یافتن مدل مستحکم برای پیش‌بینی رشد شهری مبتنی بر طبقه‌بندی کاربری اراضی و پوشش زمین: مطالعه‌ای تطبیقی از مدل‌های مبتنی بر زنجیره مارکوف سلولی (CA-Markov)، شامل LCM و MOLUSCE

نوع مقاله : مقاله پژوهشی

نویسندگان

1 Student, Department of Remote Sensing and Geoinformatics, Maharshi Dayanand Saraswati University Ajmer, India.

2 Assistant Professor, Department of Geography, School of Earth Sciences, SRT Campus Tehri, HNBGU, India.

چکیده

زمینه پژوهش: داده‌های علمی در خصوص کاربری اراضی و پوشش زمین (LULC) در آینده، برای تدوین برنامه‌ریزی‌های دقیق کاربری اراضی به‌منظور تأمین نیازهای رو به رشد بشر و رفاه عمومی، بدون ایجاد آسیب‌های ماندگار به محیط زیست، امری بنیادین محسوب می‌شود.
اهداف پژوهش: این مقاله تلاشی است برای شناسایی مدلِ مستحکم (Robust) میان مدل‌های مبتنی بر «زنجیره مارکوف سلولی» (CA-Markov)، شامل «مدل‌ساز تغییرات اراضی» (LCM) در نرم‌افزار IDRISI و مدل MOLUSCE (به‌عنوان افزونه نرم‌افزار QGIS)، جهت پیش‌بینی رشد شهری برای سال ۲۰۲۰، با استفاده از پویایی‌های کاربری اراضی/پوشش زمین در بازه زمانی ۲۰۰۰ تا ۲۰۱۰ در شهر جیپور هند و شهر آستین در ایالات متحده آمریکا.
روش‌شناسی: نقشه‌های پیش‌بینی‌شده با نقشه‌های مشاهده‌شده (واقعی) مقایسه شدند و دقت آن‌ها از طریق معادلات ریاضی و همچنین ابزار «ارزیابی آماری شاخص کاپا» (Kappa Index Statistic) در افزونه MOLUSCE مورد سنجش قرار گرفت.
یافته‌ها: نتایج حاصل از روش CA-Markov در مقایسه با مدل MOLUSCE، مستحکم‌تر و دقیق‌تر است. بنابراین، به مقامات دولتی و غیردولتی قویاً توصیه می‌شود که برای ایجاد تاب‌آوری شهری، پویایی‌های LULC و پیش‌بینی‌های رشد شهری را مورد مطالعه قرار دهند.
نتیجه‌گیری: این مطالعه همچنین بر اهمیت یکپارچه‌سازی داده‌های شبکه معابر (Road Network) در بهبود دقت پیش‌بینی تأکید دارد. یافته‌های این پژوهش به توسعه راهبردهای برنامه‌ریزی شهری پایدار در شهرهای با رشد سریع کمک می‌کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Finding robust model for Urban Growth Prediction based on Land Use Land Cover Classification: A comparative study of CA Markov based Models LCM and MOLUSCE

نویسندگان [English]

  • Nitesh Kumar Mourya 1
  • Sana Rafi 2
1 Student, Department of Remote Sensing and Geoinformatics, Maharshi Dayanand Saraswati University Ajmer, India.
2 Assistant Professor, Department of Geography, School of Earth Sciences, SRT Campus Tehri, HNBGU, India.
چکیده [English]

Background: Scientific information of future LULC is fundamental to make proper land use planning to fulfill the rising human demands and their wellbeing without causing lasting damage to environment.
Objectives: This paper is an attempt to identify the robust model among CA MARKOV based model Land Change Modeler (LCM (IDRISI) and MOLUSCE Model (QGIS Plugin) to predict urban growth for 2020 using LULC dynamics during 2000-2010 of Jaipur city, India, and Austin in America.
Methodology: The predicted maps were compared with observed map and accuracy was checked through mathematical equation as well as Kappa Index Statistic Assessment Tool of MOLUSCE Plugin.
Results: The results obtained through CA Markov method are robust and more accurate compared to MOLUSCE model.  Therefore, it is highly recommended for government and non-government officials to study LULC dynamics and urban growth predictions in order to build urban resilience.
Conclusion: The study also highlights the importance of integrating road network data in improving prediction accuracy. These findings contribute to the development of sustainable urban planning strategies in rapidly growing cities.

کلیدواژه‌ها [English]

  • Prediction
  • LULC
  • Urban Growth
  • LCM
  • MOLUSCE
  • Robust Model
  • Jaipur City

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