Channel cross-section analysis for automated stream head identification. (October 2020)

• Record Type: Journal Article
• Authors: Shavers, Ethan; Stanislawski, Lawrence V.
• Is Part Of: Environmental modelling & software. Volume 132(2020)
• Publication Date: 2020-10
• Subjects: Lidar -- DEM -- Stream head -- Headwater -- Change point -- Cross-sections; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104809
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: Test signal processing methods and elevation derivative thresholding for automated pruning of flow accumulation lines. It is found that channel cross-section statistics correlate with the presence of channel and stream heads. A catchment specific statistic is found to be reliable for identifying the upslope extent of channels in varied landscapes.