On the quality checking of persistent scatterer interferometry data by spatial-temporal modelling
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Mohammad Omidalizarandi
,
Bahareh Mohammadivojdan
,
Hamza Alkhatib
,
Jens-André Paffenholz
and
Ingo Neumann
Abstract
Today, rapid growth in infrastructure development and urbanisation process increases the attention for accurate deformation monitoring on a relatively large-scale. Furthermore, such deformation monitoring is of great importance in the assessment and management of natural hazard processes like landslides, earthquakes, and floods. In this study, the Persistent Scatterer Interferometry (PSI) technique is applied using open-source Synthetic Aperture Radar (SAR) data from the satellite Sentinel-1. It allows point-wise deformation monitoring based on time series analysis of specific points. It also enables performing spatio-temporal area-based deformation monitoring. Currently, these data do not have a sophisticated quality assurance process to judge the significance of deformations. To obtain different quality classes of the Persistent Scatterer (PS) data points, the first step is to classify them into buildings and ground types using LoD2 building models. Next, time series analysis of the PS points is performed to model systematic and random errors. It allows estimation of the offset and the deformation rate for each point. Finally, spatio-temporal modelling of neighbourhood relations of the PS points is carried out using local geometric patches which are approximated with a mathematical model, such as, e.g., multilevel B-Splines. Subsequently, the quality of SAR data from temporal and spatial neighbourhood relations is checked. Having an appropriate spatio-temporal quality model of the PS data, a deformation analysis is performed for areas of interest in the city of Hamburg. In the end, the results of the deformation analysis are compared with the BodenBewegungsdienst Deutschland (Ground Motion Service Germany) provided by the Federal Institute for Geosciences and Natural Resources (BGR), Germany.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Gabriel, AK, Goldstein, RM, Zebker, HA. Mapping small elevation changes over large areas: differential radar interferometry. J Geophys Res Solid Earth 1989;94:9183–91. https://doi.org/10.1029/jb094ib07p09183.Search in Google Scholar
2. Massonnet, D, Rossi, M, Carmona, C, Adragna, F, Peltzer, G, Feigl, K, et al.. The displacement field of the landers earthquake mapped by radar interferometry. Nature 1993;364:138–42. https://doi.org/10.1038/364138a0.Search in Google Scholar
3. Goldstein, RM, Engelhardt, H, Kamb, B, Frolich, RM. Satellite radar interferometry for monitoring ice sheet motion: application to an antarctic ice stream. Science 1993;262:1525–30. https://doi.org/10.1126/science.262.5139.1525.Search in Google Scholar PubMed
4. Massonnet, D, Briole, P, Arnaud, A. Deflation of mount etna monitored by spaceborne radar interferometry. Nature 1995;375:567–70. https://doi.org/10.1038/375567a0.Search in Google Scholar
5. Fruneau, B, Achache, J, Delacourt, C. Observation and modelling of the saint-etienne-de-tinée landslide using sar interferometry. Tectonophysics 1996;265:181–90. https://doi.org/10.1016/s0040-1951(96)00047-9.Search in Google Scholar
6. Crosetto, M, Gili, JA, Monserrat, O, Cuevas-González, M, Corominas, J, Serral, D. Interferometric sar monitoring of the vallcebre landslide (Spain) using corner reflectors. Nat Hazards Earth Syst Sci 2013;13:923–33. https://doi.org/10.5194/nhess-13-923-2013.Search in Google Scholar
7. van der Kooij, M. Land subsidence measurements at the belridge oil fields from ers insar data (int). In: Third ERS symposium on space at the service of our environment; 1997, vol 414:1853 p.Search in Google Scholar
8. Amelung, F, Galloway, DL, Bell, JW, Zebker, HA, Laczniak, RJ. Sensing the ups and downs of las vegas: insar reveals structural control of land subsidence and aquifer-system deformation. Geology 1999;27:483–6.10.1130/0091-7613(1999)027<0483:STUADO>2.3.CO;2Search in Google Scholar
9. Lin, H, Chen, FL, Jiang, LM, Zhao, Q, Cheng, S. Preliminary research on large-scale man-made linear features deformation monitoring using multi-baseline differential sar interferometry. Geo Inf Sci 2010;12:718–25.Search in Google Scholar
10. Dai, K, Liu, G, Li, Z, Li, T, Yu, B, Wang, X, et al.. Extracting vertical displacement rates in shanghai (China) with multi-platform sar images. Remote Sens 2015;7:9542–62. https://doi.org/10.3390/rs70809542.Search in Google Scholar
11. Tosi, L, Da Lio, C, Strozzi, T, Teatini, P. Combining l-and x-band sar interferometry to assess ground displacements in heterogeneous coastal environments: the po river delta and venice lagoon, Italy. Remote Sens 2016;8:308. https://doi.org/10.3390/rs8040308.Search in Google Scholar
12. Crosetto, M, Biescas, E, Duro, J, Closa, J, Arnaud, A. Generation of advanced ers and envisat interferometric sar products using the stable point network technique. Photogramm Eng Remote Sens 2008;74:443–50. https://doi.org/10.14358/pers.74.4.443.Search in Google Scholar
13. Eineder, M, Adam, N, Bamler, R, Yague-Martinez, N, Breit, H. Spaceborne spotlight sar interferometry with terrasar-x. IEEE Trans Geosci Remote Sens 2009;47:1524–35. https://doi.org/10.1109/tgrs.2008.2004714.Search in Google Scholar
14. Gernhardt, S, Adam, N, Eineder, M, Bamler, R. Potential of very high resolution sar for persistent scatterer interferometry in urban areas. Ann GIS 2010;16:103–11. https://doi.org/10.1080/19475683.2010.492126.Search in Google Scholar
15. Huang, Q, Crosetto, M, Monserrat, O, Crippa, B. Displacement monitoring and modelling of a high-speed railway bridge using c-band sentinel-1 data. ISPRS J Photogramm Remote Sens 2017;128:204–11. https://doi.org/10.1016/j.isprsjprs.2017.03.016.Search in Google Scholar
16. Van Leijen, FJ. Persistent scatterer interferometry based on geodetic estimation theory [Ph.D. thesis]; 2014.Search in Google Scholar
17. Ito, H, Susaki, J, Anahara, T. Integrating multi-temporal sar images and gps data to monitor three-dimensional land subsidence. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2019;4:9–16. https://doi.org/10.5194/isprs-annals-iv-3-w1-9-2019.Search in Google Scholar
18. Gernhardt, S, Hinz, S. Advanced displacement estimation for psi using high resolution sar data. In: GARSS 2008-2008 IEEE international geoscience and remote sensing symposium. IEEE; 2008, vol. 3:1276–79 pp.10.1109/IGARSS.2008.4779591Search in Google Scholar
19. Artese, G, Fiaschi, S, Di Martire, D, Tessitore, S, Fabris, M, Achilli, V, et al.. Monitoring of land subsidence in ravenna municipality using integrated sar-gps techniques: description and first results. Int Arch Photogram Remote Sens Spatial Inf Sci 2016;7:23–8.10.5194/isprs-archives-XLI-B7-23-2016Search in Google Scholar
20. Montazeri, S, Ansari, H, De Zan, F, Mania, R, Shau, R, Beker, T, et al.. Insar and machine learning for surface displacement monitoring in South America. In: EGU general assembly conference abstracts; 2021:EGU21–6086 pp.10.5194/egusphere-egu21-6086Search in Google Scholar
21. Yin, X. Einflüsse geometrischer Radar-Aufnahmekonstellationen auf die Qualität der kombinativ berechneten Bodenbewegungskomponenten [Ph.D. thesis]. Technische Universität Clausthal; 2020.Search in Google Scholar
22. Brockmeyer, M, Schnack, C, Jahn, CH. Datenanalyse und flächenhafte modellierung der psi-informationen des bodenbewegungsdienst deutschlands für die landesfläche niedersachsens. Zfv - Zeitschrift für Geodäsie, Geoinformation und Landmanagement 2020;3:154–67.Search in Google Scholar
23. Mohammadivojdan, B, Brockmeyer, M, Jahn, CH, Neumann, I, Alkhatib, H. Regional ground movement detection by analysis and modeling psi observations. Remote Sens 2021;13:2246. https://doi.org/10.3390/rs13122246.Search in Google Scholar
24. Catalao, J, Raju, D, Nico, G. Insar maps of land subsidence and sea level scenarios to quantify the flood inundation risk in coastal cities: the case of Singapore. Remote Sens 2020;12:296. https://doi.org/10.3390/rs12020296.Search in Google Scholar
25. Omidalizarandi, M, Herrmann, R, Kargoll, B, Marx, S, Paffenholz, J-A, Neumann, I. A validated robust and automatic procedure for vibration analysis of bridge structures using mems accelerometers. J Appl Geodesy 2020;14:327–54. https://doi.org/10.1515/jag-2020-0010.Search in Google Scholar
26. Lomb, NR. Least-squares frequency analysis of unequally spaced data. Astrophys Space Sci 1976;39:447–62. https://doi.org/10.1007/bf00648343.Search in Google Scholar
27. Omidalizarandi, M, Kargoll, B, Paffenholz, JA, Neumann, I. Accurate vision-based displacement and vibration analysis of bridge structures by means of an image-assisted total station. Adv Mech Eng 2018;10:1687814018780052. https://doi.org/10.1177/1687814018780052.Search in Google Scholar
28. Alkhatib, H, Kargoll, B, Paffenholz, JA. Further results on a robust multivariate time series analysis in nonlinear models with autoregressive and t-distributed errors. In Time series analysis and forecasting. ITISE 2017. Contributions to statistics. Springer, Cham 2018;25–38 pp.10.1007/978-3-319-96944-2_3Search in Google Scholar
29. Kargoll, B, Omidalizarandi, M, Loth, I, Paffenholz, JA, Alkhatib, H. An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations. J Geodesy 2018;92:271–97. https://doi.org/10.1007/s00190-017-1062-6.Search in Google Scholar
30. Omidalizarandi, M. Robust deformation monitoring of bridge structures using MEMS accelerometers and image-assisted total stations [Ph.D. thesis]. Deutsche Geodätische Kommission: C, 2020, vol 859.Search in Google Scholar
31. Piegl, L, Tiller, W. Symbolic operators for nurbs. Comput Aided Des 1997;29:361–8. https://doi.org/10.1016/s0010-4485(96)00074-7.Search in Google Scholar
32. Straub, C. Recent crustal deformation and strain accumulation in the Marmara Sea region, NW Anatolia, inferred from GPS measurements [Ph.D. thesis]. ETH Zurich; 1996.10.1029/95GL02219Search in Google Scholar
33. Williams, CK, Rasmussen, CE. Gaussian processes for machine learning. MA: MIT press Cambridge; 2006, vol 2.10.7551/mitpress/3206.001.0001Search in Google Scholar
34. Montero, JM, Fernández-Avilés, G, Mateu, J. Spatial and spatio-temporal geostatistical modeling and kriging. New York: John Wiley & Sons; 2015, vol. 998.10.1002/9781118762387Search in Google Scholar
35. Mohammadivojdan, B, Alkhatib, H, Brockmeyer, M, Jahn, CH, Neumann, I. Surface based modelling of ground motion areas in lower saxony. Geomonitoring 2020;2020:107–23.Search in Google Scholar
36. Lee, S, Wolberg, G, Shin, SY. Scattered data interpolation with multilevel b-splines. IEEE Trans Visual Comput Graph 1997;3:228–44. https://doi.org/10.1109/2945.620490.Search in Google Scholar
37. Ferretti, A, Prati, C, Rocca, F. Permanent scatterers in sar interferometry. IEEE Trans Geosci Remote Sens 2001;39:8–20. https://doi.org/10.1109/36.898661.Search in Google Scholar
© 2023 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Guest Editorial
- Special Issue: Deformation Monitoring
- Research Articles
- High-precision intermode beating electro-optic distance measurement for mitigation of atmospheric delays
- EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results
- A mobile robot for monitoring floor flatness in real-time
- On the quality checking of persistent scatterer interferometry data by spatial-temporal modelling
- Image segmentation of breakwater blocks by edge-base Hough transformation
- Real movement or systematic errors? – TLS-based deformation analysis of a concrete wall
- Investigation of space-continuous deformation from point clouds of structured surfaces
- Supervoxel-based targetless registration and identification of stable areas for deformed point clouds
- Forecasting post-earthquake rockfall activity
Articles in the same Issue
- Frontmatter
- Guest Editorial
- Special Issue: Deformation Monitoring
- Research Articles
- High-precision intermode beating electro-optic distance measurement for mitigation of atmospheric delays
- EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results
- A mobile robot for monitoring floor flatness in real-time
- On the quality checking of persistent scatterer interferometry data by spatial-temporal modelling
- Image segmentation of breakwater blocks by edge-base Hough transformation
- Real movement or systematic errors? – TLS-based deformation analysis of a concrete wall
- Investigation of space-continuous deformation from point clouds of structured surfaces
- Supervoxel-based targetless registration and identification of stable areas for deformed point clouds
- Forecasting post-earthquake rockfall activity
