Najmeh Mozaffaree Pour
ABSTRACT
This research modelled "urban expansion" in the Harju County in Estonia, which includes the capital of the country, using remote sensing data of CORINE land cover database. Urban land use changes were detected by applying Markov model. Then, Markov transition probability maps were produced. Using MCE (Non_ Boolean Standardization and Weighted Linear Combination (WLC), suitability maps were extracted and these inputs used in the process of CA-Markov in IDRISI software to simulate the future scenario for the year 2046.
- Arsanjani, J. J., M. Helbich, W. Kainz, and A. D. Boloorani. 2013. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation 21:265--275.Google Scholar
Cross Ref
- Caruso, G., M. Rounsevell, and G. Cojocaru. 2005. Exploring a spatio-dynamic neighbourhood-based model of residential behaviour in the Brussels periurban area. International Journal of Geographical Information Science 19 (2):103--123.Google ScholarCross Ref
- Dahal, K. R., S. Benner, and E. Lindquist. 2017. Urban hypotheses and spatiotemporal characterization of urban growth in the Treasure Valley of Idaho, USA. Applied Geography 79:11--25.Google ScholarCross Ref
- Guan, D., Z. Zhao, and J. Tan. 2019. Dynamic simulation of land use change based on logistic-CA-Markov and WLC-CA-Markov models: a case study in three gorges reservoir area of Chongqing, China. Environmental Science and Pollution Research 26 (20):20669--20688.Google ScholarCross Ref
- He, C., N. Okada, Q. Zhang, P. Shi, and J. Li. 2008. Modelling dynamic urban expansion processes incorporating a potential model with cellular automata. Landscape and Urban Planning 86 (1):79--91.Google ScholarCross Ref
- Keshtkar, H., and W. Voigt. 2015. A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment 2 (1).Google Scholar
- Lau, K. H., and B. H. Kam. 2005. A Cellular Automata Model for Urban Land-Use Simulation. Environment and Planning B: Planning and Design 32 (2):247--263.Google ScholarCross Ref
- Li, X., and P. Gong. 2016. Urban growth models: progress and perspective. Science Bulletin 61 (21):1637--1650.Google ScholarCross Ref
- Li, X., P. Gong, L. Yu, and T. Hu. 2017. A segment derived patch-based logistic cellular automata for urban growth modeling with heuristic rules. Computers, Environment and Urban Systems 65:140--149.Google ScholarCross Ref
- Liu, X., X. Li, Y. Chen, Z. Tan, S. Li, and B. Ai. 2010. A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landscape Ecology 25 (5):671--682.Google ScholarCross Ref
- Liu, Y. 2009. Modelling urban development with geographical information systems and cellular automata. Boca Raton, FL: CRC Press.Google Scholar
- Luck, M., J. Wu. 2002. Landscape Ecology 17 (4):327--339.Google Scholar
- Maithani, S. 2010. Cellular Automata Based Model of Urban Spatial Growth. Journal of the Indian Society of Remote Sensing 38 (4):604--610.Google ScholarCross Ref
- Rimal, B., L. Zhang, H. Keshtkar, N. Wang, and Y. Lin. 2017. Monitoring and Modeling of Spatiotemporal Urban Expansion and Land-Use/Land-Cover Change Using Integrated Markov Chain Cellular Automata Model. ISPRS International Journal of Geo-Information 6 (9):288.Google ScholarCross Ref
- Tobler, W. R. 1970. A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 46:234.Google ScholarCross Ref
- Triantakonstantis, D., and G. Mountrakis. 2012. Urban Growth Prediction: A Review of Computational Models and Human Perceptions. Journal of Geographic Information System 04 (06):555--587.Google ScholarCross Ref
- Wolfram, S. 1984. Cellular automata as models of complexity, Nature; 311(5985), 419--424Google Scholar
- Wray, C., and K. Cheruiyot. 2015. Key Challenges and Potential Urban Modelling Opportunities in South Africa, with Specific Reference to the Gauteng City-Region. South African Journal of Geomatics 4 (1):14.Google ScholarCross Ref
Index Terms
- Simulation of Urban Expansion in Estonia for 2046 Using Cellular Automata Model Based on the CORINE Land Cover Database
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