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جغرافیا و برنامه ریزی محیطی، جلد ۲۶، شماره ۱، صفحات ۱۲۹-۱۴۶
عنوان فارسی ارزیابی اثر بخار آب ستونی جو بر محاسبات جابجایی سطح زمین در روش DInSAR (سنجنده ASAR)
چکیده فارسی مقاله چکیده علی‌رغم مزایای استفاده از داده هایSAR نسبت به سایر داده‌های دورسنجی، می توان از اتمسفر و علی‌الخصوص بخار آب ستونی جو به عنوان اصلی ترین عامل محدود کننده در دقت محاسبات تداخل‌سنجی راداری نام برد. بخار آب می‌تواند با اثر بر روی فاز امواج راداری دقت محاسبات را به شدت تحت تاثیر قرار دهد. در این پژوهش با استفاده از داده های همزمان بخار آب سنجنده MERIS و داده های راداری سنجنده ASAR، رابطه بین تغییرات مقدار ستونی بخار آب موجود در جو و خطای ناشی از آن در محاسبات جابجایی سطح زمین در 16 ایستگاه مختلف در ایران مورد بررسی قرار گرفت. با مشاهده وضعیت تغییرات بخار آب اتمسفری و مقایسه مقادیر جابجایی ثبت شده توسط GPS و روش DInSAR مشخص گردید که اولاً در روش تفاضل تداخل‌سنجی دو گذره افزایش مجموع میزان بخار آب در زمانهایاصلی و فرعی در یکتداخل نگار به 4 گرم بر سانتیمتر مربع می‌تواند خطایی تا 6 سانتی متر در برآورد میزان جابجایی عمودی سطح زمین ایجاد کند. ثانیاً مشخص گردید که با افزایش میزان بخار آب، خطای ناشی از آن بر روی محاسبات تداخل‌سنجی جابجایی به صورت یک تابع تواندار افزایش پیدا می‌کند. در انتها جهت تصحیح مقادیر جابجایی بدست آمده از سنجنده ASAR در حالت بدون تصحیح اتمسفری در سایر مطالعات، رابطه‌ای ارائه گردید.
کلیدواژه‌های فارسی مقاله تداخل‌سنجی راداری، بخار آب اتمسفری، MERIS، ASAR،
عنوان انگلیسی
چکیده انگلیسی مقاله 1-Introduction In Interferometry Synthetic Aperture RADAR (InSAR) method, we use phase values of radar pulse. Interferometric radar remote sensing techniques are those in which the phase difference "image" or interferogram, of radar images pairs is the prime observable, with geophysical descriptions of the surface derived thence. Interferometric analysis can be estimate distance with the accurate interpretation of pulse phase temporal and differential dispute. This distance values will be convert to topography, velocity and displacement vector, according to imagine geometry. This technique has advantages of surface displacement monitoring ability with centimeter precision on the wide area (about square kilometer) and acquisition steadily.In this study, we have introduced a relation between atmospheric column water vapor content and their error induced on interferometric earth surface displacement measuring in DInSAR method 2-pass mode for Envisat/ASAR sensor. We have used MERIS water vapor product and ASAR SAR simultaneous data and GPS vertical displacement values at 16 regions in Iran.Data used in this project divided into two categories: satellite and ground data. Satellite data prepared from European Space Agency (ESA) covering Envisat/ASAR and MERIS product and ground data prepared from the Iran National Cartographic Center (INCC) covering GPS data on the Iran Permanent GPS Network (IPGN). 2- Methodology DInSAR is the estimation of differences in surface location (both plan position and height) from interferometry. The principle is the same as the use of ground surveying to collect data from which contour maps are made. Assume that we have a raster (digital) contour map of a survey conducted 5 years ago and a second raster contour map that was completed last week. (Raster means made up of pixels in this case, the pixel values represent elevation). If the two maps are exactly the same then subtracting one from the other on a pixel-by-pixel basis will produce an array of zeros. If any pixel in the difference map has a non-zero value then change has occurred, and the magnitude of that change is proportional to the pixel value in the difference image. In effect, we have used the first (old) raster contour map to ‘remove the topography’ from the newer map. The same could be done with two interferometric DEMs derived from SAR images collected before and after a significant event such as an earthquake. The difference between the two interferometric DEMs shows the changing of the surface geometry resulting from the earthquakeor any same event. Phase difference between two SAR images acquisited in two various time and same location, create a pattern as Fring in interferograms. Figure 4 represent interferogram obtained from 2009 and 2010 ASAR data in Mashhad Region of Iran. 3– Discussion First, vertical displacement values obtained by GPS network has compared with displacement values by DInSAR, for data analysis and making model between atmospheric column water vapor content and interferometric earth surface displacement measuring error induced. Figure 5 represents a procedure strategy for this aim. Note that in first step, displacement changes to be checked for each station (in terms of having or not having displacement) with evaluate actual displacement values extracted from IPGN network. λ/2 is assessment criteria in DInSAR method if λ equal wavelength. Any ASAR sensor with λ wavelength, can be estimate surface displacement to λ/2 with millimeter precision because any color cycle fring equal λ/2 displacement. In here our threshold of having or not having displacement is 28mm because we use ASAR sensor (C band) with 5.6cm wavelength.The vertical displacement value was extracted from all IPGN ground networks in the temporal epoch of Envisat Images. Negative and positive values represent ground subsidence and uplift respectively. Displacement values derived by IPGN represent in Appendix-1. Also, vertical displacement values derived from Differential Interferometry SAR method and Envisat/ASAR data in all regions and stations. Stations were selected based on wavelength sensitivity and 28mm displacement threshold. These stations are places where can achieve to displacement with DInSAR method and C band SAR data. But for other stations, they have not been formed fring in interferogram and in term radar has not displaced while it really has been displace. It is therefore, remains displacement values can be imputed to atmospheric water vapor effects. This remains values derived from compare IPGN and DInSAR results. In continue, MERIS atmospheric water vapor content Values were calculated in master and slave date. Before modelling between variables, first must evaluate correlation of gathering results. We used correlation index (R2) and correlation (R) in this study. The correlation (R) can be used to results evaluation before modelling but the correlation index (R2) can be used to model assessment after modelling.After evaluating correlation results of DInSAR and IPGN values and ensure the high correlation must before modelling, first fit a linear regression on the MERIS atmospheric column water vapor content and displacement measured error induced. This step is necessary for ensuring the variable behavior type and more precision in the final model, in continuing. So in this step, it was evaluated correlation results between water vapor content and difference values of DInSAR and IPGN displacement. After assessing variable behavior and ensuring the high correlation in compare to each other, should be determining their behavior type and models. In here, besides the linear function, we fitted multinomial, exponential and power regression on the variables in comparing to each other. These functions were applied on the 2 region from 3 regions separately until to be used for model testing on the third region.In this step, for a closer look at models in comparison to each other and models testing results comparison, correlation index and RMSE values estimated in all regions. 4– Conclusion In overall, various regressions in AZR and KRJ region have more correlated in compare with other regions because the data gathered of this region have maximum correlation values. Also, determined that power function is more accurate in comparing with other regressions. According to obtained results, RMSE values obtained from various models in three regions are very near to each other. In this study, were found that atmosphere is limiting factor in displacement measuring of DInSAR method, due to comparison of GPS values in IPGN and DInSAR values. Also we demonstrated that increasing atmospheric column water vapor content can be increase induce error of displacement measuring that confirms Zebker (1997) comment.
کلیدواژه‌های انگلیسی مقاله Radar Interferometry, Atmospheric Water Vapor, MERIS, ASAR
نویسندگان مقاله محمد صادق پاکدامن | mohammad sadegh


نشانی اینترنتی http://gep.ui.ac.ir/article_18713_0a55845d544a566bd3486e7ed44d1f4c.pdf
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