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طبقه‌بندی و شناسایی تغییرات اراضی ساخته‌شده با استفاده از تصاویر سنجش‌ازدور | ||
| پژوهش‌های جغرافیای برنامه‌ریزی شهری | ||
| مقاله 6، دوره 5، شماره 3، مهر 1396، صفحه 445-468 اصل مقاله (1.34 M) | ||
| نوع مقاله: پژوهشی - کاربردی | ||
| شناسه دیجیتال (DOI): 10.22059/jurbangeo.2018.229640.687 | ||
| نویسندگان | ||
| کیوان عزی مند1؛ عطاءاله عبدالهی کاکرودی* 2؛ مجید کیاورز مقدم2 | ||
| 1دانشجوی کارشناسی ارشد سنجش ازدور و سیستم اطلاعات جغرافیایی دانشگاه تهران | ||
| 2استادیار گروه سنجش ازدور و سیستم اطلاعات جغرافیایی دانشکدة جغرافیای دانشگاه تهران | ||
| چکیده | ||
| در پی شهرنشینی بی‌سابقه در دهه‌های گذشته و افزایش جمعیت شهرها، چشم‌اندازهای طبیعی در حال تبدیل‌شدن به چشم‌اندازهای انسانی است و فضاهای باز شهری به اراضی ساخته‌شده مبدل شده است. در این بین، تغییرات کاربری اراضی مدیران شهری را مجاب می‌کند که همواره اطلاعات به‌روزی از این تغییرات داشته باشند تا بتوانند دربارة مدیریت شهری سریع‌تر تصمیم‌گیری کنند. هدف از انجام این مطالعه طبقه‌بندی اراضی ساخته‌شده و شناسایی میزان تغییرات این اراضی در شهر تهران است. همچنین این مطالعه به بررسی و عملکرد هفت شاخص طیفی به‌منظور طبقه‌بندی و تشخیص تغییر اراضی ساخته‌شده با استفاده از تصاویر ماهوارة لندست 7 سنجندة ETM + و تصاویر ماهوارة لندست 8 سنجندة OLI / TIRS می‌پردازد. محدودة مورد مطالعه در این تحقیق شهر تهران با وسعت 68995 هکتار است. روش انجام این تحقیق نیز بدین گونه است که ابتدا برای جداسازی سطوح دارای آب از سطوح بدون آب بر روی تصاویر، از شاخصMNDWI و روش آستانه‌گذاری اتسو استفاده شده است. پس از آن به‌منظور توجه مطلق بر مناطق بدون آب، یک ماسک آب تولید، و برای پوشاندن آب در تمام تصاویر به‌کار رفته است. درنهایت با استفاده از روش اتسو برای تمامی شاخص‌ها اراضی ساخته‌شده و ساخته‌نشده از یکدیگر جدا و طبقه‌بندی شده‌اند. دقت طبقه‌بندی نیز با استفاده از 3500 نقطة مرجع برای هر تصویر بررسی شده است. نتایج نشان می‌دهد شاخص VbSWIR1-BI با دقت کلی 88/92 درصد (لندست 7) و 68/92 درصد (لندست 8)، دقت کلی بیشتری دارد. همچنین نتایج تغییرات اراضی ساخته‌شدة شهر تهران براساس شاخص VbSWIR1-BI در بازة زمانی 2001 تا 2015 به میزان 38/6 درصد بوده است. گفتنی است بیشترین تغییرات مکانی اراضی ساخته‌شده در بخش‌های غربی و جنوب غربی شهر تهران دیده می‌شود. | ||
| کلیدواژه‌ها | ||
| تصاویر لندست 7 و لندست 8؛ شاخص‌های طیفی؛ شناسایی تغییرات؛ طبقه‌بندی؛ گسترش شهری | ||
| عنوان مقاله [English] | ||
| Classification and Change Detection of Urban Built-up Lands Using Remote Sensing Images | ||
| نویسندگان [English] | ||
| Keyvan Ezimand1؛ Ataallah Abdollahi Kakroodi2؛ Majid Kiavarz Moghaddam2 | ||
| 1MSc Student in Remote Sensing and GIS, University of Tehran, Faculty of Geography, Department of Remote Sensing and GIS, Iran | ||
| 2Assistant Professor of Remote Sensing and GIS, University of Tehran, Faculty of Geography, Department of Remote Sensing and GIS, Iran | ||
| چکیده [English] | ||
| Introduction Urbanization and use of urban lands is the result of social and economic development. Urbanization is a major concern in many parts of the world. By 2050, the world's urban population is expected to double from about 3.3 billion in 2007 to 6.4 billion in 2050. Today, changes in land use occur without clear planning and little attention to their environmental impacts. At present, the built-up lands cover 400,000 square kilometers of the Earth's surface and it is expected to increase to 120,000 square kilometers by 2030. Recently, urban studies, classification of built-up lands and land-use change detection in urban areas using remote sensing data have been highlighted on an unprecedented manner. Various spectral indices have been proposed for rapid detection and accurate classification of built-up lands using satellite images. The purpose of this study is to compare the performance of the indices and the introduction of a new index for classification of the built-lands using satellite images to determine spatial and temporal differences of land-use in the city of Tehran. Methodology The data used in this study is Landsat 7 ETM + and Landsat 8 OLI / TIRS satellite images for Tehran. In this research, we have initially used the MNDWI index and the Otsu thresholding method to separate water surfaces from the waterless surfaces. Then, for the purpose of masking the water in the image, water mask was created. Finally, using indices such as Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI), Normalized Difference Impervious Surface Index (NDISI), visible red/green-based built-up indices (VrNIR-BI and VgNIR-BI), visible blue based built-up index (VbSWIR1-BI) and Otsu , the built-up lands are separated and classified. The accuracy of the classification was examined using 3500 reference points for each image. Results and discussion The histogram of the spectral indices of two satellite images and the Otsu method has showed that for the ETM + sensor, all indices except NDBI and VrNIR-BI show double distribution signs. For the OLI / TIRS sensor, only the IBI, VgNIR-BI and VbSWIR1-BI indices show signs of a dual distribution. The classification accuracy results show that the VbSWIR1-BI index has the highest overall accuracy and the NDISI index has the lowest overall accuracy for both Landsat 7 and Landsat 8 images. The temporal and spatial variations of the built-up lands indicate that the highest increase of built-up lands can be found geographically in the western and southwestern part of Tehran. According to the results of the VbSWIR-BI index, built-up lands in the studied area between 2001 and 2015 increased to 6.38%. Conclusion The rapid development of geography and remote sensing technology has led to creation of different spectral indexes for classification. A review of studies on spectral indices indicates that the blue band coupled with the near infrared band, has not been used for classification of built-up and non-built-up lands and the results of this study have shown that this index is good and has been able to classify the built-up lands and increased classification accuracy. This index also enables the determination of changes in spatial and temporal built-up lands in Tehran accurately. | ||
| کلیدواژه‌ها [English] | ||
| : Landsat 7 and Landsat 8 images, Classification, spectral indices, Change detection, Urban Growth | ||
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