Journal
REMOTE SENSING OF ENVIRONMENT
Volume 241, Issue -, Pages -Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2020.111738
Keywords
Empirical model; Time series InSAR; Landslide forecasting; Landslide volume; Risk
Funding
- National Key Research and Development Program of China [2018YFC1504704, 2017YFC1501005]
- National Natural Science Foundation of China [41702292, 41602348]
- Science and Technology Major Project of Gansu Province [19ZD2FA002]
- Fundamental Research Funds for the Central Universities [lzujbky-2019-28]
- BGS ODA Programme (NC-ODA grant) [NE/R000069/1]
- NERC [come30001, bgs06002] Funding Source: UKRI
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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 (A(L)) to volume (V-L) (V-L = 0.333 x A(L)(1.399)). 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 x 10(2) m(2) <= A(L) <= 8.1 x 10(4) m(2)). 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.
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