Machine learning for cross-scale geomorphometric classification of landforms: a day at the beach
- Published
- Accepted
- Subject Areas
- Computational Science, Spatial and Geographic Information Science
- Keywords
- random forests, sand dunes, scale, lidar digital elevation model, geomorphometry
- Copyright
- © 2018 Shortridge 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. Machine learning for cross-scale geomorphometric classification of landforms: a day at the beach. PeerJ Preprints 6:e27112v1 https://doi.org/10.7287/peerj.preprints.27112v1
Abstract
This paper investigates the use of random forests and spatial random forests (RFsp) for the classification of coastal dune areas along 41km of Lake Michigan’s shoreline using a lidar- derived DEM. Terrain variables across a range of spatial neighborhood scales are utilized, and for two different cell resolutions. Distance is explicitly incorporated into the RFsp models through the calculation of buffer distances around small numbers (6-13) of gridded points in the study area. While classification accuracy is high generally, RFsp produced much more accurate results. At the fine scale, topographic variables and their neighborhood ranges were not predictive of dune areas, perhaps because large (> 0.1 hectare) neighborhoods were not tested at that scale. At the coarse scale these variables were much more important. The use of small numbers of gridded (non-sample) points to improve spatial prediction warrants further investigation.
Author Comment
This short paper is an accepted submission to Geomorphometry 2018, a conference held in Boulder, Colorado in August, 2018. This is a preprint.