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Accuracy of local topographic variables derived from digital elevation models
Abstract
We study the accuracy of data on some local topographic attributes derived from digital elevation models (DEMs). First, we carry out a test for the precision of four methods for calculation of partial derivatives of elevations. We find that the Evans method is the most precision algorithm of this kind. Second, we produce formulae for root mean square errors of four local topographic variables (gradient, aspect, horizontal and vertical landsurface curvatures), provided that these variables are evaluated with the Evans method. Third, we demonstrate that mapping is the most convenient and pictorial way for the practical implementation of the formulae derived. A DEM of a part of the Kursk Region (Russia) is used as an example. We find that high errors of data on local topographic variables are typical for flat areas. Results of the study can be used to improve landscape investigations with digital terrain models.
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Cited by
Causes and consequences of error in digital elevation models
Fisher, P. F., & Tate, N. J. (2006) · Progress in Physical Geography: Earth and Environment · doi:10.1191/0309133306pp492ra · Cited by 405
Thomas, J., Joseph, S., Thrivikramji, K. P., & Arunkumar, K. S. (2014) · Geoscience Frontiers · doi:10.1016/j.gsf.2013.12.008 · Cited by 67
Metadata
- Authors
- FLORINSKY, I. V.
- Venue
- International Journal of Geographical Information Science
- Publisher
- Informa UK Limited
- Published
- 1998-01
- Volume
- 12
- Issue
- 1
- Pages
- 47-62
- ISSN
- 0269-3798 (ISSN-L)
- 1365-8816
- 1362-3087
- OpenAlex
- W2134248412
- Cited by
- 290
- References
- 0
- FWCI
- 9.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- FLORINSKY, I. V. (1998). Accuracy of local topographic variables derived from digital elevation models. International Journal of Geographical Information Science. https://doi.org/10.1080/136588198242003
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
