Causes and consequences of error in digital elevation models
By Peter F. Fisher, Nicholas J. Tate. 2006
501 citations recorded by the source corpus.
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.
Subfields: Environmental Engineering, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/a8a6b490a3385fe06c572ac9395b4e7a3a0922cc
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Citations in the corpus
- Accuracy assessment of digital elevation models by means of robust statistical methods (2009, methodology): “Recent publications, which deal in detail with accuracy assessment, are for example Carlisle (2005), Höhle and Potuckova (2006), Fisher and Tate (2007), Aguilar et al. (2007a) and Zandbergen (2008).”
- Airborne LiDAR for DEM generation: some critical issues (2008, background): “There seems to be no single interpolation method that is the most accurate for the interpolation of terrain data (Fisher and Tate, 2006).”
- An evaluation of void‐filling interpolation methods for SRTM data (2007, background): “If auxiliary data are used, KR performed best up to void size class F compared with up to void 998 H. I. Reuter et al. V o id -fillin g in terp o la tio n m eth o d s 9 9 9 1 0 0 0 H .”
- Analyzing High Resolution Topography for Advancing the Understanding of Mass and Energy Transfer Through Landscapes: A Review (2015, background): “Raster users need to carefully consider the sources of uncertainty mentioned in Section 2.3 as they affect DEMs (Fisher and Tate, 2006) and derived topo- graphic attributes (Sofia et al., 2013).915”