پهنه‌بندی پوشش گیاهی شهرستان صومعه‌سرا با استفاده از سری زمانی تصاویر ماهواره‌ای

نوع مقاله : علمی- پژوهشی

نویسندگان

1 کارشناس پژوهشی گروه مطالعات ناحیه‌ای، پژوهشکده محیط‌زیست جهاد دانشگاهی، رشت، ایران

2 استادیار، گروه مطالعات ناحیه‌ای، پژوهشکده محیط‌زیست جهاد دانشگاهی، رشت، ایران

3 پژوهشگر پسا دکتری جغرافیا و برنامه ریزی روستایی، دانشکده علوم انسانی، دانشگاه گیلان، رشت، ایران

10.22092/ijfpr.2023.361480.2088

چکیده

 تهیه نقشه‌های پهنه‌بندی در مناطق دارای پوشش‌های مشابه از نظر طیفی، به‌عنوان یک چالش در حوزه سنجش از دور شناخته می‌شود. هدف از پژوهش پیش‌رو، افزایش صحت طبقه‌بندی پوشش گیاهی با تمرکز بر شناسایی صنوبرکاری‌های‌ شهرستان صومعه‌سرا در استان گیلان بود. اطلاعات مستخرج از تصاویر ماهواره‌ای به‌همراه نمونه‌های زمینی در روش طبقه‌بندی جنگل تصادفی برای پهنه‌بندی باغ‌های صنوبرکاری‌شده استفاده شد. این اطلاعات از تلفیق باندها و شاخص‌های طیفی تصاویر نوری ماهواره سنتینل 2 و باندهای راداری ماهواره سنتینل 1 به‌صورت سری زمانی و مدل رقومی ارتفاعی  که توسط ماهواره ALOSpalsar تهیه شده است، به‌دست آمد. پس از تجزیه‌وتحلیل‌های سری زمانی، بهترین تصاویر از نظر قدرت تفکیک مناطق صنوبرکاری‌ از مناطق دیگر برای ورود به الگوریتم طبقه‌بندی انتخاب شدند. نتایج نشان داد که تصاویر نوری نسبت‌به راداری توانایی بهتری برای پهنه‌بندی داشتند. همچنین، استفاده از سری زمانی به‌جای تک‌تصویر و استفاده از شاخص‌ها به‌طور میانگین تا سه درصد صحت کلی طبقه‌بندی را افزایش داد. به‌طور کلی، تصاویر نوری و راداری ماهواره‌های سنتینل 1 و سنتینل 2 و ایلوس با استفاده از روش ارائه‌شده از قابلیت زیادی در طبقه‌بندی درختان صنوبر در مناطق وسیع دارند. مساحت این مناطق در شهرستان صومعه‌سرا 7778 هکتار برآورد شد.

کلیدواژه‌ها

عنوان مقاله [English]

Land cover mapping of the Sowmeh Sara city using time series of satellite imagery

نویسندگان [English]

  • S.M. Hatami Shah Khali 1
  • Sh. Sharifi Hashjin 2
  • F. Nasiri Aghajan 1
  • S.F. Emami 3
1 Research Expert, Department of Regional Studies, Jihad University Environmental Research Institute, Rasht, Iran
2 Assistant Prof, in the Department of Regional Studies, Jihad University Environmental Research Institute, Rasht, Iran
3 Postdoctoral Researcher in Geography and Rural Planning, Faculty of Humanities, Guilan University, Rasht, Iran
چکیده [English]

The remote sensing field faces a significant challenge in preparing land use maps in regions with similar spectral coverages. This research aimed to enhance crop classification accuracy by prioritizing poplar in Guilan province's Soomesara county. To classify poplar plantations, the researchers used data extracted from satellite imagery and field samples employing a random forest classification method. They utilized Sentinel-2 optical images, Sentinel-1 radar polarization data, and the ALOSpalsar digital elevation model produced in a time series to obtain this information. The team selected optimal images with high separability power for distinguishing poplar farms from other classes after analyzing the time series. Results demonstrated that optical images had better capabilities for land use classification than radar images. Additionally, implementing time series data instead of single images and using indices increased overall classification accuracy up to three percent. In conclusion, the researchers found that the proposed method utilizing Sentinel-1, Sentinel-2, and ALOS optical and radar satellite images has a high potential for poplar mapping in large areas. The estimated area of these regions in Soomesara County was 7,778 hectares.

کلیدواژه‌ها [English]

  • Poplar
  • sentinel satellite image
  • spectral indices

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