Predicting Land use and land cover spatiotemporal changes utilizing CA-Markov model in Duhok district between 1999 and 2033
The process of spatiotemporal changes in land use land cover (LULC) and predicting their future changes are highly important for LULC managers. one of the most important challenges in LULC studies is considered to be the creation of simulation of future change in LULC that involve spatial modeling. the purpose of this study is to use GIS and remote sensing to classify LULC classes in Duhok district between 1999 and 2018, and their results calculated using an integrated cellular automaton and ca-markov chain model to simulate LULC changes in 2033. in this study, satellite images from landsat7 ETM and landsat8 oli used for Duhok district which is located in the northern part of Iraq obtained from united states geological survey (USGS) for the periods (1999 and 2018) analyzed using remote sensing and GIS techniques in addition to the ground control points, for each class 60 ground points have taken. to simulate future LULC changes for 2033, integrated approaches of cellular automata and ca-markov models utilized in Idrisi selva software. the outcomes demonstrate that Duhok district has experienced a total of 184.91km changes during the period (table 4). the prediction also indicates that the changes will equal to 235.4 km by 2033 (table 8). soil and grass constitute the majority of changes among LULC classes and are increasing continuously. the achieved kappa values for the model accuracy assessment higher than 0.93 and 0.85 for 1999 and 2018 respectively showed the model’s capability to forecast future LULC changes in Duhok district. thus, analyzing trends of LULC changes from past to now and predict future applying ca-markov model can play an important role in land use planning, policy making, and managing randomly utilized LULC classes in the proposed study area.
2. Akyürek, D., Koç, Ö., Akbaba, E. M., & Sunar, F. (2018). Land use/land cover change detection using multi-temporal satellite dataset: A case study in Istanbul new airport. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 42, 17-22.
3. Anderson, J.R., 1976. A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.
4. Araya, Y.H. and Cabral, P., 2010. Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2(6), pp.1549-1563.
5. Arsanjani, J.J., Helbich, M., Kainz, W. and Boloorani, A.D., 2013. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21, pp.265-275.
6. Azizi, A., Malakmohamadi, B. and Jafari, H.R., 2016. Land use and land cover spatiotemporal dynamic pattern and predicting changes using integrated CA-Markov model. Global Journal of Environmental Science and Management, 2(3), pp.223-234.
7. Boori, M.S., Voženílek, V. and Choudhary, K., 2015. Land use/cover disturbance due to tourism in Jeseníky Mountain, Czech Republic: A remote sensing and GIS based approach. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp.17-26.
8. Brown, D.G., Pijanowski, B.C. and Duh, J.D., 2000. Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management, 59(4), pp.247-263.
9. Butt, A., Shabbir, R., Ahmad, S.S. and Aziz, N., 2015. Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 18(2), pp.251-259.
10. Chen, J., Gong, P., He, C., Pu, R. and Shi, P., 2003. Land-use/land-cover change detection using improved change-vector analysis. Photogrammetric Engineering & Remote Sensing, 69(4), pp.369-379.
11. D BEHERA, M.U.K.U.N.D.A., Borate, S.N., Panda, S.N., Behera, P.R. and Roy, P.S., 2012. Modelling and analyzing the watershed dynamics using Cellular Automata (CA)–Markov model–A geo-information based approach. Journal of earth system science, 121(4), pp.1011-1024.
12. Dimyati, M., Mizuno, K., Kobayashi, S. and Kitamura, T., 1996. An analysis of land use/cover change in Indonesia. International Journal of Remote Sensing, 17(5), pp.931-944.
13. Gashaw, T., Tulu, T., Argaw, M. and Worqlul, A.W., 2018. Modeling the hydrological impacts of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia. Science of the Total Environment, 619, pp.1394-1408.
14. Houet, T. and Hubert-Moy, L., 2006. Modeling and projecting land-use and land-cover changes with Cellular Automaton in considering landscape trajectories.
15. Jiang, W., Chen, Z., Lei, X., Jia, K. and Wu, Y., 2015. Simulating urban land use change by incorporating an autologistic regression model into a CLUE-S model. Journal of Geographical Sciences, 25(7), pp.836-850.
16. Kamusoko, C., Aniya, M., Adi, B. and Manjoro, M., 2009. Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), pp.435-447.
17. Lu, D., Mausel, P., Brondizio, E. and Moran, E., 2004. Change detection techniques. International journal of remote sensing, 25(12), pp.2365-2401.
18. Mayunga, S.D., 2018. Monitoring of Land Use/Cover Change Using Remote Sensing and GIS techniques: A case study of Loliondo Game Controlled Area, Tanzania. Trends Journal of Sciences Research, 3(1), pp.18-32.
19. Muller, M.R. and Middleton, J., 1994. A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), pp.151-157.
20. Nejadi, A., Jafari, H.R., Makhdoum, M.F. and Mahmoudi, M., 2012. Modeling Plausible Impacts of land use change on wildlife habitats, Application and validation: Lisar protected area, Iran. International Journal of Environmental Research, 6(4), pp.883-892.
21. Parker, D.C., Manson, S.M., Janssen, M.A., Hoffmann, M.J. and Deadman, P., 2003. Multi-agent systems for the simulation of land-use and land-cover change: a review. Annals of the association of American Geographers, 93(2), pp.314-337.
22. Rundquist, D.C., Narumalani, S., Narayanan, R.M., 2001. A review of wetlands remote sensing and defining new considerations. Remote Sens. Rev. 20, 207–226.
23. Rawat, J.S. and Kumar, M., 2015. Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp.77-84.
24. Sang, L., Zhang, C., Yang, J., Zhu, D. and Yun, W., 2011. Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3-4), pp.938-943.
25. SENSING, D.U.R., 2014. Land use and land cover change detection using remote sensing and GIS in parts of Coimbatore and Tiruppur districts, Tamil Nadu, India.
26. Singh, S.K., Mustak, S., Srivastava, P.K., Szabó, S. and Islam, T., 2015. Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environmental Processes, 2(1), pp.61-78.
27. Subedi, P., Subedi, K. and Thapa, B., 2013. Application of a hybrid cellular automaton–Markov (CA-Markov) model in land-use change prediction: a case study of Saddle Creek Drainage Basin, Florida. Applied Ecology and Environmental Sciences, 1(6), pp.126-132.
28. Torrens, P.M. and O'Sullivan, D., 2001. Cellular automata and urban simulation: where do we go from here?.
29. Wolfram, S., 1983. Statistical mechanics of cellular automata. Reviews of modern physics, 55(3), p.601.
30. Wu, F. and Webster, C.J., 2000. Simulating artificial cities in a GIS environment: urban growth under alternative regulation regimes. International Journal of Geographical Information Science, 14(7), pp.625-648.
31. Yuan, F., Sawaya, K.E., Loeffelholz, B.C. and Bauer, M.E., 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote sensing of Environment, 98(2-3), pp.317-328.
32. Zhang, H., Jin, X., Wang, L., Zhou, Y. and Shu, B., 2015. Multi-agent based modeling of spatiotemporal dynamical urban growth in developing countries: simulating future scenarios of Lianyungang city, China. Stochastic environmental research
33. Zhilong, Z., Xue, W., Yili, Z. and Jungang, G., 2017. Assessment of changes in the value of ecosystem services in the Koshi River Basin, central high Himalayas based on land cover changes and the CA-Markov model. Journal of resources and ecology, 8(1), pp.67-77
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