author version Zhang et al Forecasting etc.pdf (3.23 MB)
Download file

Forecasting the magnitude of potential landslides based on InSAR techniques

Download (3.23 MB)
journal contribution
posted on 26.01.2021, 11:31 by Y Zhang, XM Meng, Tom DijkstraTom Dijkstra, CJ Jordan, G Chen, RQ Zeng, A Novellino
© 2020 Elsevier Inc. 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.

Funding

National Key Research and Development Program of China (Grant Nos. 2018YFC1504704 and 2017YFC1501005)

National Natural Science Foundation of China (Grant Nos. 41702292, 41602348)

Science and Technology Major Project of Gansu Province (Grant Nos. 19ZD2FA002)

Fundamental Research Funds for the Central Universities (Grant Nos. lzujbky-2019-28)

History

School

  • Architecture, Building and Civil Engineering

Published in

Remote Sensing of Environment

Volume

241

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Remote Sensing of Environment and the definitive published version is available at https://doi.org/10.1016/j.rse.2020.111738

Acceptance date

22/02/2020

Publication date

2020-03-02

Copyright date

2020

ISSN

0034-4257

eISSN

1879-0704

Language

en

Depositor

Dr Tom Dijkstra Deposit date: 22 January 2021

Article number

111738