Calibration and Assessment of Multitemporal Image-based Cellular Automata for Urban Growth Modeling

This paper discusses the calibration and assessment of a cellular automata model for urban growth modeling. A number of transition rules are introduced in the cellular automata model to consider the most influential urbanization factors, such as land-cover maps obtained from satellite images and population density from the census. The transition rules are calibrated both spatially and temporally to ensure the modeling accuracy. Spatially, each township (about 6 miles 3 6 miles) in the study area is used as a calibration unit such that the spatial variability of the urban growth process can be taken into account. The temporal calibration is performed by using a sequence of remote sensing images from which the land-cover information at different years is extracted. As for the assessment, fitness (for urban level match) and two types of modeling errors (for urban pattern match) are introduced as the evaluation criteria. The study shows that the use of images reduces the need for a large number of input data. Evaluation on the rule variogram reveals that the transition rule values are correlated spatially and vary with the urbanization level. The paper reports the study outcome over the city of Indianapolis, Indiana for the past three decades using Landsat images and the population data.