The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland
Open accessAbstract
Surface roughness is an important geomorphological variable which has been used in the Earth and planetary sciences to infer material properties, current/past processes, and the time elapsed since formation. No single definition exists; however, within the context of geomorphometry, we use surface roughness as an expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest. Six techniques for the calculation of surface roughness were selected for an assessment of the parameter's behavior at different spatial scales and data-set resolutions. Area ratio operated independently of scale, providing consistent results across spatial resolutions. Vector dispersion produced results with increasing roughness and homogenization of terrain at coarser resolutions and larger window sizes. Standard deviation of residual topography highlighted local features and did not detect regional relief. Standard deviation of elevation correctly identified breaks of slope and was good at detecting regional relief. Standard deviation of slope $(\hbox{SD}_{\rm slope})$ also correctly identified smooth sloping areas and breaks of slope, providing the best results for geomorphological analysis. Standard deviation of profile curvature identified the breaks of slope, although not as strongly as $\hbox{SD}_{\rm slope}$ , and it is sensitive to noise and spurious data. In general, $\hbox{SD}_{\rm slope}$ offered good performance at a variety of scales, while the simplicity of calculation is perhaps its single greatest benefit.
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Self-citationThe Use of LiDAR Terrain Data in Characterizing Surface Roughness and Microtopography
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Metadata
- Authors
- Grohmann, C. H., Smith, M. J., & Riccomini, C.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2011-04
- Volume
- 49
- Issue
- 4
- Pages
- 1200-1213
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2146016555
- Cited by
- 329
- References
- 29
- FWCI
- 7.95
- Type
- journal article
- Language
- en
- Open access
- green
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