نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشکده منابع طبیعی، دانشگاه ارومیه، ایران

2 دانشجوی دکتری، علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه ارومیه، ایران

چکیده

امروزه سنجش از دور، به­‌عنوان منبع تولید اطلاعات مکانی در جهت مطالعه پوشش زمین و کاربری­‌های اراضی شناخته شده ­است. حوضه باراندوزچای از لحاظ وسعت و فراوانی بالای اراضی زراعی و باغی در بین حوضه­‌های دریاچه ارومیه از اهمیت بالایی برخوردار است. به‌­منظور مطالعه تغییرات کاربری اراضی حوضه مذکور تصاویر ماهواره‌­ای Landsat و Sentinel سنجنده TM و 2A مربوط به سال‌های 2005، 2010 و 2016 مورد ارزیابی قرار گرفت. از الگوریتم بیشینه شدت احتمال، به‌­منظور تهیه نقشه کاربری اراضی استفاده شد. نتایج حاصل از ارزیابی صحت و دقت و همچنین، بررسی ضرایب توافق کاپا نشان از دقت بالای نقشه­‌های مستخرج از الگوریتم داشته است. به­‌منظور آشکار­سازی تغییرات از روش تفاضل تصویر استفاده شد. نتایج نشان می‌دهد که مساحت اراضی درختزار و درختان غیرمثمر طی سال­‌های 2010 تا 2016 در حوضه باراندوزچای با افزایش روبرو بوده است. اراضی دیم و اراضی آیش نیز جز طبقاتی بوده‌اند که در طی سال­‌های 2010 تا 2016 با رشد مواجه بوده‌اند. طبق نتایج به‌دست آمده، مشاهده شد که در بازه­ زمانی 2010 -2005 و 2016-2010 به‌­ترتیب بیشترین تغییر مساحت صورت گرفته در این حوضه، مربوط به سطوح نفوذناپذیر و اراضی آیش و کمترین آن، مربوط به اراضی دیم و درختزار بوده است.

کلیدواژه‌ها

عنوان مقاله [English]

Land use change detection in Barandouzchay Watershed from Lake Urmia River Basin using remotely sensed Landsat5 and Sentinel imagery

نویسندگان [English]

  • Mirhassan Miryaghoubzadeh 1
  • Seyed-Amin Khosravi 2

1 Assistant Professor, Department of Watershed Management Engineering, Faculty of Natural Resources, Urmia University, Iran

2 Ph.D Student, Faculty of Natural Resources, Urmia University, Iran

چکیده [English]

Nowadays remote sensing is known as practical method for studying Land Use (LU)/Land Cover (LC) changes. Due to the vast area of agricultural lands, Barandouzchay Basin is one of the important watersheds among all of watersheds in Lake Urmia River Basin. In this study, in order to evaluate LU/LC change, Landsat-5 TM and Sentinel-2A satellite images were used from 2005 to 2016. The maximum likelihood classification method was used to prepare LU/LC maps. The results of overall accuracy and Kappa coefficient showed high accuracy of maximum likelihood classification method. In order to extract the change detection maps, image difference method was used. Results showed that orchard and nonproductive trees have been increased during 2010-2016 years in Barandouzchay Basin. In the years before 2010, trees were relocated by young trees in Barandouzchay Basin. Drylands and bare lands are classified in the 2005-2010 years which has been increased. The most land use change was related to urban and lowest change was related to rainfed area from 2005 to 2010 and the most land use change is related to bare lands and lowest rate is related to nonproductive tree area from 2010 to 2016.

کلیدواژه‌ها [English]

  • Image difference
  • Kappa coefficient
  • Maximum likelihood
  • Remote sensing
  • Young trees
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