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 کارشناسی ارشد ، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران

2 استادیار، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران

3 استاد، 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 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
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

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رادار
دوره 10، شماره 1 - شماره پیاپی 27
شماره پیاپی 27، فصلنامه بهار و تابستان
تیر 1401
صفحه 14-27

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سابقه مقاله

  • تاریخ دریافت: 02 اردیبهشت 1401
  • تاریخ بازنگری: 26 تیر 1401
  • تاریخ پذیرش: 25 مرداد 1401
  • تاریخ انتشار: 30 شهریور 1401

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