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Monitoring land subsidence in the Peshawar District, Pakistan, with a multi-track PS-InSAR technique

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Abstract

Peshawar is one of the most densely populated cities of Pakistan with high urbanization rate. The city overexploits groundwater resources for household and commercial usage which has caused land subsidence. Land subsidence has long been an issue in Peshawar due to insufficient groundwater removal. In this research, we employ the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique with Sentinel-1 imaging data to observe the yearly land subsidence and generate accumulative time-series maps for the years (2018 to 2020) using the SAR PROcessing tool (SARPROZ). The PS-InSAR findings from two contiguous paths are combined by considering the variance over the overlap** area. The subsidence rates in the Peshawar are from −59 to 17 mm/yr. The results show that subsidence is −28.48 mm/yr in 2018, the subsidence reached −49.02 mm/yr in 2019, while in 2020, the subsidence reached −49.90 mm/yr. The findings indicate a notable rise in land subsidence between the years 2018 and 2020. Subsidence is predicted in the research region primarily due to excessive groundwater removal and soil consolidation induced by surficial loads. The correlation of land subsidence observations with groundwater levels and precipitation data revealed some relationships. Overall, the proposed method efficiently monitors, maps, and detects subsidence-prone areas. The utilization of land subsidence maps will enhance the efficiency of urban planning, construction of surface infrastructure, and the management of risks associated with subsidence.

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Acknowledgements

This research is supported by the Chinese Government Scholarship.

Data availability

The data presented in the study are available on request from the first and corresponding author.

Funding

The study was supported by the National Natural Science Foundation of China (No. 41871305); National key R & D program of China (No.2017YFC0602204); Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. CUGQY1945); Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education; and Fundamental Research Funds for the Central Universities (No. GLAB2019ZR02).

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Muhammad Afaq Hussain & Zhanlong Chen

  2. Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padova, 35131, Padua, Italy

    Junaid Khan

Authors
  1. Muhammad Afaq Hussain
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  2. Zhanlong Chen
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  3. Junaid Khan
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Contributions

Muhammad Afaq Hussain: Conceptualization, Methodology, Writing—original draft, Visualization, Data curation. Junaid Khan: Validation, Formal analysis. Zhanlong Chen: Writing—review & editing, Supervision, Investigation.

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Correspondence to Zhanlong Chen.

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Hussain, M.A., Chen, Z. & Khan, J. Monitoring land subsidence in the Peshawar District, Pakistan, with a multi-track PS-InSAR technique. Environ Sci Pollut Res 31, 12271–12287 (2024). https://doi.org/10.1007/s11356-024-31995-x

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  • DOI: https://doi.org/10.1007/s11356-024-31995-x

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