Early Identification of Geological Hazards Along the Power Transmission Line in Weinan Based on SBAS-InSAR
Source abstract
Abstract
Landslides and ground subsidence pose significant threats to the successful construction and operation of transmission line projects in the Loess Plateau region. This study aims to explore an accurate early identification method for geological hazards, providing support for the construction and smooth operation of the transmission project along the route from Baishui County, Weinan City, Shaanxi Province to Lantian County, Xi’an City, Shaanxi Province. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to acquire the surface deformation data of the study area from 4 February 2018 to 21 May 2023. The deformation data were spatially analyzed through kernel density analysis, which quickly and intuitively identified 52 potential geological hazard points in the region, including eight landslides and 44 ground subsidence. Detailed field investigations of the hazards confirmed the accuracy of the identification results. A thorough analysis of typical hazards, such as landslide No. 9 and ground subsidence No. 29, revealed severe deformation, posing a threat to the proposed transmission project. This study indicates that combining InSAR, kernel density analysis, and field investigations can accurately and quickly identify geological hazards around transmission lines, providing support for the site selection and implementation of transmission projects.
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Research context
OpenAlex topics: Synthetic Aperture Radar (SAR) Applications and Techniques · Landslides and related hazards · Geophysical Methods and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Northeast Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Changchun 130000, China
College of Construction Engineering, Jilin University, Changchun 130026, China
Funding
Technology Project of Northeast Electric Power Design Institute CO., LTD. of China Power Engineering Consulting Group
References (35)
Show source-provided references
1. Geological hazards in loess terrain, with particular reference to the loess regions of China10.1016/S0012-8252(01)00050-2 ↗
2. Chen, G., Zhang, Y., Zeng, R., Yang, Z., Chen, X., Zhao, F., and Meng, X. (2018). Detection of Land Subsidence Associated with Land Creation and Rapid Urbanization in the Chinese Loess Plateau Using Time Series InSAR: A Case Study of Lanzhou New District. Remote Sens., 10.10.3390/rs10020270 ↗
3. Bai, M., Cui, Z., and Mou, K. (2024). Early Detection and Stability Assessment of Hazardous Rock Masses in Steep Slopes. Appl. Sci., 14.10.3390/app14146317 ↗
4. Gamperl, M., Singer, J., Garcia-Londoño, C., Seiler, L., Castañeda, J., Cerón-Hernandez, D., and Thuro, K. (2023). Recommendations for Landslide Early Warning Systems in Informal Settlements Based on a Case Study in Medellín, Colombia. Land, 12.10.5194/nhess-2023-20 ↗
5. Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER
6. Sinkhole monitoring and early warning: An experimental and successful GB-InSAR application10.1016/j.geomorph.2015.04.018 ↗
7. Alimuddin, I., Bayuaji, L., Maddi, H.C., Sri Sumantyo, J.T., and Kuze, H. (2013). Developing tropical landslide susceptibility map using DInSAR technique of JERS-1 SAR data. Int. J. Remote Sens. Earth Sci., 8.10.30536/j.ijreses.2011.v8.a1739 ↗
8. Wei, Z., Li, Y., Dong, J., Cao, S., Ma, W., Wang, X., Wang, H., Tang, R., Zhao, J., and Liu, X. (2024). The Identification and Influence Factor Analysis of Landslides Using SBAS-InSAR Technique: A Case Study of Hongya Village, China. Appl. Sci., 14.10.3390/app14188413 ↗
9. Chen, X., Chen, J., Wang, G., Zhang, Q., and Zheng, Y. (2024). Mining Subsidence Based on Integrated SBAS-InSAR and Unmanned Aerial Vehicles Technology. J. Ocean. Univ. China, 1–17.10.1007/s11802-025-6009-6 ↗
10. Zheng, Z., Xie, C., He, Y., Zhu, M., Huang, W., and Shao, T. (2022). Monitoring Potential Geological Hazards with Different InSAR Algorithms: The Case of Western Sichuan. Remote Sens., 14.10.3390/rs14092049 ↗
11. Cao, C., Zhu, K., Song, T., Bai, J., Zhang, W., Chen, J., and Song, S. (2022). Comparative Study on Potential Landslide Identification with ALOS-2 and Sentinel-1A Data in Heavy Forest Reach, Upstream of the Jinsha River. Remote Sens., 14.10.3390/rs14091962 ↗
12. Wang, S., Sun, H., Wei, L., Pi, P., Zeng, M., Pan, Y., Xue, Z., Jiang, X., and Tapete, D. (2024). Integrated Assessment of Coastal Subsidence in Nansha District, Guangzhou City, China: Insights from SBAS-InSAR Monitoring and Risk Evaluation. Remote Sens., 16.10.3390/rs16020248 ↗
13. SAR data applications in earth observation: An overview10.1016/j.eswa.2022.117342 ↗
14. OpenSARUrban: A Sentinel-1 SAR image dataset for urban interpretation10.1109/JSTARS.2019.2954850 ↗
15. Spatiotemporal analysis of strain localization in dense granular materials10.1007/s11440-018-0685-y ↗
16. Influence of the sampling density on the noise level in displacement and strain maps obtained by processing periodic patterns10.1016/j.measurement.2020.108570 ↗
17. Garza-Gisholt, E., Hemmi, J.M., Hart, N.S., and Collin, S.P. (2014). A comparison of spatial analysis methods for the construction of topographic maps of retinal cell density. PLoS ONE, 9.10.1371/journal.pone.0093485 ↗
18. 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 ↗
19. Analysing infrequently sampled animal tracking data by incorporating generalized movement trajectories with kernel density estimation10.1016/j.compenvurbsys.2011.12.001 ↗
20. A multiple kernel-based kernel density estimator for multimodal probability density functions10.1016/j.engappai.2024.107979 ↗
21. Forecasting the magnitude of potential landslides based on InSAR techniques10.1016/j.rse.2020.111738 ↗
22. Zhang, R., Zhang, L., Fang, Z., Oguchi, T., Merghadi, A., Fu, Z., Dong, A., and Dou, J. (2024). Interferometric synthetic aperture Radar (InSAR)-based absence sampling for machine-learning-based landslide susceptibility mapping: The Three Gorges Reservoir area, China. Remote Sens., 16.10.3390/rs16132394 ↗
23. An integrated InSAR-machine learning approach for ground deformation rate modeling in arid areas10.1016/j.jhydrol.2022.127627 ↗
24. Relationship between landslides and active normal faulting in the epicentral area of the AD 1556 M~ 8.5 Huaxian Earthquake, SE Weihe Graben (Central China)10.1007/s12583-017-0900-z ↗
25. Liu, X., Shao, S., and Shao, S. (2024). Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Great Xi’an Region, China. Sci. Rep., 14.10.1038/s41598-024-53630-y ↗
26. Probability prediction model for landslide occurrences in Xi’an, Shaanxi Province, China10.1007/s11629-013-2809-z ↗
27. Social risk assessments of the Hutoumao landslide in Xi’an City, Shaanxi
28. Surface deformation of Long Valley caldera and Mono Basin, California, investigated with the SBAS-InSAR approach10.1016/j.rse.2006.11.015 ↗
29. Spatio-temporal evolution of land subsidence and susceptibility zonation of associated ground fissures in the urban area of loess plateau: A case in Xianyang city, China10.1080/19475705.2024.2341181 ↗
30. Shi, X., Zhang, L., Zhong, Y., Zhang, L., and Liao, M. (2020). Detection and Characterization of Active Slope Deformations with Sentinel-1 InSAR Analyses in the Southwest Area of Shanxi, China. Remote Sens., 12.10.3390/rs12030392 ↗
31. Spatiotemporal clustering and analysis of road accident hotspots by exploiting GIS technology and Kernel density estimation10.1093/comjnl/bxz158 ↗
32. Hot spot analysis based on network spatial weights to determine spatial statistics of traffic accidents in Rize, Turkey10.1007/s12517-018-3492-8 ↗
33. Afolayan, A., Easa, S.M., Abiola, O.S., Alayaki, F.M., and Folorunso, O. (2022). GIS-based spatial analysis of accident hotspots: A Nigerian case study. Infrastructures, 7.10.20944/preprints202206.0003.v1 ↗
34. Zheng, L., Wang, Q., Cao, C., Shan, B., Jin, T., Zhu, K., and Li, Z. (2024). Development and Comparison of InSAR-Based Land Subsidence Prediction Models. Remote Sens., 16.10.3390/rs16173345 ↗
35. Wang, B., Zhao, C., Zhang, Q., and Peng, M. (2019). Sequential InSAR Time Series Deformation Monitoring of Land Subsidence and Rebound in Xi’an, China. Remote Sens., 11.10.3390/rs11232854 ↗