ارزیابی صحت روش‌های سنجش از دور در استخراج و پایش تغییرات پهنۀ آبی دریاچۀ زریبار

نوع مقاله : پژوهشی

نویسندگان

1 استاد، مرکز سنجش‏ازدور، دانشگاه علوم کشاورزی و منابع طبیعی ساری

2 کارشناس ارشد، دانشکدۀ علوم محیطی، مؤسسۀ آموزش عالی آبان هراز آمل

3 استادیار، گروه مهندسی آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری

10.22059/ije.2023.342056.1632

چکیده

منابع آب نقش بسیار مهمی در زندگی انسان، گیاهان و جانوران ایفا می‏کند. استخراج و تعیین میزان تغییرات نواحی آبی می‏تواند در پیش‏بینی بسیاری از مشکلات کارساز باشد. امروزه، روش‏ها و الگوریتم‏های زیادی با استفاده از داده‏های سنجش‏ازدور برای پایش تغییرات آب معرفی ‏شده است. بنابراین، بررسی صحت این روش‏ها بسیار لازم و ضروری است. در همین راستا، هدف مطالعۀ حاضر ارزیابی صحت روش‏های سنجش‏ازدور برای پایش آب دریاچۀ زریبار طی یک دورۀ 33 ساله (1984 تا 2017) است. از این‏رو، بعد از تصحیحات تصاویر پهنۀ دریاچۀ زریبار با استفاده از الگوریتم‏های حداکثر احتمال، حداقل فاصله، فاصلۀ ماهالانویی، ماشین بردار پشتیبان و شاخص‏های NDWI، MNDWI و AWEI در محیط نرم‏افزارهای ENVI5.3 و ArcGIS10.4 استخراج شد و سپس، اعتبارسنجی روش‏ها با استفاده از نقاط کنترل زمینی انجام گرفت. مطابق نتایج به‏دست‏آمده تمامی روش‏ها از صحت کلی80 درصد برخوردار هستند، اما الگوریتم‏های ماشین بردار پشتیبان و حداکثر احتمال با صحت کلی بیش از 90 درصد صحت بیشتری دار‌ند. همچنین، نتایج بررسی تغییرات آب دریاچه نشان می‎دهد طی دورۀ 1984 تا 2017 مساحت آب دریاچه بر اساس الگوریتم‏های ماشین بردار پشتیبان و حداکثر احتمال به‌ترتیب 46/738 هکتار (83/46 درصد) و 06/613 هکتار (45/42 درصد) کاهش‏ یافته است. همچنین، در همین دوره بر اساس الگوریتم‏های ماشین بردار پشتیبان و الگوریتم حداکثر احتمال به‌ترتیب 35/47 درصد و 22/36 درصد به مساحت نیزارهای سطح دریاچه افزوده ‏شده است. با توجه به روند کاهشی آب دریاچه و به‌خصوص روند افزایشی نیزارهای سطح دریاچه لزوم برنامه‏ریزی صحیح برای جلوگیری از هدررفت آب و از بین بردن نیزارها در این ناحیه بیش‏ از پیش احساس می‏شود.

کلیدواژه‌ها


عنوان مقاله [English]

Accuracy assessment of remote sensing methods for extraction and monitoring of Zrebar Lake, Iran

نویسندگان [English]

  • Karim Solaimani 1
  • Shadman Darvishi 2
  • Fatemeh Shokrian 3
1 Professor, Remote Sensing Centre, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
2 M.Sc. Faculty of Environmental Sciences, Aban Haraz Institute of Higher Education, Amol, Iran
3 Assistant Professor, Dept. of Watershed Management, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
چکیده [English]

Water resources play a very important role in the life of humans, plants and animals. Extracting and monitoring the extent of changes in water areas can be effective in predicting many problems. Today, many methods and algorithms have been introduced using remote sensing data to monitor water changes, so it is very necessary to study the accuracy of these methods. In this regard, the aim of the present study is to evaluate the accuracy of remote sensing methods for monitoring the water of Zarebar Lake over a period of 33 years (1984 to 2017). Therefore, after images preprocessing, water body of Zaribar Lake was extracted using maximum likelihood algorithms, minimum distance, Mahalanobis distance, support vector machine and NDWI, MNDWI and AWEI indices in ENVI5.3 and ArcGIS10.4 softwares, then these method was validated using ground control points. According to the results, all methods have an accuracy of 80%, but the support vector machine and maximum likelihood algorithms have a higher accuracy with a kappa coefficient above 85%. Also, the results of the study of lake water changes show that during the period 1984 to 2017, the water body based on support vector machine and maximum likelihood algorithms has decreased by 738.46/ha (46.83 percent) and 613.06/ha (42.45 percent), respectively. Also, in the same period, based on support vector machine algorithms and maximum likelihood algorithm 47.35% and 36.22% have been added to the reed bed on the lake. Due to the decreasing trend of lake and invasion of vegetation, it is necessary to consider a plane to prevent the lake water body for future.

کلیدواژه‌ها [English]

  • Water changes
  • spectral indices
  • supervised algorithms
  • Landsat
  • Zaribar Lake
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