Semi-automated delineation of reticulate channel networks in low-gradient floodplain wetlands using LiDAR-derived DEMs

William Farebrother, Timothy J. Ralph

    Research output: Chapter in Book/Report/Conference proceedingConference abstract

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    The study of catchment drainage networks and river channel delineation using Digital Elevation Models (DEMs) has been the focus of many studies, both domestically and internationally. However, little attention has been given to the mapping of complex reticulate channel networks that characterise many low-gradient floodplain wetlands in the drylands of Australia, and elsewhere around the world. This ongoing pilot study has identified and mapped the drainage network present in the Macquarie Marshes, New South Wales, to better understand the channel hierarchy, patterns of flow dispersal, channel morphology and connectivity within this system. Several different tools commonly used to derive drainage networks using DEMs in Geographical Information System (GIS) software packages, such as ArcGIS, GRASS GIS and SAGA GIS, were applied to a high-resolution DEM of the Macquarie Marshes derived from Light Detection and Ranging (LiDAR) data obtained by the NSW Office of Environment and Heritage. The outputs were compared to determine which method offered the most accurate channel network and provided the most efficient technique for further morphometric and pattern analyses. It was found that methods that did not require DEM-filling prior to flow direction and accumulation modelling and that took multiple flow directions into account offered the most accurate channel networks in this system and were more computationally efficient.
    Original languageEnglish
    Title of host publicationWIDS2017 Dynamic Landscapes
    Subtitle of host publicationproceedings of the Wetlands in Drylands Research Network Conference
    EditorsTimothy J. Ralph
    Place of PublicationSydney, Australia
    PublisherMacquarie University
    Number of pages2
    ISBN (Print)9781741384543
    Publication statusPublished - 24 Jul 2017

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