Published

2025-04-21

Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data

Monitoreo del hundimiento de tierra en una área urbana de Tamilnadu, India, a través de datos temporales del radar de apertura sintética

DOI:

https://doi.org/10.15446/esrj.v29n1.110897

Keywords:

SAR,, PS-InSAR, Subsidence, SARPROZ (en)
radar de apertura sintética, radar de apertura sintética interferométrico de dispersión persistente, hundimiento, SARPROZ (es)

Downloads

Authors

  • Kishan Singh Rawat Centre for Remote Sensing and Geo-Informatics (A Joint Initiative of ISRO & Sathyabama University)

PS-InSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) technique is one of the most effective methods for detecting land displacement in selected areas. It combines the advantages of radar interferometry and remote sensing, and it is based on long-term coherent radar measurements collected from the SAR (Synthetic Aperture Radar) images. As for this specific study, 186 Sentinel-1 images were collected in the study area from 2016 to 2022. The authors used well-known SARPROZ software to process PS-InSAR techniques. The measured rate of cumulative displacement in this region was close to -60mm/year. On the contrary, the remaining areas of the study area experienced lower subsidence rates or no subsidence compared to the urbanized part. It demonstrated that high levels of urbanization and industrial activities led to high subsidence in the urban part of the study area, with the rest showing low or no subsidence at all. The results of the research highlight the necessity of planning concerning groundwater management to mitigate the nefarious implications of over-extraction. Sustainable practices can be implemented; however, ensuring stability and sustainability in the face of urbanization and industrial development is the key.

El radar de apertura sintética interferométrico de dispersión persistente es uno de los métodos más efectivos para detectar el desplazamiento de tierras en áreas seleccionadas. Este combina las ventajas del radar interferométrico y la detección remota, y se basa en mediciones de radar coherentes a largo plazo recopiladas a partir de imágenes SAR (radar de apertura sintética). Para este trabajo en específico se recolectaron 186 imágenes del satélite Sentinel-1 en el área de estudio entre el 2016 y el 2022. Los autores usaron el software SARPROZ para aplicar el método  de radar de apertura sintética interferométrico de dispersión persistente. El ritmo medido de desplazamiento acumulativo en la región fue de -60mm por año. Por el contrario, las áreas de la zona de estudio que no están urbanizadas experimentaron rimos de hundimiento muy bajos o de no hundimiento. Esto demuestra que los altos índices de actividades urbanísticas e industriales conllevan a un alto índice de hundimiento en las zonas urbanas del área de estudio, mientras las otras partes no tienen hundimiento o es muy poco. Los resultados de la investigación resaltan la necesidad de una planeación y administración de las aguas subterráneas para mitigar los efectos de la sobreexplotación. Se pueden implementar prácticas sostenibles; sin embargo, la clave está en asegurar la estabilidad y sostenibilidad en el desarrollo urbanístico e industrial. 

References

Abdikan, S., Arikan, M., Sanli, F. B., &Cakir, Z. (2014). Monitoring of coal mining subsidence in peri-urban area of Zonguldak city (NW Turkey) with persistent scatterer interferometry using ALOS-PALSAR; Environ. Earth Sci. 71 4081–4089.

Abdikan, S., Hooper, .A, Arikan, M., Balik, S. F., Cakir, Z., & Kemaldere, H. (2011). InSAR time series analysis of coal mining in Zonguldak city, Northwestern Turkey; In: Fringe Workshop.

Akshar, T., &Reet, K. T. (2022). Synergetic utilization of sentinel-1 SAR and sentinel-2 optical remote sensing data for surface soil moisture estimation for Rupnagar, Punjab, India, Geocarto International, 37:8, 2215-2236, DOI: 10.1080/10106049.2020.1815865.

Amelung, F., et al. (1999). Sensing the ups and downs of las vegas: insar reveals structural control of land subsidence and aquifer-system deformation. Geology, 27(6), pp.483-486.

Ashwani, R., Ritika, N., Anjali, S. &Kapil, M. (2022). Multi-temporal analysis of groundwater depletion-induced land subsidence in Central Ganga Alluvial plain, Northern India, Geocarto International, 37:26, 11732-11755, DOI: 10.1080/10106049.2022.2060322.

Bakon, M., et al. (2016). Multi-sensor InSAR deformation monitoring over urban area of Bratislava (Slovakia). Procedia Comput Sci. 2016;100:1127–1134.

Bock, Y., Wdowinski, S., & Ferretti, A. (2012). Recent subsidence of the Venice Lagoon from continuous GPS and interferometric synthetic aperture radar. Geochem GeophysGeosyst. 2012;13:Q03023.

Chatterjee, R. S. (2006). Coal Bre mapping from satellite thermal IR data – a case example in Jharia Coalfield, Jharkhand, India; ISPRS J. Photogramm. Remote Sens. 60 113–128.

Crosetto, M., Devanthery, N., Cuevas-Gonzalez, M., Monserrat, O., & Crippa, B. (2015). Exploitation of the full potential of PSI data for subsidence monitoring. Proc. IAHS 2015, 372, 311–314.

Davila-Hernandez, N., Madrigal, D., Exposito, J. L., & Antonio, X. (2014). Multi-temporal analysis of land subsidence in Toluca Valley (Mexico) through a combination of Persistent Scatterer Interferometry (PSI) and historical piezometric data; Adv. Remote Sens. 3 49.

Dumka, R. K., Prajapati, S., SuriBabu, D., Swamy, K. V., Kothyari, G. C., & Malik, K. (2023). GPS and InSAR derived evidences of intra-basin stress and strike-slip tectonics in the vicinity of 2001 (M7.7) earthquake, Kachchh, western India. Geological Journal, 58( 2), 683– 699.

Ferretti, A., Prati, C., & Rocca, F. (2000). Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry; IEEE Trans. Geosci. Remote Sens. 38(5) 2202–2212.

Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry; IEEE Trans. Geosci. Remote Sens. 39 8–20.

Guang, L., Huadong, G., Xiaofang, G., Perski, Z. and Huanyin, Y. (2009). Mining area subsidence monitoring using multi-band SAR data; In: Urban Remote Sensing Event, pp. 1–6.

Gueguen, Y., Deffontaines, B., Fruneau, B., Al Heib, M., De Michele, M., Raucoules, D., Guise, Y., &Planchenault, J. (2009). Monitoring residual mining subsidence of Nord/Pas-deCalais coal basin from differential and Persistent Scatterer Interferometry (Northern France); J. Appl. Geophys. 69 24–34.

Gupta, M., Mohanty, K. K., Kumar, D. and Banerjee, R. (2014). Monitoring surface elevation changes in JhariacoalBeld, J. Earth Syst. Sci. (2020) 129:146 Page 9 of 10 146 India using synthetic aperture radar interferometry; Environ. Earth Sci. 71 2875–2883.

Ishwar, S. G., & Kumar, D. (2017). Application of DInSAR in mine surface subsidence monitoring and prediction; Curr. Sci. 112 46–51.

Jalayer, S., Sharifi, A., Abbasi-Moghadam, D., Tariq, A., & Qin, S. (2023). Assessment of spatiotemporal characteristic of droughts using in situ and remote sensing-based drought indices, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens. 16 (2023) 1483–1502, https://doi.org/10.1109/JSTARS.2023.3237380.

Jian, W. (2011). Coal mining GPS subsidence monitoring technology and its application. Mining Science and Technology (China). 21(004), pp. 463-467.

Jiang, L., Lin, H., Ma, J., Kong, B., & Wang, Y. (2011). Potential of small-baseline SAR interferometry for monitoring land subsidence related to underground coal Bres: Wuda (Northern China) case study; Remote Sens. Environ. 115 257.

Jianjun, S., Chunjian, H., Ping, L., Junwei, Z., Deyuan, L., Minde, J., Lin, Z., Jingkai, Z. & Jianying, S. (2012). Quantitative prediction of mining subsidence and its impact on the environment; Int. J. Min. Sci. Technol. 22 69–73.

Kapil, M., Dheeraj, K., & Daniele, P. (2019). Assessment of subsidence in Delhi NCR due to groundwater depletion using TerraSAR-X and persistent scatterers interferometry, The Imaging Science Journal, 67:1, 1-7.

Khorrami, M. (2020). Extreme subsidence in a populated city (Mashhad) detected by PSInSAR considering groundwater withdrawal and geotechnical properties. Sci. Rep. 10, 11357. https://doi.org/10.1038/s41598-020-67989-1.

Kumar, S., Kumar, D., & Chaudhary, S.K. (2020). Land subsidence mapping and monitoring using modified persistent scatterer interferometric synthetic aperture radar in Jharia Coalfield, India. J Earth SystSci 129, 146. https://doi.org/10.1007/s12040-020-01413-0.

Mahmoodinasab, F. &Mohseni, N., 2021. A spatiotemporal analysis of the relationship between groundwater level and ground surface displacement using Sentinel-1 SAR data. Arab. J. Geosci. 14, 1106. https://doi.org/10.1007/s12517-021-07497-2.

Majeed, M., Lu, L., Anwar, M. M., Tariq, A., Qin, S., El-Hefnawy, M. E., El-Sharnouby, M.., Li, Q., Alasmari, A., 2023. Prediction of flash flood susceptibility using integrating analytic hierarchy process (AHP) and frequency ratio (FR) algorithms, Front. Environ. Sci. 10 (2023) 1–14, https://doi.org/10.3389/fenvs.2022.1037547.

Miao, F., Yan, M., Qi, X., Ye, C., Wang, B., Liu, R. & Chen, J. (2008). Application of DInSAR and GIS for underground mine subsidence monitoring; Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 37 251–256.

Pacheco-Martínez, J., Hernandez-Marín, M., & Burbey, T. J. (2013). Land subsidence and ground failure associated to groundwater exploitation in the Aguascalientes Valley, México. Engineering Geology, 164: 172–186.

Perissin, D. & Wang, T. (2012). Repeat-pass SAR interferometry with partially coherent targets; IEEE Trans. Geosci. Remote Sens. 50 271–280.

Tariq, A., Mumtaz,F., Majeed, M. & Zeng, X. (2023). Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan, Environ. Monit. Assess. 195 (2023) 114, https://doi.org/10.1007/s10661-022-10738-w.

Thapa, S., Chatterjee, R. S., Singh, K. B., & Kumar, D. (2016). Land subsidence monitoring using PS-InSAR technique for l-band SAR data; ISPRS – Int. Arch. Photogramm. Remote Sens. Spat. Info. Sci., https://doi.org/10.5194/isprsarchives-xli-b7-995-2016.

Tripathi, A., Attri, L. & Tiwari, R.K. (2021). Spaceborne C-band SAR remote sensing–based flood mapping and runoff estimation for 2019 flood scenario in Rupnagar, Punjab, India. Environ Monit Assess 193, 110. https://doi.org/10.1007/s10661-021-08902-9.

Tripathi, A., Moniruzzaman, Md., Reshi, A.R., Malik, K., Tiwari, R.K., Bhatt, C.M., &Rahaman, K.R. (2023). ‘Chamoli Flash Floods of 7th February 2021 and Recent Deformation a PSInSAR and Deep Learning Neural Network (DLNN) based perspective’. Natural Hazards Research. https://doi.org/10.1016/j.nhres.2023.03.003.

Tripathi, A., Reshi, A., Moniruzzaman, Md., Rahaman, K. & Tiwari, R., & Malik, K. (2022). Interoperability of C-band Sentinel-1 SAR and GRACE satellite sensors on PSInSAR based urban surface subsidence mapping of Varanasi, India. IEEE Sensors Journal. 1-1. 10.1109/JSEN.2022.3208117.

Xiong, S. (2021). Time-Series Analysis on Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) Derived Displacements of the Hong Kong-Zhuhai-Macao Bridge (HZMB) from Sentinel-1A Observations. Remote Sensing, 13(4), pp. 546.

How to Cite

APA

Rawat, K. S. (2025). Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data. Earth Sciences Research Journal, 29(1), 89–99. https://doi.org/10.15446/esrj.v29n1.110897

ACM

[1]
Rawat, K.S. 2025. Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data. Earth Sciences Research Journal. 29, 1 (Apr. 2025), 89–99. DOI:https://doi.org/10.15446/esrj.v29n1.110897.

ACS

(1)
Rawat, K. S. Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data. Earth sci. res. j. 2025, 29, 89-99.

ABNT

RAWAT, K. S. Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data. Earth Sciences Research Journal, [S. l.], v. 29, n. 1, p. 89–99, 2025. DOI: 10.15446/esrj.v29n1.110897. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/110897. Acesso em: 20 jun. 2025.

Chicago

Rawat, Kishan Singh. 2025. “Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data”. Earth Sciences Research Journal 29 (1):89-99. https://doi.org/10.15446/esrj.v29n1.110897.

Harvard

Rawat, K. S. (2025) “Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data”, Earth Sciences Research Journal, 29(1), pp. 89–99. doi: 10.15446/esrj.v29n1.110897.

IEEE

[1]
K. S. Rawat, “Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data”, Earth sci. res. j., vol. 29, no. 1, pp. 89–99, Apr. 2025.

MLA

Rawat, K. S. “Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data”. Earth Sciences Research Journal, vol. 29, no. 1, Apr. 2025, pp. 89-99, doi:10.15446/esrj.v29n1.110897.

Turabian

Rawat, Kishan Singh. “Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data”. Earth Sciences Research Journal 29, no. 1 (April 21, 2025): 89–99. Accessed June 20, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/110897.

Vancouver

1.
Rawat KS. Monitoring land subsidence in an urban area of Tamilnadu, India using SAR time series data. Earth sci. res. j. [Internet]. 2025 Apr. 21 [cited 2025 Jun. 20];29(1):89-9. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/110897

Download Citation

CrossRef Cited-by

CrossRef citations0

Dimensions

PlumX

Article abstract page views

13

Downloads

Download data is not yet available.