Accuracy of Interpolated Bathymetry in Digital Elevation Models

Abstract

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Estimating Coastal Digital Elevation Model Uncertainty

“…Current state-of-the-art linear-mode LIDAR sensors have a data density of approximately 2-4 elevation returns/m 2 , and emerging single-photon LIDAR and Geiger-mode LIDAR have a data density of approximately 23 and 25 elevation returns/m 2 for open terrain, respectively (Stoker et al, 2016). NOAA NCEI coastal DEMs have spatial resolutions that usually range from approximately 3 to 10 m (Amante and Eakins, 2016;Eakins and Grothe, 2014), resulting in multiple measurements per DEM grid cell where there is LIDAR coverage. DEM values typically represent a distance-weighted mean of all measurements located within an individual DEM grid cell when using an exact interpolation technique (Amante and Eakins, 2016;Caress and Chayes, 1996).…”

“…DEM uncertainty represents the lack of knowledge of the vertical errors, and a DEM uncertainty surface is a statistical assessment of the likely magnitude and spatial distribution of these errors (Hunter and Goodchild, 1997;International Hydrographic Organization, 2008;Li et al, 2018;Wechsler, 2007). Accuracy is defined in this manuscript as a general term for the agreement of values to known or accepted values (Amante and Eakins, 2016), and is typically assessed by statistical measures, such as root mean square error (RMSE).…”

“…Numerous studies address uncertainty in topographic DEMs (e.g., Bater and Coops, 2009;Goulden et al, 2016;Leon, Heuvelink, and Phinn, 2014;Spaete et al, 2011;Su and Bork, 2006) and uncertainty in bathymetric DEMs (e.g., Amante and Eakins, 2016;Calder, 2006;Elmore et al, 2012;Jakobsson, Calder, and Mayer, 2002), but there is currently a lack of research on deriving uncertainty surfaces for coastal DEMs developed from multiple bathymetric and topographic data sets of disparate quality and age. Jakobsson, Calder, and Mayer (2002) and Calder (2006) create uncertainty surfaces that reflect potential measurement uncertainty for bathymetric data sets from different time periods, and the interpolation uncertainty between sparse measurements.…”

Monitoring remote ocean waves using P‐wave microseisms

“…C P was calculated using the water depth and take‐off angle appropriate for each grid point using the formulation of Gualtieri et al . [], taking water‐depth from the ETOP01 data set [ Amante and Eakins , ] and using the ObsyPy package [ Beyreuther et al ., ] to calculate take‐off angle based on the distance from the array center. Values for spatially varying crustal density ρ c and water density ρ w were taken from the upper crust layer and water layer of the global crustal model CRUST1.0 (http://igppweb.ucsd.edu/~gabi/rem.html).…”

Rapid variability of Antarctic Bottom Water transport into the Pacific Ocean inferred from GRACE

“…Regional bathymetry and bottom pressure measurements. (a) Bathymetry [ Amante and Eakins , ] (m) of the analysis region. The dots denote P06 station locations.…”