Abstract
This work focuses on understanding to what degree the remote sensing tool of Interferometric Synthetic Aperture Radar (InSAR) can be used to track sub-surface ground motion in deep-seated landslides. We also consider the uncertainties that may arise out of using a remote sensing tool to track ground motion, as opposed to traditional boreholes, and how InSAR can be used to understand this uncertainty. The landslide case study of interest in this work is the El-Forn landslide in Canilllo, Andorra. These objectives of this work will be completed by focusing on the utilization of available Sentinel-1 data. Sentinel-1 data was processed and were used to generate a stack of Sentinel-1 images using small temporal and spatial baseline subsets (SBAS), which relies on many SAR acquisitions and implements a combination of the multi-look interferograms computed from the original SAR acquisitions, generating mean deformation velocity maps and time series (Berardino et al., IEEE Trans Geosci Remote Sens 40:2375–2383, 2002; Handwerger et al., Sci Rep 9: 1–12, 2019; Yunjun et al., Comput Geosci 133:104–331, 2019). From there, the interferogram pairs were inverted with the Miami Insar Time series software in Python (MintPy), InSAR Scientific Computing Environment (ISCE), and Hyp3 toolboxes/open-repositories to create displacement time series (Yunjun et al., Comput Geosci 133:104–331, 2019) over the main scarp. The displacement time series from InSAR was compared to in-situ ground motion measurements, suggesting that InSAR-based displacement data can be used to track sub-surface ground motion trends. Similarly, InSAR was found to indicate extreme sub-surface events in ground motion time seriry kriging was used as a method of geospatial interpolation over the landslide in order to visualize the quantity of remote observations necessary to minimize error in scarp reconstruction, suggesting that around 20 total observations lowers the normalized root mean squared error for the geospatially interpolated reconstruciton of the El Forn landslide surface.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Berardino P, Fornaro G, Lanari R, Sansosti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential sar interferograms. IEEE Trans Geosci Remote Sens 40:2375–2383. https://doi.org/10.1109/TGRS.2002.803792
Cascini L, Fornaro G, Peduto D (2010) Advanced low- and full-resolution dinsar map generation for slow-moving landslide analysis at different scales. Eng Geol 112:29–42
Cascini L, Fornaro G, Peduto D (2009) Analysis at medium scale of low-resolution dinsar data in slow-moving landslide-affected areas. ISPRS J Photogrammetry Remote Sens 64:598–611. https://doi.org/10.1016/j.isprsjprs.2009.05.003. [Online]. Available: http://dup.esrin.esa.it
Cigna F, Bianchini S, Casagli N (2013) How to assess landslide activity and intensity with persistent scatterer interferometry (psi): the psi-based matrix approach. Landslides 10:267–283. https://doi.org/10.1007/S10346-012-0335-7/FIGURES/11
Fiorucci F et al. (2011) Seasonal landslide map** and estimation of landslide mobilization rates using aerial and satellite images. Geomorphology 129:59–70. https://doi.org/10.1016/J.GEOMORPH.2011.01.013
Guzzetti F et al. (2009) Analysis of ground deformation detected using the sbas-dinsar technique in umbria, central Italy. Pure Appl Geophys 166(8):1425–1459. https://doi.org/10.1007/S00024-009-0491-4
Handwerger AL, Huang MH, Fielding EJ, Booth AM, Bürgmann R (2019) A shift from drought to extreme rainfall drives a stable landslide to catastrophic failure. Sci Rep 9:1–12. https://doi.org/10.1038/s41598-018-38300-0
Hooper A, Zebker H, Segall P, Kampes B (2004) 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
Hueckel T, Pellegrini R (2002) Reactive plasticity for clays: Application to a natural analog of long-term geomechanical effects of nuclear waste disposal. Eng. Geol 64:195–215. https://doi.org/10.1016/S0013-7952(01)00114-4
Krzeminska D et al (2013) Hydrology and earth system sciences. Eur Geosci Union 17:947–959. https://doi.org/10.5194/hess-17-947-2013
Lanari R et al (2007) An overview of the small baseline subset algorithm: a dinsar technique for surface deformation analysis. Pure Appl Geophys 164:637–661. https://doi.org/10.1007/S00024-007-0192-9
Michoud C, Jaboyedoff M, Derron M-H, Nadim F, Leroi E (2012) (pdf) New classification of landslide-inducing anthropogenic activities. [Online]. Available: https://www.researchgate.net/publication/258616160_New_classification_of_landslide-inducing_anthropogenic_activities
Mitchell JK, Campanella RG, Singh A (1968) Soil creep as a rate process. Am Soc Civil Eng 94:231–253. https://doi.org/10.1061/JSFEAQ.0001085
Mora P et al (2003) Global positioning systems and digital photogrammetry for the monitoring of mass movements: Application to the ca’di malta landslide (northern apennines, italy). Eng Geol 68:103–121. https://doi.org/10.1016/S0013-7952(02)00200-4
Reid M (1994) A pore-pressure diffusion model for estimating landslide-inducing rainfall. J Geol 102(6):709–717
Saito M (1969) Forecasting the time of occurrence of a slope failure. 10:135–142
Seguí C, Veveakis M (2021) Continuous assessment of landslides by measuring their basal temperature. Landslides 18:3953–3961. https://doi.org/10.1007/S10346-021-01762-X/FIGURES/3
Seguí C, Veveakis M (2022) Forecasting and mitigating landslide collapse by fusing physics-based and datadriven approaches. Geomech Energy Environ 32:100412. https://doi.org/10.1016/J.GETE.2022.100412
Smalley IJ (1978) Rockslides and avalanches. Nature 272:654–654, 5654. https://doi.org/10.1038/272654b0. [Online]. Available: https://www.nature.com/articles/272654b0
Spek JEVD, Bogaard TA, Bakker M (2013) Characterization of groundwater dynamics in landslides in varved clays. Hydrol Earth Syst Sci 17:2171–2183. https://doi.org/10.5194/HESS-17-2171-2013
Usai S (2003) A least squares database approach for sar interferometric data. IEEE Trans Geosci Remote Sens 41:753–760. https://doi.org/10.1109/TGRS.2003.810675
Voight B (1988) A method for prediction of volcanic eruptions. Nature 332(6160):125–130. https://doi.org/10.1038/332125a0. [Online]. Available: https://www.nature.com/articles/332125a0
Werner C, Wegm ̈uller U, Strozzi T, Wiesmann A (2003) Interferometric point target analysis for deformation map**. Int Geosci Remote Sens Symp (IGARSS) 7:4362–4364. https://doi.org/10.1109/IGARSS.2003.1295516
Yunjun Z, Fattahi H, Amelung F (2019) Small baseline insar time series analysis: Unwrap** error correction and noise reduction. Comput Geosci 133:104–331. https://doi.org/10.1016/J.CAGEO.2019.104331. [Online]. Available: https://miami.pure.elsevier.com/en/publications/small-baseline-insar-time-series-analysis-unwrap**-error-correc
Conflicts of Interest
The author(s) declare(s) that there is no conflict of interest regarding the publication of this article.
Funding
This work was supported by the National Science Foundation [CMMI grant numbers 2006150 and 2042325] and the Fulbright Open Study Award.
Author information
Authors and Affiliations
Contributions
R. Lau conceived, designed, and carried out the analysis, as well as wrote the entirety of the manuscript. C. Segui provided helpful information regarding geophysical modeling and geology of the El Forn landslide. T. Waterman provided helpful guidance in getting set up and troubleshooting working on the computing cluster. N. Chaney provided space on his lab’s computing cluster, as well as helpful guidance on spatial data analysis. A. Handwerger provided critical guidance on the use of InSAR, ISCE+, and MintPy for this work. M. Veveakis provided guidance and oversight throughout the entirety of the research process.
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
All data used in the construction of the interferograms is publicly available on the Alaska Satellite Facility's Vertex Platform. The in-situ data is restricted based on an existing partnership with the government of Andorra, but is available upon request from the corresponding author.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Lau, R., Seguí, C., Waterman, T., Chaney, N., Veveakis, M. (2024). Quantitative Assessment of Interferometric Synthetic Aperture Radar (INSAR) for Landslide Monitoring and Mitigation. In: Sarkar, R., Saha, S., Adhikari, B.R., Shaw, R. (eds) Geomorphic Risk Reduction Using Geospatial Methods and Tools. Disaster Risk Reduction. Springer, Singapore. https://doi.org/10.1007/978-981-99-7707-9_9
Download citation
DOI: https://doi.org/10.1007/978-981-99-7707-9_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7706-2
Online ISBN: 978-981-99-7707-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)