نشریه محیط زیست طبیعی

مقایسه روش های پیکسل‌مبنا و شئ‌گرا در طبقه‌بندی کاربری اراضی (مطالعه موردی: حوضه سملقان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه آب دانشگاه بیرجند

2 دانشیار و رئیس دانشکده کشاورزی و محیط زیست

3 دانشیار گروه مرتع و آبخیزداری، مهندسی منابع طبیعی، دانشکده منابع طبیعی و محیط زیست، دانشگاه بیرجند

چکیده

برنامه‌ریزی و استفاده بهینه از منابع و کنترل و مهار تغییرات غیر اصولی در آینده، نیازمند مطالعه میزان تغییرات و تخریب منابع می‌باشد. در واقع برنامه‌ریزان برای تصمیم‌گیری‌های اصولی، بایستی شناخت کاملی از کاربری اراضی، آشکارسازی، پیش‌بینی تغییرات کاربری اراضی و پوشش زمین به منظور مدیریت بهتر منابع طبیعی در بلندمدت داشته باشند. این مطالعه با هدف ارزیابی صحت الگوریتم‌های مختلف طبقه‌بندی نظارت شده پیکسل‌مبنا و شئ‌گرا در استخراج کاربری اراضی حوضه سملقان در سه مقطع زمانی 1987، 2002 و 2019 انجام شد. نتایج نشان داد که الگوریتم‌های ماشین بردار پشتیبان برای تصاویر سال‌های 1987 و 2019 و شبکه عصب برای تصویر سال 2002 در روش طبقه‌بندی پیکسل مبنا از بیشترین مقدار صحت کلی و ضریب کاپا برخوردار می‌باشد. همچنین، واضح‌ترین تغییری که با مقایسه نقشه‌های کاربری تهیه شده مشاهده می‌شود، تغییر سطح کاربری‌ها با رشد مناطق مسکونی، دیم و جنگل است و این گسترش به صورت مستمری با کاهش کاربری مرتعی همراه بوده است. بدینصورت که از سال 1987 تا سال 2019 مساحت کاربری مسکونی بیش از 197/9 کیلومتر مربع و اراضی دیم در طی این سال‌ها به میزان 89/130 کیلومتر مربع و جنگل 92/118 کیلومتر مربع افزایش، اراضی کشاورزی آبی نیز 45/44 کیلومتر مربع افزایش و کاربری مرتع نیز به میزان 3/272 کیلومتر مربع کاهش یافته است.

کلیدواژه‌ها

عنوان مقاله [English]

Comparison of the accuracy of pixel-based and object-oriented methods in land use classification (case study: Samalghan Watershed)

نویسندگان [English]

  • zahra zeraatkar 1
  • Ali Shahidi 2
  • Hadi Memarian khalilabad 3

1 department of water

2 Associate Prof. Department of Water Science Engineering, University of Birjand

3 3Associate Prof. Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand

چکیده [English]

Planning and optimal use of resources and controlling and unprincipled changes in the future, requires studying the extent of change and destruction of resources. In fact, planners for principled decisions must have a full knowledge of land use, detection, prediction of land use change and land cover in order to better manage natural resources in the long time. The aim of this study was to evaluate the accuracy of different supervised classification algorithms of basic and object-oriented pixels in land use extraction in Samalghan watershed in three periods of time 1987, 2002 and 2019. The results showed that the support vector machine algorithms for the images of 1987 and 2019 and the neural network for the 2002 image in the pixel-based classification method have the highest overall accuracy and kappa coefficient. Also, the most obvious change that can be seen by comparing the prepared user maps is the change in the level of land uses with the growth of residential areas, thus this expansion has been continuously associated with a decrease in rangeland land use. Thus, from the years of 1987 to 2019, the residential land use area increased by more than 9.197 km2 and dryland lands during these years increased by 130.89 km2, irrigated agricultural lands also increased from 44.45 km2 and Rangeland use has also decreased by 272.3 km2.

کلیدواژه‌ها [English]

  • Land use
  • Pixel Based
  • object-oriented
  • supervised classification
  • Kappa coefficient
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نشریه محیط زیست طبیعی
دوره 73، شماره 4
اسفند 1399
صفحه 687-700