An approach for multi-dimensional land subsidence velocity estimation using time-series Sentinel-1 SAR datasets by applying persistent scatterer interferometry technique

Awasthi, Shubham; Jain, Kamal; Mishra, Virendra; Kumar, Ajeet

doi:10.1080/10106049.2020.1831624

Cited by 12 publications

(11 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, due to the characteristics of SAR side-view imaging, the SBAS-InSAR technique based on single-track data can only capture the projection of the real surface deformation in the radar line of sight (LOS) direction. For regional subsidence structures with both vertical deformation and horizontal movement, the results generated by the conventional SBAS-InSAR technique are highly inaccurate and do not represent the true surface deformation 23 . The Multidimensional SBAS-InSAR (MSBAS-InSAR) technique was developed to compensate for the shortcomings of the single-track InSAR technique, which can only capture LOS deformation.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and analysis of surface deformation in alpine valley areas based on multidimensional InSAR technology

Fan

Ren

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Joshimath has received much attention for its massive ground subsidence at the beginning of the year. Rapid urbanization and its unique geographical location may have been one of the factors contributing to the occurrence of this geological disaster. In high mountain valley areas, the complex occurrence mechanism and diverse disaster patterns of geological hazards highlight the inadequacy of manual monitoring. To address this problem, the inversion of deformation of the Joshimath surface in multiple directions can be achieved by multidimensional InSAR techniques. Therefore, in this paper, the multidimensional SBAS-InSAR technique was used to process the lift-track Sentinel-1 data from 2020 to 2023 to obtain the two-dimensional vertical and horizontal deformation rates and time series characteristics of the Joshimath ground surface. To discover the causes of deformation and its correlation with anthropogenic activities and natural disasters by analyzing the spatial and temporal evolution of surface deformation. The results show that the area with the largest cumulative deformation is located in the northeastern part of the town, with a maximum cumulative subsidence of 271.2 mm and a cumulative horizontal movement of 336.5 mm. The spatial distribution of surface deformation is based on the lower part of the hill and develops towards the upper part of the hill, showing a trend of expansion from the bottom to the top. The temporal evolution is divided into two phases: gentle to rapid, and it is tentatively concluded that the decisive factor that caused the significant change in the rate of surface deformation and the early onset of the geological subsidence hazard was triggered by the 4.7 magnitude earthquake that struck near the town on 11 September 2021.

show abstract

Section: Introductionmentioning

confidence: 99%

Monitoring and analysis of surface deformation in alpine valley areas based on multidimensional InSAR technology

Fan

Ren

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The amplitude dispersion index is calculated as the ratio of the standard deviation ( ) to the mean of the amplitude values for each pixel in the set of SLC images. The phase-based approach considers the components that make up the interferogram, including the deformation phase, phase due to precise orbit errors, atmospheric phase screen, topographic correction errors, and uncorrelated noise-phases 28 . The deformation phases exhibit minimal spatial and temporal correlation.…”

Section: Methodsmentioning

confidence: 99%

“…The PSInSAR has been used to analyze earth surface dynamics and processes including land subsidence, groundwater level change, earthquakes, and landslides 28 , 32 , 33 . Subsequently, another study used PSInSAR for analyzing the extremely long-term spatiotemporal evolution of land subsidence in the region of Taiyuan, China, and concluded that ground deformation was concentrical around the areas of high groundwater loss 34 .…”

Section: Introductionmentioning

confidence: 99%

“…Various studies have also used Cossi-Corr based methods for measuring surface dynamics happening due to geological processes, climate change scenarios, or anthropogenic activities 14,[23][24][25] . Time-Series InSAR-based methods, like PSInSAR, provide high-resolution land deformation measurements with very high accuracy, nearly on a millimeter scale [26][27][28] . The PSInSAR method was initially developed by Ferretti et al in 2007 based on the approach of selecting permanent scattering candidate information from the interferometric stack using amplitude dispersion methods 29 .…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

Awasthi,

Jain,

Sahoo

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Differential InSAR (DInSAR), which is the classical InSAR method, detects surface displacement between two SAR acquisition times. However, this technique restricts the monitoring of the temporal change in surface deformation and has a few disadvantages that limit its usage [ 19 , 20 , 21 , 22 , 23 ]. These disadvantages consist of errors such as perpendicular baseline, spatial–temporal decorrelation, digital elevation model error, and tropospheric delay.…”

Section: Introductionmentioning

confidence: 99%

Land Subsidence and Its Relations with Sinkhole Activity in Karapınar Region, Turkey: A Multi-Sensor InSAR Time Series Study

Orhan

Oliver-Cabrera

Wdowinski

et al. 2021

Sensors

View full text Add to dashboard Cite

The Karapinar basin, located in the Central Anatolian part of Turkey, is subjected to land subsidence and sinkhole activity due to extensive groundwater withdrawal that began in the early 2000s. In this study, we use Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS), and groundwater level data to monitor and better understand the relations between groundwater extraction, land subsidence, and sinkhole formation in the Karapinar basin. The main observations used in the study are InSAR-derived subsidence velocity maps calculated from both Sentinel-1 (2014–2018) and COSMO-SkyMed (2016–2017) SAR data. Our analysis reveals broad areas of subsidence with rates exceeding 70 mm/yr. The InSAR-derived subsidence was compared with GNSS data acquired by a continuously operating GNSS station located in the study area, which show a similar rate of subsidence. The temporal characteristic of both InSAR and GNSS time series indicate a long-term subsidence signal superimposed by seasonal variability, which follows the overall groundwater level changes, with over 80% cross-correlation consistency. Our results also indicate that sinkhole activity is limited to slow subsidence areas, reflecting strong cohesion of near-surface rock layers that resist subsidence but yield to collapse in response to aquifer system deformation induced by groundwater extraction.

show abstract

An approach for multi-dimensional land subsidence velocity estimation using time-series Sentinel-1 SAR datasets by applying persistent scatterer interferometry technique

Cited by 12 publications

References 48 publications

Monitoring and analysis of surface deformation in alpine valley areas based on multidimensional InSAR technology

Monitoring and analysis of surface deformation in alpine valley areas based on multidimensional InSAR technology

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

Land Subsidence and Its Relations with Sinkhole Activity in Karapınar Region, Turkey: A Multi-Sensor InSAR Time Series Study

Contact Info

Product

Resources

About