Details
Konferenz
| Konferenz | 23. Geokinematischer Tag |
|---|---|
| Land/Gebiet | Deutschland |
| Ort | Freiberg |
| Zeitraum | 16 Mai 2024 → 17 Mai 2024 |
| Internetadresse |
Abstract
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2024. 23. Geokinematischer Tag, Freiberg, Sachsen, Deutschland.
Publikation: Konferenzbeitrag › Vortragsfolien › Forschung
}
TY - CONF
T1 - Accurate and large-scale monitoring of civil engineering infrastructures through quality ensured Persistent Scatterer Interferometry , corner reflectors, and GNSS equipment
AU - Omidalizarandi, Mohammad
AU - Shahryarinia, Kourosh
AU - Mohammadivojdan, Bahareh
AU - Iqbal, Waseem
AU - Wübbena, Jannes B.
AU - Rüffer, Jürgen
AU - Paffenholz, Jens-André
AU - Neumann, Ingo
PY - 2024/5/16
Y1 - 2024/5/16
N2 - Accurate and large-scale deformation monitoring of civil infrastructures such as buildings, bridges, and railways or natural objects in a long-term and low-cost (or freely available) manner is still challenging. This study employs the Persistent Scatterer Interferometry (PSI) technique using open-source Synthetic Aperture Radar (SAR) data from the satellite Sentinel-1. To judge the significance of the deformation, the quality assurance is performed in advance for different classes of the Persistent Scatterer (PS) data points. Since direct calculation of the sub-pixel phase is not feasible, the Sinc-interpolation up-sampling technique is employed to determine the positions of dominant scatterers within pixels. Subsequently, the position of highest intensity within every pixel is determined. Two corner reflectors (CRs) equipped with GNSS equipment are installed in the area of interest, which are also visible in amplitude (intensity) of Interferometric SAR (InSAR) images. The geolocation problem of the PS points is solved using GNSS coordinates together with the CRs as reference points. Next, the PS points are classified into buildings and ground types using LoD2 building models. The univariate time series analysis provides a comprehensive understanding of the PSI time series regarding temporal behaviours. It involves modelling and analysing PSI time series to estimate deterministic and stochastic parameters, such as offset, deformation rate, standard deviation, and corresponding distributional parameters for each PS point. A spatio-temporal modelling is employed to establish neighbourhood relations among the PS points using the Multilevel B-Splines Approximation for local geometric patches. A 95% confidence interval is estimated for the approximated surface within the local geometric patches using a bootstrapping approach. Subsequently, an appropriate quality model for the PS points is derived from the above-mentioned temporal and spatial modelling. It further enables to perform deformation analysis for the area of interest in a case study in the city of Minden. In the end, the results of the deformation analysis are compared with the Ground Motion Service Germany (BodenBewegungsdienst Deutschland) provided by the Federal Institute for Geosciences and Natural Resources (BGR), Germany.
AB - Accurate and large-scale deformation monitoring of civil infrastructures such as buildings, bridges, and railways or natural objects in a long-term and low-cost (or freely available) manner is still challenging. This study employs the Persistent Scatterer Interferometry (PSI) technique using open-source Synthetic Aperture Radar (SAR) data from the satellite Sentinel-1. To judge the significance of the deformation, the quality assurance is performed in advance for different classes of the Persistent Scatterer (PS) data points. Since direct calculation of the sub-pixel phase is not feasible, the Sinc-interpolation up-sampling technique is employed to determine the positions of dominant scatterers within pixels. Subsequently, the position of highest intensity within every pixel is determined. Two corner reflectors (CRs) equipped with GNSS equipment are installed in the area of interest, which are also visible in amplitude (intensity) of Interferometric SAR (InSAR) images. The geolocation problem of the PS points is solved using GNSS coordinates together with the CRs as reference points. Next, the PS points are classified into buildings and ground types using LoD2 building models. The univariate time series analysis provides a comprehensive understanding of the PSI time series regarding temporal behaviours. It involves modelling and analysing PSI time series to estimate deterministic and stochastic parameters, such as offset, deformation rate, standard deviation, and corresponding distributional parameters for each PS point. A spatio-temporal modelling is employed to establish neighbourhood relations among the PS points using the Multilevel B-Splines Approximation for local geometric patches. A 95% confidence interval is estimated for the approximated surface within the local geometric patches using a bootstrapping approach. Subsequently, an appropriate quality model for the PS points is derived from the above-mentioned temporal and spatial modelling. It further enables to perform deformation analysis for the area of interest in a case study in the city of Minden. In the end, the results of the deformation analysis are compared with the Ground Motion Service Germany (BodenBewegungsdienst Deutschland) provided by the Federal Institute for Geosciences and Natural Resources (BGR), Germany.
M3 - Slides to presentation
T2 - 23. Geokinematischer Tag
Y2 - 16 May 2024 through 17 May 2024
ER -