title: [Land use simulation and landscape assessment by using genetic algorithm based on cellular automata under different sampling schemes].
abstract: Simulating land use change scenarios with cellular automata (CA) can help to the policy makers in understanding the mechanisms of land change, and support the spatial decision-making for the sustainable use of land resources. Genetic algorithm (GA), an intelligent approach originally conceived from the biological process of evolution, has the capability of minimizing the difference between simulated and observed land use patterns with optimum chromosomes (i.e., feasible CA parameters) obtained through a set of selection, crossover, and mutation operations. In this paper, GA-based CA model was developed, and applied to simulate the land use change in Jiaxing City of Zhejiang Province in 1992-2008. This model was calibrated with 6% (66 samples km(-2)) and 3% (33 samples km(-2)) samplings, and the simulation results were evaluated based on confusion matrix, Kappa coefficient, and landscape metrics analysis. Over 80% of the land use features generated by the GA-based CA model matched the observed classification of land features geographically, and much higher simulation accuracy could be obtained with a larger sample. The simulation accuracy and the landscape metrics for 2001 were better than those for 2008, suggesting a tendency that the model's accuracy decreased over the simulating process.
authors.affiliation: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China. yjfeng@shou.edu.cn
authors.forename: Yong-jiu
authors.initials: YJ
authors.lastname: Feng
authors.forename: Yan
authors.initials: Y
authors.lastname: Liu
authors.forename: Zhen
authors.initials: Z
authors.lastname: Han
mesh_terms: D000383:Agriculture / Q000379:methods* / Q000639:trends; D000465:Algorithms; D002681:China; D002958:City Planning / Q000379:methods / Q000639:trends; D003198:Computer Simulation; D003247:Conservation of Natural Resources / Q000379:methods* / Q000639:trends; D017753:Ecosystem; D005544:Forecasting; D008962:Models, Theoretical