Monitoring the Process of Land Use/cover Changes Using Markov CA Model: a Case Study of Kermanshah City

Document Type : Research article

Authors

1 Associate Professor of Climatology, University of Tehran, Iran

2 MA in Satellite Climatology, University of Tehran, Iran

3 PhD Candidate in GIS, University of Tehran, Iran

Abstract

Introduction
Land is one of the primary natural resources required for many activities in cities. A city is expanded not only by population, but also by changing in the spatial dimensions. Changes of land are a natural process and can't be stopped, but it can be organized. Supervising land zoning in the rules of city zoning to residential, commercial, industrial, and administrative areas is one of the important issues of urban life. Land use is one of the basic concepts in urban and regional planning. Thus, in optimized urban and environmental management, it is necessary to know about the proportion of land use changes / land cover and their causes. Remote sensing is considered for monitoring and supporting decision making for effective tools related to urban planning.  The modeling for prediction of land use changes by remote sensing data is also a helpful tool that can manifest a good recognition of land use changes and present good solutions for management.The goal of the current article is to survey changes of Kermanshah city's zones through Landsat satellite images in the past three decades (1985-2013) and to predict changes until 2026 by using a combination of regression logistic, Markov chain and Markov CA models.    
Methodology   

In order to produce the land use maps, satellite images of TM Landsat 5 and OLI Landsat 8 with the resolution of 30m, for 1985, 2000, and 2013, all in July, have been used. General stages of the investigation can be categorized in four sections, which are as follow:
Providing land use maps of three periods and manifesting changes.
Checking the factors influencing the urban growth, land use change, and providing the potential map of town expanding in the future periods. This has been done by the regression logistic model.
Estimating land use changes and spatial distribution of them by analytical methods of Markov chain.
Running the Markov CA model and predicting land use changes over the study area.

For the classification, the number of classes was determined by the available images and maps, conditions of the studied region, and the classes needed for vegetation maps. Finally, the classification has been done through maximum likelihood algorithm. To determine the changes, we used post-classification comparison method. Following the procedure, the potential change map was produced through regression logistic, as one of the extended linear models. Markov model was used for calibration to extract changed area matrix and change potential of each class. Finally, the change prediction map of 2026 was provided through Markov CA model. 
Results and discussion
The results showed that in the first period (1985-2000) the pure changes of reduction in areas of vegetation and water surfaces is 4153 and 14 hectares, respectively. The pure changes of area increase in urban areas and mountains are 3947 and 221 hectares. In the second period (2000-2013), the area reduction in the mountains and the areas with water surfaces is 3261 and 22 hectares, respectively. The area increasing in towns and the areas with vegetation is 2594 and 689 hectares. In the last three decades, the most area reduction is for the vegetation and water areas, for example Ghare-sou River, and it's up to 3465 and 35 hectares, respectively.   
The change prediction results with Markov CA model shows that, according to the past event, the most changes will occur in the built urban areas. This is in a way that these changes that are 9565 hectares in 2013 will increase to 2790 hectares in 2026.  After the above-mentioned use, the vegetation area will increase to 1053 hectares in comparison with that of 2013. This is probably resulted from the afforestation plan of Kermanshah which has been started since 2015 by Kermanshah's municipality, Assistance of Parks and Green Spaces. Again, some parts of mountain areas will be placed in the vegetation class which is because of increase in the green spaces and tree planting establishment, causing a decrease in the level of the above-mentioned areas. However, the water bodies in 2026 will increase by 52 hectares. This is due to evacuating a very large amount of the waste water entering into the Ghare-Sou River, according to the present recognition of the region. This can make an increase in this class.
Conclusion
 One of the principal properties of the developing cities is the fast and unplanned urban residency. This is one of the main factors of land use changes on the earth. The purpose of the current study is to predict the process of Kermanshah city expansion in order to provide a comprehensive plan for developing the city in the future through the prediction models. The results of monitoring and evaluating the changes of land use/vegetation of Kermanshah during the studied years showed that 6540.48 hectares were added to Kermanshah area from 1985 to 2013. Moreover, the results of Markov CA, urban growth, and land use changes of Kermanshah for 2026 show that 1426 hectares of vegetation cover, 2462.3 hectares of mountain areas, and 63 hectares of water areas will change to urban use until that year.Using these results in Kermanshah city plans and decision makings helps us prevent the urban growth to inappropriate areas in the future and avoid undesirable problems. Besides, it is important to state that the physical development of Kermanshah can be effective if it is in a controlled and monitored way and before any growth the appropriate options for this purpose should be evaluated.

Keywords

References

  1. Abdi, N., Zanganeh Shahraki, S.; Morsouzi, N., Rostami, S. 2015. Estimation and prediction of the optimal route of Sanandaj urban development using auto-Markov cells. Urban Planning Geography, Volume 3, Issue 4, pp. 431-446.
  2. Aburas M. M, Hoa Y. M, Ramlib M. F, Ash’aari Z. H. 2017. Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio. International Journal of Applied Earth Observation and Geoinformation. 59: 65-78.
  3. Ahangnejad, M, Ghasemi, A, Kazemi, L, 2013, Using the Urban Development Pattern for Green Planning (Case Study: Zanjan City Area). Geography Quarterly and Zagros Eye Planning, 2014, No. 19. P. 7-21.
  4. Ali Mommadi Sarab, A., Metcanan, A., Mirbagheri, B, 2010, Evaluation of the Efficiency of Automatic Cell Models in the Simulation of Urban Distribution in the Suburbs of Southwest of Tehran. Space Planning and Design (Mads of the Humanities), 2: 81-102.
  5. Asanjani, J.J., Helbich, M., Kainz, W., and Darvishi Boloorani, A., 2013, Integration of regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geo information, 21: 265-275.
  6. Azizi Qalati, S.; Darghzan; K; Sadi'i; J; Heydarian; P.; Taghizzadeh, A. 2016. Estimation of Land Use Land Use Change Process Using Markov-CA Chain Model (Case Study: Kohmareh Sorkhi Region of Fars Province). Remote Sensing and Geographic Information Systems in Natural Resources, 1: 59-71.
  7. Bagheri, R; Shtaei Joybari, Sh. 2010. Modeling of Widespread Forest Degradation Using Logistic Regression (Case Study: Chehel-Chai Watershed, Golestan Province). Forest of iran Volume 2, Issue 3, pp. 243-252.
  8. Barat A, Kh; Malekpour, M; Irandost, K. 2011. The role of rural-urban urbanization in physical development without the program of Kermanshah city. Ferdowsi University of Mashhad.
  9. Coppin, P.; Jonckheere, I.; Nackaerts, K.; Muys, B.; (2004). Digital change detection methods in ecosystem monitoring, International Journal of RemoteSensing, 25 (9), 1565-1596.

10. Dai, F.C, and Lee, CF., 2002, Landslid characteristics and slope instability modeling using GIS, Lantau Isiand, Hong kong. Geomorphology. 42: 213-228.

11. Dawelbait, M., and Morai, F., 2012, Monitoring desertification in a savannah region in Sudan using Landsat images and spectral mixture analysis. Journal of Arid Environments. 80: 45-55.

12. Deep, SH; Saklani, A, 2014, Urban sprawl modeling using cellular automata. The Egyptian Journal of Remote Sensing and Space Sciences. 17, 179-187.

13. Doygun, H, 2008, Effects of urban sprawl on agricultural land: a case study of Kahramanmaras, Turkey. Environ Monit Assess. 158, 471-478.

14. Fallahtakar, Q.; Sfyanyan, A.; Khwajaedin, S.; Ziaee, H., 2009, investigating the ability of the CA Markov model to predict landslide map (Case study: Isfahan city). Geomatics conference 88. Tehran. National mapping agency.

15. Feyisa, G.L.; Meilby, H., Jenerette, G.D, and Pauliet, S., 2016, locally optimized separability enhancement indices for urban land cover mapping: Exploring thermal environmental consequences of rapid urbanization in Addis Ababa, Ethiopia. Remote Sensing of Environment. 175: 14-31.

16. Ghosh P, Mukhopadhyay A, Chanda A, Mondal P, Akhand A, Mukherjee S, Nayak S.K, Ghosh S, Mitra D, Ghosh T, Hazra S. Application of Cellular automata and Markov-chain model in geospatial environmental modeling- A review. Remote Sensing Applications: Society and Environment 5: 64–77.

17. Gonga W, Yuanb L, Fanc W, StottdaCollege Ph. Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata—Markov modelling. International Journal of Applied Earth Observation and Geoinformation 34: 207–216.

18. Jalibian, A.; Soltanian, M. 2016. Assessment and prediction of desertification changes in eastern and southern Isfahan with CA-Markov model. Environmental spatial analysis journal, Third year, No. 88-71.

19. Jensen, J. R.; (2005). Introductory digital image processing: A remote sensing perspective (3rd Edn), Upper Saddle River, NJ: Prentice-Hall.

20. Jensen, J.R., 2007, Remote Sensing of the Environment: An Earth Resource Perspective, Pearson Prentice Hall, p: 592.

21. Karimi, K.; Bayram Kameki, Q; 2015. Monitoring, evaluation and prediction of land use / land cover change patterns using the Markov chain model. Remote sensing of GIS in natural resources. Year 6, Issue 2, 88-75.

22. Kaveh N., Ebrahimi, A, 2013, Estimation of land use / land cover changes with CA Markov model (case study: Aqbaghag river). Remote Sensing Application and GIS in Natural Resources Science, 2: 41-51.

23. Kazem, A., Hossein Ali, A. Sheikh; 2015. Modeling of Urban Growth Using Medium-Sized Satellite Images Based on Cellular Automata (Case Study: Tehran City). Quarterly Journal of Geographic Information Systems Volume 24, Issue 94,

24. Koomen, E; Stillwell, J; Bakema, A; and Scholten, H.J., 2007, Modelling Land-use Change, Progress and Applications, Netherlands, Springer, p: 410.

25. Lu, D; Mausel, P; Brondizio, E; Moran, E., 2004, Change detection techniques. International Journal of Remote sensing, 25(12): 2365- 2401.

26. Mahini, Hon., H; Ronald Eastman, J. 2010, its nonsense and geographic information systems with Idrisi software. Publisher Mehr Mehdi. Number of pages 610.

27. Marwa Waseem A. Halmy , Paul E. Gessler , Jeffrey A. Hicke , Boshra B. Salem. 2015. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography 63: 101-112.

28. Mayes, M.T., Mustard, J.F. and Melillo, J.M., 2015, Forest cover change in Miombo Woodlands: Modeling land cover of Africa dry tropical forests with linear spectral mixture analysis. Remote Sensing of Environment. 165: 203-215.

29. Mirzaeezadeh, and Niknejad, M., Oladi Khodikilai, C, 2015. Evaluation of Non-parametric Non-parametric Classification Algorithms for Land Cover Mapping Using Landsat Images 8. Remote Sensing and Geographic Information System in Natural Resources, Vol. 6, no. 3, 44-29.

30. Mitsova, D; Shuster, W; Wang, X., 2011. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning, 99(2): 141-153.

31. Nouri, J; Gharagozlou, A; and Arjmandi, R., 2014, Predicting urban Land Use Changes Using a CA-Markov Model. Research ARTICLE – Earth Sciences. 1-9.

32. Richard, M; Hosseinzadeh, M; Exchange, M, Panahi, R. 2011. Study of Urbanization and Physical Development in Kermanshah, First National Conference of Arman Shahr Iran, Noor, Islamic Azad University, Noor Branch.

33. Rowbotham, D.N.; and Dudycha, D., 1998, GIS modelling of slope stability in Phewa Tal watershed, Nepal. Geomorphology 26: 151-170.

34. Sang, L; Zhang, C; Yang, J; Zhu, D; Yun, W., 2011, Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Mathematical and Computer Modelling, 54(3):938-943.

35. Sidi Luo, M., Amini, A., Hamza, F. 2016. Evaluation of Physical Exposure of Rabat Karim and its Impact on Agricultural Lands Using Remote Sensing Technology and Geographic Information System. International Conference on Architectural and Urban Planning. Tehran.

36. Solo, M., Jozzi, S, 2011. Prognosis of vegetation changes using Markov model (case study: District 4 of Tehran Municipality). Application of Remote Sensing and GIS in Natural Resources Science, 2: 83-96.

37. u Han, Haifeng Jia. 2016. Simulating the spatial dynamics of urban growth with an integrated modeling approach: A case study of Foshan, China. Ecological Modelling 353:  107–116.

38. Weng, Q., 2002, Land use change analysis in the remote sensing, GIS and stochastic modelling. Journal of Environmental Management, 64(3): 273-284.

39. Wolfram, Stephen. "Cellular automata as models of complexity." Nature311.5985 (1984): 419-424.

40. Wu, Qiong, Li, Hong-qing, Wang, Ru-song., Paulussen, Juergen, He, Yong, Wang, Min, Wang, Bi-hui, Wang, zhen. (2006). Monitoring and predicting land use change in Beijing using remote sensing and GIS, Landscape and urban planning, Article in press.

41. Wyman, M. S.; Stein, T.V.; (2010). Modeling social and land-use/land-cover change data to assess drivers of smallholder deforestation in Belize, Applied Geography, 30(3), 329-342.

42. Yang X, Zheng X. O, Chen R. 2014. A land use change model: Integrating landscape pattern indexes andMarkov-CA. Ecological Modelling 283: 1–7.

43. Zanganeh Shahraki, S.; Kazemzadeh, A., Hashemi Dareh Badami, S, 2014. Time-Manic Analysis of Physical Expansion of Mashhad City and Monitoring of Land Use Change. Geography of Urban Planning, Volume 2, Issue 4, pp. 483-499.

Geographical Urban Planning Research (GUPR)
Volume 5, Issue 3
October 2017
Pages 495-514

Files

Share

How to cite

Statistics