Forecasting the magnitude of potential landslides based on InSAR techniques

Zhang, Y.; Meng, X.M.; Dijkstra, T.A.; Jordan, C.J.; Chen, G.; Zeng, R.Q.; Novellino, A.. 2020 Forecasting the magnitude of potential landslides based on InSAR techniques. Remote Sensing of Environment, 241, 111738. https://doi.org/10.1016/j.rse.2020.111738

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Official URL: http://dx.doi.org/10.1016/j.rse.2020.111738

Abstract/Summary

A new method, combining empirical modeling with time series Interferometric Synthetic Aperture Radar (InSAR) data, is proposed to provide an assessment of potential landslide volume and area. The method was developed to evaluate potential landslides in the Heitai river terrace of the Yellow River in central Gansu Province, China. The elevated terrace has a substantial loess cover and along the terrace edges many landslides have been triggered by gradually rising groundwater levels following continuous irrigation since 1968. These landslides can have significant impact on communities, affecting lives and livelihoods. Developing effective landslide risk management requires better understanding of potential landslide magnitude. Fifty mapped landslides were used to construct an empirical power-law relationship linking landslide area (AL) to volume (VL) (VL = 0.333 × AL1.399). InSAR-derived ground displacement ranges from −64 mm/y to 24 mm/y along line of sight (LOS). Further interpretation of patterns based on remote sensing (InSAR & optical image) and field survey enabled the identification of an additional 54 potential landslides (1.9 × 102 m2 ≤ AL ≤ 8.1 × 104 m2). In turn this enabled construction of a map that shows the magnitude of potential landslide activity. This research provides significant further scientific insights to inform landslide hazard and risk management, in a context of ongoing landscape evolution. It also provides further evidence that this methodology can be used to quantify the magnitude of potential landslides and thus contribute essential information towards landslide risk management.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.rse.2020.111738
ISSN:	00344257
Date made live:	16 Apr 2020 14:43 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/527494

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.rse.2020.111738

ISSN:

00344257

Date made live:

16 Apr 2020 14:43 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/527494