Assimilating process context information of cellular automata into change detection for monitoring land use changes

Abstract

This article presents a new method of assimilating process context information into change detection for monitoring land use changes. The accurate information about land use changes is important for implementing many global and regional environmental models. Two types of models have been independently developed to obtain such information, including change detection models (e.g. pixel-to-pixel comparison, post-classification comparison and object-based change analysis) and simulation models (e.g. cellular automata (CA) and agent-based modelling). These models may have limitations in capturing land use dynamics when used alone. In this study, the ensemble Kalman filter is used to obtain the best estimate of land use changes by combining remote-sensing observations with urban simulation. Urban simulation is able to provide process context information such as diffusion and coalescence of urban development. This type of complementary information is useful for improving the performance of change detection. Compared with traditional change detection models, this integrated model has the potential to improve the performance of change detection in terms of accuracies and landscape metrics. For example, the assimilating (MLC + CA) method can show improvement of the total accuracy and the kappa coefficient by 2.5–5.2% and 3.6–7.4%, respectively, in this study.

Keywords:

• process context
• cellular automata
• change detection
• data assimilation
• ensemble Kalman filter

Acknowledgements

This study was supported by National Basic Research Program of China (973 Program) (Grant No. 2011CB707103), the Key National Natural Science Foundation of China (Grant No. 40830532) and National Natural Science Foundation of China (Grant No. 41171308).