نشریه علمی - پژوهشی مرتع و آبخیزداری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد ارزیابی و آمایش سرزمین، گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه تهران،کرج، ایران.

2 دانشیار، گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

3 استادیار، گروه مهندسی برق، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی اراک، اراک، ایران.

10.22059/jrwm.2023.351534.1687

چکیده

آشکار سازی و پیش بینی تغییرات پوشش/کاربری سرزمین علاوه بر درک عملکرد و سلامت اکوسیستم ها، امکان مدیریت و برنامه ریزی سرزمین را خصوصا در مناطق با تغییرات سریع و اغلب ناهمسو با طرح‌های آمایش سرزمین فراهم می سازد. مطالعه حاضر سعی دارد علاوه بر معرفی قابلیت‌های گوگل ارث انجین با استفاده از مدل ترکیبی سلول خودکار و زنجیره مارکوف به پایش الگوی تغییرات سرزمین و پیش بینی الگوی تغییرات آتی بپردازد. جهت انجام تحقیق ابتدا سه تصویر لندست (2006، 2014 و 2021) با استفاده از روش طبقه‌بندی‌کننده ماشین بردار پشتیبان طبقه‌بندی شدند و با استفاده از نقشه های طبقه بندی شده (2014-2006) و (2021-2014)، و مدل ترکیبی سلول حودکار و زنجیره مارکوف برای سال های 2021 و 2035 شبیه سازی انجام شد. جهت ارزیابی دقت نقشه پیش بینی شده 2021 از نقشه طبقه بندی شده همان سال استفاده شد. دقت توافق بین نقشه‌های طبقه بندی شده و مدل‌سازی شده به ترتیب812/0 Kno=، 816/0Klocation=، 786/0 Kstandard= بود. ارزیابی روند تغییرات نشان می‌دهد که بین سال‌های 2006 تا 2035، مساحت طبقه انسان ساخت از 01/4839 هکتار به 76/7199 هکتار خواهد رسید و 75/2360 هکتار افزایش را شاهد خواهیم بود. این نتایج بیانگر لزوم توجه به برنامه های آمایشی در فرایند برنامه ریزی سرزمین است. استفاده از مدل های شبیه سازی می تواند خطرهای تصمیم گیری بلندمدت را در مدیریت سرزمین کاهش دهد. همچنین استفاده از گوگل ارث انجین موجب کاهش هزینه و زمان طبقه بندی و پردازش تصاویر ماهواره ای خواهد شد.

کلیدواژه‌ها

عنوان مقاله [English]

Development of an integrated model based on Cellular automata and Markov chain to predict land use/cover changes, Case Study: Karaj City

نویسندگان [English]

  • Haniyeh Rezaie 1
  • Sharareh Pourebrahim 2
  • Mohammad Karimadini 3

1 Master student of Land Assessment and Spatial Planning, Department of Environment, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

2 Associate Professor, Department of Environment, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

3 Assistant Professor, Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Arak University of Technology, Arak, Iran.

چکیده [English]

Due to the ability of land use/cover changes monitoring and predicting to understand the performance and health of ecosystems, this purposed method can provide possibility of sustainable land use management and planning, especially in the rapid change areas without master/land use plan. The present study has aimed to introduce Google Earth Engine to evaluate the pattern of land changes during 2006- 2021 and predict the pattern of future changes by using an integrated model based on Cellular automata and Markov chain using Google Earth Engine system. Three Landsat images (2006, 2014 and 2021) were classified using the support vector machine classifier method, and were simulated using the integrated model of cellular automata and Markov chain. In order to evaluate the accuracy of the predicted map of 2021, the classified map of the same year was applied. The accuracy of classified and simulated maps was Kno=0.812, Klocation=0.816, Kstandard=0.786 respectively. Evaluation of the land use/cover changes shows that between 2006 and 2035, the buildup areas will reach from 4839.01 hectares to 7199.76 hectares with increasing of 2360.75 hectares. These results indicate the necessity of land use planning principles. Simulation models can reduce the risks of long-term decision-making in land use management and Google Earth Engine can reduce the time and cost for classification and satellite image processing.

کلیدواژه‌ها [English]

  • : Logistic Regression
  • Google Earth Engine
  • Support vector machine
  • Auxiliary variables
  • Sustainable land use management
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