Extracting and visualising glacial ice flow directions from Digital Elevation Models using greyscale thinning and directional trend analyses

Abstract Flow pattern reconstructions for past glaciations are based on the analysis of the spatial distribution of subglacial landforms, and streamlined subglacial landforms (oriented parallel or sub-parallel to the ice flow) are regarded as the main indicators of the previous ice flow. Manual mapping of these landforms is a time consuming and subjective process, making semi-automated mapping (SAM) methods attractive. We present a novel SAM method that extracts mean directional trends from a digital elevation model (DEM) by performing greyscale thinning on its derived slope raster, and produce directional statistics from the lines extracted from the resulting skeleton. The method has been tested on artificial (synthetically generated) surfaces to demonstrate its potential for detecting directions of idealized landforms. The tests carried out on the artificial surfaces reveal that a landform feature has to be at least six (raster) cells wide for its direction to be properly detected by the method. The application of the method on real-world terrain data shows that the method is capable of reconstructing glacial flow patterns, and that it is robust to reasonable variations in the method parameters. We believe that the method should also have great potential for detecting directional trends in the general field of geomorphology. The user friendliness of the method and the simple interpretation of the results should make this a useful tool for geomorphological studies.

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