Land subsidence

Owned by Maiwenn Herpe
Last updated: Jun 24, 2021 by Frederika Mourot
3 min read

Satellite(s)

Sentinel-1, ERS-1/-2, Tandem-X, ALOS PALSAR
& ALOS-2 PALSAR-2.

Monitoring element

SAR backscatter, LiDAR backscatter.

Satellite(s)

Sentinel-1, ERS-1/-2, Tandem-X, ALOS PALSAR
& ALOS-2 PALSAR-2.

Monitoring element

SAR backscatter, LiDAR backscatter.

Description technique

Previously measured using field GPS, subsidence monitoring is now generally achieved using Differential Informetric Synthetic Aperture Radar (D-InSAR) from both spaceborne and aerial platforms (Solar et al, 2018).
D-InSAR exploits the phase measurement of SAR data, which can be used to calculate ground displacement via an interferogram, which compares phase between two coincident SAR images captured at different times (Solar et al., 2018). This approach has been improved in recent years via the increased availability and quality of spaceborne SAR data, particularly the open access Sentinel-1 (Blasco et al, 2019). This has precipitated a shift from comparing observation pairs to a fully multitemporal approach to subsidence monitoring, which better separates ground displacement from topographic, atmospheric and random noise, while presenting a deformation velocity surface as opposed to a single measurement surface (Solar et al, 2018).

Berardino et al (2002) present the SBAS technique, which utilises a large number of SAR acquisitions distributed in small baseline (difference in sensor position) subsets to reduce some of the spatial correlation issues which can be problematic when comparing portions SAR images captured from different orbital positions.

Accuracy / Resolution

Variable spatial and temporal resolution according to sensors.

Case study

Blasco et al. (2019) demonstrate subsidence monitoring within an urban environment using Sentinel-1 D-InSAR, extending the process to include displacements of infrastructure such as roads and buildings.

By utilising a time series of data, Raspini et al. (2018) demonstrate how Sentinel-1 can be used in a continuous manner to monitor for ground displacement in Central Italy.

Pawluszek-Filipiak and Borkowski (2020) compared D-InSAR and SBAS techniques to measure ground displacement resulting from mining activity in Poland. The results showed that SBAS better captured the often rapid and nonlinear deformation that characterises mining related subsidence.

Benefits

  • Provides a far higher number of measurements than can be achieved with ground stations, with a continuous surface describing deformation velocity generated as opposed to a heavily interpolated result (Solar et al, 2009).

  • Significantly cheaper than establishing and maintaining a GPS monitoring network.

Limitations

  • Traditional D-InSAR exploits single interferometric SAR pairs, the accuracy of this technique is limited by a number of factors, particularly spatial and temporal correlations and atmospheric, orbital or topographic artifacts (Pawluszek-Filipiak and Borkowski, 2020).

  • InSAR applications can experience issues when significant vegetation cover is present or the target area is oriented away from the sensor in a manner that results in radar shadow, while also being sensitive to ground moisture content (Behling et al., 2015, Barra et al., 2016 - see landslides section).

Applicability for Northland

Yes, this approach is applicable to Northland.

The D-InSAR or SBAS approaches would be applicable to monitor subsidence in the Northland region using Sentinel-1 as base data. The retired coal mining areas around Kamo and Hikurangi would likely be excellent test cases.

Publication references

Sowter, A., Amat, M.B.C., Cigna, F., Marsh, S., Athab, A. and Alshammari, L., 2016. Mexico City land subsidence in 2014–2015 with Sentinel-1 IW TOPS: Results using the Intermittent SBAS (ISBAS) technique. International journal of applied earth observation and geoinformation, 52, pp.230-242.
https://www.sciencedirect.com/science/article/pii/S0303243416300976

Solari, L., Del Soldato, M., Bianchini, S., Ciampalini, A., Ezquerro, P., Montalti, R., Raspini, F. and Moretti, S., 2018. From ERS 1/2 to Sentinel-1: subsidence monitoring in Italy in the last two decades. Frontiers in Earth Science, 6, p.149.
https://www.frontiersin.org/articles/10.3389/feart.2018.00149/full

Delgado Blasco, J.M., Foumelis, M., Stewart, C. and Hooper, A., 2019. Measuring urban subsidence in the Rome metropolitan area (Italy) with Sentinel-1 SNAP-StaMPS persistent scatterer interferometry. Remote Sensing, 11(2), p.129.
https://www.mdpi.com/2072-4292/11/2/129

Aimaiti, Y., Yamazaki, F. and Liu, W., 2018. Multi-sensor InSAR analysis of progressive land subsidence over the Coastal City of Urayasu, Japan. Remote Sensing, 10(8), p.1304.
https://www.mdpi.com/2072-4292/10/8/1304

Raspini, F., Bianchini, S., Ciampalini, A., Del Soldato, M., Solari, L., Novali, F., Del Conte, S., Rucci, A., Ferretti, A. and Casagli, N., 2018. Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites. Scientific reports, 8(1), pp.1-11.
https://www.nature.com/articles/s41598-018-25369-w

Pawluszek-Filipiak, K. and Borkowski, A., 2020. Integration of DInSAR and SBAS Techniques to determine mining-related deformations using sentinel-1 data: The case study of Rydułtowy mine in Poland. Remote Sensing, 12(2), p.242.
https://www.mdpi.com/2072-4292/12/2/242

Berardino, P., Fornaro, G., Lanari, R. and Sansosti, E., 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on geoscience and remote sensing, 40(11), pp.2375-2383.

https://ieeexplore.ieee.org/document/1166596