A cellular automata model for monitoring and simulating urban land use/cover changes toward sustainability
Abstract
This study presents an integrated model based on cellular automata for assessing and simulating land use/cover changes and their impact on the environment. Satellite images from Landsat TM and ETM + sensors from the time period of 1985 to 2014 were applied. Seven static and five dynamic variables were applied. These included elevation, slope, aspect, soil salinity, soil texture, distance from rivers, and roads, and distance from the five classes of land use. The model was validated by a fuzzy reciprocal similarity method. The results showed that this model is suitable for simulating changes in periods of less than 15 years and patches with areas greater than 25 hectares. The model was run for 15 years, beginning with the year 2014. The results for the study area predict that settlement areas will expand; agricultural land, rangeland and barren areas will decline; and forests will remain unchanged until 2029.
Keyword : simulation, cellular automata, land use/cover change, land use planning, remote sensing, GIS
How to Cite
Khatibi, A., Pourebrahim, S., & Danehkar, A. (2018). A cellular automata model for monitoring and simulating urban land use/cover changes toward sustainability. Journal of Environmental Engineering and Landscape Management, 26(1), 1-7. https://doi.org/10.3846/16486897.2017.1284666
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Alphan, H.; Derse, M. A. 2013. Change detection in Southern Turkey using normalized difference vegetation index (NDVI), Journal of Environmental Engineering and Landscape Management 21(1): 12–18. https://doi.org/10.3846/16486897.2012.663091
Alphan, H; Guvensoy, L. 2016. Detecting coastal urbanization and land use change in southern Turkey, Journal of Environmental Engineering and Landscape Management 24(2): 97–107. https://doi.org/10.3846/16486897.2015.1113976
Bellinger, G. 2004. Modeling & simulation: an introduction [online], [cited 6 January 2015]. Available from Internet: http://www.systems-thinking.org/modsim/modsim.htm
Benenson, I.; Torrens, P. M. 2004. Geosimulation: Automata–based modelling of urban phenomena. West Sussex: Wiley. 312 p. https://doi.org/10.1002/0470020997
Cheng, J.; Masser, I. 2003. Urban growth modeling: a case study of Wuhan City, PR China, Landscape and Urban Planning 62: 199–217. https://doi.org/10.1016/S0169-2046(02)00150-0
Fisher, P.; Unwin, D. J. 2005. Re–presenting GIS. Wiley, London. 296 p.
Gregorio, A. D.; Jansen, L. J. 2005. Land cover classification system: Classification concepts and use manual [online], [cited 7 January 2015] Available from Internet: http://www.fao.org/gtos/doc/ECVs/T09/ECV–T9–landcover–ref25–LCCS.pdf
Hagen, A. 2003. Fuzzy set approach to assessing similarity of categorical maps, International Journal of Geographical Information Science 17(3): 235–249. https://doi.org/10.1080/13658810210157822
Kamusoko, C.; Gamba, J. 2015. Simulating urban growth using a random forest–cellular automata (RF–CA) model, ISPRS International Journal of Geo–Information 4(2): 447–470. https://doi.org/10.3390/ijgi4020447
Kucas, A. 2010. Location prioritization by means of multicriteria spatial decision‐support systems: a case study of forest fragmentation‐based ranking of forest administrative areas, Journal of Environmental Engineering and Landscape Management 18(4): 312–320. https://doi.org/10.3846/jeelm.2010.36
KwadwoNti, I. 2013. geospatial process modelling for land use cover change: PhD thesis. Auckland University of Technology. 216 p
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Makhdoom, M.; Darvishsefat, A. A.; Jafarzadeh, H.; Makhdoom, A. 2001. Environmental assessment and planning by using GIS. University of Tehran press. 304 p. (in Persian).
Mas, J.; Paegelow, M.; De Jong, B.; Masera, O.; Guerrero, G.; Follador, M.; Olguin, M.; Diaz, J. R.; Castillo, M. A.; Garcia, T. 2007. Modelling tropical deforestation: a comparison of approaches, in 32rd Symposium on Remote Sensing of Environment, June 2007, San José, Costa Rica. 3 p. en CD. https://halshs.archives-ouvertes.fr/halshs-01063568
Mcclean, C. J. 1995. Land use planning: a decision support system, Journal of Environmental Planning and Management 38: 77–92. https://doi.org/10.1080/09640569513129
Ministry of Roads and Urban Development of Iran. 2011. Strategic and structural planning for Karaj city. 194 p.
Pourebrahim S.; Mokhtar, M. B. 2016. Conservation priority assessment of the coastal area in the Kuala Lumpur mega–urban region using extent analysis and TOPSIS, Environmental Earth Science 75: 348–355. https://doi.org/10.1007/s12665-016-5276-3
Pourebrahim, S.; Hadipour, M.; Mokhtar, M. B. 2011. Integration of spatial analysis for land use planning in coastal areas; case of Kuala District, Selangor, Malaysia, Landscape and Urban Planning 101(1): 84–97. https://doi.org/10.1016/j.landurbplan.2011.01.007
Ramachandra, T. V.; Bharath, S.; Bharath, A. 2014. Spatio-temporal dynamics along the terrain gradient of diverse landscape, Journal of Environmental Engineering and Landscape Management 22(1): 50–63. https://doi.org/10.3846/16486897.2013.808639
Sakieh, Y. 2013. Determining of sustainable development index for Karaj city based on urban growth simulation and environment capability: MSc thesis. Faculty of natural resources, University of Tehran. 152 p.
Sheng, J.; Qing, G.; Chun-yu, W.; Bei, L.; Xiao-dong, L.; Guang-ming, Z.; Zhong-xing, Y.; Jie, L. 2012. Ecological suitability evaluation for urban growth boundary in red soil hilly areas based on fuzzy theory, Journal of Central South University 19: 1364–1369. https://doi.org/10.1007/s11771-012-1151-x
Soares-Filho, B.; Lima, L.; Bowman, M.; Viana, L. 2012. Challenges for low–carbon agriculture and forest conservation in challenges for low–carbon agriculture and forest conservation in Brazil. Inter-American Development Bank. 40 p.
Soares-Filho, B.; Rodrigues, H.; Costa, W.; Schlesinger, P. 2009. Modeling environmental dynamics with dinamica EGO. Belo Horizonte. 116 p.
Tang, J.; Wang, L.; Yao, Z. 2007. Spatio–temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm, International Journal of Remote Sensing 28(15): 3255–3271. https://doi.org/10.1080/01431160600962749
Torre, R. D.; Jimenez, M. D.; Ramirez, A.; Mola, I.; Casado, M. A.; Balaguer, L. 2015. Use of restoration plantings to enhance bird seed dispersal at the roadside: failures and prospects, Journal of Environmental Engineering and Landscape Management 23(4): 302–311. https://doi.org/10.3846/16486897.2015.1079529
Verburg, P. H.; Kok, K.; Pontius, R. G.; Veldkamp, A. 2006. Modeling land–use and cover change, in Land–use and land cover change: local processes and global impacts. Springer-Verlag Germany, Berlin, 117–135. https://doi.org/10.1007/3-540-32202-7_5
Visser, S. 2004. The map comparison KIT: methods, software and applications. Bilthoven. 127 p.
Wolfram, S. 1984. Universality and complexity in cellular automata, Physica 10: 1–35. https://doi.org/10.1016/0167-2789(84)90245-8
Yuanjing, Zh.; Binyang, Y.; Ashraf, M. A. 2015. Ecological security pattern for the landscape of mesoscale and microscale land: a case study of the Harbin City center, Journal of Environmental Engineering and Landscape Management 23(3): 192–201. https://doi.org/10.3846/16486897.2015.1036872
Zare-Garizi, A.; Sheykh, V.; Sadaldin, A.; Mahini, A. 2011. Spatio–temporal simulation of forest changes in Chehel Chay basin of Golestan province using combination of cellular automata and Mrkov chain, Journal of Iranian forest research 20(2): 273–285.
Zehtabian, Gh. 2012. National atlas of Iranian desertification. University of Tehran. 255 p.