Modelling spatial changes in coastal areas of Samsun (Turkey) using a Cellular Automata-Markov Chain method
The evolution of cities over a specific time period and the determination of relevant trends are important to simulate for the proper development and planning of a city. In particular, coastal areas experience intense pressure from developers with respect to settlements, tourism, trade and industry,...
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2017-01-01
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| Series: | Tehnički Vjesnik |
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| Online Access: | https://hrcak.srce.hr/file/267115 |
| Summary: | The evolution of cities over a specific time period and the determination of relevant trends are important to simulate for the proper development and planning of a city. In particular, coastal areas experience intense pressure from developers with respect to settlements, tourism, trade and industry, and built-up areas are being observed near coasts. The inability to direct development and control growth is destroying natural resources in coastal areas at a rapid pace. Thus, determining these trends is the key component for ensuring the protection of natural resources and planned growth. Because a complete estimation of urban expansion is not possible, likely changes can be determined using simulations. In this study, a 30-year urban expansion simulation (2004-2034) was obtained using land use/land cover (LU/LC) data for 1987 and 2004 at Samsun (Turkey) coastal areas and using the Cellular Automata-Markov Chain (CA-Markov) method. To verify the method, urban expansion simulation for the year 2014 was compared with real LU/LC data for the same year, and the kappa value was found to be 0,82. To determine LU/LC, Landsat TM/ETM+/OLI satellite images were used, and the analyses were realised in a geographic information system (GIS) environment. As a result of the study, the CA-Markov Chain approach integrated with GIS and remote sensing was shown to be effective in the study of urban growth dynamics. Using the simulation for the year 2034, probable urban expansion in the 2014-2034 period was estimated as approximately 3683 ha and the probable destructions of absolute agricultural lands, forests and pastures were predicted as approximately 968 ha, 228 ha and 24 ha, respectively. |
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| ISSN: | 1330-3651 1848-6339 |