Application of Remote Sensing and GIS Techniques for Simulating Urban Development (Case Study: The City of Mahabad

جغرافیا و توسعه فضای شهری

Article 6, Volume 3, Issue 2 - Serial Number 5, March 2017, Pages 161-175   

Document Type: Scientific-Research

DOI: 10.22067/gusd.v3i2.55931

Authors

Hassan Mahmoudzadeh^* ; Khalil Didhban; Syed Ali Sadeghzadeh Sadat

University of Tabriz

Abstract

Extended Abstract
1. Introduction
The investigation of land cover and land use changes has been important since ancient eras and these changes mainly occur in two ways: the first types of these changes take place by the means of natural factors such as erosion, tectonic forces and floods and the second types take place by human factors. In recent years in many parts of the world, human oriented activities were the most effective factors in changing land use, land cover and urban development. Nowadays population growth,on one hand and limites resources,on the other hand, result in changing of the application of lands. Land use changes in agricultural areas neighboring the cities is an important factor which has a vital role in supplying food and maintaining food security as well as earning money for urban population which have rapid growth. Vicinity of agricultural and horticultural areas with cities of Iran as ecological reserves, has increased the necessity of conducting this study. The present study aims to minimize the extent of use changes and lessen challenge of ecological destruction via development scenarios.
2. Methodology
In this study logistic regression model has been used, which is a special kind of multiple regressions and in which the dependent variable is discrete. Contrary to other linear models, the least square method was used in logistic regression. Maximum probability method was used for estimation of characteristics and factors of each independentvariable. Therefore,the logistic regression equation is as follows:
{ }+{ }+{ }+….{ }
Different data and softwares were used toward implementing the regression model. The soft wares used are:Ecognition،Idrisiselva and Arc GIS. Images were received through satellite Landsat 5 in 1986 and Landsat 8 in 2014 from USGS site (United State Geological Site). Regarding the location of Mahabad which is situated in pass 168 and rows 34 and 35, we received two frames each year. Pre-process operations including image mosaic, subset and radiometric correction were implemented on images before classification operation by Flaash method in ENVI environment. For increasing accuracy of multi-spectral images of Landsat 8 bands which have low local resolution and high spectral resolution fusion operations were implemented combined with 15 meters panchromatic band. Classification was conducted through the Fuzzy method in the Ecognition environment. Then, classified images were transformed into ASCII format in Arc GIS environment for entering the software environment of Idrisiselva, and their format were changed into RST format in IDRISI environment software for preparing variables. Logistic regression command was used for modeling in the software Idrisi environment. To do this, we entered the amount of change in urban development from 1986 to 2014 into the model environment as a dependent variable and other involved factors in urban development as independent variables, respectively. We used Fuzzy method for scenario building and zoning of developed areas, in which developed areas were categorized into four classes: very proper, proper, improper and very improper.
3. Results
The investigation of land use changes from 1986 to 2014 determined that land uses decreased with urban development every year. Land use changes are as follow: building lands increased from 989.86 ha to 2000.02 ha. Water resources (dikes and rivers) had decreasing trend from 841.85 ha to 564.16ha. Irrigated arable lands decreased from 2889.17 ha to 1195.378 ha in 1986, and also rocky lands decreased from 10045.42 ha to 7572.78 ha. In this study, investigation of urban development from 1986 to 2041 shows that land use change will be increased in proportion with population growth.

4. Conclusion
Results of the study showed that in the city of Mahabad, land use changes doubled with the twofold growth of the population in the mentioned period. Development in the third period was lower than twofold. Generally, changing land use could be predicted for future years through using different models of forecasting and through using geographical information system techniques and remote sensing as well, and allowing controlling and management of urban development trends, considering ecological and risk factors, and prohibit from entering residential areas into risky areas. These bestow help and information to urban managers to solve the problems. By introducing solutions urban development can be managed and controlled in a desired way. Considering urban development and reducing arable lands by making urban belt prohibits urban development in agricultural lands and prohibits approaching to natural risks areas, stabilizes land use in a programmed form and prevents hasty urban development. On the other hand, for identifying areas prone to urban development, and for preventing unlawful building by making barrier in the way of rushed urban development,we should establish an exact plan from an economic point of view.

Keywords

Logistic regression model; Mahabad; Land use change; Urban development

References

1. بهرام‌سلطانی، ک. (1371). مجموعه‌مباحث و روش‌های شهرسازی. تهران: مرکز مطالعات تحقیقات و معماری ایران

2. بیبر ان. آر.، و هیگینز، ک. (1381). برنامه‌ریزی محیطی و توسعۀ زمین؛ راهنمایی برای برنامه‌ریزی و طراحی محلی پایدار. ترجمۀ حسین بحرینی و کیوان کریمی. تهران: انتشارات دانشگاه تهران

3. ربیعی، ح. ا.، ضیائیان، پ.، و محمدی، ع. (1384). کشف و بازیابی تغییرات کاربری و پوشش اراضی شهر اصفهان به کمک سنجش از دور و سیستم اطلاعات جغرافیایی. ویژه‌نامه جغرافیایی، (4)، 19-32

4. رسولی، ع. (1387). مبانی سنجش از دور کاربردی با تأکید بر پردازش تصاویر ماهواره‌ای. تبریز: انتشارات دانشگاه تبریز

5. رسولی، ع.، زرین‌بال، م.، و شفیعی، م. (1388). کاربرد تصاویر ماهواره‌ای با هدف تشخیص تغییرات کاربری اراضی و ارزیابی تأثیرات محیط زیستی. پژوهش‌های آبخیزداری، 21(82)، 1-11

6. شمس، م.، و حجی‌ملایری، پ. (1388). توسعۀ فیزیکی و تأثیر آن بر تغییرات کاربری شهر ملایر در دورۀ زمانی 1365-1385. فصلنامه جغرافیایی آمایش، (7)، 75-91

7. عبداللهی، م. (1382). مدیریت بحران در نواحی شهری. تهران: انتشارات سازمان شهرداری‌های کشور

8. عزیزقلاتی، س.، رنگزن، ک.، تقی‌زاده، ا.، و احمدی، ش. (1393). مدل‌سازی تغییرات کاربری اراضی با استفاده از روش رگرسیون لجستیک در مدل Lcm؛ منطقۀ مورد مطالعه: کوهمره سرخی استان فارس. علمی پژوهشی تحقیقات جنگل و صنوبر ایران، 22(4)، 585-596

9. فیضی‌زاده، ب.، جعفری، ف.، و نظم‌فر، ح. (1387). کاربرد داده‌های سنجش از دور در آشکارسازی تغییرات کاربری‌های اراضی شهری (مطالعۀ موردی فضای سبز شهر تبریز). هنرهای زیبا، (34)، 17-24

10. قربانی، م.، مهرابی، ع.، ثروتی، م.، و نظری‌سامانی، ع. (1389). بررسی تغییرات جمعیتی و اثرگذاری‌های آن بر تغییرات کاربری اراضی (منطقۀ مورد مطالعه: بالا طالقان). منابع طبیعی، 63(1) 75-88

11. کامیاب، ح.، ماهینی، ع.، حسینی، س. م.، و غلامعلی‌فرد، م. (1389). اتخاذ رهیافت اطلاعات ‌حور با کاربرد روش رگرسیون لجستیک برای مد‌ل‌سازی توسعۀ شهری گرگان. محیط‌شناسی، (54)، 89-96

12. کرم، ا.، حجه‌فروش‌نیا، ش.، و حکیمی، ح. (1389). مدل‌سازی فضایی گسترش شهری با استفاده از روش رگرسیون لجستیک. تحقیقات کاربردی علوم جغرافیایی،14(17)، 41-64

13. محمدزاده، ر. (1386). بررسی اثرات زیست‌محیطی توسعۀ فیزیکی شتابان شهرها با تأکید بر شهرهای تهران و تبریز. مجلۀ جغرافیا و توسعۀ ناحیه ای، (9)، 95-112

14. المدرسی‌الحسینی، س. ع.، کرمی، ج.، و روشن‌بخش، س. (1393). مدل‌سازی توسعۀ شهری همدان با استفاده از روش رگرسیون لجستیک سال‌های 2003-2009. در مجموعه‌مقالات نخستین همایش ملی کاربرد مدل‌های پیشرفتۀ تحلیل فضایی (سنجش از دور و GIS ایران) در آمایش سرزمین، به‌همت سیدعلی المدرسی، ص. 412-423

15. Aldrich, J. H., & Nelson, F. D. (1986). Linear probability, logit and probit models (3rd edition). Beverly Hills, CA: Sage Publications

16. Alig, R. J., Kline, J. D., & Lichtenstein, M. (2004). Urbanization on the US landscape: Looking ahead in the 21st century. Landscape Urban Plan, 69, 219–234

17. Clark, W. A., & Hosking, P. L. (1986). Statistical methods for geographers. New York, NY: John Wiley and Sons

18. Domencich, T.A., McFadden, D. (1975). Urban travel demand: Behavioural analysis. Amsterdam: North- Holland Publishing Co

19. Geist, H. L., & Lambin, E. F. (2002). Proximate causes and underlying driving forces of tropica deforestation. Bioscience, 52(2), 143-150

20. Henry, S., Boyle, P., & Lambin, E. F. (2003). Modeling inter-provincial migration in Burkina Faso, West Africa: The role of socio-demographic and environmental factors. Applied Geography, 23, 115–136

21. Hu, Zh., & Lo, C. P. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31, 667-688

22. Jongman, R. H., Bunce, R.G., & Elena-Rossello, R. (1998). A European perspective on the definition of landscape character and biodiversity: Key concepts in landscape ecology. In J. W. Dover, & R. G. H. Bunce (Eds.), Proceedings of the 1998 European congress of the International Association of Landscape Ecology (pp. 1–35). England, University of Nottingham Press

23. Kamyab, H., Salman Mahini, A., Hosseini, S.M., & Gholamalifard, M. (2010). Adopt a datadriven approach using logistic regression to model urban development Gorgan. Journal of Ecology, 36, 89-96

24. Maithani, S., Jain, R. K., & Arora, M. K. (2009). An artificial neural network based approach for modelling urban spatial growth. ITPI Journal, 4, 43–51

25. Mas, J. F., Puig, H., Palacio, J. L., & Sosa- Lopez, A. (2004). Modelling deforestation using GIS and artificial neural networks. Environmental Modelling & Software, 19, 461–471

26. Pan, D., Domon, G., De Blois, S., & Bouchard, A. (1999). Temporal (1958–1993) and spatial patterns of land use changes in Haut-Saint-Laurent (Quebec, Canada) and their relation to landscape physical attributes. Landscape ecology, 14(1), 35-52

27. Quarantelli, E. L. (2003). Urban vulnerability to disasters in developing countries, managing risks, In A. Kreimer, M. Arnold, & A. Carlin (Eds.), Building safer cities: The future of disaster risk (pp. 211-232). Washington, D.C.: The International Bank for Reconstruction and Development/ The World Bank

28. Tayyebi, A, Perry, Ph. Ch., & Tayyebi, A. H .(2013). Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster–vector routines with remote sensing and GIS. International Journal of Geographical Information Science, 28(4), 639-659

29. Wu, Q., Li, H., Wang, R., Paulussen, J., He, Y., Wang, M., Wang, B. & Wang, Z. (2006). Monitoring and predicting land use change in Beijing using remote sensing and GIS . Landscape and Urban Planning, 78(4), 322-333

30. Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the twin cities (Minnesota) Metropolitan Area by multi-temporal landsat remote sensing. Remote Sensing of Environment 98(2-3), 317-328

31. Zhang, J., & Zhang, Y. (2007). Remote sensing research issues of the national land use change program of China. ISPRS Journal of Photogrammetry & Remote Sensing, 26(6), 461–472