مقایسه روش‌های پرکردن پیکسل‌های فاقد داده در تصاویر ماهواره لندست 7 ETM+ در برآورد نقشه ضریب گیاهی

نوع مقاله: مقاله پژوهشی

نویسندگان

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

موضوعات


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
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