Analysis of the future land cover change in Beijing using CA–Markov chain model

Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)

“…The growth and development in wetland farming as an essential source of food crops and cash crops for the city's inhabitants expanded the vegetation coverage. This result is in contrast with recent studies that indicate a continuous decrease in vegetation cover due to urban growth and development [3,18,19]. In this case, reforestation and massive investment in Fadama agricultural projects by federal, state, and various non-governmental organizations in Zaria city has contributed to the significant increase in agricultural/vegetable areas and the consequent decrease in barren land.…”

“…Similarly, the city of Ravansar in Iran is further expected to experience an unsustainable natural environment changing into built-up areas in the future due to excessive human activities [3]. Studies in China highlight rapid urbanization as a driving factor for its extensive construction activities [18]. This development will lead to a future reduction in the country's agricultural land and counter the state's ability to achieve sustainable urban expansion unless the government achieves a reduced urban construction.…”

“…The use of satellite remote sensing (SRS) data and geographical information system (GIS) techniques have been long established for several decades as an efficient tool for land use and other natural resource management [17]. The availability of remotely sensed data has provided a rapid variety of spectral, temporal, and spatial resolution images utilized in detecting changes on the surface of the earth at a faster and cheaper cost than traditional ground survey methods [3,[18][19][20][21]. Satellite remotely sensed data had been employed as inputs for generating land use/land cover maps.…”

“…A study of LULC transition in Beijing demonstrated the efficiency of using the CA-Markov model for land cover prediction. The results foresee a reduction in farmland by about 507.08 km 2 from 2012 to 2020 and an increase in construction land by 262.57 km 2 [18]. Aneesha Satya et al [19] used open-source GIS that comprises cellular automata and Markov chain-based models to predict the 2052 land use scenario in the city of Warangal, Telangana, India.…”

Simulation of the Dynamics of Land Cover and Land Use in the Lobo River Watershed Upstream of Nibéhibé (Center-West of Côte d’Ivoire)

“…The work of [7] shows that the CA-MARKOV combination integrates the stochastic function of Markov Method with the stochastic spatial characteristic of Technology CA. CA-MARKOV a powerful tool that has enabled several authors including [8] [9] [10] to simulate land cover and land use changes.…”