محاسبه فرونشست با روش SBAS و بررسی رابطه آن با داده ‌های پیزومتری با استفاده از تصاویر سری زمانی ماهواره SENTINEL-1

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد مهندسی سنجش از دور، گروه مهندسی نقشه برداری، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، کرمان، ایران

2 استادیار، گروه مهندسی نقشه برداری، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، کرمان، ایران

3 استاد، گروه فتوگرامتری و اطلاعات جغرافیایی، دانشگاه لایبنیتس، هانوفر، آلمان

چکیده

فرونشست پدیده ای است بسیار مخرب و خطرناک که علاوه بر خطرات جانی برای انسان ها، می تواند به تاسیسات زیربنایی شهرها نیز آسیب برساند. یکی از دلایل ایجاد آن استخراج بی رویه آب زیرزمینی می باشد که به طور گسترده در دشت‌های ایران اتفاق می‌افتد. تداخل سنجی سری زمانی تصاویر راداری یکی از روش‌های مهم برای بررسی دقیق و پیوسته فرونشست است. اما مشکل اصلی این روش حذف پیکسل‌ها با همبستگی پایین در چرخه پردازش است. در این تحقیق برای غلبه بر این مشکل، فرونشست دشت رفسنجان با استفاده از روش سری زمانی SBAS بهبود یافته برپایه همدوسی بررسی شده است. داده‌های مورد استفاده 15 تصویر ماهواره SENTINEL-1 مربوط به محدوده زمانی مهرماه 1394 تا مهرماه 1395 است و50 تداخل‌نگاشت تولید شده است. نتایج حاصله توانایی این روش در استفاده از پیکسل‌ها با همبستگی پایین مربوط به مناطق پوشش گیاهی را نشان می‌دهد. بیشترین مقدار نرخ فرونشست 284میلی‌متر در سال برای محدوه دشت رفسنجان-بهرمان و 252میلی‌متر درسال برای محدوده دشت رفسنجان-کشکوییه در راستای خط دید ماهواره بدست آمد. برای بررسی رابطه بین نتایج SBAS بهبود یافته و سطح آب چاه‌های منطقه از ضریب همبستگی پیرسون و جهت مدل کردن رابطه از مدل رگرسیون خطی استفاده شد که نتایج بیانگر رابطه خطی مستقیم قوی است. همچنین مدل رگرسیون خطی قابلیت مدل کردن رابطه را با سطح اطمینان 95% دارا می‌باشد. برای بررسی معنی دار بودن مدل رگرسیون خطی از آزمون تحلیل واریانس (ANOVA) و به منظور بررسی خودهمبستگی باقی ماندها از آزمون دوربین- واتسون استفاده شد که نتایج آن معنی دار بودن مدل و استقلال مشاهدات را تایید می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

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

نویسندگان [English]

  • Ali Roozban 1
  • Ali Esmaeili 2
  • Mehdi Motagh 3
1 Senior Expert in Remote Sensing Engineering, Dept. of Mapping Engineering, Graduate University of Industrial and Advanced Technology, Kerman, Kerman, Iran
2 Assistant Professor, Dept. of Mapping Engineering, Graduate University of Industrial and Advanced Technology, Kerman, Kerman, Iran
3 Professor, Department of Photogrammetry and Geographic Information, Leibniz University, Hannover, Germany
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Subsidence
  • Time-Series
  • InSAR
  • Improved SBAS
  • SENTINEL-1
  • Linear Regression Model
  • Pearson Correlation Coefficient

Smiley face

مراجع

  1. Sharifikia, "Determining the extent and amplitude of land subsidence using radar interferometry (D-InSAR) method in Nogh-Bahrman plain " (in Persian), The Journal of Spatial Planning, vol. 16, no. 3, pp. 55-77, 2012.
  2. E. Hunt, Geologic hazards: a field guide for geotechnical engineers. CRC Press, 2007.
  3. Toufigh and B. Sabet, "Prediction of future land subsidence in Kerman, Iran, due to groundwater withdrawal," in International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 1996, vol. 8, no. 33, p. 344A.
  4. M. Mousavi, A. Shamsai, M. H. E. Naggar, and M. Khamehchian, "A GPS-based monitoring program of land subsidence due to groundwater withdrawal in Iran," Canadian journal of civil engineering, vol. 28, no. 3, pp. 452-464, 2001.
  5. Motagh et al., "Land subsidence in Iran caused by widespread water reservoir overexploitation," Geophysical Research Letters, vol. 35, no. 16, 2008.
  6. Dehghani, M. Rastegarfar, R. A. Ashrafi, N. Ghazipour, and H. R. Khorramrooz, "Interferometric SAR and geospatial techniques used for subsidence study in the Rafsanjan plain," Am J Environ Eng, vol. 4, no. 2, pp. 32-40, 2014.
  7. Bagheri, M. Dehghani, A. Esmaeily, and V. Akbari, "Assessment of land subsidence using interferometric synthetic aperture radar time series analysis and artificial neural network in a geospatial information system: a case study of Rafsanjan Plain," Journal of Applied Remote Sensing, vol. 13, no. 4, p. 044530, 2019.
  8. Tavakkoli-Estahbanati, M. Dehghani, and A. R. Amiri-Simkooei, "Assessment of Conventional Unwrapping Methods Presented to Unwrap Interferometric Phases," (in Persian), Journal of “Radar”, vol. 5, no. 3, pp. 1-14, 2017.
  9. Awasthi, K. Jain, V. Mishra, and A. Kumar, "An approach for multi-dimensional land subsidence velocity estimation using time-series Sentinel-1 SAR datasets by applying persistent scatterer interferometry technique," Geocarto International, pp. 1-32, 2020.
  10. Yastika, N. Shimizu, and H. Abidin, "Monitoring of long-term land subsidence from 2003 to 2017 in coastal area of Semarang, Indonesia by SBAS DInSAR analyses using Envisat-ASAR, ALOS-PALSAR, and Sentinel-1A SAR data," Advances in Space Research, vol. 63, no. 5, pp. 1719-1736, 2019.
  11. [11] Fárová, J. Jelének, V. Kopačková-Strnadová, and P. Kycl, "Comparing DInSAR and PSI techniques employed to Sentinel-1 data to monitor highway stability: A case study of a massive dobkovičky landslide, Czech Republic," Remote Sensing, vol. 11, no. 22, p. 2670, 2019.
  12. Pawluszek-Filipiak and A. Borkowski, "Integration of DInSAR and SBAS Techniques to determine mining-related deformations using sentinel-1 data: The case study of Rydułtowy mine in Poland," Remote Sensing, vol. 12, no. 2, p. 242, 2020.
  13. Bedini, "Land Subsidence Assessment by Using Persistent Scatterer Interferometry of Sentinel-1 Data: A Study of Vienna City, Austria," International Journal of Innovative Technology and Interdisciplinary Sciences, vol. 4, no. 1, pp. 604-611, 2021.
  14. J. Delgado Blasco, M. Foumelis, C. Stewart, and A. Hooper, "Measuring urban subsidence in the Rome metropolitan area (Italy) with Sentinel-1 SNAP-StaMPS persistent scatterer interferometry," Remote Sensing, vol. 11, no. 2, p. 129, 2019.
  15. J. D’Aranno, A. Di Benedetto, M. Fiani, M. Marsella, I. Moriero, and J. A. Palenzuela Baena, "An Application of Persistent Scatterer Interferometry (PSI) Technique for Infrastructure Monitoring," Remote Sensing, vol. 13, no. 6, p. 1052, 2021.
  16. Orhan, "Monitoring of land subsidence due to excessive groundwater extraction using small baseline subset technique in Konya, Turkey," Environmental Monitoring and Assessment, vol. 193, no. 4, pp. 1-17, 2021.
  17. Yalvac, "Validating InSAR-SBAS results by means of different GNSS analysis techniques in medium-and high-grade deformation areas," Environmental monitoring and assessment, vol. 192, no. 2, pp. 1-12, 2020.
  18. Tong and D. Schmidt, "Active movement of the Cascade landslide complex in Washington from a coherence-based InSAR time series method," Remote Sensing of Environment, vol. 186, pp. 405-415, 2016.
  19. Mehryar, R. Sliuzas, A. Sharifi, and M. Van Maarseveen, "The water crisis and socio-ecological development profile of Rafsanjan Township, Iran," WIT Transactions on Ecology and the Environment, vol. 199, pp. 271-285, 2015.
  20. Motagh et al., "Quantifying groundwater exploitation induced subsidence in the Rafsanjan plain, southeastern Iran, using InSAR time-series and in situ measurements," Engineering Geology, vol. 218, pp. 134-151, 2017.
  21. Gatelli, A. M. Guamieri, F. Parizzi, P. Pasquali, C. Prati, and F. Rocca, "The wavenumber shift in SAR interferometry," IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no. 4, pp. 855-865, 1994.
  22. Usai, A New Approach for Longterm Monitoring of Deformations by Differential SAR Interferometry. TU Delft, Delft University of Technology, 2001.
  23. Berardino, G. Fornaro, R. Lanari, and E. Sansosti, "A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms," IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 11, pp. 2375-2383, 2002.
  24. Lanari, O. Mora, M. Manunta, J. J. Mallorquí, P. Berardino, and E. Sansosti, "A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms," IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 7, pp. 1377-1386, 2004.
  25. R. Lauknes, H. A. Zebker, and Y. Larsen, "InSAR deformation time series using an-norm small-baseline approach," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 1, pp. 536-546, 2011.
  26. Agram and M. Simons, "A noise model for InSAR time series," Journal of Geophysical Research: Solid Earth, vol. 120, no. 4, pp. 2752-2771, 2015.
  27. A. Rosen et al., "Synthetic aperture radar interferometry," Proceedings of the IEEE, vol. 88, no. 3, pp. 333-382, 2000.
  28. Ferretti, C. Prati, and F. Rocca, "Permanent scatterers in SAR interferometry," IEEE Transactions on geoscience and remote sensing, vol. 39, no. 1, pp. 8-20, 2001.
  29. Sandwell, R. Mellors, X. Tong, M. Wei, and P. Wessel, "Gmtsar: An InSAR processing system based on generic mapping tools," Scripps Institution of Oceanography, 2011.
  30. Sansosti, P. Berardino, M. Manunta, F. Serafino, and G. Fornaro, "Geometrical SAR image registration," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 10, p. 2861, 2006.
  31. Miranda, "Definition of the TOPS SLC deramping function for products generated by the S-1 IPF," Eur. Space Agency, Paris, France, Tech. Rep, 2014.
  32. C. Montgomery and G. C. Runger, Applied statistics and probability for engineers, Sixth Edition ed. John Wiley & Sons, 2010.
رادار
دوره 10، شماره 1 - شماره پیاپی 27
شماره پیاپی 27، فصلنامه بهار و تابستان
تیر 1401
صفحه 14-27

فایل ها

سابقه مقاله

  • تاریخ دریافت: 02 اردیبهشت 1400
  • تاریخ بازنگری: 05 بهمن 1400
  • تاریخ پذیرش: 02 اسفند 1401

هم رسانی

ارجاع به این مقاله

آمار