Integrating spatiotemporal co-evolution patterns of land types with cellular automata to enhance the reliability of land use projections

Abstract

Land use and land cover change (LUCC) simulation aids the interpretation of the causes and consequences of future landscape dynamics under various scenarios, which in turn supports policy decisions. The essence of LUCC simulation lies in representing complex spatiotemporal associations among land types, including competitions and interactions. Currently, analyses of complex spatiotemporal LUCC associations mainly focus on the spatial configuration of land use while ignoring the intricate spatiotemporal co-evolution patterns of land types. Therefore, by integrating spatiotemporal co-evolution pattern mining (STC) in a future land use simulation (FLUS) model, a land use change simulation model named STC-FLUS was developed in this study. The proposed model is innovative because it can accurately quantify the spatiotemporal co-evolution patterns of land types, which can be effectively incorporated into LUCC simulations. A set of simulations indicate that the STC-FLUS model is more accurate than the classical FLUS model, with a figure of merit score of 0.135 compared with 0.114. Simulation results under five localized shared socioeconomic pathway scenarios from 2020 to 2040 demonstrate that the proposed model is effective for future LUCC simulation under a set of development scenarios. We conclude that spatiotemporal co-evolution patterns of land types can enhance the reliability of land use projections. Moreover, the STC-FLUS model can serve as a useful tool to understand future land use dynamics.