Detecting Very Slow Ground Motion in Schleswig-Holstein and Hamburg from Radar Satellite Data

Abstract

The near-surface geology of northern Germany is characterised by glacial deposits, which are deformed and penetrated by rising permian and upper triassic salt structures. The salt structures mainly rise along tectonic fault zones and today partly trace the structures of the Glückstadt Graben. The observation of ground motion potentially associated to these processes poses a special challenge for geophysics. It requires the measurement of motion rates with an accuracy of only a few millimetres per year, a sufficient spatial coverage of tenth of square kilometres, and a spatial density of the measurement points of less than one per square kilometre.
To measure ground motion, we use radar interferometric time series data that are based on SAR (Synthetic Aperture Radar) images acquired by ESA satellites ERS-1 and ERS-2 between 1992 and 2000 over Schleswig-Holstein and Hamburg. Such radar interferometric time series analyses are possible for temporally stable backscattering objects (persistent scatterers) on the ground. Generally, this results in spatially dense observations over built-up areas and less dense observations in rural areas. We use a set of geostatistical and statistical methods to analyse these time series data. We detect signals of large-scale surface-deforming processes such as the subsidence of the marshes along the Elbe or the use of large gas caverns, as well as signals of small-scale processes such as the swelling of anhydrite at the Segeberger "Kalkberg" and subsidence processes at the edges of the historic old town of Lübeck. We are introducing techniques that allow us to derive ground motion even in areas with low scatterer density. Finally, we specify quality criteria to keep even such data consistent, which suffer from large time gaps in the acquisitions. Furthermore, we show how our methods can be used to link ERS data with the newer Sentinel-1 data.

Our work extends the area of application of the PS-InSAR technique, from areas with high motion rates to regions with particularly low motion rates. It shows limitations of the current practices, particularly resulting from the missing decomposition of long term and short term effects in the PS-InSAR time series, a geographical resolution too coarse for precise monitoring and the low density of observations over important installations like the dikes along the west coast of Schleswig-Holstein.

Full text

Detecting (very slow) ground motion
in Schleswig-Holstein from radar satellite data
Dieter Hoogestraat, Kiel University CAU
Henriette Sudhaus, Kiel University CAU
Andreas Omlin, Geological Survey of Schleswig-Holstein

Challenges A – geological setting
Fig. 1: The Permian Zechstein Sea with the Danish Basin in the north and
the North German Basin in the south (yellow). The German federal states
of Schleswig-Holstein and Hamburg are marked red.

Challenges B – the data
Fig. 2: Persistent Scatterer from ERS-1/-2 mission between
1992 And 2001 over Schleswig-Holstein and Hamburg. Instead
of the conventional red-green-blue (“jet”) color ramp, a
colourblind safe color ramp is used.
Displacement
rate mm/a

Data preparation
Fig. 3: Arbitrary time series
from an ERS-1/-2 track

Data preparation
Fig. 4: ERS-PS from track 22
over Hamburg without
stationarity correction (left)
and with stationarity correction
and 7ltering
Displacement
rate mm/a

Sigma-classes
Fig. 5: Colorblind safe color
ramp based an classes of
standard deviations. This one
is calculated from the ERS
missions track 22 over
Northern Germany.

Sigma-classes
Fig. 6: ERS-PS from track 22
over Hamburg with a linear
color ramp (left) and a color
ramp based on sigma-classes
(right) as on the slide before
(see thumbnail here)
Displacement
rate mm/a

Elbe marshlands
Fig. 7: Displacement rates in mm/a for
persistent scatterer over Elbe marshlands
(left)
Histogram of displacement rate distribution
over the Elbe marshlands (below)

Elbe marshlands
Fig. 8: Displacement rates in mm/a for
persistent scatterer over Elbe marshlands in
relation to the soil types in this region (left)
Histogram of displacement rate distribution
over the Elbe marshlands (below)

Lübeck’s UNESCO World Heritage
Lübeck’s historic city (UNESCO World Heritage)
Fig. 9 (above, left): Displacement rates over the region of Lübeck’s historic town calculated form ERS data
(left) und Sentinel-1 data (middle) (above, right): Histograms for displacement rates over Lübeck
calculated from ERS data (uppermost) and Sentinel 1 data

Transmission towers
Fig. 10: Displacement rates over an industrial area in Brunsbüttel, on the left side without any
correction, an the right side corrected with mean displacement rates of scatterers at deep founded
transmission towers.
Displacement
rate mm/a

Conclusion
●PS-InSAR satellite displacement rates can be used to detect and measure even
very slow ground motion processes
●To do so, scales in terms of classes of standard deviations around the mean of a
displacement rate distribution allows to see processes that do not show up when
using linear scales
●Where appropriate installations like transmission towers with well known
displacement characteristics can be used to calibrate diplacement rates
.

Thank you very much for your attention.
And if there’s something left:
→ dieter.hoogestraat@gmail.com
Download this presentation at
https://www.bridges.uni-kiel.de/en/publications
Get in contact!

References
All ERS data was provided by Nico Adam through the German Aerospace Center (DLR). The
Sentinel-1 data is part of the Bodenbewegungsdienst, BGR 2021
All Sentinel data is part of the publicly available German Ground Motion Service
(Bodenbewegungsdienst) and is also provided by a web interface at
https://bodenbewegungsdienst.bgr.de/mapapps/resources/apps/bbd/index.html?lang=en
Detailed information on the initial approach can be found in: Hoogestraat, D. H. (2019):
Auswertung von Zeitreihen stabiler Streupunkte…, Bsc-Thesis, Kiel University (DE), Inst. f.
Geosciences