An identification method of potential landslide zones using InSAR data and landslide susceptibility

Abstract

Landslides are destructive to property, infrastructure and people in potential landslide zones. Identifying potential landslides is an important step in landslide preparedness and will help develop sustainable landslide risk management. Interferometric synthetic aperture radar (InSAR) and landslide susceptibility assessment (LSA) have poor reliability in individually identifying potential landslide zones. This study proposes a threshold model for identifying potential landslide zones that fuses the InSAR two-dimensional (vertical direction and east-west direction) deformation rates and LSA results. The deformation rate threshold for this threshold model is |D_U| or |D_E|>10 mm/year (D_U and D_E are the vertical and the east-west deformation rates, respectively), with threshold levels of LSA set to high and very high susceptibility. The criterion of potential landslide zones is ((|D_U| or |D_E|>10 mm/year) ∩ (high or very high susceptibility of LSA)), and points with similar deformation and susceptibility are clustered by the K-means algorithm, and the potential landslide zones are obtained by elimination, smooth and speckle removal operations. The results showed that the InSAR two-dimensional deformation rates D_U and D_E were −32.71 − 12.72 mm/year and −14.88 − 24.81 mm/year, respectively, during 2015–2020 in Lanzhou city. The LSA showed that very low, low, medium, high, and very high susceptibility accounted for 55.36%, 10.54%, 21.37%, 9.63%, 3.1% of the total area, respectively. Using the proposed threshold model, 117 potential landslide zones were identified in Lanzhou. The overlap rate between potential landslide zones and the landslide inventory was 40.17%, indicating that about 40% of the potential landslide zones overlapped with the landslide inventory and that about 60% were new potential landslide zones in Lanzhou. The feasibility of the threshold model in identifying potential landslides was confirmed by field research and time-series InSAR analysis on typical areas (L1, L2, L3, and L4), which had large deformation variables and landslide features. The proposed method can quickly determine the spatial location of potential landslides, providing targeting data for landslide field investigations, technical support for rapid early landslide identification, and data support for landslide management and prevention in Lanzhou.

Keywords:

• Potential landslide zones
• SBAS-InSAR
• landslide susceptibility assessment
• threshold model

Acknowledgments

The authors would like to thank the United States Geological Survey (USGS) for providing access to the SRTM DEM, Tsinghua University for providing access to the land cover types, Geological Survey (CGS) for providing access to Lithology and fault, Department of Resources of Lanzhou for providing access to River and road. At the same time, we would like to thank the reviewers and editors for their valuable suggestions and their hard work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

All data included in this study are available upon request by contact with the corresponding author.

Additional information

Funding

This work is funded by the Project Supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources (KF-2021-06-014), Tianyou Youth Talent Lift Program of Lanzhou Jiaotong University, Education Department of Gansu Province: Young Doctoral Fund Project (2022QB-058), State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM (2022-03-03), Natural Science Foundation of Gansu Province (20JR10RA249) and Jiayuguan City 2021 Science and Technology Plan Projects (437069).