پیشبینی و مدلسازی تغییرات پوشش زمین در استان قم با استفاده از تصاویر ماهورهای لندست
محورهای موضوعی : منابع طبیعی و مدیریت زیست محیطی
1 - عضو هیئت علمی دانشگاه امام حسین
2 - گروه سنجش از دور و سیستمهای اطلاعات جغرافیایی
کلید واژه: مدلسازی, پیشبینی, تغییرات پوشش زمین, زنجیره مارکوف ,
چکیده مقاله :
مدلسازی و پیشبینی تغییرات پوشش زمین برای برنامهریزی استفاده پایدار از زمین امری ضروری به شمار میرود. هدف این پژوهش، مدلسازی تغییرات پوشش زمین در استان قم با استفاده از تصاویر ماهوارهای لندست در بازه زمانی 2015 تا 2023 و پیشبینی تغییرات تا سال 2027 میباشد. برای این منظور، از شاخصهای طیفی پوشش گیاهی نرمالشده (NDVI) و ساختوساز نرمالشده (NDBI) و روش طبقهبندی ماشین بردار پشتیبان (SVM) استفاده شده است. همچنین، با بهرهگیری از ترکیب مدل زنجیره مارکوف و اتوماتای سلولی، روند تاریخی تغییرات پوشش زمین از 2015 تا 2023 مدلسازی و برای سال 2027 پیش بینی تغییرات صورت گرفت. نتایج تجربی بهدستآمده نشان میدهند که پوشش گیاهی پس از سال 2019 به وضعیت سال 2015 بازگشته، در حالی که رشد شهری به طور متوالی در حال افزایش است. پیشبینیها حاکی از رشد 2700 متر مربعی مناطق شهری در سال 2027 هستند. این نتایج میتوانند به تصمیمگیریهای بهتر در مدیریت منابع طبیعی، توسعه زیرساختهای شهری و دستیابی به توسعه پایدار منطقه کمک کنند. علاوه بر این، یافتههای تحقیق ابزار مفیدی برای برنامهریزی آتی خواهند بود.
Modeling and predicting land cover changes are essential for sustainable land use planning. This study aims to model land cover changes in Qom Province using Landsat satellite imagery from 2015 to 2023 and to predict changes up to 2027. For this purpose, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were employed, along with the Support Vector Machine (SVM) classification method. Additionally, a combination of the Markov Chain model and Cellular Automata was utilized to simulate historical land cover change trends from 2015 to 2023 and to predict future changes for 2027. The results indicate that vegetation cover returned to its 2015 state after 2019, while urban growth has continued to increase steadily. The predictions suggest an expansion of 2,700 square meters in urban areas by 2027. These findings can support improved decision-making in natural resource management, urban infrastructure development, and efforts to achieve regional sustainable development. Moreover, the research outcomes provide a valuable tool for future planning.
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