Abstract
Abstract. Long-term urban built-up area changes of the Ulaanbaatar city has accelerated since the 1950s and due to rapid urbanization most of the Mongolian population, or about 68%, live in urban areas. The systematic understanding of urban land expansion is a crucial clue for urban land use planning and sustainable land development. Therefore, in this paper, we used a Markov chain model and cellular automata (CA) to simulate and predict current and future built-up areas expansion is Ulaanbaatar. Landsat imageries (Landsat TM 5, Landsat ETM 7 and Landsat OLI 8) of 1988, 1998, 2008, and 2017 were used to derive main land use classes. Clark Lab’s (Clark University) Geospatial Monitoring and Model software had been used for the urban expansion prediction. The results are innovated to comparable to validate with other study results by using a different kind of methods. Built-up area expansion modeled and predicted 2028’s trends based on a historical expansion of the Ulaanbaatar city between 1988 and 2017, which are prepared according to input model requirements. The built-up area was 7282 hectares (ha) in 1988 and has expanded to 31144 ha in 2017. The built-up area growth of the Ulaanbaatar city has reached 4.3 times over the past 30 years, and from 2017 to 2028 the expansion of the built-up area will be 1.5 times. A comparison of urban expansion from 1988 to 2017 has revealed a rapid built-up invasion to the previous areas of agriculture, grassland, and forest. Simulation performance of Markov chain with the cellular automata model can be used for an improvement in the understanding of the urban expansion processes while allowing helpful for better planning of Ulaanbaatar city, as well as for other rapidly developing towns of Mongolia.
Highlights
The main task of governments and communities struggle in our day is how to manage expansion for areas for developing areas in a sustainable way without degrading land values (Myagmartseren et al, 2017; Purevtseren et al, 2018)
The purpose of this study is to show that a simulation performance of Markov chains with a cellular automata (CA) model can be used for improving the understanding of urban expansion processes while allowing for better planning of the urban development of Ulaanbaatar city the urban expansion other rapid developing towns of Mongolia
No scientific studies have reported on urban geography or sprawl expansion prediction in Mongolia using a Markov chain model and CA
Summary
The main task of governments and communities struggle in our day is how to manage expansion for areas for developing areas in a sustainable way without degrading land values (Myagmartseren et al, 2017; Purevtseren et al, 2018). In the “ger district” (circular nomad’s tent-yurts detached to a land lot is a type of slum settlement district in Mongolia) sprawl area, which covers about 32% of all territory of the city, urban expansion has accelerated so far so it adversely impacts green belt areas, wetlands, riparian zones, open space, and public land (Myagmartseren et al, 2017). In this case, Gantumur et al (2020) assessed the spatiotemporal dynamics of urban expansion and simulated urban area in 2030 and 2040, were sampled in Ulaanbaatar, Mongolia. Mongolian urban studies need new conceptual remote sensing tools, some of which are presented in this paper
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