Parallel Channel Identification and Elimination Method Based on the Spatial Position Relationship of Different Channels

• Published in: ISPRS international journal of geo-information Vol. 13; no. 1; p. 13
• Main Authors: Zhao, Mingwei, Ju, Xiaoxiao, Wang, Ni, Wang, Chun, Zeng, Weibo, Xu, Yan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2024
• Subjects: Algorithms; Analysis; channel level; Channels; Classification; DEM; Digital Elevation Models; Evolution; Fluid mechanics; Geomorphology; Geospatial data; Hydrology; Identification methods; Measurement; Methods; Morphology; parallel channels; Radiation; Simulation; Soil erosion; spatial location relationship; Terrain analysis; Vegetation; Watersheds; China
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi13010013

Extracting a channel network based on the Digital Elevation Model (DEM) is one of the key research topics in digital terrain analysis. However, when the channel area is wide and flat, it is easy to form parallel channels, which seriously affect the accuracy of channel network extraction. To solve this problem, this study proposes a method to identify and eliminate parallel channels extracted by classical methods. First, the channel level in the study area is marked based on the flow accumulation data, and the parallel channels are then identified using the positional relationship between the different channel levels. Finally, the modification point of the identified parallel channels is determined to eliminate the parallel channels, with the help of the change relationship between the parallel channel and its upper-level channel. In this study, two watersheds in southeast China are selected as examples for method verification and analysis. Experimental results show that the parallel channel identification method proposed in this paper can accurately identify all parallel channels and eliminate the identified parallel channels one by one. The location relationship of the modified channels is consistent with the actual situation, indicating that the proposed method has good application potential in DEM-based channel extraction networks.