Land use / land cover change modelling using Markov chain and Cellular Automata (Case study: Hamedan province)

Subject Areas : environmental management

Jalil Imani Harsini ¹ , Mohammad kaboli ² , Jahangir Feghhi ³ , Ali Taherzadeh ⁴

1 - Ph.D of Environmental Sciences, Department of Agricultural Sciences and Natural Resources, Gorgan-University, Gorgan, Iran*(Corresponding Author).
2 - Associate Professor Department of Environmental Sciences, Faculty of Natural Resources, Tehran University, Tehran, Iran
3 - Associate Professor Department of foresty and forest Economics, Faculty of Natural Resources, Tehran University, Tehran, Iran.
4 - - MSc of remote sensing in Iranian Space Agency, Iran.

Received: 2015-06-12 Accepted : 2015-12-09 Published : 2017-03-21

Keywords: modelling, Change Process, Markov chain, Cellular Automata, Hamedan Province,

Abstract :

Background and Objective: The extent of spread and source degradation would be determined using prediction of land use/ land cover changes. In this way these changes would be guided in the right directions. The aim of this study is modeling the process of land use / land cover changes of Hamedan province using Landsat TM satellite image of 1989 and IRS LISS III image of 2008. Method: After running the necessary corrections, land use/ land cover maps of the study area in the past two years were obtained using supervised classification with maximum likelihood algorithm. Then probability matrix of land use transition (to each other) were calculated using Markov chain with respect to land use/ land cover map. In the next step, Cellular Automata method was used to geo specified these changes. Findings: Finally land use/ land cover map of Hamedan province for 19 years later (2024) was obtained and the area of each land use/ land cover was calculated. Discussion and Counclusion: The results of this research shows that natural land use/ land covers will be decreased and transmited to human land uses in future. These changes are conceivable due to population growth and increasing human needs to exploit the nature; but this process should be considered to exploit the natural resources in a sustainable manner to avoid severe consequences in future.  

References:

1. Coppin P I., Jonckheere K., Nackaerts B., Muys. 2004. Digital change detection methods in ecosystem monitoring: a review. Remote Sensing, VOL. 25, NO. 9, 1565–1596.


2. Milne A. K. 1988. Change direction analysis using Landsat imagery: a review of methodology. Proceedings of the IGARSS’88 Symposium Edinburgh, Scotland, ESA SP-284 (Noordwijk, Netherlands: ESA): 541–544.


3. Lund H. G. 1983. Change now you see it—now you don’t. Proceedings of the International Conference on Renewable Resource Inventories for Monitoring Changes and Trends, Oregon State University, Corvallis, OR, USA (Corvallis, OR: Oregon State University). 211–213.


4. LU D. S., Mausel P., Brondizio E. S., Moran E. 2004. Change detection techniques. International journal of remote sensing, Washington, DC. Vol, 25, NO. 12, 2365–2407.


5. Turner, B. L. and Meyer, W. B. 1991. Land use and land cover in global environmental change: considerations for study. International Social Sciences Journal, No,130, 669–667.


6. Candau J.T. 2002. Temporal Calibration Sensitivity of the SLEUTH Urban Growth Model. MSc Thesis. Santa Barbara University. 116pp.


7. Soffianian, A., M. A. Nadoushan, L. Yaghmaei and S. Falahatkar. 2010 Mapping and analyzing urban expansion using remotely sensed imagery in Isfahan, Iran, World Applied Sciences Journal, Vol 9(12), 1370-1378.


8. Sarvestani, M., A. Latifi Ibrahim and P. Kanaroglou. 2011. Three decades of urban growth in the city of Shiraz, Iran: A remote sensing and geographic information systems application. Cities, Vol, 28(4), 320-329.


9. Singh A. K. 2003. Modeling Landuse Landcover Changes using Cellular automata in geo-spatial Environment. MSc Thesis ITC. Netherland.58pp.


10. Torrens P.M. 2000. Cellular Automata and Urban Simulation: Where do we go from here, Environment and Planning,Vol, 28. No, 3, 163-168.


11. Stevens D., Dragicevic S., Rothley K: iCity. 2007. A GIS-CA modeling tool for urban planning and decision making”, Environmental Modeling & Software, Vol, 22, 761-773


12. Ye, B. and Bai, Z. 2008. Simulation land use/ cover changes of Nenjiang country based on CA Markov model, Computer and Computing Technologies in Agriculture, Vol. 1, 321–329.


13. Muller M. R., J. Middleton. 1994. A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, pp. 151-157.


14. Brown D. G., Pinjanowski B. C., Duh J. D. 2000. Modeling the relationship between landuse and landcover on private lands in the upper Midwest. Journal of environmental management. Vol, 59, 247-263.


15. یمانی. م، مزیدی. ا، 1386،  بررسی تغییرات سطح و پوشش گیاهی کویر سیاه­کوه با استفاده از داده­های سنجش از دور، پژوهش های جغرافیایی، شماره 64، صص 1-12.


16. Tso B., Mather P. M. 2001. Classification methods for remotely sensed data. Taylor and Francis. New York.


17. ماهینی. ع و کامیاب. ح، 1388، سنجش از دور و سامانه های اطلاعات جغرافیایی کاربردی با نرم افزار ایدریسی، انتشارات مهر مهدیس، تهران ،582 ص.


18. Batty M., Xie Y., Sun Z. 1999. Modeling urban dynamics through GIS- based cellular automata. Computers, Environment and Urban Systems: Vol, 23, 205-233.

9. شیخ گودرزی. م، علیزاده شعبانی. ا، سلمان ماهینی. ع. ر. و فقهی. ج، 1391، بررسی آثار ناشی از توسعه شهر بر مطلوبیت پهنه‌های حفاظتی با رویکرد سیمای سرزمین (مطالعه موردی: حوزه آب­خیز گرگان­رود). اکولوژی کاربردی. شماره 1، صص43- 30.


10. بابایی اقدم. ف، و ابراهیم‌زاده. ح، 1390، مدل­سازی تغییرات کاربری اراضی زراعی و بایر سطوح ساخته شده در منطقه شهری اردبیل با استفاده از مدل CLUE_S، جغرافیا و توسعه، شماره 26، صص 34-21.

21. Yaung G., Li X., Shi X. 2008. Cellular automata for simulating landuse changes based on support vector machines. Computers & geosciences: Vol, 34, 592-602.