Land use change detection and prediction using Markov-CA and publishing on the web with platform map server, case study: Qom Metropolis, Iran

Mojtaba Pirnazar; Nasrin Haghighi; Donya Azhand; Kaveh Ostad-Ali-Askari; Saeid Eslamian; Nicolas R. Dalezios; Vijay P. Singh

doi:10.24294/jgc.v4i1.453

Land use change detection and prediction using Markov-CA and publishing on the web with platform map server, case study: Qom Metropolis, Iran

Mojtaba Pirnazar, Nasrin Haghighi, Donya Azhand, Kaveh Ostad-Ali-Askari, Saeid Eslamian, Nicolas R. Dalezios, Vijay P. Singh

Article ID: 453
Vol 4, Issue 1, 2021

VIEWS - 1340 (Abstract) 561 (PDF)

Abstract

To achieve sustainable development, detailed planning, control and management of land cover changes that occur naturally or by human caused artificial factors, are essential. Urban managers and planners need a tool that represents them the information accurate, fast and in exact time. In this study, land use changes of 3 periods, 1994-2002, 2002-2009, 2009-2015 and predictions of 2009, 2015 and 2023 were assessed. In this paper, Maximum Likelihood method was used to classify the images, so that after evaluation of accuracy, amount of overall accuracy for images of 2013 was 85.55% and its Kappa coefficient was 80.03%. To predict land use changes, Markov-CA model was used after assessing the accuracy, and the amount of overall accuracy for 2009 was 82.57% and for 2015 was 93.865%. Then web GIS application was designed via map server application and evoked shape files through map file and open layers to browser environment and for design of appearance of website CSS, HTML and JavaScript languages were used. HTML is responsible for creating the foundation and overall structure of webpage but beautifying and layout design on CSS.

Keywords

Land Use Change; Urban Growth; Markov-CA; Web Design; Map Server; Web GIS

Full Text:

PDF

References

1. Rindfuss RR, Walsh SJ, Turner BL, et al. Developing a science of land change: Challenges and methodological issues. Proceedings of the National Academy of Sciences 2004; 101(39): 13976–13981. doi: 10.1073/pnas.0401545101.

2. Valbuena D, Verburg PH, Bregt AK. A method to define a typology for agent-based analysis in regional land-use research. Agriculture, Ecosystems & Environment 2008; 128(1-2): 27–36. doi: 10.101 6/j.agee.2008.04.015.

3. Arsanjani JJ, Helbich M, Kainz W, et al. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation & Geo-information 2013; 21: 265–275. doi: 10.1016/j.jag.2 011.12.014.

4. Liu H, Zhou Q. Developing urban growth predictions from spatial indicators based on multi-temporal images. Computers Environment & Urban Systems 2005; 29(5): 580–594. doi: 10.1016/j.comp envurbsys.2005.01.004.

5. Pilehforooshha P, Karimi M, Taleai M. A GIS-based agricultural land-use allocation model coupling increase and decrease in land demand. Agricultural Systems 2014; 130: 116–125. doi: 10.1016/ j.agsy.2014.07.001.

6. Tan M, Li X, Xie H, et al. Urban land expansion and arable land loss in China—A case study of Beijing–Tianjin–Hebei region. Land Use Policy 2005; 22(3): 187–196. doi: 10.1016/j.landusepol.20 04.03.003.

7. Weber C, Puissant A. Urbanization pressure and modeling of urban growth: example of the Tunis Metropolitan Area. Remote Sensing of Environment 2003; 86(3): 341–352. doi: 10.1016/s0034-4257(03) 00077-4.

8. Gong W, Yuan L, Fan W, et al. 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 & Geoinformation 2015; 34: 207–216. doi: 10.1016/j.jag.2014.07.005.

9. Ramachandra TV, Aithal BH, Sanna DD. Insights to urban dynamics through landscape spatial pattern analysis. International Journal of Applied Earth Observations & Geoinformation 2012; 18: 329–343. doi: 10.1016/j.jag.2012.03.005.

10. Li X, Yeh AG. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Systems 2002; 16(4): 323–343. doi: 10.1 080/13658810210137004.

11. Yang X, Zheng X, Lv L. A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling 2012; 233(2): 11–19. doi: 10.1016/j.ecolmodel.2012.03.011.

12. Le Q, Park SJ, Vlek P, et al. Land-Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human–landscape system. I. Structure and theoretical specification. Ecological Informatics 2008; 3(2): 135–153. doi: 10.1016/j.ecoinf.2008.04.003.

13. Coulter LL, Stow DA, Tsai YH, et al. Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+ imagery. Remote Sensing of Environment 2016; 184: 396–409. doi: 10.1016/j.rse.2016.07.016.

14. Estoque RC, Murayama Y. Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological Indicators 2015; 56: 205–217. doi: 10.1016/j. ecolind.2015.03.037.

15. Jhonnerie R, Siregar VP, Nababan B, et al. Random forest classification for mangrove land cover mapping using landsat 5 TM and Alos Palsar Imageries. Procedia Environmental Sciences 2015; 24: 215–221. doi: 10.1016/j.proenv.2015.03.028.

16. Mei A, Manzo C, Fontinovo G, et al. Assessment of land cover changes in Lampedusa Island (Italy) using Landsat TM and OLI data. Journal of African Earth Sciences 2016; 122: 15–24. doi: 10.1016/j.jafrearsci.2015.05.014.

17. Vaz EDN, Nijkamp P, Painho M, et al. A multi-scenario forecast of urban change: A study on urban growth in the Algarve. Landscape and Urban Planning 2012; 104(2): 201–211. doi: 10.1016/j.landurbplan.2011.10.007.

18. Jia Y, Zhao H, Niu C, et al. A WebGIS-based system for rainfall-runoff prediction and real-time water resources assessment for Beijing. Computers & Geosciences 2009; 35(7): 1517–1528. doi: 10.1016/ j.cageo.2008.10.004.

19. Adam F, Patrick H. Encyclopedia of decision making and decision support technologies. Humphreys, Patrick. New York: Hershey Publication; 2008.

20. Myint SW, Wang L. Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Canadian Journal of Remote Sensing 2006; 32(6): 390–404. doi: 10.5589/m06-032.

21. Stokey E, Zeckhauser R. A primer for policy analysis: New York: W.W. Norton; 1978.

22. Sang L, Chao Z, Yang J, et al. Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical & Computer Modelling 2011; 54(3-4): 938–943. doi: http://dx.do i.org/10.1016/j.mcm.2010.11.019.

23. Clarke KC, Hoppen S, Gaydos LJ. A self-modifying cellular automaton model of historical ur-banization in the San Francisco Bay area. Environment & Planning B Planning & Design 1997; 24(2): 247–261.

24. Araya YH, Cabral P. Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing 2010; 2(6): 1549–1563.

DOI: https://doi.org/10.24294/jgc.v4i1.453