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مقایسه روشهای پرکردن پیکسلهای فاقد داده در تصاویر ماهواره لندست 7 ETM+ در برآورد نقشه ضریب گیاهی | ||
| تحقیقات آب و خاک ایران | ||
| مقاله 3، دوره 47، شماره 4، دی 1395، صفحه 665-676 اصل مقاله (1.13 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2016.59974 | ||
| نویسندگان | ||
| مریم طاهرپرور؛ نادر پیرمرادیان* ؛ مجید وظیفه دوست | ||
| دانشگاه گیلان | ||
| چکیده | ||
| دادههای ماهوارهای لندست 7 ETM+ بهطور گستردهای در مطالعات پوشش گیاهی و توزیع مکانی ضریب گیاه در مقیاس منطقهای و جهانی استفاده میشوند اما شکست تصحیح کننده خط اسکن (SLC) در سال 2003 تا حد زیادی سودمندی آن را کاهش داده است. علاوه بر این، شکست مذکور دائمی است و تلاشهای متعاقب آن برای بازیابی تصحیح کننده خط اسکن ناموفق بوده، بنابراین راه لازم و عملی برای رسیدگی به این مشکل پر کردن پیکسلهای فاقد داده در تصایر SLC-off است. اگرچه روشهای پیشنهادی مختلفی برای پر کردن شکافها وجود دارد اما کیفیت تصاویر پر شده در مناطق ناهمگن هنوز هم برای بیشتر برنامههای کاربردی رضایتبخش نیست. این پژوهش به مقایسه دو روش زمین آماری و استفاده از دادههای کمکی مودیس برای پر کردن شکافها در تصاویر SLC-off در تصویر لندست 7 ETM+ و با هدف برآورد مقادیر ضریب گیاهی گیاه برنج در بخش شرقی واحد عمرانی F1 از شبکه آبیاری و زهکشی سفیدرود پرداخته است. نتایج نشان داد که برآوردها در روش IDW با مقدار NRMSE برابر 09/6 درصد دارای بیشترین دقت بوده و روشهای FGMAD و FAD بهترتیب با مقدار NRMSE برابر 75/14 و 97/14 در رتبههای بعدی از نظر دقت برآورد قرار میگیرند. روش FDCAD، کمترین دقت را در برآوردها داشت. | ||
| کلیدواژه‌ها | ||
| سنجش از دور؛ زمین آمار؛ تبخیر-تعرق | ||
| عنوان مقاله [English] | ||
| Comparison of gap filling methods in Landsat 7 ETM+ images to estimate crop coefficient | ||
| نویسندگان [English] | ||
| Maryam Taherparvar؛ Nader Pirmoradian؛ Majid Vazifedoust | ||
| University of Guilan | ||
| چکیده [English] | ||
| Landsat 7 ETM+ data is widely used in studies of the spatial distribution Kc and vegetation cover parameters in regional and global scales but SLC failure has greatly reduces its usefulness. Additionally, the failure is permanent and has failed subsequent attempts to recover the SLC, so required and practical way to address this problem is filling the pixels of missed data in the SLC-off images. Although, there are several proposed methods to fill the gap, but still have filled images quality in heterogeneous area is not satisfactory for more applications. This study was conducted to compare the geostatistics and MODIS auxiliary data methods to fill the pixels of missed data in the SLC-off images. The results showed that the IDW method with NRMSE 6.09% was the best method. The fusion with auxiliary images (MODIS) and ordinary Kriging methods resulted in NRMSE 14.75 and 16.9, respectively. The method of fusion with classified auxiliary images (MODIS) presented the lowest accuracy in estimating missed data. | ||
| کلیدواژه‌ها [English] | ||
| remote sensing, Geostatistics, Evapotranspiration | ||
| مراجع | ||
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