Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model

The cellular automata (CA) based models have been extensively used in urban sprawl modeling to support sustainable urban planning. However, in most existing urban CA models, only abrupt conversion (i.e., from non-urban to urban) was considered, whereas the difference in urbanization levels among different grids, as well as the nature of continuous urban evolution process with gradually increasing urban densities, were commonly ignored. Here, we proposed an impervious surface area (ISA) based urban CA model that can simulate the urban fractional change within each grid, using annual urban extent time series data from satellite observations. We implemented the developed ISA-based urban CA model in Beijing (China) and evaluated its performance through model comparison and scenario analyses. We found the ISA-based urban CA model can well capture the dynamics of urban sprawl with improved performance compared to the traditional urban CA model. The spatial pattern of modeled ISA is generally consistent with observed satellite data, with the root mean square error between the modeled and referred results of 0.14 across all changed pixels. Besides, results from our developed model agree well with the binary urban CA model, with notably reduced overestimation errors. The urban fractional change within each grid revealed in our model can provide more details than the traditional binary urban CA models, showing great potential in supporting sustainable urban development. Furthermore, the developed ISA-based urban CA model can be used for regional, even global scale urban sprawl modeling with urban fractional information in each grid, to support decision-making on the sustainable management and conservation of natural land resources.