Determining future scenarios of urban areas with cellular automata/Markov Chain Model method; example of Ereğli District Konya-Türkiye (2030–2040)

Abstract

AbstractAs a result of the rapid increase in the world population, the earth surface has started to be damaged due to natural and artificial effects. The extent of the damage to nature can be determined by examining the temporal changes of land use and land cover (LULC). In order to offer healthier and more sustainable living spaces, scientists have produced many studies on the changes in nature. Within the scope of this study, 5 basic training classes were created with the help of Landsat satellite images and CORINE data, covering the period of 1985–2018 for Ereğli-Bor Sub-Basin, which is one of the 9 sub-basins of Konya Closed Basin located in the Central Anatolian Region of Türkiye. Landsat Satellite images, Google Earth Program and CORINE data were overlaid to create a basic training class as artificial areas, agricultural areas—pasture areas—forest areas and wetlands and these areas were classified by supervised classification method. The study was carried out on an area of approximately 331057 ha in and around Ereğli district. Modeling was carried out with the Cellular Automata (CA) Markov Chain Model to determine the urban development potential in the region. In order to estimate the modeling accuracy, the 2018 prediction model was created according to the 2018 reference map, and the validation between the two data was analyzed with the kappa statistics. According to kappa statistics values, it was determined that K_location and K_standard values were 0.9301 and 0.8935, respectively. As a result of the validation in sufficient standards, future prediction models were applied; future models and result maps were prepared for the years 2030–2040. According to the modeling results, it is estimated that the artificial area class in Ereğli district will reach 122.74 km2 by 2030 and 142.24 km2 in 2040. In addition, it was expressed in detail with the prediction results and maps that there will be a decrease in pasture, forest and agricultural areas in the region until 2030 and 2040. As a result, it is predicted that the ecological balance in the region will change and agricultural production may decrease as a result of the decline in agricultural pasture and forest areas. For this reason, it has been revealed that it is important for the future of humanity that plans such as environmental layout and master development plans to be made by regional manager in the region for the future should be planned in line with the results to be obtained as a result of future prediction models.