Bank gully extraction from DEMs utilizing the geomorphologic features of a loess hilly area in China

Abstract

As one of most active gully types in the Chinese Loess Plateau, bank gullies generally indicate soil loss and land degradation. This study addressed the lack of detailed, large scale monitoring of bank gullies and proposed a semi-automatic method for extracting bank gullies, given typical topographic features based on 5 m resolution DEMs. First, channel networks, including bank gullies, are extracted through an iterative channel burn-in algorithm. Second, gully heads are correctly positioned based on the spatial relationship between gully heads and their corresponding gully shoulder lines. Third, bank gullies are distinguished from other gullies using the newly proposed topographic measurement of “relative gully depth (RGD).” The experimental results from the loess hilly area of the Linjiajian watershed in the Chinese Loess Plateau show that the producer accuracy reaches 87.5%. The accuracy is affected by the DEM resolution and RGD parameters, as well as the accuracy of the gully shoulder line. The application in the Madigou watershed with a high DEM resolution validated the duplicability of this method in other areas. The overall performance shows that bank gullies can be extracted with acceptable accuracy over a large area, which provides essential information for research on soil erosion, geomorphology, and environmental ecology.

Keywords

bank gully / DEMs / topographic features / loess shoulder line / relative gully depth

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Nos. 41771415, 41471316, and 41271438) and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions No. 164320H116. We thank Dr. Liyang XIONG who gave us many useful suggestions, Dr. Rui ZHU from The Hong Kong Polytechnic University for the language improvement, three anonymous reviewers for their useful suggestions, and Jilong LI, Wen DAI, Min LI, and Dr. Kai LIU for their field work assistance.

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