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Research Articles

Exploring Ancillary Parameters for Quantifying Interpolation Uncertainty in Digital Bathymetric Models

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Pages 289-323 | Received 02 Jan 2024, Accepted 29 Mar 2024, Published online: 17 Apr 2024

References

  • Agatonovic-Kustrin, S., and R. Beresford. 2000. “Basic Concepts of Artificial Neural Network (ANN) Modeling and Its Application in Pharmaceutical Research.” Journal of Pharmaceutical and Biomedical Analysis 22 (5): 717–727. https://doi.org/10.1016/s0731-7085(99)00272-1
  • Aguilar, F. J., F. Agüera, M. A. Aguilar, and F. Carvajal. 2005. “Effects of Terrain Morphology, Sampling Density, and Interpolation Methods on Grid DEM Accuracy.” Photogrammetric Engineering & Remote Sensing 71 (7): 805–816. https://doi.org/10.14358/PERS.71.7.805
  • Alcaras, E., P. P. Amoroso, and C. Parente. 2022. “The Influence of Interpolated Point Location and Density on 3D Bathymetric Models Generated by Kriging Methods: An Application on the Giglio Island Seabed (Italy).” Geosciences 12 (2): 62. https://doi.org/10.3390/geosciences12020062
  • Amante, C. J. 2012. Accuracy of Interpolated Bathymetric Digital Elevation Models. Boulder, CO: University of Colorado.
  • Amante, C. J. 2018. “Estimating Coastal Digital Elevation Model Uncertainty.” Journal of Coastal Research 34 (6): 1382–1397. https://doi.org/10.2112/JCOASTRES-D-17-00211.1
  • Amante, C. J., and B. W. Eakins. 2016. “Accuracy of Interpolated Bathymetry in Digital Elevation Models.” Journal of Coastal Research 76 (sp1): 123–133. https://doi.org/10.2112/SI76-011
  • Amoroso, P. P., F. J. Aguilar, C. Parente, and M. A. Aguilar. 2023. “Statistical Assessment of Some Interpolation Methods for Building Grid Format Digital Bathymetric Models.” Remote Sensing 15 (8): 2072. https://doi.org/10.3390/rs15082072
  • Anderson, E. S., J. A. Thompson, and R. E. Austin. 2005. “LIDAR Density and Linear Interpolator Effects on Elevation Estimates.” International Journal of Remote Sensing 26 (18): 3889–3900. https://doi.org/10.1080/01431160500181671
  • Arun, P. V. 2013. “A Comparative Analysis of Different DEM Interpolation Methods.” The Egyptian Journal of Remote Sensing and Space Science 16 (2): 133–139. https://doi.org/10.1016/j.ejrs.2013.09.001
  • Bojanov, B. D., H. A. Hakopian, and A. A. Sahakian. 1993. Spline Functions and Multivariate Interpolations. Dordrecht: Springer Netherlands.
  • Bongiovanni, C. 2018. Quantifying Vertical Uncertainty and the Temporal Variability of the Seafloor to Inform Hydrographic Survey Priorities. Durham, NC: University of New Hampshire.
  • Bongiovanni, C., A. Armstrong, B. Calder, and T. Lippman. 2021. “Identifying Future Hydrographic Survey Priorities: A Quantitative Uncertainty Based Approach.” International Hydrographic Review 25: 33–53.
  • Burrough, P. A., and R. A. McDonnell. 1998. Principles of Geographical Information Systems. Oxford: Oxford University Press.
  • Calder, B. 2006. “On the Uncertainty of Archive Hydrographic Data Sets.” IEEE Journal of Oceanic Engineering 31 (2): 249–265. https://doi.org/10.1109/JOE.2006.872215
  • Caruso, C., and F. Quarta. 1998. “Interpolation Methods Comparison.” Computers & Mathematics with Applications 35 (12): 109–126. https://doi.org/10.1016/S0898-1221(98)00101-1
  • Chaplot, V., F. Darboux, H. Bourennane, S. Leguédois, N. Silvera, and K. Phachomphon. 2006. “Accuracy of Interpolation Techniques for the Derivation of Digital Elevation Models in Relation to Landform Types and Data Density.” Geomorphology 77 (1–2): 126–141. https://doi.org/10.1016/j.geomorph.2005.12.010
  • Childs, C. 2004. “Interpolating Surfaces in ArcGIS Spatial Analyst.” In ArcUser. Redlands: ESRI Press.
  • Chowdhury, E., Q. Hassan, G. Achari, and A. Gupta. 2017. “Use of Bathymetric and LiDAR Data in Generating Digital Elevation Model over the Lower Athabasca River Watershed in Alberta, Canada.” Water 9 (1): 19. https://doi.org/10.3390/w9010019
  • Curtarelli, M., J. Leão, I. Ogashawara, J. Lorenzzetti, and J. Stech. 2015. “Assessment of Spatial Interpolation Methods to Map the Bathymetry of an Amazonian Hydroelectric Reservoir to Aid in Decision Making for Water Management.” ISPRS International Journal of Geo-Information 4 (1): 220–235. https://doi.org/10.3390/ijgi4010220
  • Davis, B. M. 1987. “Uses and Abuses of Cross-Validation in Geostatistics.” Mathematical Geology 19 (3): 241–248. https://doi.org/10.1007/BF00897749
  • Declercq, F. A. N. 1996. “Interpolation Methods for Scattered Sample Data: Accuracy, Spatial Patterns, Processing Time.” Cartography and Geographic Information Systems 23 (3): 128–144. https://doi.org/10.1559/152304096782438882
  • Eakins, B. W., and L. A. Taylor. 2010. “Seamlessly Integrating Bathymetric and Topographic Data to Support Tsunami Modeling and Forecasting Efforts.” In: J. Breman, ed. Ocean Globe. Redlands: ESRI Press, 37–56.
  • Elmore, P. A., D. H. Fabre, R. T. Sawyer, R. W. Ladner, P. A. Elmore, D. H. Fabre, R. T. Sawyer, and R. W. Ladner. 2012. “Uncertainty Estimation for Databased Bathymetry Using a Bayesian Network Approach.” Geochemistry, Geophysics, Geosystems 13 (9): 9011. https://doi.org/10.1029/2012GC004144
  • Erdogan, S. 2009. “A Comparison of Interpolation Methods for Producing Digital Elevation Models at the Field Scale.” Earth Surface Processes and Landforms 34 (3): 366–376. https://doi.org/10.1002/esp.1731
  • Erdogan, S. 2010. “Modelling the Spatial Distribution of DEM Error with Geographically Weighted Regression: An Experimental Study.” Computers & Geosciences 36 (1): 34–43.
  • Gunarathna, M. H. J. P., K. G. S. Nirmanee, and M. K. N. Kumari. 2016. “Are Geostatistical Interpolation Methods Better than Deterministic Interpolation Methods in Mapping Salinity of Groundwater?” International Journal of Research and Innovations in Earth Science 3 (3): 59–64.
  • Guo, Q., W. Li, H. Yu, and O. Alvarez. 2010. “Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods.” Photogrammetric Engineering & Remote Sensing 76 (6): 701–712. https://doi.org/10.14358/PERS.76.6.701
  • Hare, R., B. Eakins, and C. Amante. 2011. “Modelling Bathymetric Uncertainty.” International Hydrographic Review 6: 31–42.
  • Henrico, I. 2021. “Optimal Interpolation Method to Predict the Bathymetry of Saldanha Bay.” Transactions in GIS 25 (4): 1991–2009. https://doi.org/10.1111/tgis.12783
  • Horn, B. K. P. 1981. “Hill Shading and the Reflectance Map.” Proceedings of the IEEE 69 (1): 14–47. https://doi.org/10.1109/PROC.1981.11918
  • Hughes Clarke, J. E., L. A. Mayer, and D. E. Wells. 1996. “Shallow-Water Imaging Multibeam Sonars: A New Tool for Investigating Seafloor Processes in the Coastal Zone and on the Continental Shelf.” Marine Geophysical Researches 18 (6): 607–629. https://doi.org/10.1007/BF00313877
  • IHO. 2014. IHO Transfer Standard for Digital Hydrographic Data Publication S-57.
  • IHO. 2020. International Hydrographic Organization Standards for Hydrographic Surveys S-44 Edition 6.0.0.
  • IHO. 2022. International Hydrographic Organization S-101 Annex A, Data Classification and Encoding Guide.
  • Isaaks, E. H., and R. M. Srivastava. 1989. An Introduction to Applied Geostatistics. New York, NY: Oxford University Press.
  • Jakobsson, M., B. Calder, and L. Mayer. 2002. “On the Effect of Random Errors in Gridded Bathymetric Compilations.” Journal of Geophysical Research: Solid Earth 107 (B12): ETG 14-1–ETG 14-11. https://doi.org/10.1029/2001JB000616
  • Jakobsson, M., L. A. Mayer, C. Bringensparr, C. F. Castro, R. Mohammad, P. Johnson, T. Ketter, et al. 2020. “The International Bathymetric Chart of the Arctic Ocean Version 4.0.” Scientific Data 7 (1): 176. https://doi.org/10.1038/s41597-020-0520-9
  • Jakobsson, Martin, Christian Stranne, Matt O'Regan, Sarah L. Greenwood, Bo Gustafsson, Christoph Humborg, and Elizabeth Weidner. 2019. “Bathymetric Properties of the Baltic Sea.” Ocean Science 15 (4): 905–924. https://doi.org/10.5194/os-15-905-2019
  • Kastrisios, C., and C. Ware. 2022. “Textures for Coding Bathymetric Data Quality Sectors on Electronic Navigational Chart Displays: Design and Evaluation.” Cartography and Geographic Information Science 49 (6): 492–511. https://doi.org/10.1080/15230406.2022.2059572
  • Lam, N. S.-N. 1983. “Spatial Interpolation Methods: A Review.” The American Cartographer 10 (2): 129–150. https://doi.org/10.1559/152304083783914958
  • Legleiter, C. J., and P. C. Kyriakidis. 2006. “Forward and Inverse Transformations between Cartesian and Channel-Fitted Coordinate Systems for Meandering Rivers.” Mathematical Geology 38 (8): 927–958. https://doi.org/10.1007/s11004-006-9056-6
  • Li, J., and A. D. Heap. 2008. A Review of Spatial Interpolation Methods for Environmental Scientists. Canberra: Geoscience Australia.
  • Li, J., and A. D. Heap. 2014. “Spatial Interpolation Methods Applied in the Environmental Sciences: A Review.” Environmental Modelling & Software 53: 173–189. https://doi.org/10.1016/j.envsoft.2013.12.008
  • Liu, X., Z. Zhang, J. Peterson, and S. Chandra. 2007. “LiDAR-Derived High Quality Ground Control Information and DEM for Image Orthorectification.” GeoInformatica 11 (1): 37–53. https://doi.org/10.1007/s10707-006-0005-9
  • Lloyd, C. D., and P. M. Atkinson. 2002. “Deriving DSMs from LiDAR Data with Kriging.” International Journal of Remote Sensing 23 (12): 2519–2524. https://doi.org/10.1080/01431160110097998
  • MacEachren, A. M., and J. V. Davidson. 1987. “Sampling and Isometric Mapping of Continuous Geographic Surfaces.” The American Cartographer 14 (4): 299–320. https://doi.org/10.1559/152304087783875723
  • Mayer, L., M. Jakobsson, G. Allen, B. Dorschel, R. Falconer, V. Ferrini, G. Lamarche, H. Snaith, and P. Weatherall. 2018. “The Nippon Foundation—GEBCO Seabed 2030 Project: The Quest to See the World’s Oceans Completely Mapped by 2030.” Geosciences 8 (2): 63. https://doi.org/10.3390/geosciences8020063
  • Merwade, V. 2009. “Effect of Spatial Trends on Interpolation of River Bathymetry.” Journal of Hydrology 371 (1-4): 169–181. https://doi.org/10.1016/j.jhydrol.2009.03.026
  • Merwade, V. M., D. R. Maidment, and J. A. Goff. 2006. “Anisotropic Considerations While Interpolating River Channel Bathymetry.” Journal of Hydrology 331 (3-4): 731–741. https://doi.org/10.1016/j.jhydrol.2006.06.018
  • Negreiros, J., M. Painho, F. Aguilar, and M. Aguilar. 2010. “Geographical Information Systems Principles of Ordinary Kriging Interpolator.” Journal of Applied Sciences 10 (11): 852–867. https://doi.org/10.3923/jas.2010.852.867
  • Negreiros, J., M. Painho, M. A. Aguilar, and F. J. Aguilar. 2008. “Spatial Error and Interpolation Uncertainty Appraisal within Geographic Information Systems.” Research Journal of Applied Sciences 3: 471–479.
  • Nippon Foundation-GEBCO. 2023. Our Mission—Seabed 2030 [online]. Accessed December 8, 2023. https://seabed2030.org/our-mission/.
  • Özdamar, L., M. Demirhan, and A. Özpınar. 1999. “A Comparison of Spatial Interpolation Methods and a Fuzzy Areal Evaluation Scheme in Environmental Site Characterization.” Computers, Environment and Urban Systems 23 (5): 399–422. https://doi.org/10.1016/S0198-9715(99)00032-0
  • Panhalakr, S. S., and A. P. Jarag. 2016. “Assessment of Spatial Interpolation Techniques for River Bathymetry Generation of Panchganga River Basin Using Geoinformatic Techniques.” Asian Journal of Geoinformatics 15 (3): 9–12.
  • Rice, G., K. Wyllie, B. Gallagher, and P. Geleg. 2023. “The National Bathymetric Source.” In OCEANS 2023 - MTS/IEEE U.S. Gulf Coast. IEEE, 1–7. https://doi.org/10.23919/OCEANS52994.2023.10337401
  • Ryan, William B. F., Suzanne M. Carbotte, Justin O. Coplan, Suzanne O'Hara, Andrew Melkonian, Robert Arko, Rose Anne Weissel, et al. 2009. “Global Multi‐Resolution Topography Synthesis.” Geochemistry, Geophysics, Geosystems 10 (3), Q03014. https://doi.org/10.1029/2008GC002332
  • Schaap, D. M. A, and T. Schmitt. 2020. EMODnet Bathymetry—Further developing a high resolution digital bathymetry for European seas. EGU2020.
  • Šiljeg, A., S. Lozić, and S. Šiljeg. 2014. “A Comparison of Interpolation Methods on the Basis of Data Obtained from a Bathymetric Survey of Lake Vrana, Croatia.” Hydrology & Earth System Sciences Discussions 19 (8): 3653–3666.
  • Smith, W. H. F., and D. T. Sandwell. 1997. “Global Sea Floor Topography from Satellite Altimetry and Ship Depth Soundings.” Science 277 (5334): 1956–1962. https://doi.org/10.1126/science.277.5334.1956
  • Tobler, W. R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46: 234–240. https://doi.org/10.2307/143141
  • Tomczak, M. 1998. Spatial Interpolation and Its Uncertainty Using Automated Anisotropic Inverse Distance Weighting (IDW)—Cross-Validation/Jackknife Approach. Journal of Geographic Information and Decision Analysis 2 (2): 18–30.
  • Vetter, M., B. Höfle, G. Mandlburger, and M. Rutzinger. 2011. “Estimating Changes of Riverine Landscapes and Riverbeds by Using Airborne LiDAR Data and River Cross-Sections.” Zeitschrift Für Geomorphologie, Supplementary Issues 55 (2): 51–65. https://doi.org/10.1127/0372-8854/2011/0055S2-0045
  • Voltz, M., and R. Webster. 1990. “A Comparison of Kriging, Cubic Splines and Classification for Predicting Soil Properties from Sample Information.” Journal of Soil Science 41 (3): 473–490. https://doi.org/10.1111/j.1365-2389.1990.tb00080.x
  • Ware, C., and C. Kastrisios. 2022. “Evaluating Countable Texture Elements to Represent Bathymetric Uncertainty.” In EuroVis. Rome, Italy: The Eurographics Association.
  • Weatherall, P., K. M. Marks, M. Jakobsson, T. Schmitt, S. Tani, J. E. Arndt, M. Rovere, D. Chayes, V. Ferrini, and R. Wigley. 2015. “A New Digital Bathymetric Model of the World’s Oceans.” Earth and Space Science 2 (8): 331–345. https://doi.org/10.1002/2015EA000107
  • Wilson, M. F. J., B. O’Connell, C. Brown, J. C. Guinan, and A. J. Grehan. 2007. “Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope.” Marine Geodesy 30 (1–2): 3–35. https://doi.org/10.1080/01490410701295962
  • Wu, C. Y., J. Mossa, L. Mao, and M. Almulla. 2019. “Comparison of Different Spatial Interpolation Methods for Historical Hydrographic Data of the Lowermost Mississippi River.” Annals of GIS 25 (2): 133–151. https://doi.org/10.1080/19475683.2019.1588781
  • Zar, J. H. 1999. Biostatistical Analysis. Edgewood Cliffs, NJ: Prentice Hall.

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