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Article

The Influence of Interpolated Point Location and Density on 3D Bathymetric Models Generated by Kriging Methods: An Application on the Giglio Island Seabed (Italy)

1
International PhD Programme “Environment, Resources and Sustainable Development”, Department of Science and Technology, Parthenope University of Naples, Centro Direzionale, Isola C4, 80143 Naples, Italy
2
DIST—Department of Science and Technology, Parthenope University of Naples, Centro Direzionale, Isola C4, 80143 Naples, Italy
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Author to whom correspondence should be addressed.
Academic Editors: Vincent Lecours and Jesus Martinez-Frias
Geosciences 2022, 12(2), 62; https://doi.org/10.3390/geosciences12020062
Received: 28 December 2021 / Revised: 23 January 2022 / Accepted: 27 January 2022 / Published: 29 January 2022
In relation to 3D bathymetric modelling, this article aims to analyze the performance of Kriging approaches in dependence of the location and density of the measured depth points. The experiments were carried out on a multi-beam sonar (MBS) dataset that includes 240,000 soundings covering a sea-bottom area near Giglio Island (Italy). Seven subsets were derived in random way from the initial regular MBS dataset, selecting an increasing number of points uniformly spaced. Seven models were generated for both Ordinary Kriging and Universal Kriging. Each model was submitted to leave-one-out cross-validation to define the exactness of the predictive values and compared with the initial grid to better evaluate the accuracy in dependence of the point number and dissemination. To investigate this relationship, a new index called MVI (Morphological Variation Index) was introduced as a measurement of the level of variation of seabed morphology. The results validate the efficiency of the Kriging methods and remark the influence of the dataset distribution on the 3D model, highlighting MVI as a useful index to represent the seabed variation as a unique value. Finally, in no rugged areas using 1 point every 1000 m2, the RMSE of the differences between measured and interpolated values falls below 1 m, while a further increment of soundings is required in the presence of a high level of variation of seabed morphology. View Full-Text
Keywords: interpolation; topography; 3D model; DEM; multi-beam; point density; bathymetry; Kriging interpolation; topography; 3D model; DEM; multi-beam; point density; bathymetry; Kriging
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MDPI and ACS Style

Alcaras, E.; Amoroso, P.P.; Parente, C. 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 2022, 12, 62. https://doi.org/10.3390/geosciences12020062

AMA Style

Alcaras E, Amoroso PP, Parente C. 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. 2022; 12(2):62. https://doi.org/10.3390/geosciences12020062

Chicago/Turabian Style

Alcaras, Emanuele, Pier Paolo Amoroso, and Claudio 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, no. 2: 62. https://doi.org/10.3390/geosciences12020062

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