GeoNet: An open source software for the automatic and objective extraction of channel heads,channel network,and channel morphology from high resolution topography data |
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| Affiliation: | 1. Department of Civil, Architectural and Environmental Engineering, Center for Research in Water Resources, The University of Texas at Austin, Austin, TX, USA;2. Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, USA;1. Division of Soil and Water Management, Department of Earth & Environmental Sciences, KU Leuven, Celestijnenlaan 200E ⿿ bus 2411, B-3001 Leuven, Belgium;2. Division of Forest, Nature and Landscape, Department of Earth & Environmental Sciences, KU Leuven, Celestijnenlaan 200E ⿿ bus 2411, B-3001 Leuven, Belgium;1. Department of Geography, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada;1. Quantitative Landscape Ecology, Institute for Environmental Sciences, University of Koblenz-Landau, Fortstraße 7, 76829 Landau in der Pfalz, Germany;2. GIS and Remote Sensing Platform, Biodiversity and Molecular Ecology Department, Research and Innovation Centre – Fondazione Edmund Mach, Via E. Mach 1, 38010 S. Michele all''Adige (TN), Italy |
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| Abstract: | Extracting hydrologic and geomorphic features from high resolution topography data is a challenging and computationally demanding task. We illustrate the new capabilities and features of GeoNet, an open source software for the extraction of channel heads, channel networks, and channel morphology from high resolution topography data. The method has been further developed and includes a median filtering operation to remove roads in engineered landscapes and the calculation of hillslope lengths to inform the channel head identification procedure. The software is now available in both MATLAB and Python, allowing it to handle datasets larger than the ones previously analyzed. We present the workflow of GeoNet using three different test cases; natural high relief, engineered low relief, and urban landscapes. We analyze default and user-defined parameters, provide guidance on setting parameter values, and discuss the parameter effect on the extraction results. Metrics on computational time versus dataset size are also presented. We show the ability of GeoNet to objectively and accurately extract channel features in terrains of various characteristics. |
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| Keywords: | Lidar High resolution topography Geodesics Channel heads Channel network Channel morphology |
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