Land Subsidence Monitoring in Jagadhri City Using Sentinel 1 Data and DInSAR Processing

Abstract

DInSAR is a renowned method for estimating land subsidence based on the principles of interferometric synthetic aperture radar using different series of the temporal dataset. The present study has been performed using GMTSAR software with Sentinel 1 SAR data of C band for the duration of 2017-2019 (January to April) and focused particularly over the area of Jagadhri city which is situated 100 km away from Chandigarh, which has been identified under the potential threat of land subsidence. The DInSAR method has been applied in this study that came up with an outcome of three interferograms and yearly displacement that broadcast an update on the diagnosis of subsidence activity in the area. A total of six Single Look Complex (SLC) datasets were selectively chosen with a minimum temporal and spatial baseline so that the problem of decorrelation would be minimal. Goldstein filtering has been applied to the deburst interferograms which reduced the noise and, in turn, improved the quality of output. The city is located on the western bank of river Yamuna and about 55 km on the east of Ambala. Due to the presence of unconsolidated sediments in the aquifer system and over-exploitation of groundwater to meet the domestic needs has led to surface deformation in and around the city area. The outcome of this study identifies the area of depression quite distinctly while the accuracy has been assessed by ground survey. The rate of subsidence estimated approximately 4.98 cm/year which can prove to be disastrous over the course of time.

Does Environment has any role in recovery from COVID-19 pandemic? A case study from India.

“…Spatial data for total six different meteorological parameters -2-m Air Temperature (AT), Bias-corrected Total Precipitation (PRC), Speci c Humidity (Hm), Cloud Cover (CLD), Incoming Short-wave Radiation (ISWR), Wind Speed (WS), and six different air quality parameters -Aerosol Optical Depth (AOD), Tropospheric Column Nitrogen di-Oxide (NO 2 ), Total Column Ozone (O 3 ), Surface concentration of Carbon Monoxide (CO), Sulphur di-Oxide (SO 2 ) and Black Carbon (BC) have been incorporated in the present study. Remote Sensing based datasets are highly useful for various environmental studies (Bhatt et al, 2021;Das et al, 2017;Das and Gupta, 2021;Gupta et al, 2019;Moniruzzam et al, 2018;Nanda et al, 2018;Rousta et al, 2020), hence we have used such dataset from different sources. Monthly mean of these twelve environmental variables were further processed in ArcGIS software, adjoined and related with the monthly cumulative counts of con rmed and recovery cases for each months for each of those selected districts.…”

Correlation between the environmental parameters with outbreak pattern of COVID-19: A district level investigation based on yearlong period in India

“…Spatial data for total six different meteorological parameters - 2-m Air Temperature (AT), Bias-corrected Total Precipitation (PRC), Specific Humidity (Hm), Cloud Cover (CLD), Incoming Short-wave Radiation (ISWR), Wind Speed (WS), and six different air quality parameters - Aerosol Optical Depth (AOD), Tropospheric Column Nitrogen di-Oxide (NO 2 ), Total Column Ozone (O 3 ), Surface concentration of Carbon Monoxide (CO), Sulphur di-Oxide (SO 2 ) and Black Carbon (BC) have been incorporated in the present study. Remote Sensing based datasets are highly useful for various environmental studies (Bhatt et al, 2021; Das et al, 2017; Das and Gupta, 2021; Gupta et al, 2020b; Gupta et al, 2019; Moniruzzam et al, 2018; Nanda et al, 2018; Rousta et al, 2020), hence we have used such dataset from different sources. Monthly mean of these twelve environmental variables were further processed in ArcGIS software, adjoined and related with the monthly cumulative counts of confirmed and recovery cases for each months for each of those selected districts.…”

InSAR Time Series Analysis to Evaluate Subsidence Risk of Monumental Chandigarh City (India) and Surroundings

“…Due to the limited spatial resolution of GRACE, the detailed distributions of groundwater depletion and consequent ground subsidence have not been well understood but are within the resolving scope of recent InSAR surveys [25,26]. In recent years, the groundwater in the northern Indian cities, New Delhi [27] and Jagadhri [28] have been affected by subsidence, which was mapped by the InSAR time series analysis.…”