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Sensitivity of DEM, slope, aspect and watershed attributes to LiDAR measurement uncertainty

Goulden, T., Hopkinson, C., Jamieson, R., Sterling, S.·2016·Remote Sensing of Environment
DOI 10.1016/j.rse.2016.03.005
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Abstract

Knowledge of LiDAR measurement uncertainty and its propagation into derived geospatial products is important for conscientious application of LiDAR products to environmental modeling, planning and decision making. This study simulates theoretical error limits of LiDAR elevations based on survey acquisition parameters and terrain conditions and applies a Monte Carlo simulation approach to determine the uncertainty in a DEM, grid based slope and aspect maps, watershed area and a stream network delineation. A cell-by-cell assessment of the DEM for hydrological network extraction is also provided through the Gyasi-Agyei et al. (1995) suitability metric. Results show that DEM uncertainty generally ranged between 0.025 and 0.060m, slope uncertainty ranged between 0.6 and 1.5°, and aspect uncertainty ranged between 2.7 and 24.1° for the given case study site. DEM uncertainty and slope uncertainty were correlated with the incidence angle between the laser pulse and the terrain, described with a piecewise linear function. Uncertainty in aspect showed a correlation with terrain slope and could be described with a decreasing power function. The variation in watershed area was less than 1% of total watershed area indicating it was not sensitive to LiDAR measurement errors for the selected case study site. Stream network length variability was 1.5% of total length, but increased as the analyzed area decreased. Areas of 1ha showed relative standard deviations of stream network length of ~2…

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  • DEM inaccuracies can significantly influence derived information such as watershed delineation, slope, aspect, and drainage network.

    “DEM inaccuracies can significantly influence the information derived, such as watershed delineation, slope, aspect, and drainage network [56,58].”

    From ANADEM: A Digital Terrain Model for South America (2024) · cites this paper as [58]

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Research works printed but not linked: 44

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Cited by

Sorted by citations

Metadata

Authors
Goulden, T., Hopkinson, C., Jamieson, R., & Sterling, S.
ORCID
Goulden, T.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2016-06
Volume
179
Pages
23-35
ISSN
0034-4257
DOI
10.1016/j.rse.2016.03.005
Cited by
51
References
0
Language
en
Licence
Funders
  • Natural Sciences and Engineering Research Council of Canada
  • Canada Foundation for Innovation
  • Atlantic Canada Opportunities Agency

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