Analyzing urbanization induced groundwater stress and land deformation using time-series Sentinel-1 datasets applying PSInSAR approach

Awasthi, Shubham; Jain, Kamal; Bhattacharjee, Sutapa; Gupta, Vivek; Varade, Divyesh; Singh, Hemant; Narayan, Avadh Bihari; Buddilon, Alesandra

doi:10.1016/j.scitotenv.2022.157103

Cited by 24 publications

(16 citation statements)

References 73 publications

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“…During the estimation of land deformation velocity, the Sentinel-1 datasets were pre-processed by analyzing the Sentinel-1 SLC SAR datasets based on spatial baselines (B ⊥ ) and temporal baselines (B Temp ), selecting a master image, co-registering all the Sentinel-1 images with the master image, and applying a DEM assisted geometric correction. Next, the interferogram formation was carried out by calculating the phase difference between two complex SAR observations 41 . Then, the Sentinel-1 TOPS data was combined, and individual bursts present in the datasets were merged.…”

Section: Methodsmentioning

confidence: 99%

“…Lastly, uncorrelated noise phases exhibit high spatial correlation and low temporal correlation 30 , 75 . The following equations are used to estimate the PS candidates in the InSAR stack 30 , 41 : …”

Section: Methodsmentioning

confidence: 99%

“…After the phase unwrapping process, the spatial correlation of the look angle error (SCLA) in the persistent scattering pixels is calculated. Applying high-pass time and low-pass space filters on the unwrapped phase data yields the SCLA error 41 . The residual phase resulting from deformation is created by subtracting the SCLA error from the still present phase.…”

Section: Methodsmentioning

confidence: 99%

“…Their analysis showed the dominant uplift in the valley and surrounding hill ranges 40 . Awasthi 41 used the PSInSAR method to measure land deformation caused by groundwater stress as a result of fast urbanization. This study successfully differentiated the specific areas experiencing land subsidence using ground water and surface water as sources based on observed urban land subsidence in the region.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

Awasthi,

Jain,

Sahoo

et al. 2024

Sci Rep

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The Himalayas are highly susceptible to various natural disasters, such as the tectonically induced land deformation, earthquakes, landslides, and extreme climatic events. Recently, the Joshimath town witnessed a significantly large land subsidence activity. The phenomenon resulted in the development of large cracks in roads and in over 868 civil structures, posing a significant risk to inhabitants and infrastructure of the area. This study uses a time-series synthetic aperture radar (SAR) interferometry-based PSInSAR approach to monitor land deformation utilizing multi-temporal Sentinel-1 datasets. The line of sight (LOS) land deformation velocity for the Joshimath region, calculated for the year 2022–2023 using a PSInSAR-based approach, varies from − 89.326 to + 94.46 mm/year. The + ve sign indicates the LOS velocity/displacement away from the SAR sensor, whereas − ve sign signifies the earth's movement towards the SAR sensor in the direction of LOS. In addition, the study investigates feature tracking land displacement analysis using multi-temporal high-resolution Planet datasets. The result of this analysis is consistent with the PSInSAR results. The study also estimated the land deformation for the periods 2016–2017, 2018–2019, and 2020–2021 separately. Our results show that the Joshimath region experienced the highest land deformation during the year 2022–2023. During this period, the maximum land subsidence was observed in the north-western part of the town. The maximum LOS land deformation velocity + 60.45 mm/year to + 94.46 mm/year (2022–2023), occurred around Singhdwar, whereas the north and central region of the Joshimath town experienced moderate to high subsidence of the order of + 10.45 mm/year to + 60.45 mm/year (2022–2023), whereas the south-west part experienced an expansion of the order of 84.65 mm/year to − 13.13 mm/year (2022–2023). Towards the south-east, the town experienced rapid land subsidence, − 13.13 mm/year to − 5 mm/year (2022–2023). The study analyzes the causative factors of the observed land deformation in the region. Furthermore, this work assesses the ground conditions of the Joshimath region using UAV datasets acquired in the most critically affected areas such as Singhdhaar, Hotel Mountain View, Malhari Hotel, and Manoharbagh. Finally, the study provides recommendations and future prospects for the development policies that need to be adopted in the critical Himalayan regions susceptible to land deformation. The study suggests that land deformation in the region is primarily attributed to uncontrolled anthropogenic activities, infrastructural development, along with inadequate drainage systems.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

Awasthi,

Jain,

Sahoo

et al. 2024

Sci Rep

Self Cite

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“…It plays a pivotal role in detecting and measuring deformations associated with seismic events, contributing significantly to earthquake research [13]. It aids in studying the impact of urbanization on groundwater stress and land deformation [14]. Its utility extends to assessing the structural stability and safety of artificial structures including dams [15], bridges [16], tunnels [17], and buildings [18].…”

Section: Introductionmentioning

confidence: 99%

Non-Linear PSInSAR Analysis of Deformation Patterns in Islamabad/Rawalpindi Region: Unveiling Tectonics and Earthquake-Driven Changes

Afzal,

Balz,

Asghar

2024

Remote Sensing

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The standard Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique, which is commonly used for surface motion analysis, assumes linear deformation velocities. While effective for monitoring urban subsidence over short periods, it falls short when dealing with non-linear, earthquake-related deformations over extended timeframes. To address this limitation, we use a non-linear PSInSAR technique, which is an enhancement of PSInSAR, to identify non-linear deformation patterns. We processed Sentinel-1A images from ascending and descending orbits in the Islamabad/Rawalpindi region from December 2015 to January 2023 using non-linear PSInSAR. By calculating the differences in deformation, we analyzed surface movements and assessed the impact of the 2017 earthquake on urban areas. Our findings reveal that the earthquake significantly increased the deformation in ascending and descending orbit tracks, with an average deformation of up to 70 mm/yr and a line-of-sight movement of up to 30 mm/yr. Our observations indicate that the deformation is directed towards the line of sight in the north and south of the deformed area, suggesting subsidence between the two uplifting faults, potentially linked to a concealed fault line along the deformation zone boundary. This contradicts previous arguments, suggesting that water extraction is the leading cause of deformation. Our analysis with non-linear PSInSAR demonstrates that tectonics play a significant role in deformation, providing valuable insights into tectonic-activity-induced deformations in urban areas over the long term.

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Hydrometeorological analysis of July-2023 floods in Himachal Pradesh, India

Gupta,

Syed,

Pathania

et al. 2024

Nat Hazards

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Analyzing urbanization induced groundwater stress and land deformation using time-series Sentinel-1 datasets applying PSInSAR approach

Cited by 24 publications

References 73 publications

Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

Non-Linear PSInSAR Analysis of Deformation Patterns in Islamabad/Rawalpindi Region: Unveiling Tectonics and Earthquake-Driven Changes

Hydrometeorological analysis of July-2023 floods in Himachal Pradesh, India

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