journal article·2021-09-26Open access

Feasibility of Artificial Slope Hazards Identification in Regional Mountainous Highway Using SBAS-InSAR Technique: A Case Study in Lishui, Zhejiang

Zhi Hu, Danqiang Xiao, Wei Zhan, Yang Yu, Yiqiang Yu, Xin Yan, Chunmei Pan

Applied Sciences· Volume 11· Issue 19

Abstract

Safety status of artificial slopes is significant for the operation and maintenance of highway to mitigate the risk; thus, slope hazard identification is necessary. In order to realize large-area and low-cost application for regional highway, taking the Longqing Highway (length of 55 km) as a case study, the SBAS-InSAR (Small Baseline Subset-Interferometric Synthetic Aperture Radar) technique is adopted to detect the ground deformation and conduct hazard identification based on slope dip, aspect, geological data and historical hazard record. Field survey is carried out to verify the identified potential hazards. Results show that the detected potential hazards are distributed mainly in the areas consisting of granite residual and the Quaternary soil. Six potential hazards identified by the SBAS-InSAR-based method are roughly in accordance with the on-site verification. It is suggested that the SBAS-InSAR technique has the ability to obtain the slope deformation accurately and reveal the safe condition of the slopes. The SBAS-InSAR technique can be suitable for assistance in regional highway slope inspection.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Synthetic Aperture Radar (SAR) Applications and Techniques · Landslides and related hazards · Cryospheric studies and observations

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Key Laboratory of Road and Bridge Detection and Maintenance Technology Research of Zhejiang Province, Hangzhou 310023, China

Zhejiang Scientific Research Institute of Transport, Hangzhou 310023, China

Ocean College, Zhejiang University, Zhoushan 316021, China

Lishui Management Center, Zhejiang Communications Investment Expressway Operation Management Co., Ltd., Lishui 323000, China

Funding

National Key R&D Program of China · Science and Technology Project of Zhejiang Provincial Department of Transport

References (43)

Show source-provided references
1. Risk assessment of highway structures in natural disaster for the property insurance10.1007/s11069-020-04291-3 ↗
2. Monitoring and evaluating the stability of soil slopes: A review on various available methods and feasibility of acoustic emission technique10.1016/j.crte.2014.01.003 ↗
3. Liang, H., Li, X., Zhang, L., Chen, R.-F., Ding, X., Chen, K.-L., Wang, C.-S., Chang, C.-S., and Chi, C.-Y. (2020). Investigation of slow-moving artificial slope failure with multi-temporal InSAR by combining persistent and distributed scatterers: A case study in Northern Taiwan. Remote Sens., 12.10.3390/rs12152403 ↗
4. Aimaiti, Y., Yamazaki, F., Liu, W., and Kasimu, A. (2017). Monitoring of land-surface deformation in the Karamay Oilfield, Xinjiang, China, using SAR interferometry. Appl. Sci., 7.10.3390/app7080772 ↗
5. Aslan, G., Cakır, Z., Ergintav, S., Lasserre, C., and Renard, F. (2018). Analysis of secular ground motions in Istanbul from a long-term InSAR time-series (1992–2017). Remote Sens., 10.10.3390/rs10030408 ↗
6. Zhao, F., Meng, X., Zhang, Y., Chen, G., Su, X., and Yue, D. (2019). Landslide susceptibility mapping of Karakorum highway combined with the application of SBAS-InSAR technology. Sensors, 19.10.3390/s19122685 ↗
7. L- and X-band multi-temporal InSAR analysis of Tianjin subsidence10.3390/rs6097933 ↗
8. Xing, X., Chang, H.-C., Chen, L., Zhang, J., Yuan, Z., and Shi, Z. (2019). Radar interferometry time series to investigate deformation of soft clay subgrade settlement—A case study of Lungui Highway, China. Remote Sens., 11.10.3390/rs11040429 ↗
9. The applications of InSAR technique for natural hazard detection in smart society10.1088/1742-6596/1572/1/012067 ↗
10. Qin, Y., Hoppe, E., and Perissin, D. (2020). Slope hazard monitoring using high-resolution satellite remote sensing: Lessons learned from a case study. ISPRS Int. J. Geo-Inf., 9.10.3390/ijgi9020131 ↗
11. Zhu, K., Xu, P., Cao, C., Zheng, L., Liu, Y., and Dong, X. (2021). Preliminary identification of geological hazards from songpinggou to feihong in Mao county along the Minjiang river using SBAS-InSAR technique integrated multiple spatial analysis methods. Sustainability, 13.10.3390/su13031017 ↗
12. Zhuo, G., Dai, K., Huang, H., Li, S., Shi, X., Feng, Y., Li, T., Dong, X., and Deng, J. (2020). Evaluating potential ground subsidence geo-hazard of Xiamen Xiang’an new airport on reclaimed land by SAR Interferometry. Sustainability, 12.10.3390/su12176991 ↗
13. Casagli, N., Bianchini, S., Ciampalini, A., Del Soldato, M., Ezquierro, P., Montalti, R., Shan, M., Solari, L., and Raspini, F. (2020). Sentinel-1 InSAR data for the continuous monitoring of ground deformation and infrastructures at regional scale. Advances in Remote Sensing for Infrastructure Monitoring, Springer.10.1007/978-3-030-59109-0_3 ↗
14. Hayati, N., Niemeier, W., and Sadarviana, V. (2020). Ground deformation in the Ciloto landslides area revealed by multi-temporal InSAR. Geosciences, 10.10.3390/geosciences10050156 ↗
15. Ts-Insar analysis for monitoring ground deformation in Lanzhou new district, the loess plateau of China, from 2017 to 201910.1016/j.asr.2020.11.004 ↗
16. Ramirez, R., Lee, S.-R., and Kwon, T.-H. (2020). Long-term remote monitoring of ground deformation using sentinel-1 interferometric Synthetic Aperture Radar (InSAR): Applications and insights into geotechnical engineering practices. Appl. Sci., 10.10.3390/app10217447 ↗
17. Wang, Y., Guo, Y., Hu, S., Li, Y., Wang, J., Liu, X., and Wang, L. (2019). Ground deformation analysis using InSAR and backpropagation prediction with influencing factors in Erhai region, China. Sustainability, 11.10.3390/su11102853 ↗
18. Integration of InSAR and GPR techniques for monitoring transition areas in railway bridges10.1016/j.ndteint.2020.102291 ↗
19. Zhang, L., Sun, Q., and Hu, J. (2018). Potential of TCPInSAR in monitoring linear infrastructure with a small dataset of SAR images: Application of the Donghai Bridge, China. Appl. Sci., 8.10.3390/app8030425 ↗
20. Wu, W., Cui, H., Hu, J., and Yao, L. (2019). Detection and 3d visualization of deformations for high-rise buildings in Shenzhen, China from high-resolution TerrasSAR-X datasets. Appl. Sci., 9.10.3390/app9183818 ↗
21. Entering the era of Earth observation-based landslide warning systems: A novel and exciting framework10.1109/MGRS.2019.2954395 ↗
22. Evaluation of polarimetric capabilities of dual polarized sentinel-1 and TerraSAR-X data to improve the psinsar algorithm using amplitude dispersion index optimization
23. How to assess landslide activity and intensity with Persistent Scatterer Interferometry (PSI): The PSI-based matrix approach10.1007/s10346-012-0335-7 ↗
24. Fiaschi, S., Holohan, E., Sheehy, M., and Floris, M. (2019). Ps-InSAR analysis of sentinel-1 data for detecting ground motion in temperate oceanic climate zones: A case study in the republic of Ireland. Remote Sens., 11.10.3390/rs11030348 ↗
25. Fryksten, J., and Nilfouroushan, F. (2019). Analysis of clay-induced land subsidence in Uppsala city using sentinel-1 SAR data and precise leveling. Remote Sens., 11.10.3390/rs11232764 ↗
26. Time-series analysis of subsidence associated with rapid urbanization in Shanghai, China measured with SBAS InSAR method10.1007/s12665-013-2990-y ↗
27. Long-term land subsidence monitoring of Beijing (China) using the Small Baseline Subset (SBAS) technique10.3390/rs6053648 ↗
28. Validating InSAR-SBAS results by means of different GNSS analysis techniques in medium- and high-grade deformation areas10.1007/s10661-019-8009-8 ↗
29. Zhang, Y., Liu, Y., Jin, M., Jing, Y., Liu, Y., Liu, Y., Sun, W., Wei, J., and Chen, Y. (2019). Monitoring land subsidence in Wuhan city (China) using the Sbas-InSAR method with radarsat-2 imagery data. Sensors, 19.10.3390/s19030743 ↗
30. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms10.1109/TGRS.2002.803792 ↗
31. Using advanced InSAR techniques to monitor landslide deformations induced by tunneling in the Northern Apennines, Italy10.1016/j.enggeo.2017.03.026 ↗
32. Zhu, Y., Xing, X., Chen, L., Yuan, Z., and Tang, P. (2019). Ground subsidence investigation in Fuoshan, China, based on SBAS-InSAR technology with TerraSAR-X images. Appl. Sci., 9.10.3390/app9102038 ↗
33. Foumelis, M., Blasco, J.M.D., Desnos, Y.-L., Engdahl, M., Fernandez, D., Veci, L., Lu, J., and Wong, C. (2018, January 22–27). ESA SNAP—StaMPS integrated processing for sentinel-1 persistent scatterer interferometry. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.10.1109/IGARSS.2018.8519545 ↗
34. Mancini, F., Grassi, F., and Cenni, N. (2021). A workflow based on SNAP–StaMPS open-source tools and GNSS data for PSI-Based ground deformation using dual-orbit sentinel-1 data: Accuracy assessment with error propagation analysis. Remote Sens., 13.10.3390/rs13040753 ↗
35. Delgado Blasco, J., Foumelis, M., Stewart, C., and Hooper, A. (2019). Measuring urban subsidence in the Rome metropolitan area (Italy) with sentinel-1 SNAP-StaMPS persistent scatterer interferometry. Remote Sens., 11.10.3390/rs11020129 ↗
36. Du, Y., Feng, G., Li, Z., Peng, X., Zhu, J., and Ren, Z. (2017). Effects of external digital elevation model inaccuracy on StaMPS-PS processing: A case study in Shenzhen, China. Remote Sens., 9.10.3390/rs9111115 ↗
37. A new model for three-dimensional deformation extraction with single-track InSAR based on mining subsidence characteristics
38. Power, D., Youden, J., English, J., Russell, K., Croshaw, S., and Hanson, R. (2006). InSAR Applications for Highway Transportation Projects.
39. Zhejiang Provincial Bureau of Quality and Technical Supervision (2018). Technical Specification for Expressway Slope Maintenance (DB 33/T 2099-2018).
40. GEO (2018). Guide to Slope Maintenance. Geoguide 5, Continuously Updated E-Version Released on 20 November 2018.
41. Landslide monitoring by combining of CR-InSAR and GPS techniques10.1016/j.asr.2013.12.003 ↗
42. Landslide monitoring for risk mitigation by using corner reflector and satellite SAR interferometry: The large landslide of Carlantino (Italy)10.1016/j.catena.2016.12.006 ↗
43. Huggel, C., Frey, H., Klimeš, J., Strozzi, T., Caduff, R., and Cochachin, A. (2021, September 13). Contribution of Earth Observation to Landslide Early Warning, Available online: http://www.geo.uzh.ch/~chuggel/files_download/alcantara/eo_ews_white_paper_esa_alcantara_2017.pdf.