Enhancing DEMs for geomorphometric research through digital filtering
- Published
- Accepted
- Subject Areas
- Spatial and Geographic Information Systems
- Keywords
- DEM, Filter, Geomorphometry, Morocco
- Copyright
- © 2018 Habib et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Enhancing DEMs for geomorphometric research through digital filtering. PeerJ Preprints 6:e27105v1 https://doi.org/10.7287/peerj.preprints.27105v1
Abstract
Currently, Digital Elevation Models (DEMs) are the main source for representing Earth’s surface and have been an integral part of any geospatial analysis, in particular, geomorphometric research. Among existing techniques, photogrammetry is considered as a common method for obtaining high-resolution elevation data, especially over large or/and inaccessible areas. However, this DEMs are disposed to a severe amount of uncertainty. The aim of this research is to provide an optimal filtering strategy that can remove possible errors and improve the quality of raw photogrammetric DEMs for geomorphometric research over regions with relatively low relief. Results reveal that a combination of three digital filters, namely Gaussian filter, Median filter, and Slope Based filter, is able to reduce different sources of errors and improve the elevation accuracy of DEM by more than 5%, resulting in improved quality of the derived geomorphometric parameters.
Author Comment
This paper was presented at the Geomorphometry 2018 conference in Boulder, CO in August 2018.