Coupling Mixed Pixel Decomposition and Mixed-cell Simulation for Land Cover Change Deduction
Cellular Automata (CA) provides an important tool for land use/land cover change simulation. However,previous CA models based on pure cells ignore the mixed land cover structure within cells,making it difficult to simulate the continuous evolution of mixed land systems during rapid urbanization. The Mixed-Cell Cellular Automata (MCCA) can address this issue,but its widespread application is hindered by the difficulty in obtaining fine-scale mixed structure data. To solve these problems,this study proposes a simulation analysis framework that couples the mixed pixel decomposition method with the MCCA model. This framework uses the mixed pixel decomposition algorithm to directly obtain the sub-pixel scale mixed structure data required by the MCCA model from Landsat images. The SHAP method is utilized to explore the driving forces of sub-pixel scale land cover change. To verify the proposed framework,we conducts an experiment in Wuhan city. Results show that:1) The decomposition accuracy of the land cover data is above 0.8,and the mcFoM index of the simulation results is 0.38,indicating that this coupled model has high accuracy in characterizing the spatial pattern of mixed land structures and simulating future changes;2) The proposed coupling model can effectively simulate the fine-scale dynamic changes of land cover proportions and discover relevant patterns of regional land use changes. For example,future land cover structure changes will mainly concentrate in built-up areas,and land mixture will experience a process of increasing first and then decreasing. Socio-economic factors such as proximity to companies,the municipal government,and high population and GDP are important driving factors for the expansion of impervious surfaces,and impervious surfaces in urban centers relatively far from high-speed railway stations grow more rapidly;3) The future land cover change trends simulated by the proposed model are consistent with the future planning layout of Wuhan. The comparison between multiple scenarios demonstrates the MCCA model's ability to accurately capture the subtle differences in land cover proportion between pixels. This method couples the mixed pixel decomposition method from the field of remote sensing with the mixed Cellular Automata (CA) model from the field of GIS,solving the problem of lacking fine-scale data sources for simulating mixed land cover structures. It simulates future changes in mixed land cover structures at the sub-pixel scale,which can enrich existing research on mixed land structures and provide a certain theoretical basis for urban development decisions. Additionally,it opens up new avenues for the application of CA models in other areas.
land use changeland covergeographic simulationmixed cellcellular automatamixed pixel decomposition
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