Investigation of groundwater induced land subsidence in Ludhiana City using InSAR and Sentinel-1 data Article Swipe
Hari Shankar
,
Dharmendra Singh
,
Prakash Chauhan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.qsa.2023.100151
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.qsa.2023.100151
Land Subsidence (LS) is a vertically downward motion of land surface due to various reasons such as natural processes and anthropogenic activities especially excessive exploitation of groundwater. LS has adverse effects on substantial infrastructural damage, severe environmental problems, extensive economic losses, and significant societal impacts. In this study, we investigated the LS of Ludhiana city, a densely populated, largest and major industrial city of Punjab state, India. We presented a comprehensive methodology of Short BAseline Subset Interferometric Synthetic Aperture Radar (SBASInSAR) for LS measurement using an open-source computational environment. We generated 197 interferograms from 65 Sentinel-1A images acquired in descending pass between September 2019–July 2022 for deriving radar line of sight (LOS) displacement time-series and mean LOS velocity. Deformation results showed that the southern, and south-eastern parts of the city had been consistently moving downward with a mean subsidence rate of 24.7 mm/yr, while in western, few small patches in eastern and northern regions it ranges from 2 to 21 mm/yr, 3–20 mm/yr and 4–16 mm/yr respectively. The standard deviation of mean LOS velocity was observed between 0.7 and 3.3 mm/yr with majority values < 2 mm/yr. SBASInSAR results were compared with six Groundwater Level (GWL) wells observations, and both measurements generally agreed well at GWL locations (W1–W6) except W1–W2. For assessing the influence of groundwater change on LS, a correlation analysis was performed, and correlation of 0.50, 0.47, 0.81, 0.77, 0.84, and 0.64 was observed at GWL stations (W1–W6) respectively. Overall comparison of InSAR and GWL measurements are found in good agreement and significantly correlated, which can provide sufficiently detailed information about LS.
Related Topics
Concepts
Interferometric synthetic aperture radar
Groundwater
Baseline (sea)
Subsidence
Standard deviation
Geology
Environmental science
Hydrology (agriculture)
Synthetic aperture radar
Geodesy
Physical geography
Remote sensing
Geography
Geomorphology
Statistics
Geotechnical engineering
Mathematics
Structural basin
Oceanography
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.qsa.2023.100151
- OA Status
- gold
- Cited By
- 7
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389410515
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4389410515Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.qsa.2023.100151Digital Object Identifier
- Title
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Investigation of groundwater induced land subsidence in Ludhiana City using InSAR and Sentinel-1 dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-06Full publication date if available
- Authors
-
Hari Shankar, Dharmendra Singh, Prakash ChauhanList of authors in order
- Landing page
-
https://doi.org/10.1016/j.qsa.2023.100151Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.qsa.2023.100151Direct OA link when available
- Concepts
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Interferometric synthetic aperture radar, Groundwater, Baseline (sea), Subsidence, Standard deviation, Geology, Environmental science, Hydrology (agriculture), Synthetic aperture radar, Geodesy, Physical geography, Remote sensing, Geography, Geomorphology, Statistics, Geotechnical engineering, Mathematics, Structural basin, OceanographyTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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