ارزیابی قابلیت تصاویر ماهواره‌ای سنتینل-2 و لندست 8 و تلفیق تصاویر در پهنه‌بندی فصلی شاخص‌های کیفی nsfwqi و irwqisc در آب‌های سطحی نمونه‌ پژوهش: رودخانه‌ کارون

در این پژوهش قابلیت دو نوع تصویر ماهواره‌ای اپتیکی سنتینل2 (s2b) و لندست 8 (l8) و همچنین قابلیت تلفیق این تصاویر در پهنه‌بندی فصلی شاخص‌های کیفی آب رودخانه‌ کارون ارزیابی شده است. برای هر فصل، 18 باند حاصل از تصاویر اولیه و تصاویر تلفیق‌شده به چهار روش شدت رنگ اشباع (ihs)، گرام اشمیدت (gst)، تبدیل براووی (bt) و تبدیل موجک (wt) به‌منظور یافتن بیشترین ضریب همبستگی با شاخص‌های کیفی nsfwqi و irwqisc و درنتیجه محاسبه‌ مدل‌های رگرسیون بهینه به کار رفته است. دو ایده‌ ارزیابی تاثیر مقدار تک‌پیکسل (1p) یا درنظرگرفتن میانگین یک پنجره‌ 3*3 (9p) تصاویر ورودی در بهبود ضریب همبستگی بین تصاویر و داده‌های میدانی نیز بررسی شده است.نتایج نشان داد بهترین نقشه‌های پهنه‌بندی شاخص nsfwqi در فصول بهار، تابستان، پاییز و زمستان به‌ترتیب براساس به‌کارگیری باند 2 تصویر مبتنی بر bt (حالت 9p)، باند 2 تصویر مبتنی بر ihs (حالت 1p)، باند 2 تصویر l8 (حالت 1p) و باند 4 تصویر l8 (حالت 1p) هستند. بهترین نقشه‌های پهنه‌بندی شاخص irwqisc در فصول بهار، تابستان، پاییز و زمستان به‌ترتیب براساس به‌کارگیری باند 1 تصویر  l8(حالت 1p)، باند 2 تصویر s2b (حالت 1p)، باند 2 تصویر  l8(حالت 1p) و باند 2 تصویر bt (حالت 1p) به دست آمده‌اند؛ همچنین برمبنای نتایج، روند تغییرات هر دو شاخص در نقشه‌های پهنه‌بندی کیفی از شمال به جنوب منطقه‌ پژوهش در هر فصل بسیار مشابه است. نتایج ارزیابی فصلی هر دو شاخص حاکی از وضعیت نامناسب آب رودخانه در بیشتر روزهای سال است.

Evaluation of Sentinel-2 and Landsat-8 Satellite Images Capability and Evaluation of Image Fusion Capability in Seasonal Zoning of NSFWQI and IRWQIsc Qualitative Indices in Surface Water(Case Study: Karoun River)

Extended AbstractIntroductionGiven the importance of surface water for drinking, agriculture, and industry, the protection of these resources against pollution is of great importance for national and regional health.The integration of satellite imagery to produce data with a higher information level is called image fusion. Fusion methods are generally divided into three categories: pixelbased methods, featurebased methods, and decisionbased methods.The main objective of this research is to optimize seasonal zoning of the water quality indices of Karoun River based on two indices of NSFWQI (National Health Foundation Water Quality Index) and IRWQIsc (Iran Surface Water Quality Index for Conventional Pollutants) and therefore, the fusion of Sentinel2 and Landsat 8 images will be evaluated. It is noteworthy that based on our knowledge the evaluation of image fusion methods in the field of qualitative indices zoning has not been conducted yet. Materials and MethodsMeasured Quality ParametersSeven sampling stations were selected for seasonal measurement of water quality parameters along a part of the Karoun River in 2018. The measured qualitative parameters included chemical oxygen demand, biological oxygen demand, dissolved oxygen, electrical conductivity, fecal coliform, PH, nitrate, phosphate, total hardness, and turbidity. NSFWQINSFWQI was introduced by the US National Health Organization in 1970. In this index, several questionnaires were filled out by experts in the United States, which are the basis for adjusting this index. Based on the answers, a curve was plotted for each parameter to determine the subindices of each parameter. IRWQIscTo evaluate the quality of rivers and compare their pollution rates in Iran concerning natural conditions and water resources problems, an index (IRWQIsc) was introduced to provide a good perspective about the quality status of water resources in Iran. Utilized satellite imageries Four pairs of geometrically and atmospherically corrected Sentinel2 and Landsat8 images were used to seasonally map the water quality indices in the Karoun River. Results and discussionCalculation of NSFWQI quality index and its seasonal zoningUsing the measured field qualitative parameters and equation, the NSFWQI parameter value was calculated. Next, by calculating the CR value between the input images and this index, optimal regression models based on the maximum CR values were developed to seasonally map the entire region. Calculating the IRWQIsc and its seasonal zoningBased on the measured qualitative field parameters, the value of the IRWQIsc index was calculated. Next, by calculating the CR value between the input images and this index, optimal regression models based on the maximum CR values were developed to seasonally map the entire region. ConclusionComparison of the output maps of NSFWQI and IRWQIsc indices showed that Karoun River’s quality status is inappropriate during most of the year and in most areas and it is best to use it with water treatment. According to the NSFWQI index in the NSFWQI2 map of the spring, the qualitative status of the study area changes from bad to moderate and to bad again considering north to south direction. In the summer NSFWQI2 map, the quality of the Karoun River changes from bad to moderate. In the autumn, based on the NSFWQI1 map, the qualitative status changes from bad to medium and again to bad. The qualitative status of the river in the winter shows moderate status. A survey of the qualitative status of the study area based on the IRWQIsc index also shows that in the spring, the river status changes from bad to relatively bad and again to bad. The qualitative change of the region in summer based on the IRWQI2 map changes from bad and relatively bad to relatively good and finally to relatively bad. In the autumn, the IRWQI1 map shows the change from bad to relatively bad and again to bad. In winter, according to the IRWQI2 map, the quality of the Karoun River changes from relatively good to relatively bad.