مقایسه سه روش تفسیر چشمی، طبقه‌بندی شیءگرا و طبقه‌بندی نظارت شده در تهیه نقشه کاربری/پوشش اراضی حوزه آبخیز ملا احمد اردبیل

محورهای موضوعی : کاربری اراضی

آزاد کاکه ممی ¹ , اردوان قربانی ²

1 - دانش‌آموخته کارشناسی ارشد مرتعداری دانشگاه محقق اردبیلی
2 - دانشیار گروه مرتع و آبخیزداری دانشگاه محقق اردبیلی

تاریخ دریافت : 1396/04/10 تاریخ پذیرش : 1396/10/16 تاریخ انتشار : 1396/12/01

کلید واژه: تفسیر چشمی, طبقه‌بندی شیءگرا, سنجنده OLI, ماهواره Quick bird, طبقه‌بندی نظارت‌شده,

چکیده مقاله :

با توجه به رشد روزافزون جمعیت و در نتیجه آن، تخریب و تبدیل غیراصولی کاربری اراضی، آگاهی از وضع موجود سرزمین، کاربری و پوشش آن ضرورت دارد. هدف اصلی تحقیق حاضر تهیه نقشه کاربری/پوشش اراضی حوزه آبخیز ملا احمد بوده است. در این تحقیق از دو نوع تصاویر ماهواره QuickBird موجود در سرور گوگل‌ ارث (سال 2013) و سنجنده (Operational Land Imager) OLI ماهواره لندست 8 (سال 2014) در قالب سه روش تفسیر چشمی (تصاویر گوگل ‌ارث)، طبقه‌بندی شیءگرا (تصاویر گوگل ارث) و طبقه‌بندی نظارت‌شده (تصویر لندست 8) استفاده شد. به‌منظور ارزیابی نقشه کاربری/پوشش اراضی تولیدشده از سه روش ذکر شده، 51 نقطه به‌عنوان نقاط کنترلی جهت ارزیابی صحت انتخاب گردید. نتایج نشان داد که صحت کلی نقشه تولید شده از روش تفسیر چشمی، طبقه‌بندی شیءگرا و طبقه‌بندی نظارت‌شده، به ترتیب 100، 90 و 72 درصد و ضریب کاپای آن‌ها نیز به ترتیب 1، 85/0 و 6/0 است، که صحت بالای نقشه‌های تولید شده از دو روش تفسیر چشمی و شیءگرا را نشان داد. روش تفسیر چشمی با استفاده از تصاویر با قدرت تفکیک بین 65/0 تا 9/2 متری ماهواره QuickBird (سرور گوگل ارث)، صحیح‌ترین روش می‌باشد؛ هرچند که روش طبقه‌بندی شیءگرا با توجه به هزینه پایین آن از نظر زمانی در مناطق گسترده‌تر، به نسبت دارای صحت قابل قبولی می‌باشد.

چکیده انگلیسی:

Due to the growing population and consequently the degradation and uncontrolled use of land, awareness of the state of the land and its use is necessary. In this study, the main purpose was land use /cover mapping of the Mollah Ahmad Watershed. Two types of Google Earth images (Quickbird images, 2013) and Operational Land Imager (OLI) sensor of Landsat 8 image (2014) with three methods of visual interpretation (Google Earth images), object-based classification (Google Earth images), and supervised classification (Landsat 8) were used. In order to evaluate the land use /cover map produced from the methods, 51 points were selected as control points for the accuracy assessment. The results showed that the overall accuracy of the map generated from visual interpretation, object-based classification, and supervised classification were 100, 90, and 72 percent, respectively and their kappa coefficients were 1, 0.85 and 0.6, respectively, which the high accuracy of the maps generated from two methods of visual and object-based interpretation. The method of interpreting the visually using the high-resolution images (Quickbird with 0.65 to 2.9 m resolutions) of Google Earth is the most accurate method; however, the object-based classification method due to its low cost in terms of time in large environment has relatively acceptable accuracy.

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