Channel cross-section analysis for automated stream head identification

Ethan J. Shavers; Larry Stanislawski

doi:10.1016/j.envsoft.2020.104809

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Channel cross-section analysis for automated stream head identification

Ethan J. Shavers·Larry Stanislawski

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Abstract

Headwater streams account for more than half of the streams in the United States by length. The substantial occurrence and susceptibility to change of headwater streams makes regular updating of related maps vital to the accuracy of associated analysis and display. Here we present work testing new methods of completely automated remote headwater stream identification using metrics derived from channel Digital Elevation Model (DEM) cross-sections. A jump in standard deviation of curvature (sK) is found to correlate with the presence of stream heads. Field and remotely validated stream and channel initiation points from 4 diverse study areas in North Carolina as well as a simulated surface are used to test the sK findings. The sK value within individual catchments equal to 0.5*Tukey's upper inner fence is found to be a reliable threshold for identifying the upslope extent of channels in varied landscapes.

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Environmental Modelling & Software Article

Source-reported bounding extent: 25.08° to 49.38905° latitude; -124.68721° to -66.96466° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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BibTeX RIS

Ethan J. Shavers, Larry Stanislawski. 2020. Channel cross-section analysis for automated stream head identification. https://doi.org/10.1016/j.envsoft.2020.104809

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