Gridding heterogeneous bathymetric data sets with stacked continuous curvature splines in tension

Hell, Benjamin

doi:10.1007/s11001-011-9141-1

Cited by 41 publications

(43 citation statements)

References 28 publications

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“…The methods and results of this study advance previous research by Jakobsson, Calder, andMayer (2002), Calder (2006), and Hell and Jakobsson (2011) by integrating bathymetric and topographic data sets of disparate age, quality, data density, and vertical datums to create a seamless coastal DEM, and most notably, provide an estimate of the cell-level uncertainty that also incorporates the spatial resolution of the DEM. Uncertainty estimations using the number of measurements per DEM grid cell, as determined by the spatial resolution of the DEM, are remarkably absent from literature on estimating DEM uncertainty, despite being acknowledged by Wechsler (2007) a decade ago.…”

Section: Discussionsupporting

confidence: 63%

“…There are numerous types of kriging, such as ordinary kriging, cokriging, and simple kriging, and each type has different statistical assumptions and constraints (Meul and Van Meirvenne, 2003). A main limitation of implementing geostatistical methods such as kriging is the large computational expense needed to create the semivariogram, especially with voluminous elevation data sets (Hell and Jakobsson, 2011). Geostatistical methods typically have computational costs that scale with the cube of the number of measurements (Cressie and Johannesson, 2008;Kleiber and Nychka, 2015).…”

Section: Geostatistical Interpolation Techniquesmentioning

confidence: 99%

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Estimating Coastal Digital Elevation Model Uncertainty

Amante

2018

Journal of Coastal Research

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Section: Discussionsupporting

confidence: 63%

Section: Geostatistical Interpolation Techniquesmentioning

confidence: 99%

Estimating Coastal Digital Elevation Model Uncertainty

Amante

2018

Journal of Coastal Research

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“…Additional soundings and individual grids are regularly added to the GEBCO grid. These data are digitally incorporated using procedures such as remove-restore [Hell and Jakobsson, 2011;Smith and Sandwell, 1997] described below. These data set additions are listed in Table 1.…”

Section: Bathymetric Soundingsmentioning

confidence: 99%

A new digital bathymetric model of the world's oceans

Weatherall

Marks

Jakobsson

et al. 2015

Earth and Space Science

Self Cite

770

546

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General Bathymetric Chart of the Oceans (GEBCO) has released the GEBCO_2014 grid, a new digital bathymetric model of the world ocean floor merged with land topography from publicly available digital elevation models. GEBCO_2014 has a grid spacing of 30 arc sec and updates the 2010 release (GEBCO_08) by incorporating new versions of regional bathymetric compilations from the International Bathymetric Chart of the Arctic Ocean, the International Bathymetric Chart of the Southern Ocean, the Baltic Sea Bathymetry Database, and data from the European Marine Observation and Data network bathymetry portal, among other data sources. Approximately 33% of ocean grid cells (not area) have been updated in GEBCO_2014 from the previous version, including both new interpolated depth values and added soundings. These updates include large amounts of multibeam data collected using modern equipment and navigation techniques, improving portrayed details of the world ocean floor. Of all nonland grid cells in GEBCO_2014, approximately 18% are based on bathymetric control data, i.e., primarily multibeam and single-beam soundings or preprepared grids which may contain some interpolated values. The GEBCO_2014 grid has a mean and median depth of 3897 m and 3441 m, respectively. Hypsometric analysis reveals that 50% of the Earth's surface is composed of seafloor located 3200 m below mean sea level and that~900 ship years of surveying would be needed to obtain complete multibeam coverage of the world's oceans.

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“…In the literature, we observed that several spatial interpolation techniques were used to generate the bathymetry data based on survey samples depth, topographic data, and remote sensing based data, respectively [18,[26][27][28][29]. Among those, sample depths collected at various reaches of the river have been widely used for river bathymetry generation and hydrodynamic studies [30,31].…”

Section: Gap Fillingmentioning

confidence: 99%

Use of Bathymetric and LiDAR Data in Generating Digital Elevation Model over the Lower Athabasca River Watershed in Alberta, Canada

Chowdhury

Hassan

Achari

et al. 2017

Water

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Abstract:The lower Athabasca River watershed is one of the most important regions for Alberta and elsewhere due to fact that it counts for the third largest oil reserve in the world. In order to support the oil and gas extraction, Athabasca River provides most of the required water supply. Thus, it is critical to understand the characteristics of the river and its watershed in order to develop sustainable water management strategies. Here, our main objective was to develop a digital elevation model (DEM) over the lower Athabasca River watershed including the main river channel of Athabasca River (i.e., approximately 128 km from Fort McMurray to Firebag River confluence). In this study, the primary data were obtained from the Alberta Environmental Monitoring, Evaluation and Reporting Agency. Those were: (i) Geoswath bathymetry at 5-10 m spatial resolution; (ii) point cloud LiDAR data; and (iii) river cross-section survey data. Here, we applied spatial interpolation methods like inverse distance weighting (IDW) and ordinary kriging (OK) to generate the bathymetric surface at 5 m × 5 m spatial resolution using the Geoswath bathymetry data points. We artificially created data gaps in 24 sections each in the range of 100 to 400 m along the river and further investigated the performance of the methods based on statistical analysis. We observed that the DEM generated using the both IDW and OK methods were quite similar, i.e., r 2 , relative error, and root mean square error were approximately 0.99, 0.002, and 0.104 m, respectively. We also evaluated the performance of both methods over individual sections of interest; and overall deviation was found to be within ±2.0 m while approximately 96.5% of the data fell within ±0.25 m. Finally, we combined the Geoswath-derived DEM and LiDAR-derived DEM in generating the final DEM over the lower Athabasca River watershed at 5 m × 5 m resolution.

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Gridding heterogeneous bathymetric data sets with stacked continuous curvature splines in tension

Cited by 41 publications

References 28 publications

Estimating Coastal Digital Elevation Model Uncertainty

Estimating Coastal Digital Elevation Model Uncertainty

A new digital bathymetric model of the world's oceans

Use of Bathymetric and LiDAR Data in Generating Digital Elevation Model over the Lower Athabasca River Watershed in Alberta, Canada

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