GeoNet: An open source software for the automatic and objective extraction of channel heads, channel network, and channel morphology from high resolution topography data. (September 2016)

• Record Type: Journal Article
• Authors: Sangireddy, Harish; Stark, Colin P.; Kladzyk, Anna; Passalacqua, Paola
• Is Part Of: Environmental modelling & software. Volume 83(2016:Sep.)
• Page Start: 58
• Page End: 73
• Publication Date: 2016-09
• Subjects: Lidar -- High resolution topography -- Geodesics -- Channel heads -- Channel network -- Channel morphology; 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.2016.04.026
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: Open source software for analysis of high resolution topography data. Automatic extraction of channel networks, channel heads, and channel morphology. Includes nonlinear filtering of small scale variability and median filtering of man-made features. Objective and accurate channel extraction tested on terrains of different characteristics. First application to an urban setting shows ability to handle natural and engineered features.