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Legacy Data: How Decades of Seabed Sampling Can Produce Robust Predictions and Versatile Products

1
Centre for Environment, Fisheries and Aquaculture Science (Cefas), Pakefield Road, Lowestoft NR33 0HT, UK
2
Geological Survey of Norway (NGU), Postal Box 6315 Torgarden, 7491 Trondheim, Norway
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Author to whom correspondence should be addressed.
Geosciences 2019, 9(4), 182; https://doi.org/10.3390/geosciences9040182
Received: 22 March 2019 / Revised: 12 April 2019 / Accepted: 15 April 2019 / Published: 19 April 2019
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Abstract

Sediment maps developed from categorical data are widely applied to support marine spatial planning across various fields. However, deriving maps independently of sediment classification potentially improves our understanding of environmental gradients and reduces issues of harmonising data across jurisdictional boundaries. As the groundtruth samples are often measured for the fractions of mud, sand and gravel, this data can be utilised more effectively to produce quantitative maps of sediment composition. Using harmonised data products from a range of sources including the European Marine Observation and Data Network (EMODnet), spatial predictions of these three sediment fractions were generated for the north-west European continental shelf using the random forest algorithm. Once modelled these sediment fraction maps were classified using a range of schemes to show the versatility of such an approach, and spatial accuracy maps were generated to support their interpretation. The maps produced in this study are to date the highest resolution quantitative sediment composition maps that have been produced for a study area of this extent and are likely to be of interest for a wide range of applications such as ecological and biophysical studies. View Full-Text
Keywords: Particle size analysis; random forest; accuracy; European continental shelf; mud; sand; gravel Particle size analysis; random forest; accuracy; European continental shelf; mud; sand; gravel
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Mitchell, P.J.; Aldridge, J.; Diesing, M. Legacy Data: How Decades of Seabed Sampling Can Produce Robust Predictions and Versatile Products. Geosciences 2019, 9, 182.

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