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one publication added to basket [107236]

Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas Verfaillie, E.; Van Lancker, V.; Van Meirvenne, M. (2006). Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas. Cont. Shelf Res. 26(19): 2454-2468. https://dx.doi.org/10.1016/j.csr.2006.07.028 In: Continental Shelf Research. Pergamon Press: Oxford; New York. ISSN 0278-4343; e-ISSN 1873-6955 Is gerelateerd aan: Verfaillie, E.; Van Lancker, V.; Van Meirvenne, M. (2008). Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas, in: Verfaillie, E. Ontwikkeling en validering van een ruimtelijke verspreidingsmodellen van mariene habitats, ter ondersteuning van het ecologisch waarderen van de zeebodem = Development and validation of spatial distribution models of marine habitats, in support of the ecological valuation of the seabed. pp. 51-72, meer

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Beschikbaar in	Auteurs
VLIZ: Open Marine Archive 115139 [ download pdf ]

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Trefwoorden

Dimensions > Size > Grain size Mapping Measurement > Depth measurement > Bathymetry ANE, België, Belgisch Continentaal Plat (BCP) [Marine Regions] Marien/Kust

Author keywords

Multivariate geostatistics; Median grain-size; Bathymetry; Habitat mapping; Resource maps; Belgian continental shelf

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Auteurs		Top
Verfaillie, E. Van Lancker, V. Van Meirvenne, M.

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Abstract

Multivariate geostatistics have been used to obtain a detailed and high-quality map of the median grain-size distribution of the sand fraction at the Belgian Continental Shelf. Sandbanks and swales are the dominant geomorphological features and impose a high-spatial seafloor variability. Interpolation over complex seafloors is difficult and as such various models were investigated. In this paper, linear regression and ordinary kriging (OK) were used and compared with kriging with an external drift (KED) that makes use of secondary information to assist in the interpolation. KED proved to be the best technique since a linear correlation was found between the median grain-size and the bathymetry. The resulting map is more realistic and separates clearly the sediment distribution over the sandbanks from the swales. Both techniques were also compared with a simple linear regression of the median grain-size against the bathymetry. An independent validation showed that the linear regression yielded the largest average prediction error (almost twice as large as with KED). Unlike most static sedimentological maps, our approach allows for defining grain-size classes that can be adapted according to the needs of various applications. These relate mainly to the mapping of soft substrata habitats and of the most suitable aggregates for extraction. This information is highly valuable in a marine spatial planning context.

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