Developing urban growth predictions from spatial indicators based on multi-temporal images

Landuse change in metropolitan areas is largely focused on the dynamic nature of urban landuse change. In this research, a spatial statistical model was used to support decision-making with regard to urban growth predictions in the urban fringe of Beijing, China. The model adopted in this study was based on the integration of remote sensing, geographical information systems, and multivariate mathematical models. The model emphasises the spatial distribution of the landuse/cover units and the spatio-temporal patterns, which were modelled by landuse/cover change trajectories over a series of observation years. The main trajectories for the landuse/cover change model were based on five sets of multitemporal landuse/cover data derived from remotely sensed images. Using the integrated GIS, several spatial variables were derived, including the proximity to major roads and built-up areas. A multivariate model was established to establish relationships between urban expansion and above spatial variables. The landuse/cover change trajectories and the multivariate model were then integrated to construct a multivariate spatial model that is capable of estimating the spatial probability of the urban expansion.