آشکارسازی اراضی شالیزاری شهرستان رشت با استفاده از تصاویر چندزمانة لندستـ 8 | ||
| نشریه سنجش از دور و GIS ایران | ||
| مقاله 2، دوره 14، شماره 3 - شماره پیاپی 55، 1401، صفحه 15-30 اصل مقاله (4.07 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.52547/gisj.14.3.15 | ||
| نویسندگان | ||
| امیر هدایتی1؛ دکتر محمد حسن وحیدنیا* 2؛ حسین آقامحمدی2 | ||
| 1دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکدة منابع طبیعی و محیطزیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران | ||
| 2استادیار گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکدة منابع طبیعی و محیطزیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران | ||
| چکیده | ||
| در بسیاری از کشورها بهویژه ایران، برنج به یکی از اقلام اساسی بهلحاظ امنیت غذایی تبدیل شده است. در این تحقیق، بهمنظور تهیة نقشة سطوح شالیزاری، با توجه به ویژگیهای فنولوژیکی گیاه برنج و بهکمک دادههای سالیانة دمای سطح زمین سنجندة مادیس، ابتدا برنامة زمانی برای انتخاب تصاویر سری زمانی ماهوارة لندستـ 8 تنظیم شد. پس از دریافت دادههای ماهوارهای، بهروش شیءمبنا و با بهرهگیری از توابع فازی، به طبقهبندی تصاویر و در نهایت، استخراج اراضی شالیزاری در حوزة شهرستان رشت پرداخته شد. بهمنظور بهبود و ارتقای نتایج، در این تحقیق طی فرایند طبقهبندی تصاویر ماهوارهای، از دادههای متنوعی مانند مدل رقومی زمین، دادههای دمای سطح زمین و شاخصهای طیفی همچون NDVI، EVI، NDBI و LSWI در کنار اطلاعاتی دربارة ویژگیهای خاص عوارض و اشیای داخل تصویر، استفاده شد. با توجه به خصوصیات ویژة اراضی شالیزاری، از مدل رقومی ارتفاعی 12.5متری برای تشخیص بهتر اراضی شالیزاری از دیگر پوششهای گیاهی، بهره گرفته شد. همچنین بین نتایج حاصل از طبقهبندی بهروش شیءمبنا و پیکسلمبنا، مقایسهای صورت گرفت؛ در نهایت، مشخص شد که روش طبقهبندی شیءمبنا میتواند، با ملاحظات خاصی، نتایجی بهتر از روش پیکسلمبنا دربر داشته باشد. نتیجة طبقهبندی با روش پیکسلمبنا، پس از اعتبارسنجی، دقت کلی 92% را نشان داد و ضریب کاپا در این روش 89/0 برآورد شد. طبق روش طبقهبندی شیءمبنا، نتایجی با دقت کلی 94% بهدست آمد و ضریب کاپا نیز 92/0 حاصل شد. | ||
| کلیدواژهها | ||
| طبقهبندی شیءمبنا؛ طبقهبندی پیکسلمبنا؛ لندستـ 8؛ اراضی شالیزاری | ||
| عنوان مقاله [English] | ||
| Detection of Rice Fields in Rasht Township Using Multi-Temporal Landsat-8 Images | ||
| نویسندگان [English] | ||
| Amir Hedayati1؛ Mohammad H Vahidnia2؛ Hosseain Aghamohammadi2 | ||
| 1M.Sc. Student of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran | ||
| 2Assistant Prof., Dep. of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran | ||
| چکیده [English] | ||
| Rice has become one of the most important food security items in many countries, especially Iran. In this study, a model was proposed to select Landsat-8 satellite time-series images in order to prepare a map of paddy lands. The method is based on the phenological characteristics of rice plants and annual surface temperature data from the MODIS sensor. After preprocessing satellite images, they were classified using an object-based approach and fuzzy functions. Various data such as a digital elevation model, land surface temperature, and spectral indices including NDVI, EVI, NDBI and LSWI are used to improve the classification process. In addition, information about the segmentation of the image is employed during the process of classification. Because of the different traits of paddy fields, a digital elevation model with a resolution of 12.5 meters was used to help differentiate paddy lands from other vegetation. In addition, a comparison was made between the results of classification based on object-based and pixel-based methods. The results showed that the object-based classification yields better results than the pixel-based method with special considerations. The classification result following validation using ENVI software pixel-based classification indicated an overall accuracy of 92 percent and a kappa value of 0.89. This is in contrast to the object-based classification technique in the eCognition software, which yielded an overall accuracy of 94 percent and a kappa coefficient of 0.92. | ||
| کلیدواژهها [English] | ||
| Landsat-8, MODIS, Object-based classification, Paddy fields, Pixel-base classification | ||
| مراجع | ||
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