فصلنامه علمی- پژوهشی اطلاعات جغرافیایی « سپهر»

فصلنامه علمی- پژوهشی اطلاعات جغرافیایی « سپهر»

پایش و شبیه‌سازی تغییرات کاربری و پوشش اراضی در حوضه آبریز کارون بزرگ

نوع مقاله : مقاله پژوهشی

نویسندگان
مسعود مینایی 1
صادق بولاقی 2
بهناز شیخ 2
مریم رضا‌علی‌زاده 2
امیر حسین نجفی ده‌جلالی 2
1 دانشیار گروه جغرافیا، آزمایشگاه علم / سیستم اطلاعات جغرافیایی و سنجش از دور (GISSRS: Lab)، دانشکده ادبیات و علوم انسانی، دانشگاه فردوسی مشهد، مشهد، ایران
2 دانشجوی کارشناسی ارشد سیستم اطلاعات جغرافیایی و سنجش از دور، گروه جغرافیا، آزمایشگاه علم / سیستم اطلاعات جغرافیایی و سنجش از دور (GISSRS: Lab)، دانشکده ادبیات و علوم انسانی، دانشگاه فردوسی مشهد، مشهد، ایران
10.22131/sepehr.2025.2027958.3075
چکیده
در سال‌های اخیر افزایش جمعیت و گسترش شهرنشینی، تغییرات گسترده‌ای در کاربری و پوشش اراضی ایجاد کرده است. این تغییرات، پیامدهای زیانباری همچون کاهش تنوع زیستی، بیابان‌زایی و جنگل‌زدایی را به دنبال داشته و اکوسیستم حوضه‌های آبریز را با تهدیدات جدی زیست‌محیطی مواجه کرده است. ازاین‌رو، پایش تغییرات کاربری و پوشش اراضی در این مناطق اهمیت ویژه‌ای دارد. پژوهش حاضر به مدل‌سازی تغییرات کاربری و پوشش اراضی در حوضه آبریز کارون بزرگ می‌پردازد تا اثرات زیست‌محیطی این تغییرات تحلیل و پایش شود. در ابتدا، نقشه‌های کاربری و پوشش اراضی منطقه از داده‌های  Globeland30 تهیه شد. سپس، عوامل مؤثر بر این تغییرات تحت سناریوی رشد اراضی انسان‌ساخت استخراج شد. در ادامه ضمن تهیه لایه‌های موردنیاز این عوامل در نرم‌افزار GIS، با استفاده از دو روش PSI و MEREC، دو نقشه مبتنی بر سناریوی رشد اراضی انسان‌ساخت تولید شد. برای ارزیابی و بهبود مدل‌سازی، دو رویکرد مورد مقایسه قرار گرفتند. در رویکرد اول، خروجی روش PSI با مدل ترکیبیSVM-CA-Markov ادغام شد و در رویکرد دوم، خروجی روش MEREC با همان مدل ترکیب شد. مدل‌سازی تغییرات کاربری و پوشش اراضی این حوضه برای سال 2020 انجام و نتایج حاصل با استفاده از منحنی ROC صحت‌سنجی شد. نتایج نشان داد که الگوریتم مبتنی بر خروجی روش MEREC  و مدل ترکیبیSVM-CA-Markov، با مقدار AUC برابر با 0.89، از دقت بالاتری برخوردار است. با توجه به نتایج صحت‌سنجی، برای پیش‌بینی تغییرات کاربری و پوشش اراضی در افق 2040 از الگوریتم MEREC+SVM-CA-Markov استفاده شد. نتایج نهایی نشان داد که وسعت اراضی انسان‌ساخت تا سال 2040 به بیش از 1862 کیلومترمربع خواهد رسید. اگرچه این گسترش می‌تواند به رونق اقتصادی منطقه کمک کند، اما پیامدهای زیست‌محیطی متعددی، ازجمله تخریب منابع طبیعی و افزایش فشار بر اکوسیستم منطقه را به دنبال خواهد داشت. ازاین‌رو، تدوین برنامه‌های مدیریتی بهینه برای کاهش اثرات زیان‌بار این تغییرات ضروری است.
کلیدواژه‌ها
تغییرات کاربری/ پوشش اراضی
اراضی انسان‌ساخت
حوضه آبریز کارون بزرگ
تحلیل‌ تصمیم‌گیری چند معیاره
ماشین بردار پشتیبان
زنجیره مارکوف
سلول‌های خودکار

موضوعات

عنوان مقاله English

Monitoring and simulation of land use and land cover changes in the Great Karun Basin

نویسندگان English

Masoud Minaei 1
Sadegh Boulaghi 2
Behnaz Sheikh 2
Maryam Rezaalizadeh 2
Amir Hossein Najafi Deh Jalali 2
1 Associate professor, Department of geography (GISSRS: Lab), Faculty of literature and humanities, Ferdowsi University of Mashhad, Mashhad, Iran
2 M.Sc. Student in GIS and RS, Department of geography (GISSRS: Lab), Faculty of literature and humanities, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده English

Extended Abstract
Introduction
In recent decades, population growth and urbanization have led to extensive changes in land use and land cover (LULC), resulting in consequences such as deforestation, desertification, greenhouse gas emissions, increased surface temperature, reduced biodiversity, and decreased ecosystem service quality. The driving forces behind LULC changes are not uniform across different parts of the world and are heavily dependent on human factors. While human factors play a primary role in these changes, other factors such as climate change and biophysical characteristics can also influence the intensity and trend of these changes. Spatial-temporal analysis of LULC changes using remote sensing (RS) and geographic information systems (GIS), provides crucial information for natural resource management and sustainable development. The use of satellite data obtained from remote sensing and multi-criteria decision-making (MCDM) analyses based on GIS can significantly improve the decision-making process in land use and land cover management. These technologies enable the identification and monitoring of both minor and major changes on the earth's surface with high precision. Various models, such as CA-Markov and machine learning algorithms, are employed to predict LULC changes and make effective land management decisions. These studies can offer important policy recommendations for ecosystem management and natural disaster monitoring. Previous studies have predominantly focused on smaller scales, such as sections of a basin, cities, or sub-basins. However, the current research is conducted on a larger scale, specifically the Great Karun Basin. In this study, the modeling scenario combines PSI and MEREC models, which are efficient multi-criteria decision-making (MCDM) methods based on GIS.
Materials & Methods
In this research, to model land use/land cover (LULC) changes in the Great Karun Basin, the driving factors of built-up areas growth were first identified, and digital maps of these criteria were prepared in a GIS environment. Then, using the PSI and MEREC methods, each criterion was weighted, and two maps were produced for the potential growth of built-up areas for the year 2010. Multi-temporal LULC maps from the Globeland30 data were used as input for the model. The LULC changes in the region for the year 2020 were simulated using two algorithmic combinations: PSI+SVM-CA-Markov and MEREC+SVM-CA-Markov in the TerrSet software. Finally, the simulation's accuracy was validated using the ROC curve, and the optimal algorithm for simulating LULC in 2040 was selected.
Results & Discussion
In this study, the driving factors for the growth of built-up areas in the Great Karun Basin were weighted using the PSI and MEREC methods. The results indicated that the criterion of distance from built-up areas had the highest importance, while the distance from power transmission lines had the least importance. Potential growth maps for the year 2010 were prepared, showing that both models indicate the northeast-southwest axis and the eastern areas of the region have suitable potential for the growth and development of built-up areas. Subsequently, potential land use conversion maps for the year 2010 were created using the PSI+SVM-CA-Markov and MEREC+SVM-CA-Markov algorithms. The LULC changes for the year 2020 were simulated, and the results were validated against the 2020 ground truth map. Validation using the ROC curve showed that the MEREC+SVM-CA-Markov algorithm, with an AUC of 0.89, was more accurate than the PSI+SVM-CA-Markov algorithm, with an AUC of 0.86. Finally, using the MEREC+SVM-CA-Markov algorithm, the LULC changes for the year 2040 were simulated. The results showed that built-up areas would increase from 843.9075 square kilometers in 2020 to 1862.487 square kilometers in 2040, with the most significant growth occurring towards the southeast, southwest, and northeast of the region. Additionally, a significant decrease in forest areas was observed in this basin, highlighting the need for sustainable environmental management strategies.
Conclusion
Land use and land cover changes are serious environmental concerns today, directly caused by human activities. These changes cause ecological disturbances, climate changes, and destructive effects on the environment and ecosystems. LULC modeling is a fundamental tool for natural resource management that helps organizations adopt optimal strategies for basin land management. The catchment area of the Great Karun River is one of the strategic areas of Iran, where the importance of modeling land use changes is very high. The results of this research can help reduce the ecological, economic, and social consequences caused by land use changes in the Great Karun Basin. Finally, it is recommended that future research incorporates more comprehensive decision-making criteria and compares the results of the algorithm used in this study with other land use and land cover change modeling algorithms to enhance the accuracy of the outcomes. Additionally, incorporating various climate change scenarios into the modeling process could facilitate a more precise assessment of the impacts of such changes on ecosystems and land use/land cover. This, in turn, would enable the development of more effective management strategies for the conservation of natural resources and sustainable development.

کلیدواژه‌ها English

Land use/cover changes
Built-up areas
Great Karun Basin
Multi-Criteria Decision Analysis
Support vector machine
Markov chain
Cellular automata
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