Temporal development of the displacement field of the Ponzano landslide in February 2017
DOI:
https://doi.org/10.12775/bgss-2023-0039Keywords
displacement field, rapid landslide, offset-tracking, residual displacements, satellite imageryAbstract
The conducted research determined the temporal evolution of the displacement field for the Ponzano landslide case study. The offset-tracking method, so far used mainly for the relatively rapid but uniform displacement of glaciers, was tested for the 2017 study of the Ponzano landslide in Italy. The suitability of the method for high-resolution TerraSAR-X and medium-resolution Sentinel-1 imagery was investigated. The results proved the applicability of the OT method for studying processes with high and variable displacement dynamics. However, for such purposes, high-resolution radar data are crucial. With an uncertainty in the determination of residual displacements of about ±1 m, it was shown that the values of residual displacements occurring up to several days after the main phase of landslide movements are within the range of uncertainty but are determinable. The research conducted in the paper filled a gap in the analysis of the phenomenon just after the main movement phase. It allowed determination of the time and speed of extinction of landslide movements.
References
Allasia, P., Baldo, M., Giordan, D., Godone, D., Wrzesniak, A. & Lollino, G. (2019). Near Real Time Monitoring Systems and Periodic Surveys Using a Multi Sensors UAV: The Case of Ponzano Landslide. In IAEG/AEG Annual Meeting Proceedings, San Francisco, California, 2018, 1(2): 303–310. Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-93124-1_37.
Amitrano, D., Guida, R., Dell’Aglio, D., Di Martino, G., Di Martire, D., Iodice, A., Costantini, M., Malvarosa, F. & Minati, F. (2019). Long-Term Satellite Monitoring of the Slumgullion Landslide Using Space-Borne Synthetic Aperture Radar Sub-Pixel Offset Tracking. Remote Sensing, 11(3): 369. DOI: https://doi.org/10.3390/RS11030369.
Amitrano, D., Guida, R., Di Martino, G. & Iodice, A. (2019). Glacier Monitoring Using Frequency Domain Offset Tracking Applied to Sentinel-1 Images: A Product Performance Comparison. Remote Sensing, 11(11): 1322. DOI: https://doi.org/10.3390/rs11111322.
Ao, M., Zhang, L., Dong, Y., Su, L., Shi, X., Balz, T. & Liao, M. (2020). Characterizing the evolution life cycle of the Sunkoshi landslide in Nepal with multi-source SAR data. Scientific Reports, 10(1): 1–12. DOI: https://doi.org/10.1038/s41598-020-75002-y.
Auflič, M. J., Herrera, G., Mateos, R. M., Poyiadji, E., Quental, L., Severine, B., Peternel, T., Podolszki, L., Calcaterra, S., Kociu, A., Warmuz, B., Jelének, J., Hadjicharalambous, K., Becher, G. P., Dashwood, C., Ondrus, P., Minkevičius, V., Todorović, S., Møller, J.J. & Marturia, J. (2023). Landslide monitoring techniques in the Geological Surveys of Europe. Landslides, 20(5): 951–965. DOI: https://doi.org/10.1007/S10346-022-02007-1/FIGURES/7.
Cai, J., Wang, C., Mao, X., Wang, Q., Lu, Z., Li, Z., Tomas, R. & Gloaguen, R. (2017). An Adaptive Offset Tracking Method with SAR Images for Landslide Displacement Monitoring. Remote Sensing, 9(8): 830. DOI: https://doi.org/10.3390/RS9080830.
Calista, M., Miccadei, E., Piacentini, T. & Sciarra, N. (2019). Morphostructural, Meteorological and Seismic Factors Controlling Landslides in Weak Rocks: The Case Studies of Castelnuovo and Ponzano (North East Abruzzo, Central Italy). Geosciences, 9(3): 122. DOI: https://doi.org/10.3390/GEOSCIENCES9030122.
Casagli, N., Intrieri, E., Tofani, V., Gigli, G. & Raspini, F. (2023). Landslide detection, monitoring and prediction with remote-sensing techniques. Nature Reviews Earth & Environment, 4(1): 51–64. DOI: https://doi.org/10.1038/s43017-022-00373-x.
Delacourt, C., Allemand, P., Berthier, E., Raucoules, D., Casson, B., Grandjean, P., Pambrun, C. & Varel, E. (2007). Remote-sensing techniques for analysing landslide kinematics: a review. Bulletin de La Société Géologique de France, 178(2): 89–100. DOI: https://doi.org/10.2113/GSSGFBULL.178.2.89.
Fell, R., Glastonbury, J. & Hunter, G. (2007). Rapid landslides: The importance of understanding mechanisms and rupture surface mechanics. Quarterly Journal of Engineering Geology and Hydrogeology, 40(1): 9–27. DOI: https://doi.org/10.1144/1470-9236/06-030.
Gariano, S.L. & Guzzetti, F. (2016). Landslides in a changing climate. Earth-Science Reviews, 162: 227–252. DOI: https://doi.org/10.1016/j.earscirev.2016.08.011.
Haque, U., Blum, P., Haque, U. & Blum, P. (2016). Costs and deaths of landslides in Europe. EGU General Assembly, 18, EPSC2016-12758. https://ui.adsabs.harvard.edu/abs/2016EGUGA..1812758H/abstract
Huang, H., Ju, S., Duan, W., Jiang, D., Gao, Z. & Liu, H. (2023). Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR. Sensors, 23(7): 3383. DOI: https://doi.org/10.3390/S23073383.
Hungr, O. (2007). Dynamics of rapid landslides. Progress in Landslide Science, 47–57. DOI: https://doi.org/10.1007/978-3-540-70965-7_4/COVER.
Jakob, M. (2022). Landslides in a changing climate. In Landslide Hazards, Risks, and Disasters, 505–579. Elsevier. DOI: https://doi.org/10.1016/B978-0-12-818464-6.00003-2.
Jia, H., Wang, Y., Ge, D., Deng, Y. & Wang, R. (2020). Improved offset tracking for predisaster deformation monitoring of the 2018 Jinsha River landslide (Tibet, China). Remote Sensing of Environment, 247: 111899. DOI: https://doi.org/10.1016/J.RSE.2020.111899.
Kim, H., Lee, J.-H., Park, H.-J. & Heo, J.-H. (2021). Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis. Engineering Geology, 294: 106372. DOI: https://doi.org/10.1016/j.enggeo.2021.106372.
Li, M., Zhang, L., Shi, X., Liao, M. & Yang, M. (2019). Monitoring active motion of the Guobu landslide near the Laxiwa Hydropower Station in China by time-series point-like targets offset tracking. Remote Sensing of Environment, 221: 80–93. DOI: https://doi.org/10.1016/J.RSE.2018.11.006.
Maraun, D., Knevels, R., Mishra, A. N., Truhetz, H., Bevacqua, E., Proske, H., Zappa, G., Brenning, A., Petschko, H., Schaffer, A., Leopold, P. & Puxley, B.L. (2022). A severe landslide event in the Alpine foreland under possible future climate and land-use changes. Communications Earth & Environment, 3(1): 87. DOI: https://doi.org/10.1038/s43247-022-00408-7.
Nikolakopoulos, K. G., Kyriou, A., Koukouvelas, I. K., Tomaras, N. & Lyros, E. (2023). UAV, GNSS, and InSAR Data Analyses for Landslide Monitoring in a Mountainous Village in Western Greece. Remote Sensing, 15(11): 2870. DOI: https://doi.org/10.3390/RS15112870.
Raspini, F., Bianchini, S., Ciampalini, A., Del Soldato, M., Montalti, R., Solari, L., Tofani, V. & Casagli, N. (2019). Persistent Scatterers continuous streaming for landslide monitoring and mapping: the case of the Tuscany region (Italy). Landslides, 16(10): 2033–2044. DOI: https://doi.org/10.1007/S10346-019-01249-W/FIGURES/7.
Refice, A., Spalluto, L., Bovenga, F., Fiore, A., Miccoli, M.N., Muzzicato, P., Nitti, D.O., Nutricato, R. & Pasquariello, G. (2019). Integration of persistent scatterer interferometry and ground data for landslide monitoring: the Pianello landslide (Bovino, Southern Italy). Landslides, 16(3): 447–468. DOI: https://doi.org/10.1007/S10346-018-01124-0/FIGURES/16.
Scaioni, M., Longoni, L., Melillo, V. & Papini, M. (2014). Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives. Remote Sensing, 6(10): 9600–9652. DOI: https://doi.org/10.3390/RS6109600.
Schuster, R.L. (1996). Socioeconomic significance of landslides. Landslides: Investigation and mitigation, 247: 12-35.
Solari, L., Raspini, F., Del Soldato, M., Bianchini, S., Ciampalini, A., Ferrigno, F., Tucci, S. & Casagli, N. (2018). Satellite radar data for back-analyzing a landslide event: the Ponzano (Central Italy) case study. Landslides, 15(4): 773–782. DOI: https://doi.org/10.1007/s10346-018-0952-x.
Strozzi, T., Luckman, A., Murray, T., Wegmüller, U. & Werner, C.L. (2002). Glacier motion estimation using SAR offset-tracking procedures. IEEE Transactions on Geoscience and Remote Sensing, 40(11): 2384–2391. DOI: https://doi.org/10.1109/TGRS.2002.805079.
Tarquini, S., Vinci, S., Favalli, M., Doumaz, F., Fornaciai, A. & Nannipieri, L. (2012). Release of a 10-m-resolution DEM for the Italian territory: Comparison with global-coverage DEMs and anaglyph-mode exploration via the web. Computers & Geosciences, 38(1): 168–170. DOI: https://doi.org/10.1016/J.CAGEO.2011.04.018.
Wang, C., Ge, D., Zhang, G., Zhu, W., Xiong, S., Liu, Y., Yang, H., Wang, S., Xu, L. & Peng, J. (2022). Monitoring and Stability Analysis of the Deformation in the Woda Landslide Area in Tibet, China by the DS-InSAR Method. Remote Sensing, 14(3): 532. DOI: https://doi.org/10.3390/RS14030532.
Wang, C., Mao, X. & Wang, Q. (2016). Landslide Displacement Monitoring by a Fully Polarimetric SAR Offset Tracking Method. Remote Sensing 8(8): 624. DOI: https://doi.org/10.3390/RS8080624.
Wasowski, J. & Bovenga, F. (2014). Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives. Engineering Geology, 174: 103–138. DOI: https://doi.org/10.1016/J.ENGGEO.2014.03.003.
Wegmüller, U., Werner, C., Strozzi, T. & Wiessman, A. (2002). Automated and Precise Image Registration Procedures. Analysis of Multi-Temporal Remote Sensing Images, 37–49. DOI: https://doi.org/10.1142/9789812777249_0002.
Zhao, C. & Lu, Z. (2018). Remote Sensing of Landslides—A Review. Remote Sensing, 10(2): 279. DOI: https://doi.org/10.3390/RS10020279.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Magdalena Łucka, Ryszard Hejmanowski, Wojciech Witkowski
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Title, logo and layout of journal Bulletin of Geography. Socio-economic Series are reserved trademarks of Bulletin of Geography. Socio-economic Series.Stats
Number of views and downloads: 300
Number of citations: 0
