Abstract
The automatic extraction of gullies from digital elevation models (DEMs) has great application value in GIS and hydrology. Many types of algorithms have been developed to address this problem, and the well-known D8 (Deterministic eight-node) algorithm has been widely applied and implemented in some commercial GIS software such as ArcGIS. However, a key parameter called flow accumulation threshold (FAT) must be determined in this process. Numerous studies focus on how to determine an optimal value for this parameter but ignore that the optimal threshold varies for different gullies, so the universality of a different optimal threshold parameter determined by different methods is poor. To address this problem, this study designs a parameter called surface concavity index (SC-index) that can describe the shape of gullies from the perspective of surface morphology. Based on this index, the positions of different gullies’ heads are identified, and then the flow accumulation matrix calculated by the D8 algorithm is used as auxiliary data to extract the gully network in the research area. In this study, six small watersheds in the Loess Plateau in northern Shaanxi, China, were used as test areas to verify the validity of the proposed method in areas with various landform types. Experimental results show that gully heads in different test areas can be effectively identified by setting different SC-index thresholds that are related to the types of terrain in the test areas. Then, the entire gully network can be extracted in watersheds with the help of a D8 algorithm. The accuracy of the gully network extracted by the new method is better than the contrast method in all test areas. In test areas with a large area of flat land (e.g., Chunhua), the difference between the total length of gullies extracted by the new method and the reference value is −2.77 km, while the corresponding value of the contrast method is 14.50 km. In test areas with large numbers of short gullies (e.g., Jiuyuangou), the difference between the total length of gullies extracted by the new method and the reference value is −2.61 km while the corresponding value of the contrast method is −27.9 km. It is pointed out that the new method can not only avoid the extraction of pseudo gullies, but also extract short gullies effectively. Further experimental analysis shows that the dependence of the new method on DEM resolution is significantly weaker than that of the contrast method. Taking Jiuyuangou test area as an example, when the DEM cell size increases from 5 m to 30 m, the total length of gullies extracted by the new method changes only about 1 km, while the corresponding value of the contrast method exceeds 20 km.
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Data Availability
Data supporting this research are available from the corresponding author on request.
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Acknowledgments
We are thankful for the helpful comments provided by the reviewers. This study was supported by Anhui Province Universities Outstanding Talented Person Support Project (No. gxyq2022097); Major Project of Natural Science Research of Anhui Provincial Department of Education (No. KJ2021ZD0130); “113” Industry Innovation Team of Chuzhou city in Anhui province; The guiding plan project of Chuzhou science and Technology Bureau (No. 2021ZD008); General Project of Natural Science Research of Anhui Provincial Department of Education (No. KJ2020B02).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jiaojiao Qi, Yuwei Zhang, Chun Wang, and Yan Xu. The first draft of the manuscript was written by Mingwei Zhao, Xiaoxiao Ju, Jinglu Sun and all authors commented on previous version of the manuscript. All authors read and approved the final manuscript.
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Zhao, Mw., Qi, Jj., Ju, Xx. et al. A novel gully network extraction method combining hydrological confluence process and surface morphology. J. Mt. Sci. 20, 2536–2556 (2023). https://doi.org/10.1007/s11629-023-7933-9
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DOI: https://doi.org/10.1007/s11629-023-7933-9