Tzouvaras, M, Agapiou, A, Lysandrou, V, Themistocleous, K, Nisantzi, A, et al.. Earth observation technologies in service to the cultural landscape of Cyprus: risk identification and assessment. In: Fourth international conference on remote sensing and geoinformation of the environment (RSCy2016). Bellingham, WA, USA: SPIE; 2016:96880Y p.10.1117/12.2241669Suche in Google Scholar
19. Mullissa, AG, Tolpekin, V, Stein, A, Perissin, D. Polarimetric differential SAR interferometry in an arid natural environment. Int J Appl Earth Obs Geoinf 2017;59:9–18. https://doi.org/10.1016/j.jag.2017.02.019.Suche in Google Scholar
20. Da Lio, C, Tosi, L. Land subsidence in the Friuli Venezia Giulia coastal plain, Italy: 1992–2010 results from SAR-based interferometry. Sci Total Environ 2018;633:752–64. https://doi.org/10.1016/j.scitotenv.2018.03.244.Suche in Google Scholar PubMed
21. Liosis, N, Marpu, PR, Pavlopoulos, K, Ouarda, TB. Ground subsidence monitoring with SAR interferometry techniques in the rural area of Al Wagan, UAE. Remote Sens Environ 2018;216:276–88. https://doi.org/10.1016/j.rse.2018.07.001.Suche in Google Scholar
22. Tzouvaras, M, Kouhartsiouk, D, Agapiou, A, Danezis, C, Hadjimitsis, DG. The use of sentinel-1 synthetic aperture radar (SAR) images and open-source software for cultural heritage: an example from Paphos area in Cyprus for mapping landscape changes after a 5.6 magnitude earthquake. Remote Sens 2019;11:1766. https://doi.org/10.3390/rs11151766.Suche in Google Scholar
23. Zhou, W, Chen, F, Guo, H. Differential radar interferometry for structural and ground deformation monitoring: a new tool for the conservation and sustainability of cultural heritage sites. Sustainability 2015;7:1712–29. https://doi.org/10.3390/su7021712.Suche in Google Scholar
24. Rocca, F, Prati, C, Guarnieri, AM, Ferretti, A. Sar interferometry and its applications. Surv Geophys 2000;21:159–76. https://doi.org/10.1023/a:1006710731155.10.1023/A:1006710731155Suche in Google Scholar
25. Rabus, B, Pichierri, M. A new InSAR phase demodulation technique developed for a typical example of a complex, multi-lobed landslide displacement field, Fels Glacier Slide, Alaska. Remote Sens 2018;10:995. https://doi.org/10.3390/rs10070995.Suche in Google Scholar
26. ESA. Sentinel data access overview – sentinel online. https://sentinel.esa.int/web/sentinel/sentinel-data-access [Accessed 26 June 2019].Suche in Google Scholar
27. Themistocleous, K, Cuca, B, Agapiou, A, Lysandrou, V, Tzouvaras, M, Hadjimitsis, DG, et al.. The protection of cultural heritage sites from geo-hazards: the PROTHEGO Project. In: EUROMED 2016, Volume LNCS 10059. Cham, Switzerland: Springer; 2016:91–8 pp.10.1007/978-3-319-48974-2_11Suche in Google Scholar
28. Hungr, O, Leroueil, S, Picarelli, L. The varnes classification of landslide types, an update. Landslides 2014;11:167–94. https://doi.org/10.1007/s10346-013-0436-y.Suche in Google Scholar
29. International Geotechnical Society’s UNESCO Working Party on World Landslide Inventory (WP/WLI). A suggested method for describing the rate of movement of a landslide. Bull Int Assoc Eng Geol 1995;52:75–8. https://doi.org/10.1007/bf02602683.Suche in Google Scholar
30. Wegnüller, U, Werner, C, Strozzi, T, Wiesmann, A, Frey, O, Santoro, M. Sentinel-1 support in the GAMMA software. Procedia Comput Sci 2016;100:1305–12. https://doi.org/10.1016/j.procs.2016.09.246.Suche in Google Scholar
31. https://earthobservatory.nasa.gov/images/151851/storm-aftermath-in-derna-libya.Suche in Google Scholar
32. Zebker, HA, Villasenor, J. Decorrelation in Interferometric radar echoes. IEEE Trans Geosci Rem Sens 1992;30:950–9. https://doi.org/10.1109/36.175330.Suche in Google Scholar
33. Le Mouélic, S, Raucoules, D, Carnec, C, King, C. A least squares adjustment of multi-temporal InSAR data. Photogramm Eng Remote Sensing 2005;71:197–204.10.14358/PERS.71.2.197Suche in Google Scholar
34. Manconi, A. Technical note: limitations on the use of space borne differential SAR interferometry for systematic monitoring and failure forecast of Alpine landslides. EarthArXiv 2019;1–20. https://doi.org/10.31223/osf.io/3nmqj.Suche in Google Scholar
35. Moretto, S, Bozzano, F, Esposito, C, Mazzanti, P, Rocca, A. Assessment of landslide pre-failure monitoring and forecasting using satellite SAR interferometry. Geosciences 2017;7:36. https://doi.org/10.3390/geosciences7020036.Suche in Google Scholar
36. Manconi, A, Kourkouli, P, Caduff, R, Strozzi, T, Loew, S. Monitoring surface deformation over a failing rock slope with the ESA sentinels: insights from Moosfluh instability, Swiss Alps. Remote Sens 2018;10:672. https://doi.org/10.3390/rs10050672.Suche in Google Scholar
37. Lee, JS, Wen, H, Ainsworth, TL, Chen, K-S, Chen, AJ. Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans Geosci Rem Sens 2008;47:202–13.10.1109/TGRS.2008.2002881Suche in Google Scholar
38. Wu, Y-Y, Ren, H, Madson, A. A discussion on the Goldstein filtering parameters of the SNAP software. AGU fall meeting abstracts; 2022, vol. 2022.10.1109/IGARSS52108.2023.10282000Suche in Google Scholar
© 2023 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Original Research Articles
- Ionospheric TEC modeling using COSMIC-2 GNSS radio occultation and artificial neural networks over Egypt
- Regional GPS orbit determination using code-based pseudorange measurement with residual correction model
- Analysis of different combinations of gravity data types in gravimetric geoid determination over Bali
- Assessment of satellite images terrestrial surface temperature and WVP using GNSS radio occultation data
- GNSS positioning accuracy performance assessments on 1st and 2nd generation SBAS signals in Thailand
- Differential synthetic aperture radar (SAR) interferometry for detection land subsidence in Derna City, Libya
- Advanced topographic-geodetic surveys and GNSS methodologies in urban planning
- Detection of GNSS ionospheric scintillations in multiple directions over a low latitude station
- Spatiotemporal postseismic due to the 2018 Lombok earthquake based on insar revealed multi mechanisms with long duration afterslip
- Practical implications in the interpolation methods for constructing the regional mean sea surface model in the eastern Mediterranean Sea
- Validation of a tailored gravity field model for precise quasigeoid modelling over selected sites in Cameroon and South Africa
- Evaluation of ML-based classification algorithms for GNSS signals in ocean environment
- Development of a hybrid geoid model using a global gravity field model over Sri Lanka
- Implementation of GAGAN augmentation on smart mobile devices and development of a cooperative positioning architecture
- On the GPS signal multipath at ASG-EUPOS stations
Artikel in diesem Heft
- Frontmatter
- Original Research Articles
- Ionospheric TEC modeling using COSMIC-2 GNSS radio occultation and artificial neural networks over Egypt
- Regional GPS orbit determination using code-based pseudorange measurement with residual correction model
- Analysis of different combinations of gravity data types in gravimetric geoid determination over Bali
- Assessment of satellite images terrestrial surface temperature and WVP using GNSS radio occultation data
- GNSS positioning accuracy performance assessments on 1st and 2nd generation SBAS signals in Thailand
- Differential synthetic aperture radar (SAR) interferometry for detection land subsidence in Derna City, Libya
- Advanced topographic-geodetic surveys and GNSS methodologies in urban planning
- Detection of GNSS ionospheric scintillations in multiple directions over a low latitude station
- Spatiotemporal postseismic due to the 2018 Lombok earthquake based on insar revealed multi mechanisms with long duration afterslip
- Practical implications in the interpolation methods for constructing the regional mean sea surface model in the eastern Mediterranean Sea
- Validation of a tailored gravity field model for precise quasigeoid modelling over selected sites in Cameroon and South Africa
- Evaluation of ML-based classification algorithms for GNSS signals in ocean environment
- Development of a hybrid geoid model using a global gravity field model over Sri Lanka
- Implementation of GAGAN augmentation on smart mobile devices and development of a cooperative positioning architecture
- On the GPS signal multipath at ASG-EUPOS stations
