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Causes and consequences of error in digital elevation models

Fisher, Peter F., Tate, Nicholas J.·2006·Progress in Physical Geography: Earth and Environment
DOI 10.1191/0309133306pp492ra
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Abstract

All digital data contain error and many are uncertain. Digital models of elevation surfaces consist of files containing large numbers of measurements representing the height of the surface of the earth, and therefore a proportion of those measurements are very likely to be subject to some level of error and uncertainty. The collection and handling of such data and their associated uncertainties has been a subject of considerable research, which has focused largely upon the description of the effects of interpolation and resolution uncertainties, as well as modelling the occurrence of errors. However, digital models of elevation derived from new technologies employing active methods of laser and radar ranging are becoming more widespread, and past research will need to be re-evaluated in the near future to accommodate such new data products. In this paper we review the source and nature of errors in digital models of elevation, and in the derivatives of such models. We examine the correction of errors and assessment of fitness for use, and finally we identify some priorities for future research.

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Crossref · 10.1191/0309133306pp492ra

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  • Cost surfaces and LCPs are often viewed as infallible, ignoring that they were derived from a DEM containing random errors.

    “Instead, cost surfaces and LCPs are often viewed as 'infallible' (Brouwer Burg, 2017), ignoring that they were derived from a DEM containing random errors: that is, errors accrued from mistakes during the creation of the DEM (Fisher & Tate, 2006).”

    From Probabilistic modelling for incorporating uncertainty in least cost path results: a postdictive roman road case study (2021) · cites this paper as Fisher & Tate, 2006

  • The error fields representing vertical error within the DEM were generated from random values drawn from a normal distribution with mean 0 and standard deviation 4, the RMSE of the DEM.

    “The error fields representing vertical error within the DEM were generated from random values drawn from a normal distribution of mean = 0 and standard deviation = 4 (i.e. the RMSE of the DEM) (Fisher, 1998;Fisher & Tate, 2006;Wechsler & Kroll, 2006).”

    From Probabilistic modelling for incorporating uncertainty in least cost path results: a postdictive roman road case study (2021) · cites this paper as Fisher & Tate, 2006

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Metadata

Authors
Fisher, P. F., & Tate, N. J.
Venue
Progress in Physical Geography: Earth and Environment
Publisher
SAGE Publications
Published
2006-08
Volume
30
Issue
4
Pages
467-489
ISSN
  • 0309-1333
  • 1477-0296
DOI
10.1191/0309133306pp492ra
Cited by
405
References
0
Language
en
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

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