Landslides activity

Owned by Maiwenn Herpe
Last updated: May 31, 2021 by Frederika Mourot
4 min read

Satellite(s)

Sentinel-2, Sentinel-1, TerraSAR-X

Monitoring element

SAR backscatter, land surface reflectance.

Satellite(s)

Sentinel-2, Sentinel-1, TerraSAR-X

Monitoring element

SAR backscatter, land surface reflectance.

Description technique

A range of remote sensing applications for landslide activity are available. Optical remote sensing has been mainly used to generate landslide inventories, through application of long time series to both spatial and temporal understanding of landslide activity (Behling et al., 2015). Recent research also suggests that Sentinel-2 can be used to monitor for land movement occurring prior to landslide activity via mapping horizontal displacement using image correlation across a time series, though only large landslides may be detected due to sensor resolution and highly accurate rectification is required (Lacroix et al., 2018).

A significant number of studies utilize Synthetic Aperture Radar sensors for Interferometry (InSAR), with Sentinel-1 data favoured in recent years due its rapid revisit and open access (Raspini et al, 2018). Interferometry exploits the phase difference between two SAR observations to extract highly detailed distance observations. These studies exploit this to detect changes in ground position (surface deformation) to detect landslide activity prior to mass slips (e.g. Czikhardt et al, 2017, Barra et al., 2016).

Accuracy / Resolution

Variable spatial and temporal resolution according to sensors.

Case study

By using a combination of spectral bands sensitive to vegetation cover, Behling et al., 2015 show that the rapid change in spectra sensitive to vegetation pre and post landslide activity can be used to resolve the location, extent and date at which the event occurred.

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 preceding landslides in Central Italy while Czikhardt et al (2017) demonstrate that InSAR data (derived deformation measurements) relates well to field measurements in Slovakia.

Benefits

  • Allows for the spatial distribution and temporal behaviour of landslides to be better understood with greater coverage and update frequency than can be achieved with field based methods (Behling et al, 2016).

  • Depending on the technique employed, possibility of early warning of potential slope failures (Lacroix et al., 2018).

Limitations

  • InSAR applications can experience issues when significant vegetation cover is present or the slope is oriented relative to 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).

  • Optical applications are dependent on cloud coverage, well rectified and located imagery and can be confounded by high sun angle that occurs in winter months, limiting the window in which monitoring can take place (Lacroix et al., 2018).

  • The minimum detectable feature size is limited by the spatial resolution of the sensors, which can limit the applicability of open access imagery like Sentinel-2 and Landsat when fine grained assessment is required (Lacroix et al., 2018).

  • Processing InSAR requires significant specialist knowledge and the usage of specific software.

Applicability for Northland

Yes, possibly.

•Applications based on optical data remain a challenging prospect in Northland as a result of the persistent cloud. Testing would be required to determine the applicability of SAR based applications within Northland, but these workflows have been successfully applied in similar environments.

Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive.

Publications references

Zhao, C. and Lu, Z., 2018. Remote sensing of landslides—A review. Remote Sensing, 10(2), p.279.
Behling, R., Roessner, S., Golovko, D. and Kleinschmit, B., 2016. Derivation of long-term spatiotemporal landslide activity—A multi-sensor time series approach. Remote Sensing of Environment, 186, pp.88-104.

https://www.mdpi.com/2072-4292/10/2/279/htm


Lacroix, P., Bièvre, G., Pathier, E., Kniess, U. and Jongmans, D., 2018. Use of Sentinel-2 images for the detection of precursory motions before landslide failures. Remote Sensing of Environment, 215, pp.507-516.

https://www.sciencedirect.com/science/article/pii/S0034425718301433


Barra, A., Montserrat, O., Mazzanti, P., Esposito, C., Crosetto, M. and Scarascia Mugnozza, G., 2016. First insights on the potential of Sentinel-1 for landslides detection. Geomatics, Natural Hazards and Risk, 7(6), pp.1874-1883.

https://www.tandfonline.com/doi/full/10.1080/19475705.2016.1171258


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


Czikhardt, R., Papco, J., Bakon, M., Liscak, P., Ondrejka, P. and Zlocha, M., 2017. Ground stability monitoring of undermined and landslide prone areas by means of sentinel-1 multi-temporal InSAR, case study from Slovakia. Geosciences, 7(3), p.87.

https://www.mdpi.com/2076-3263/7/3/87/pdf/1


Behling, R., Roessner, S., Golovko, D. and Kleinschmit, B., 2016. Derivation of long-term spatiotemporal landslide activity—A multi-sensor time series approach. Remote Sensing of Environment, 186, pp.88-104.

https://www.sciencedirect.com/science/article/pii/S0034425716302747


Mantovani, M., Bossi, G., Marcato, G., Schenato, L., Tedesco, G., Titti, G. and Pasuto, A., 2019. New perspectives in landslide displacement detection using sentinel-1 datasets. Remote Sensing, 11(18), p.2135.

https://www.mdpi.com/2072-4292/11/18/2135

Other comments or information

Usage of cloud based services such as Google Earth Engine (GEE) can offer a simplified point of access for Sentinel-1 data by providing datasets which have already had extensive pre-processing, but the complex data required for InSAR is currently not available.