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Accuracy of Interpolated Bathymetry in Digital Elevation Models

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  • Journal Title:
    Journal of Coastal Research
  • Personal Author:
  • NOAA Program & Office:
    NESDIS (National Environmental Satellite, Data, and Information Service)
  • Description:
    Digital elevation models (DEMs) are used to model numerous coastal processes, including tsunamis, contaminant dispersal, and erosion. In the bathymetric realm, the distance between measurements typically increases farther from shore (i.e., deeper water), such that gridding interpolation to build a bathymetric DEM is often across large distances. This study examined the accuracy of interpolation in bathymetric DEMs using three common interpolation techniques: inverse distance weighting, spline, and triangulation. The goal was to examine the relationship between interpolation accuracy and cell sampling density, distance to the nearest depth measurement, and terrain characteristics. Kachemak Bay, Alaska, was chosen as the study area due to its heterogeneous terrain. A split-sample method was developed to randomly separate depth measurements to be used for interpolation from those used to quantify interpolation accuracy. Results show that the accuracy of the three evaluated interpolation techniques decreases (i) at smaller cell sampling densities, (ii) as the distance to the nearest measurement increases, and (iii) in areas of high slope and curvature. Spline was found to be the most accurate technique, though all techniques have approximately equivalent accuracy at large cell sampling densities and shorter interpolation distances. From these analyses, predictive equations were derived, for each interpolation technique, of the cell-level uncertainty introduced into bathymetric DEMs, as a function of the cell sampling density and interpolation distance. These equations permit the quantification of cell-level interpolation uncertainty in DEMs and, in turn, will aid in propagating that uncertainty into the modeling of coastal processes that utilize DEMs.
  • Source:
    Journal of Coastal Research, 123-133.
  • DOI:
  • Document Type:
    Journal Article
  • Rights Information:
    CC BY
  • Compliance:
    Library
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