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Channel cross-section analysis for automated stream head identification

Environmental Modelling & Software
By:  and 
https://doi.org/10.1016/j.envsoft.2020.104809

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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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    Publication type Article
    Publication Subtype Journal Article
    Title Channel cross-section analysis for automated stream head identification
    Series title Environmental Modelling & Software
    DOI 10.1016/j.envsoft.2020.104809
    Volume 132
    Year Published 2020
    Language English
    Publisher Elsevier
    Contributing office(s) Center for Geospatial Information Science (CEGIS)
    Description 104809, 11 p.
    Country United States
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