کاربرد مدل LTM در پیش‌بینی و مدل‌سازی توسعۀ فیزیکی شهر ایلخچی

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استادیار گروه جغرافیا و برنامه‌ریزی شهری، دانشکدة برنامه‌ریزی و علوم محیطی، دانشگاه تبریز

2 کارشناس ارشد سنجش‌ازدور و GIS، دانشکدة برنامه‌ریزی و علوم محیطی، دانشگاه تبریز

چکیده

رشد سریع شهرنشینی فشارهای سنگینی بر سرزمین و منابع اطراف آن‌ها وارد کرده و موجب کاهش پوشش گیاهی، کاهش فضاهای باز و مشکلات جدی اجتماعی و زیست‌محیطی شده است. هدف از این مطالعه، درک عوامل مؤثر در روند توسعة فیزیکی شهر ایلخچی با عنایت به توسعة فضایی پایدار شهری از بعد اکولوژیکی و حفظ شرایط زیست‌محیطی آن در دو دهة آتی است؛ بدین منظور با استفاده از تصاویر ماهواره‌ای چندزمانة لندست 5 و فنون پردازش شیء‌گرا، تغییرات کاربری اراضی در مقطع زمانی 1360-1390 با تأکید بر گسترش افقی شهر ایلخچی ارزیابی شده است. براساس نتایج، مقدار مساحت شهر ایلخچی از 59/94 هکتار در سال 1363 به 57/438 هکتار در سال 1390 رسیده و 84/195 هکتار از توسعة ذکرشده بر روی اراضی باغی و زراعی صورت گرفته است که لزوم مدیریت توسعة آتی مبتنی بر اصول توسعة پایدار می‌طلبد. گفتنی است عوامل مؤثر بر توسعة فیزیکی شهر ایلخچی براساس پیشینة تحقیق، در قالب دوازده شاخص شناسایی، و با استفاده از روش شبکة عصبی مصنوعی نقشة احتمال توسعة شهری تهیه شده است. پس از پیش‌بینی الگوی آتی توسعة شهری در شهر ایلخچی با استفاده از راهبرد حفاظت از باغ‌ها و فضاهای سبز در فرایند توسعة شهری با بلوک‌بندی نقشة احتمال توسعة شهری، عواملی چون دادن فضای لازم برای توسعه، استخراج کمربند سبز طبیعی شرقی-غربی به طول پنج کیلومتر، اعمال ممنوعیت توسعه در اطراف کمربند سبز پیشنهادی و حفظ ذخایر اکولوژیک شهر ایلخچی با کاهش تخریب اراضی باغی عملیاتی شده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Application of LTM Model for Modeling of Physical Development of the Ilkhichi City

نویسندگان [English]

  • Hasan Mahmoudzadeh 1
  • Amir Masoud Ranjbar Navazi 2
1 Assistant professor of geography and urban planning, Faculty of Geography and Planning, Tabriz University, Tabriz, Iran
2 MA in remote sensing and GIS, Faculty of Geography and Planning, Tabriz University, Tabriz, Iran
چکیده [English]

Introduction
Fast and uncontrollable urbanization growth causes loss of lands and its recourses. This leads to a decrease in green areas, open spaces and serious environmental and social problems. Therefore, an essential step to urban planning, management and evaluation of its effects is to simulate physical development of the city. The aim of this study is to understand parameters of physical development in Ilkhichi city with regard to sustainable spatial development of urban issues from ecological and environmental perspectives in the next two decades. 
Changes in the land uses in 1984-2011 have been evaluated with emphasis on sprawl expansion of Ilkhichi city using Landsat 5 multi temporal satellite images and object-oriented techniques. Based on the results, urban area of Bonab, with an area of 94.59 hectares in 1984 has reached to 438.57 ha in 2011. About 195.84 hectares of the mentioned lands has developed on the gardens and agricultural lands. This demands management of future development based on the principles of sustainable development. Therefore, effective factors of physical development in Ilkhichi urban area  have been classified into 12 layers.  The LTM method has been employed to produce the possibility of urban development map. After predicting the future pattern of urban development in Ilkhichi city, the protection of gardens and green spaces strategy in the urban development process was operated using hexagonal layout of possibility of urban development map..
Rapid urbanization brings opportunities to new urban developments. However, it also causes serious losses of arable lands, as occurred in other developed countries. The term urban sprawl is so cloudy and confused that more precise language is needed to characterize what is bad urban growth.
In order to keep ecosystems functioning well, it is necessary for environmental researchers, managers, and decision makers to understand the spatial dynamics of an ecosystem. Importantly, remotely sensed imagery provides an efficient means to obtain information on temporal trends and spatial distribution of urban areas for understanding, modeling, and projecting land change.
In this study, changes of land use are analyzed in Bonab County using satellite images during 1989-2005. We have also proposed recommendation for reducing settlement sprawls and environmental problems in this area.
 
Methodology
Change detection is an important process for monitoring and managing natural resources and urban development because it provides quantitative analysis of spatial distribution in the area of interest. For monitoring the changes in land use of Ilkhichi, TM digital data of Landsat have been used in this study. The path/row number of TM and ETM+ imagery is 168/34. Main reason to use TM and ETM+ data was that of high resolution images for time series is not available to extract green area land use. The Land Transformation Model is a land use forecasting model as well as a tool that can be used to examine the spatial and temporal aspects of driving forces of land use change. The model uses a set of spatial interaction rules and machine learning, through neural net technology, to determine the nature of spatial interactions of drivers such as transportation, urban infrastructure and proximity to lakes and rivers that have historically contributed toward land use change in the past. Effective factors of physical development of Ilkhichi city based on research literature is identified in 12 indicators and artificial neural network based on LTM Model for preparing Urban Development probability map.
 
Results and discussion
In 1984-2011, based on Change detection map and initial state and final state matrix, barren land area has decreased from 66.627 hectares to 52.479, the built area has increased from 59.94 hectares to 57.438, agricultural land has decreased from 572.76 hectares to 387.90 hectares and garden land has decreased from 33.93 to 22.95 hectares. In this period, the population of the city has reached from 7446 to 15231. This means reduction in the population density from 78 to 34 people. Based on the Holdern model, sprawl index of Ilkhichi city is 53 percent and the biggest role in the development of buildup area belongs to reduction of farming and agricultural lands. About 195.84 hectares of the mentioned lands has developed on the garden and agricultural land. This involves management of future development based on the principles of sustainable development.
 
Conclusion
After predicting the future pattern of urban development in Ilkhichi city, the protection of gardens and green spaces in the urban development process was operated using hexagonal layout of possibility of urban development map. This can provide the necessary space for the development, extension of the natural green belt about 5 km long, restriction of urban development in buffer of green belt, protection of the ecological reserves of Ilkhichi city, and sprawl expansion control. Urban managers are able to decrease the horizontal expansion for detailed monitoring on proposed green belt, the use of mass production methods and the high-rise building (Compact City), the use of low-yielding land available inside the city (Infill development), and the urban development far away from the agricultural land.

کلیدواژه‌ها [English]

  • : Ilkhichi city
  • Sprawl
  • Artificial Neural Networks
  • land use changes
  • Object Oriented Classification
  1. بحرینی، حسین 1368، تهران چگونه شهری است و چه باید باشد؟، مجلة محیط‌شناسی، شمارة 15، ویژه‌نامة تهران.
  2. حکمت‌نیا، حسن و میرنجف موسوی، 1392، کاربرد مدل در جغرافیا با تأکید بر برنامه‌ریزی شهری و ناحیه‌ای، انتشارات آزادپیما، چاپ سوم.
  3. رحیمی، اکبر، 1392، ارزیابی و مدل‌سازی توسعة فضایی-کالبدی با تأکید بر توسعة میان‌افزا (نمونة موردی: کلان‌شهر تبریز)، رسالة دکتری جغرافیا و برنامه‌ریزی شهری، دانشکدة جغرافیا و برنامه‌ریزی دانشگاه تبریز.
  4. رسولی، علی‌اکبر و حسن محمود زاده، 1389، مبانی سنجش‌ازدور دانش پایه، انتشارات علمیران.
  5. شکوئی، حسین 1373، دیدگاه‌های نو در جغرافیای شهری، انتشارات سمت.
  6. شیعه، اسماعیل 1377، مقدمه‌ای بر مبانی برنامه‌ریزی شهری، انتشارات دانشگاه علم و صنعت، 212.
  7. فتح‌زاده، حیدر، 1389، شاخص‌های باروری استان آذربایجان شرقی، انتشارات سازمان مدیریت و برنامه‌ریزی آذربایجان شرقی.
  8. ویلیامز کاتی، برتون الزابت و جنکنز مایک، 1383، دستیابی به شکل پایدار شهری (شکل پایداری و حمل و نقل)، ترجمه واراز مرادی مسیحی، شرکت پردازش و برنامه‌ریزی شهری، تهران.
    1. Anderson, W.P, Kanaroglou, P.S, Miller, E.J 1996, urban form, energy and the environment: a review of issues, evidence and policy, Urban Studies, Vol.33, No. 1, pp.7-35.
10. Atkinson, P., and Tatnall, A., 1997, Neural networks in remote sensing. International Journal of Remote Sensing, Vol. 18, No. 4, pp. 699–709.

11. Babaian, R., and etc, 1997, Early detection program for prostate cancer: results and identification of high-risk patient population. Urology, Vol.37, No.3, pp. 193–197.

12. Bahreyni, H., 1989, How is Tehran and what it should be? Journal of Environmental Studies,Vol. 15, No 15, pp 83-97, Special letter. (In Persian)

13. Benz, U.C., and etc, 2004, Multi-resolution, objectoriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing No. 58, pp. 239–258.

14. Bhatta, B., 2010, Analysis of Urban Growth and Sprawl from Remote Sensing, DataSpringer, London, p 191.

15. Bockstael, N., and etc, 1995, Ecological Economic Modeling and Valuation of Ecosystems, Ecological Economics ,No. 14, pp. 143-159.

16. Bogart, W., 2009, Don't Call It Sprawl: Metropolitan Structure in the 21st Century, New York: Cambridge University Press, 2006. 196 pp

17. Brown, D. G., Lusch, D. P., & Duda, K. A.,1998, Supervised classification of glaciated landscape types using digital elevation data. Geomorphology, Vol. 21, No. 3–4, pp. 233–250.

18. Drummond, S., Joshi, A., and Sudduth, K.,1998, Application of neural networks: precisionFarming. IEEE Transactions on Neural Networks, 211–215.

19. Fathzadeh, H. 2011, Indicators of Fertility Rates of the East Azarbaijan Province, Management and Plannimg Organization of East Azarbaijan Province Publication. (In Persian)

20. Fishman, M., Barr, Dean S., and Loick, W. J. ,1991, Using neural nets in market analysis, Technical Analysis of Stocks and Commodities,No.4,pp. 18–21.

21. Fukushima, K., Miyake, S., and Takayuki., 1983, Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics, SMC, Vol.13, No.5, pp. 826–834.

22. Hekmatniya, H. and Moussavi, M. N., 2006, The Use of Models in Geography with Emphasis on Urban and Regional Planning, Novin Publications, Yazd. (In Persian)

23. Katie Williams, Michael Jenks, Elizabeth Burton., 2000, Achieving Sustainable Urban Form, Taylor & Francis Publications, 388p.

24. Nancy E. McIntyre, K. Knowles-Yánez, and D. Hope. 2008, Urban Ecology as an Interdisciplinary Field: Differences in the use of Urban Between the Social and Natural Sciences, Journal of Urban Ecosystems. No. 4, pp. 5-24.

25. Pijanowski, B. C., Brown, D. G., Shellito, B. A., and Manik, G. A. 2002, Using neural networks and GIS to forecast land use changes: a land transformation model, Computers, Environment and Urban Systems, Vol. 26, No. 6, pp. 553-575.

26. Rahimi A., 2013, Evaluation and modeling urban physical and spatial structure with special reference to in-fill development: case study, Tabriz Metropolitan, Ph.D thesis Geography and Urban Planning, University Of Tabriz, Superviser: Dr. Mir Sattar Sadrmosavi

27. Rasoli, A.A. and Mahmoudzadeh, H., 2010, principal of Object Oriented Remote sensing, ElmIran Publications, Tabriz. (In Persian)

28. Ritter, N., Logan, T., and Bryant, N. 1988, Integration of neural network technologies with geographic information systems. Proceedings of the GIS symposium: integrating technology and geoscience applications (pp. 102–103). Denver, Colorado. United States Geological Survey, Washington, DC.

29. Rumelhart, D., Hinton, G., Williams, R., 1986, Learning Internal Representations by Error Propagation, In D. E. Rumelhart, and J. L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructures of Cognition,Vol. 1, No 323, pp. 318–362, Cambridge: MIT Press.

30. Shakoui, H. 1995 New Perspectives in urban geography, Samt Publications, Tehran. (In Persian)

31. Shieh, E., 1998, An introduction to the basics of urban planning, elmo v sanat university Publications, 240p. (In Persian)

32. Skapura, D. 1996, Building neural networks, New York: ACMPress.

33. Sudhira, H.S, Ramachandra, T.V 2007, Characterising Urban Sprawl from Remote Sensing Data and Using Landscape Metrics, 10th International Conference on Computers in Urban Planning and Urban Management, Iguassu Falls, PR Brazil, July 11–13.

34. Theobald, D.M., Hobbs, N.T., 1998, Forecasting Rural Land-use Change: A Comparison of Regression and Spatial Transition-based Models, Geographical and Environmental Modeling, Vol. 2, No. 1, pp. 65–82.