Measuring subsidence using the SBAS method and SENTINEL-1 time-series data in relation to piezometric data

Document Type : Original Article

Authors

1 Master's degree, Graduate University of Industrial and Advanced Technology, Kerman, Iran

2 Assistant Professor, Graduate University of Industrial and Advanced Technology, Kerman, Iran

3 Professor, Leibniz University, Hannover, Germany

Abstract

Subsidence is a very destructive and dangerous phenomenon that, in addition to endangering human life, can also damage the infrastructure of cities. One of the reasons for its creation is the uncontrolled extraction of groundwater, which occurs widely in the plains of Iran. The time Series InSAR method is one of the important methods for accurate and continuous monitoring of subsidence. But the main problem with this method is the removal of pixels with low correlation in the processing cycle. In this study, to overcome this problem, subsidence of Rafsanjan plain has been investigated using the improved SBAS time series method based on coherence. The data used are 15 images of SENTINEL-1 satellite related to the period from October 2015 to October 2016 and 50 interferograms are generated. The results show the ability of this method to use all pixels of the interferogram, even pixels related to vegetation areas with low correlation. The highest subsidence rate was 284 mm per year for Rafsanjan-Bahrman plain and 252 mm per year for Rafsanjan-Kashkoyeh plain along the satellite line of sight. To investigate the relationship between the improved SBAS results and the water level of wells in the region, Pearson correlation coefficient was used, and to model the relationship, a linear regression model was used. The results indicate a strong direct linear relationship. Also, the linear regression model has the ability to model the relationship with a 95% confidence level. Analysis of variance (ANOVA) was used to test the significance of the linear regression model and Durbin–Watson test was used to evaluate the autocorrelation in the residuals. The results confirm the significance of the model and the independence of the observations.

Keywords

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References

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History

  • Receive Date: 22 April 2022
  • Revise Date: 17 July 2022
  • Accept Date: 16 August 2022
  • Publish Date: 21 September 2022

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