واکاوی تغییرات مکانی سیمای سرزمین شهرها (مطالعه موردی: شهر کرمانشاه)
آرشیو
چکیده
گسترش شهرها علاوه بر تغییرات کاربری ها، موجب تغییرات در الگوی سیمای سرزمین شهرها شده است و این پدیده اثرات متعددی برروی ساختار، کارکرد و تغییرات سیستم های اکولوژیک دارد. بنابراین با توجه به آثار منفی ناشی از استفاده نامناسب از سرزمین و تغییر کاربری اراضی، آگاهی و شناخت روند تغییر پذیری، در ارزیابی آثار محیط زیستی ناشی از توسعه به منظور طرح ریزی و مدیریت پایدار سرزمین ضروری است . این مطالعه با هدف بررسی روند تغییرات سیمای سرزمین،سنجش وضعیت لکه ها و تحلیل روند توسعه شهر کرمانشاه انجام شده است. این تحقیق از این نظر دارای نوآوری بوده که کاربرد اصول اکولوژی سیمای سرزمین را در سطح لکه ها ارزیابی می نماید. به منظور بررسی و طبقه بندی تغییرات پوشش اراضی، محاسبه متریک سیمای سرزمین،پیش پردازش و انجام تصحیحات هندسی و آتمسفریک در منطقه مورد مطالعه از نرم افزار های Envi.5.5 و Fragstats بهره گرفته شد. همچنین جهت تهیه نقشه های پوشش سرزمین و تحلیل تغییرات، به ترتیب از تصاویر ماهواره ای (1380) TMو (1398) OLIو متریک های مساحت طبقه، تعداد لکه، متوسط اندازه لکه، تراکم حاشیه و متوسط شاخص شکل استفاده شد. تجزیه و تحلیل متریک های سیمای سرزمین بیانگر جایگزینی گسترده فضای سبز، باغ، جنگل و مرتع توسط اراضی مسکونی، کشاورزی و آبی بوده است. نتایج بدست آمده نشان داد، با افزایش مساحت لکه، تعداد لکه و تراکم حاشیه کاهش می باید، یا به عبارتی رابطه عکس با هم دارند. تغییر در خصوصیات مکانی، در کارکرد اکولوژیک منطقه تاثیرگذار است و بایستی در برنامه ریزی و آمایش سرزمین مود توجه قرار گیرد.Analysis Of Spatial Changes In The Landscape Of Cties (Case Study: Kermanshah City)
Urban sprawl, in addition to land use changes, cause of changes in urban landscape pattern has been caused and this phenomenon has several effects on the structure and functioning of ecological systems changes. So, with considering to negative effects caused by the improper use of land and land use changes, Knowledge and understanding of the variability trend is essential for assessing of environmental impacts arising from the development to the planning and sustainable management of land. In this research, Landsat TM satellite images related to May 2001 and OLI sensors related to June 1398 have been used. Also, field visit information and Google Earth images and comprehensive 1: 2000 maps of Kermanshah city have been used. In this study, for geometric correction, topographic maps with a scale of 1: 50,000 prepared by the Army Geographical Organization were used. At this stage, geometric corrections were applied to the images and the OLI image sensor in 1398 was referenced using the vector image method. For this purpose, 24 ground control points with suitable distribution and at the intersection of roads, waterways, etc. were used, so that the mathematical model used to find unknown coefficients in the equation has less error. To convert the coordinates of the corrected image to the uncorrected image, the first degree function was used and to re-sample the pixel value of the uncorrected image, the nearest neighbor method was used and finally the OLI sensor with RMS error equal to 0.35 ground was referenced. Geometric correction of TM image in 2001 was done by image-by-image method. For this purpose, the image of 1398 after geometric correction was considered as a basis. First, the control points were selected, then the points that had a lot of errors were removed from the relevant table, and finally, by removing 7 ground control points, the TM image was corrected with 35 control points with an error of 0.32. In order to determine the changes in addition to the coordinates of the images, the dimensions of their pixels must be the same, which in this study is the size of all pixels is 28.5 meters. First, it was observed that the highest producer accuracy, above 98%, was related to residential and rangeland lands (for two periods). This indicates the high spectral resolution for these classes. Secondly, according to the results, it was observed that The lowest accuracy of the manufacturer has been for the garden class. This class has been classified with %48.82producer accuracy for the image of this area (for 2001). It was also observed that the highest user accuracy was related to forest (2001) and garden (2017) land uses, which are %100 classified. The lowest user accuracy has been for the green space floor. This class is categorized with % 92.62user accuracy for the image of this area. This could be due to the complexity or proximity of the high spectral similarity boundaries to other classes and pixels mixed in the training and experimental samples.
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