An automatic and objective approach to hydro-flatten high resolution topographic data

Abstract Hydro-flattening is an operation required to generate deliverable terrain products after lidar survey collections that usually entails extensive manual intervention. In this paper, we develop new modules of GeoNet, a computational tool for the automatic extraction of geomorphic channel features from high resolution topography, for detecting channel banks and produce a hydro-flattened Digital Terrain Model (DTM). We first review the original GeoNet workflow and then describe how it has been modified to extract channels as the least-cost-paths from given channel heads to outlets. A new code component enables hydro-flattening on a given reference network based on curvature and connectivity. A raster-based routine for extracting the geomorphic channel zone based on a statistical analysis of slope has also been added. We test our new components using three different test cases. Compared to manually delineated hydro-flattened zones and satellite imagery, our results show high consistency and the capability of our method to automatically hydro-flatten high resolution topographic data.

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