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This paper presents a new method for simulating the evolution of a gully head in a loess catchment with cellular automata(CA) based on the Fisher discriminant. The experimental site is an indoor loess catchment that was constructed in a fixed-intensity rainfall erosion test facility. Nine high-resolution digital elevation model(DEM) data sets were gathered by close range photogrammetry during different phases of the experiment. To simulate the evolution of the catchment gully head, we assumed the following. First, the 5th and 6th DEM data sets were used as a data source for acquiring the location of the catchment gully head and for obtaining spatial variables with GIS spatial analysis tools. Second, the Fisher discriminant was used to calculate the weight of the spatial variables to determine the transition probabilities. Third, CA model was structured to simulate the evolution of the gully head by iterative looping. The status of the cell in the CA models was dynamically updated at the end of each loop to obtain realistic results. Finally, the nearest neighbor, G-function, K-function, Moran′s I and fractal indexes were used to evaluate the model results. Overall, the CA model can be used to simulate the evolution of a loess gully head. The experiment demonstrated the advantages of the CA model which can simulate the dynamic evolution of gully head evolution in a catchment.
This paper presents a new method for simulating the evolution of a gully head in a loess catchment with cellular automata (CA) based on the Fisher discriminant. The experimental site is an indoor loess catchment that was constructed in a fixed-intensity rainfall erosion test facility Nine simulations of the evolution of the catchment gully head, we assume the following. First, the 5th and 6th DEM data sets were used as a data source for acquiring the location of the catchment gully head and for spatial fuel variables obtained with GIS spatial analysis tools. Second, the Fisher discriminant was used to calculate the weight of the spatial variables to determine the transition probabilities. Third, CA model was structured to simulate the evolution of the gully head by iterative looping. The status of the cell in the CA models was dynamically updated at th Finally, the nearest neighbor, G-function, K-function, Moran’s I and fractal indexes were used to evaluate the model results. Overall, the CA model can be used to simulate the evolution of a loess gully head. The experiment demonstrated the advantages of the CA model which can simulate the dynamic evolution of gully head evolution in a catchment.