Extraction and Analysis of Gully Head of Loess Plateau in China Based on Digital Elevation Model

In China′s Loess Plateau area, gully head is the most active zone of a drainage system in gully areas. The differentiation of loess gully head follows geospatial patterns and reflects the process of the loess landform development and evolution of its drainage system to some extent. In this study, the geomorphic meaning, basic characteristics, morphological structure and the basic types of loess gully heads were systematically analysed. Then, the loess gully head′s conceptual model was established, and an extraction method based on Digital Elevation Model(DEM) for loess gully head features and elements was proposed. Through analysing the achieved statistics of loess gully head features, loess gully heads have apparently similar and different characteristics depending on the different loess landforms where they are found. The loess head characteristics reflect their growth period and evolution tendency to a certain degree, and they indirectly represent evolutionary mechanisms. In addition, the loess gully developmental stages and the evolutionary processes can be deduced by using loess gully head characteristics. This study is of great significance for development and improvement of the theoretical system for describing loess gully landforms.

Simulating Evolution of a Loess Gully Head with Cellular Automata

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.