Modeling and forecasting the process of physical expansion and development of Bojnord city

Document Type : Research Paper

Authors

1 Assistant Professor of Urban Development, Faculty of Art, University of Bojnord, Bojnord, Iran

2 M.Sc. in Urban Planning, Shahid Beheshti University, Tehran, Iran

Abstract

Extended Abstract
 Introduction:
One of the most important problems of the urban network in Iran is the rapid expansion of cities and, consequently, the uneven development and growth of cities, which has occurred for various reasons, including increasing population and irregular migration. The first consequence of urban sprawl is land use change. The city of Bojnord, after being selected as the political-administrative center of North Khorasan province in 2005, faced a double population growth and demographic changes. The concentration of administrative, military, tourist, cultural and educational centers, along with the uncontrolled migration of villagers to the city, confronted the city with new urban development needs. The physical expansion of the city more than ever, the conversion of agricultural land into a city and the uncontrolled construction, is one of the main issues facing the city. In this regard, the use of cellular automation, as a technique with features such as simplicity, transparency and strong potential to simulate spatial dynamics, has caused more and more attention in modeling the spatial information system and urban affairs.
 
 Methodology:
The present study was performed by descriptive-analytical method and using cellular automation method by perceptron artificial neural networks and remote sensing data. Thus, satellite images related to the years 2011 and 2021 of Bojnord plain were the basis of the study and in order to image the spatial-temporal patterns of land use, the data of the remote sensing archive were used. Research data from satellite images related to 2011 and 2021 (a period of ten years) as well as topographic maps of the plain and the city of Bojnord along with some spatial data of the region including land price, distance from land uses, distance from fault, slope, Height, distance from the road and distance from the built areas are provided. The source and organization of this data is also related to the Surveying Organization, Google Earth and the Surveying Organization of Iran. In line with the objectives of the research, the multispectral images of Landsat 7 and Landsat 8 in relation to the city of Bojnord (path 161, row 034) have been downloaded. In order to increase the validity of the results, the output maps were adapted to Google Earth maps. Also, in the 2011 classification, the overall accuracy was 86 and the kappa coefficient was 81, and in 2021, the overall accuracy was 88 and the kappa coefficient was 85.
 
 Results and discussion:
The main findings include calculating and estimating land use changes and the share of each land use in the base years of this research, modeling and forecasting land use changes for 2031 based on mapping the potential for transfers for land uses and forming a Markov chain along with eight affecting factors: slope, height, Distance from faults, agricultural lands, main roads, main electricity network, built-up areas and land prices. In 2011, the largest area of the region was occupied by pastures, which accounted for 73.6% of the total land area. During this period, the built-up areas cover 4.2% of the area with an area of 53748901 square meters. In general, most of the pastures are in the southern and eastern regions of the area and the areas built in the central and northern part of the Bojnord plain, as well as agricultural lands are located in the central and eastern parts. According to the data obtained in 2021, the built-up areas include 65266522 square meters, agriculture 194519833 square meters, pastures 888136607 square meters and barren 130533045 square meters. The share of rangelands in land use of Bojnord plain is 69.4%, barren 10.3%, agriculture 15.2% and developed areas 5.1%. Decreasing pasture and agricultural levels and increasing built and barren levels are evident this year. In modeling land use change, the transfer force from one land use to another is modeled according to the variables; In the sense that each pixel of the image has the potential to change from one user to another. According to the calculation maps, it can be seen that the changes in the built areas in the central part of Bojnord plain and around the city of Bojnord have been mainly accompanied by changes in agricultural lands and pastures. In all parts, especially in the northwestern areas of Bojnord plain, rangelands have become agricultural lands and with the development of agriculture, these valuable lands, which are the habitats of animals and plants of different species, have been destroyed; Also, Bojnord-Esfarayen axis has become the most built areas.
 
 Conclusion:
This study to model and predict land use change in Bojnord, based on the two years based on 2011 and 2021 AD, divided land use into four main categories: constructed use, barren, agricultural and rangeland. The main findings with emphasis on the effect of eight factors affecting the slope, height, distance from the fault, agricultural lands, main roads, main electricity network, built-up areas and land prices, on the development of Bojnord based on data received from satellite images and the process of image correction to increase the validity and accuracy of research outputs shows that while the rangeland use level between 2011 and 2021 from 73.6% of total land uses decreased to 69.4% and agricultural land use level from 17.4%  decreased to 15.2%,  built-in (urban) user level increased from 4.2% to 5.1%. This increase has often occurred due to the greater impact of slope, height, distance from main roads and distance from built-up areas, and the greatest proportion of this change has occurred in the axis of Bojnord Esfarayen road (west and southwest of the city). Based on the formation of the Markov chain matrix and the created user class maps in 2011 and 2021, land use forecasting and modeling of Bojnord in the horizon of 2031 has been done.The results of the land use change forecast section confirm the increase of built-up areas and urban use and the decrease of agricultural and rangeland use in the horizon of Bojnord city development forecast in 2031.

Keywords

Main Subjects


  1. جعفرزاده، کاظم؛ سبزقیانی، غلامرضا؛ یوسفی‌خانقاه، شهرام و سلطانیان، ستار. (1397). مدل‌سازی تغییرات ساختار شهری با رویکرد برنامه‌ریزی فضایی برای رسیدن به توسعه پایدار شهری، مطالعه موردی: شهر قائم‌شهر، فصلنامه اطلاعات جغرافیایی (سپهر)، 27(107)، 209-222.
  2. حسینی، مهدی؛ برقچی، معصومه؛ باقرزاده، فهیمه و صیامی، قدیر. (1394). ارزیابی تأثیرات زیست‌محیطی گسترش بی‌رویه شهرها (مطالعه موردی: پروژه مسکن مهر شهر طرقبه)، فصلنامه برنامه‌ریزی منطقه‌ای، 5 (18)، 58-43.
  3. حمیدیان، امیرحسین و خطیبی، علی. (1395). طراحی و توسعه نرم‌افزار شبیه‌ساز سلول‌های خودکار (CAS) با رویکرد کاربرد در محیط‌زیست، محیط‌زیست طبیعی، منابع طبیعی ایران، 69 (4)، 996-981.
  4. داداش‌پور، هاشم؛ پناهی، حسین و شمس‌الدینی، علی. (1398). تحلیل عوامل متحرک و پیش‌بینی تغییرات کاربری زمین در منطقه کلان‌شهری تهران با تأکید بر یک مدل منطقه‌ای یکپارچه، فصلنامه برنامه‌ریزی منطقه‌ای، 9(35)، 39-56.
  5. دبیری، فرهاد؛ پورهاشمی، سید عباس و روستا، فخرالضحی. (1388). بررسی اصول و مفاهیم حقوق بین‌الملل محیط‌زیست با نگاهی به توسعه پایدار، علوم و تکنولوژی محیط‌زیست، 11(3)، 215- 213.
  6. زادولی، فاطمه؛ صدر موسوی، میرستار؛ کریم‌زاده، حسین و صبوری، رحیمه. (1396). بررسی و تحلیل اثرات زیست‌محیطی گسترش پراکنده شهری نمونه موردی: شهر هادی شهر، فصلنامه برنامه‌ریزی منطقه‌ای، 7(26)، 160-147.
  7. شکوهی‌فرد، حمید و اسماعیلی، علی. (1395). مدل‌سازی و پیش‌بینی روند گسترش و توسعه فیزیکی شهرها به روش اتوماتای سلولی توسط شبکه‌های عصبی مصنوعی با استفاده از داده‌های سنجش‌ازدور (مطالعه موردی شهرستان خرم‌آباد)، مجموعه مقالات اولین کنفرانس ملی علوم جغرافیا، مؤسسه حامیان زیست اندیش محیط آرمانی با حمایت مراکز آموزش عالی کشور، اردبیل، 18-1.
  8. شنانی هویزه، سیده مائده و زراعی، حیدر. (1395). بررسی تغییرات کاربری اراضی طی دو دهه دوره زمانی (مطالعه موردی: حوزه آبخیز ابوالعباس)، پژوهشنامه مدیریت حوزه آبخیز، 7(14)، 244-237.
  9. صاحبقرانی، علیرضا؛ محمدی، محمود و مالکی‌پور، احسان. (1392). مدل‌سازی گسترش شهر در اراضی پیرامونی با استفاده از سلول‌های خودکار (CA) و فرآیند تحلیل سلسله مراتبی (AHP) (مطالعه موردی: منطقه 7 اصفهان)، مطالعات و پژوهش‌های شهری و منطقه‌ای، 5(18)، 192-175.
  10. عبداللهی، علی‌اصغر؛ خبازی، مصطفی و درانی‌زاده، زهرا. (1399). مدل‌سازی تغییرات کاربری اراضی با استفاده از شبکه عصبی پرسپترون (مطالعه موردی: شهر لاهیجان)، برنامه‌ریزی و آمایش فضا، 24 (1)، 49-79.
  11. کیانی، واحد؛ علی زاده شعبانی، افشین و نظری سامانی، علی‌اکبر. (1393). ارزیابی صحت طبقه‌بندی تصویر ماهواره IRS-P6 با استفاده از پایگاه اطلاعاتی Google Earth به‌منظور تهیه نقشه پوششی/کاربری اراضی (مطالعه موردی: حوزه آبخیز طالقان)، اطلاعات جغرافیایی، 23(90)، 51-60.
  12. موسسه آموزش عالی اشراق. (1398). سند راهبردی توسعه مسکن استان خراسان شمالی، اداره کل راه و شهرسازی خراسان شمالی، گزارش پایانی.
  13. مهندسین مشاور نقش‌جهان پارس. (1389). طرح توسعه و عمران (جامع) شهر بجنورد، جلد سوم: مطالعات کالبدی (ویرایش نهایی)، سازمان مسکن و شهرسازی استان خراسان شمالی.
  14. میرباقری، بابک؛ متکان، علی‌اکبر و علی‌محمدی سراب، عباس. (1389). ارزیابی کارایی مدل سلول‌های خودکار در شبیه‌سازی گسترش اراضی شهری در حومه جنوب غرب تهران، برنامه‌ریزی و آمایش فضا، 14(2)، 102-81.
  15. نژادابراهیمی، احد و صداقتی، عاطفه. (1398). بازآفرینی تصویر ذهنی شهر بجنورد در دوره قاجار با رویکرد فرهنگی، فصلنامه تاریخ شهر و شهرنشینی در ایران و اسلام، 1(1)، 77-96.
  16. نورائی‌صفت، ایثار؛ نظری، سجاد و کریمی، سعید. (1395). بررسی روند تغییرات رشد و گسترش کالبدی شهر رشت و ارزیابی تغییرات کاربری اراضی زمین‌های اطراف آن با تصاویر ماهواره‌ای، فصلنامه جغرافیا و مطالعات محیطی، 5 (17)، 21-32.
  17. یوسفی، مریم و اشرفی، علی. (1395). مدل‌سازی رشد شهری بجنورد با استفاده از داده‌های سنجش‌ازدور (بر اساس شبکه عصبی-مارکوف و مدل‌ساز تغییرات زمین)، فصلنامه برنامه‌ریزی منطقه‌ای، 6(21)، 179-192.
  18. Agarwal, C., (2002). A review and assessment of land-use change models: dynamics of space, time, and human choice.‌
  19. Alexandratos, N., & de Haen, H., (1995). World consumption of cereals: will it double by 2025?, Food Policy, 20(4), 359-366.‌
  20. Al-sharif, A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian journal of geosciences, 7(10), 4291-4301.
  21. Anand, V., & Oinam, B. (2020) Future land use land cover prediction with special emphasis on urbanization and wetlands. Remote Sensing Letters, 11(3), 225-234.‌
  22. Baker, W. L. (1989). A review of models of landscape change. Landscape ecology, 2(2), 111-133.‌
  23. Batty, M., Couclelis, H. & Eichen, M. (1997). Urban systems as cellular automata. Environment and Planning B: Planning and Design, 24(2), 159-164.
  24. Fatemi, M., Karami, E., & Rezaei-Moghaddam, K. (2017) Determinants of land use change in Fars province, Iran. International Journal of Agricultural Resources, Governance and Ecology, 13(3), 272-293.
  25. Geist, H. J., & Lambin, E. F. (2002). Proximate Causes and Underlying Driving Forces of Tropical Deforestation Tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations. BioScience, 52(2), 143-150.‌
  26. Han, Y., & Jia, H. (2017) Simulating the spatial dynamics of urban growth with an integrated modeling approach: A case study of Foshan, China. Ecological Modelling, 353, 107-116.‌
  27. Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of remote sensing, 23(4), 725-749.
  28. Jeong, Y. S. (2017). Semiconductor wafer defect classification using support vector machine with weighted dynamic time warping kernel function. Industrial Engineering & Management Systems, 16(3), 420-426.‌
  29. Kaimowitz, D., & Angelsen, A. (1998). Economic models of tropical deforestation: a review.‌
  30. Kanchanamala, S., & Sekar, S. P. (2014). Simulation of land use changes for the planning of a metropolitan area. WIT Transactions on Ecology and the Environment, 181, pp. 385-393.‌
  31. Kuang, W. (2011). Simulating dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan Metropolitan Area. Journal of Geographical Sciences, 21(2), 317-330.‌
  32. Lambin, E. F., (1997). Modelling and monitoring land-cover change processes in tropical regions. Progress in physical geography, 21(3), 375-393
  33. Liping, C., & Yujun, S., & Saeed, S. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PloS one, 13(7), 1-23.
  34. Maimaitijiang, M., Ghulam, A., Sandoval, J. O., & Maimaitiyiming, M., (2015). Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data. International Journal of Applied Earth Observation and Geoinformation, 35, 161-174.‌
  35. Mohamed, A. & Worku, H., (2020). Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Climate, 31, 100545.
  36. Prayitno, G., Sari, N., Hasyim, A. W., & Nyoman, S. W., (2020). Land-use prediction in Pandaan District pasuruan regency. International Journal of GEOMATE, 18(65), 64-71.‌
  37. Rashidinia, J., Ghasemi, M., & Jalilian, R. (2010). Numerical solution of the nonlinear Klein–Gordon equation. Journal of Computational and Applied Mathematics, 233(8), 1866-1878.‌
  38. Seto, K. C., Woodcock, C. E., Song, C., Huang, X., Lu, J., & Kaufmann, R. K., (2002). Monitoring land-use change in the Pearl River Delta using Landsat TM. International journal of remote sensing, 23(10), 1985-2004.‌
  39. Wang, F., & Ge, Q., (2012). Estimation of urbanization bias in observed surface temperature change in China from 1980 to 2009 using satellite land-use data. Chinese Science Bulletin, 57(14), 1708-1715
  40. Yu, W., & Zang, S., & Wu, C., & Liu, W., & Na, X. (2011). Analyzing and modeling land use land cover change (LUCC) in the Daqing City, China. Applied Geography, 31(2), 600-608.