مقایسه دقت داده های ماهواره‌های لندست 8 و سنتینل 2 در طبقه بندی کاربری/پوشش زمین

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار، گروه مهندسی طبیعت، دانشکده منابع طبیعی، دانشگاه یاسوج، یاسوج، ایران

10.22034/jewe.2020.247009.1416

چکیده

این پژوهش با هدف تعیین دقت مجموعه داده­های ماهواره لندست 8 و سنتینل 2 بر مبنای الگوریتم­های احتمال حداکثر، ماشین بردار پشتیبان و شبکه عصبی در تهیه نقشه کاربری/پوشش زمین حوزه آبخیز کبگیان در استان کهگیلویه و بویراحمد انجام شد. بدین منظور، تمامی اصلاحات، آماده­سازی داده، ایجاد مجموعه داده، طبقه­بندی و تجزیه‌وتحلیل‌ها، استخراج نقشه­های موردنظر و صحت­سنجی با استفاده از نرم­افزارهای ENVI® 5.3، ArcGIS® 10.5، Google Earth Pro و Excel 2016 انجام شد. نتایج نشان داد بیش­ترین دقت کل و ضریب کاپا برای ماهواره لندست 8 با مقدار به­ترتیب 18/74% و 69/0 مربوط به الگوریتم احتمال حداکثر و برای سنتینل 2 با مقدار به­ترتیب، 84/72% و 67/0 مربوط به الگوریتم شبکه عصبی است. دقت کل الگوریتم­ها در تهیه نقشه کاربری حوضه با استفاده از داده­های لندست 8 به‌صورت احتمال حداکثر > ماشین بردار پشتیبان > شبکه عصبی و با استفاده از داده­های سنتینل 2 به‌صورت شبکه عصبی > احتمال حداکثر > ماشین بردار پشتیبان بود. چنانچه تعیین کاربری اراضی ویژه­ای مانند مراتع حوضه، هدف اصلی باشد از الگوریتم ماشین بردار پشتیبان استفاده شود؛ بدین ترتیب، مساحت هشت کاربری حوزه آبخیز کبگیان عبارت است از: زراعت 1342، مسکونی 1356، صخره 3579، جنگل 23289، پیکره آبی 407، اراضی رها شده 9571، باغ 3139 و مرتع ha 54125. بنابراین، با توجه به نوع کاربری، نوع داده ماهواره­ای در دسترس و هدف پژوهش، اولویت و تقدم استفاده از الگوریتم­ها متفاوت خواهد بود و بر مبنای آن، باید الگوریتم مناسب انتخاب شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison of Landsat 8 and Sentinel 2 Satellite Data Accuracy for Land Use Classification

نویسنده [English]

  • Mohsen Farzin
Assist. Professor, Department of Nature Engineering, Faculty of Natural Resources, Yasouj University, Yasouj, Iran
چکیده [English]

The aim of this study was to determine the accuracy of Landsat 8 and Sentinel 2 satellite data sets based on Maximum Likelihood, Support Vector Machine, Neural Network algorithms for mapping the LU/LC of Kobgian watershed in Kohgiluyeh and Boyer-Ahmad Province. For this purpose, corrections, data preparation, data set creation, classification and analysis, mapping and verification were done using ENVI® 5.3, ArcGIS® 10.5, Google Earth Pro and Excel 2016 software. The results showed that the highest total accuracy and kappa coefficient for Landsat 8 and Sentinel 2 satellites belongs to the maximum likelihood algorithm with a value of 74.18% and 0.69 and neural network algorithm with a value of 72.84% and 0.67, respectively. The overall accuracy order of the algorithms for mapping LU/LC the watershed using Landsat 8 and sentinel 2 data was as maximum likelihood > support vector machine > neural network and using data was as neural network> maximum likelihood > support vector machine, respectively. The accuracy of the algorithms indicated that if a specific LU/LC is the main goal such as basin rangelands, the support vector machine algorithm should be used. The area of eight classes of Kabgian watershed is: agriculture 1342, residential 1356, rock 3579, forest 23289, water body 407, abandoned lands 9571, garden 3139 and pasture 54125 ha. Therefore, depending on the type of LU/LC, the type of satellite data available, and the purpose of study, the priority of using algorithms will be different and based on the desired factor, suitable algorithm should be selected.

کلیدواژه‌ها [English]

  • Kabgian watershed
  • Maximum Likelihood
  • Neural Network
  • Support Vector Machine
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