Subsidence Monitoring in Railway Lines With LICSBAS Algorithm and Radar Interferometric Method (Case Study: Mashhad-Sarkhs Railway)

Document Type : Research Article

Authors

• Soroush Yasini ¹
• Hossein Etemadfard ²

¹ Environmental engineering and science Graduate Student, Ferdowsi University of Mashhad, Mashhad, Iran

² Assistant Professor in GIS, Department of Civil Engineering, Faculty of Technical and Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

10.22067/geoeh.2024.87783.1480

Abstract

In recent times, the increasing occurrence of subsidence has raised concerns, particularly in Iran's plains, urban regions, and transportation infrastructure. This study focuses on understanding how this phenomenon affects the Mashhad-Sarkhs railway, since it is located on the eastern end of Iran's rail network and serves as a vital rail link connecting to Central Asian nations, which are important trade partners. To assess the rate of subsidence along this route, we processed 151 radar images between 2017 and 2023, utilizing the new NSBAS algorithm and pre-processed data from COMET LiCSAR, to calculate the cumulative rate of land subsidence. Additionally, to reduce atmospheric effects on subsidence rate estimation, GACOS data was used. Following this, we created a land cover map of the study area with four land use classes using the Google Earth Engine to investigate the effects of land cover variations on subsidence. Finally, we generated a subsidence profile around the railroad and combined it with land use classes to visualize the correlation between croplands and subsidence occurrence in affected areas. InSAR results show three land subsidence zones along the rail line, with some areas experiencing subsidence exceeding 200 mm along the satellite line of sight. The results indicate a strong correlation between subsidence along the railway and farming within the rail corridor. Conversely, there were almost no signs of land subsidence outside the plains due to the absence of concentrated agricultural activities. In this study, the first 60 kilometers of the railroad, which contains more than 20 bridges, was identified as the most concerning subsidence zone along the rail line.
Extended Abstract
Introduction
Land subsidence, an environmental and geological phenomenon, arises from groundwater depletion and soil compaction due to human and natural factors, particularly in arid and semi-arid regions. In Iran, like many developing countries, excessive groundwater extraction to meet growing industrial, urban, and agricultural demands has a long history, exacerbating subsidence in eastern and central plains such as Mashhad, Neyshabur, and Jovin, worsened by recurring droughts. This phenomenon poses significant risks to infrastructure, including high-speed railways, roads, tunnels, and bridges, threatening economic development and safety due to potential human and financial losses. The Mashhad-Sarakhs railway, a critical transit corridor in northeastern Iran connecting to Central Asian networks, underscores the need for subsidence monitoring given its role in transporting goods and people. Radar interferometry (InSAR) offers a cost-effective, weather-independent method to monitor subsidence over large areas, unlike traditional techniques like leveling or GPS, though it faces challenges from topographic, atmospheric, and orbital errors. Advanced InSAR techniques, such as small baseline and permanent scatterer methods, enhance accuracy by reducing noise and improving coherence, while newer algorithms address gaps in data networks, especially in agricultural zones. This study investigates subsidence along the Mashhad-Sarakhs railway using modern InSAR approaches, aiming to identify high-risk zones in this vital infrastructure corridor.
 
Material and Methods
The Mashhad-Sarakhs railway, approximately 195 km long with about 5 km extending beyond Iran’s borders, is located in the northeasternmost part of the country between 59°38′ and 61°14′ east longitude and 36°1′ to 36°33′ north latitude. Operational since 1996, three years after project initiation, this route features three tunnels totaling around 6 km and includes 18 stations, 15 large bridges, and 386 medium to small bridges along its 2,700 m of bridge structures.
This study analyzed pre-processed Sentinel-1 radar imagery spanning January 2017 to 2023, covering a broad spatial area with interferograms and coherence data limited by perpendicular and temporal baselines of 200 meters and 50 days. The dataset was customized to the study region by masking irrelevant areas, and atmospheric effects were corrected using an external dataset to reduce noise that could obscure surface changes. Interferogram quality was assessed, discarding those with coherence below 0.3 due to snow, dense vegetation, or other disruptions, while unwrapping errors were detected and eliminated through a closed-loop phase method, removing interferograms exceeding an RMS threshold of 1.5. Displacement rates were calculated using a small baseline approach, assuming linear subsidence, with a reference point selected for minimal error. The standard deviation of rates was determined via repeated sampling to ensure reliability, and pixels exceeding noise thresholds were filtered out. To investigate the relationship between land cover and subsidence, a 2022 land cover map was generated using Sentinel-2 imagery at 10 × 10 m resolution with 11 classes via Google Earth Engine, then resampled to 101 × 101 m using the nearest-neighbor algorithm, and simplified into four classes: agricultural land, buildings, bare land, and grassland. Spatio-temporal filters were applied to minimize residual tropospheric, ionospheric, and orbital errors, yielding a robust time-series displacement dataset.
 
Results and Discussion
To validate radar interferometry results, ground displacement data from GNSS stations were compared with InSAR-derived time-series data at a specific point along the Mashhad-Sarakhs railway (61.09°E, 36.32°N). The comparison revealed uplift at a rate of less than 2 mm/year, alongside seasonal displacement patterns linked to regional water resource variations, confirming the accuracy of InSAR outputs with ground-based measurements. Subsidence analysis along the 3-km buffer of the railway, spanning January 2017 to December 2022, utilized 151 refined Sentinel-1 images processed with the NSBAS algorithm. The highest subsidence was observed in the southeastern outskirts of Mashhad, within the first 20 km of the route, reaching 200–260 mm in the sensor’s line of sight. Further along the path, at approximately 55 km and 190 km, maximum subsidence values of 140 mm and 75 mm were recorded, respectively. Other segments of the route showed negligible or no significant displacement. Agricultural activity was prevalent from the start to the 60-km mark and again from 180 km onward, correlating strongly with subsidence zones, particularly in the initial 0–10 km stretch where the most substantial subsidence occurred. In contrast, the middle sections of the route, dominated by barren land or sparse pastures, exhibited minimal agricultural presence and correspondingly low subsidence rates, highlighting a clear link between land use and subsidence patterns along the railway corridor.
 
Conclusion
This study identified three significant subsidence zones along the Mashhad-Sarakhs railway, with the most pronounced occurring in the first 20 km (up to 200 mm) and another between 50–60 km (up to 150 mm), linked to intensive agricultural activity and groundwater over-extraction. Subsidence rates in southeastern Mashhad have risen from under 15 mm/year (2003–2009) to over 40 mm/year (2017–2023), correlating with declining groundwater levels. The initial 20-km segment, hosting 20% of the route’s bridges, shows uneven and hazardous elevation changes, necessitating continuous monitoring. Integrating land use and hydrological data could enhance subsidence modeling, despite challenges from undocumented wells. Modern InSAR processing algorithms, offering improved accuracy and ease of use, proved effective for subsidence prediction and infrastructure risk management.
 

Keywords

• Land subsidence
• InSAR monitoring
• Railway infrastructure
• Groundwater depletion
• Agricultural impacts
• Risk assessment

Main Subjects

• Land subsidence

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References

Behniafar, A., Ghanbarzadeh, H., & Eshraghi, A. (2010). Investigation of factors affecting subsidence in the Mashhad plain and its geomorphic consequences. Zagros Perspective Geographical Quarterly, 2(5), 131–146. [In Persian]

Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40(11), 2375–2383. https://doi.org/10.1109/TGRS.2002.803792

Biggs, J., Wright, T., Lu, Z., & Parsons, B. (2007). Multi-interferogram method for measuring interseismic deformation: Denali Fault, Alaska. Geophysical Journal International, 170(3), 1165–1179. http://dx.doi.org/10.1111/j.1365-246X.2007.03415.x

Chang, L., Sakpal, N. P., Elberink, S. O., & Wang, H. (2020). Railway infrastructure classification and instability identification using Sentinel-1 SAR and laser scanning data. Sensors, 20(24), 7108. https://doi.org/10.3390/s20247108

Chen, B., Gong, H., Chen, Y., Lei, K., Zhou, C., Si, Y., ... & Gao, M. (2021). Investigating land subsidence and its causes along Beijing high-speed railway using multi-platform InSAR and a maximum entropy model. International Journal of Applied Earth Observation and Geoinformation, 96, 102284. https://doi.org/10.1016/j.jag.2020.102284

Doin, M. P., Lodge, F., Guillaso, S., Jolivet, R., Lasserre, C., Ducret, G., ... & Pinel, V. (2011). Presentation of the small baselin nsbas processing chain on a case example: The etan deformation monitoring from 2003 to 2010 using envisat data. In Fringe Symposium. https://ens.hal.science/hal-02185213

Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8–20. http://dx.doi.org/10.1109/36.898661

Ferretti, A., Savio, G., Barzaghi, R., Borghi, A., Musazzi, S., Novali, F., ... & Rocca, F. (2007). Submillimeter accuracy of InSAR time series: Experimental validation. IEEE Transactions on Geoscience and Remote Sensing, 45(5), 1142–1153. http://dx.doi.org/10.1109/TGRS.2007.894440

Foroughnia, F., Nemati, S., Maghsoudi, Y., & Perissin, D. (2019). An iterative PS-InSAR method for the analysis of large spatio-temporal baseline data stacks for land subsidence estimation. International Journal of Applied Earth Observation and Geoinformation, 74, 248–258. https://doi.org/10.1016/j.jag.2018.09.018

Gambolati, G., & Teatini, P. (2015). Geomechanics of subsurface water withdrawal and injection. Water Resources Research, 51(6), 3922-3955. https://doi.org/10.1002/2014WR016841

Gao, F., Zhao, T., Zhu, X., Zheng, L., Wang, W., & Zheng, X. (2023). Land subsidence characteristics and numerical analysis of the impact on major infrastructure in Ningbo, China. Sustainability, 15(1), 543. https://doi.org/10.3390/su15010543

Hooper, A. (2004). StaMPS (Stanford Method for PS) Manual. http://sismologia.ist.utl.pt/files/StaMPS_manual.pdf

Hooper, A. (2008). A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophysical Research Letters, 35(16), L16302. http://dx.doi.org/10.1029/2008GL034654

Hooper, A., Segall, P., & Zebker, H. (2007). Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volcan Alcedo, Galapagos. Journal of Geophysical Research: Solid Earth, 112(B7), B07407. http://dx.doi.org/10.1029/2006JB004763

Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., & Brumby, S. P. (2021). Global land use/land cover with Sentinel 2 and deep learning. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 4704–4707). https://doi.org/10.1109/IGARSS47720.2021.9553499

Khorrami, M., Abrishami, S., & Maghsoudi, Y. (2020). Mashhad Subsidence Monitoring by Interferometric Synthetic Aperture Radar Technique. Amirkabir Journal of Civil Engineering, 51(6), 1187-1204. [In Persian] https://doi.org/10.22060/ceej.2018.14300.5617

Lazecký, M., Spaans, K., González, P. J., Maghsoudi, Y., Morishita, Y., Albino, F., ... & Wright, T. J. (2020). LiCSAR: An automatic InSAR tool for measuring and monitoring tectonic and volcanic activity. Remote Sensing, 12(15), 2430. https://doi.org/10.3390/rs12152430

Luo, Q., Zhou, G., & Perissin, D. (2017). Monitoring of subsidence along Jingjin inter-city railway with high-resolution TerraSAR-X MT-InSAR analysis. Remote Sensing, 9(7), 717. https://doi.org/10.3390/rs9070717

Meng, Z., Shu, C., Wu, Q., & Yang, Y. (2021). Monitoring surface deformation of high-speed railway using time-series InSAR method in northeast China. IOP Conference Series: Earth and Environmental Science, 660(1), 012011. http://dx.doi.org/10.1088/1755-1315/660/1/012011

Minh, D. H. T., Van Trung, L., & Toan, T. L. (2015). Mapping ground subsidence phenomena in Ho Chi Minh City through the radar interferometry technique using ALOS PALSAR data. Remote Sensing, 7(7), 8543–8562. https://doi.org/10.3390/rs70708543

Ministry of Energy. (2010). Updating the integration of water resources studies, Qareh Qom watershed, Volume 2: Reviews and general specifications. Office of Basic Water Resources Studies. [In Persian]

Ministry of Energy. (2019). Prohibited plains. Office of Protection and Utilization of Water Resources and Subscriber Affairs. [In Persian]

Morishita, Y., Lazecký, M., Wright, T. J., Weiss, J. R., Elliott, J. R., & Hooper, A. (2020). LiCSBAS: An open-source InSAR time series analysis package integrated with the LiCSAR automated Sentinel-1 InSAR processor. Remote Sensing, 12(3), 424. https://doi.org/10.3390/rs12030424

Parliament Research Center. (1996). Opening of the Mashhad-Sarakhs-Tejen railway line with a look at the situation of Sarakhs. [In Persian]

Perissin, D., Wang, Z., & Wang, T. (2011). The SARPROZ InSAR tool for urban subsidence/manmade structure stability monitoring in China. In 34th International Symposium on Remote Sensing. Sydney, Australia.

Pietrzak, O., & Pietrzak, K. (2019). The role of railway in handling transport services of cities and agglomerations. Transportation Research Procedia, 39, 405-416. https://doi.org/10.1016/j.trpro.2019.06.043

Polcari, M., Moro, M., Romaniello, V., & Stramondo, S. (2019). Anthropogenic subsidence along railway and road infrastructures in Northern Italy highlighted by CosmoSkyMed satellite data. Journal of Applied Remote Sensing, 13(2), 024515. http://dx.doi.org/10.1117/1.JRS.13.024515

Rosen, P. A., Gurrola, E., Sacco, G. F., & Zebker, H. (2012). The InSAR scientific computing environment. In Proceedings of the EUSAR 2012. Nuremberg, Germany.

Sahraoui, O. H., Hassaine, B., Serief, C., & Hasni, K. (2006). Radar interferometry with Sarscape software. Photogrammetry and Remote Sensing, 1-10.

Salehi Moteahd, F., Hafezi Moghaddas, N., Lashkaripour, G., & Dehghani, M. (2019). Land Subsidence and its Consequences in Mashhad City by Integrating Radar Interferometry and Field Measurements. Journal of Engineering Geology, 13(3), 435-462. [In Persian] http://dx.doi.org/10.18869/acadpub.jeg.13.3.435

Sandwell, D., Mellors, R., Tong, X., Wei, M., & Wessel, P. (2011). Open radar interferometry software for mapping surface deformation. EOS Transactions, American Geophysical Union, 92(28), 234. https://doi.org/10.1029/2011EO280002

Shami, S., Azar, M. K., Nilfouroushan, F., Salimi, M., & Reshadi, M. A. M. (2022). Assessments of ground subsidence along the railway in the Kashan plain, Iran, using Sentinel-1 data and NSBAS algorithm. International Journal of Applied Earth Observation and Geoinformation, 112, 102898. https://doi.org/10.1016/j.jag.2022.102898

Sharifikia, M. (2010). Investigation of the consequences of the subsidence phenomenon in residential lands and plains of Iran. Journal of the Iranian Geological Engineering Association, 3(3–4), 43–58. [In Persian] https://www.jiraeg.ir/article_68238.html

Veci, L. (2015). Interferometry tutorial. Array Systems. Available online: http://sentinel1.s3. amazonaws .

Werner, C., Wegmüller, U., Strozzi, T., & Wiesmann, A. (2000). Gamma SAR and interferometric processing software. In Proceedings of the ERS-ENVISAT Symposium. Gothenburg, Sweden. https://api.semanticscholar.org/CorpusID:28598270

Yang, Z. (2015). Monitoring and predicting railway subsidence using InSAR and time series prediction techniques (Doctoral dissertation). https://etheses.bham.ac.uk/id/eprint/6377/

Yu, C., Li, Z., Penna, N., & Crippa, P. (2018). Generic atmospheric correction model for interferometric synthetic aperture radar observations. Journal of Geophysical Research: Solid Earth, 123(10), 9202–9222. https://doi.org/10.1029/2017JB015305

Zhang, Y., Fattahi, H., & Amelung, F. (2019). Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction. Computers & Geosciences, 133, 104331. https://doi.org/10.1016/j.cageo.2019.104331

Zinno, I., Elefante, S., Luca, C. D., Manunta, M., Lanari, R., & Casu, F. (2015). New advances in intensive DInSAR processing through cloud computing environments. In Proceedings of the IGARSS 2015 (pp. 5264–5267). Milan, Italy. http://dx.doi.org/10.1109/IGARSS.2015.7327022