Towards Quantitative Spatial Models of Seabed Sediment Composition.
There is a need for fit-for-purpose maps for accurately depicting the types of seabed substrate and habitat and the properties of the seabed for the benefits of research, resource management, conservation and spatial planning. The aim of this study is to determine whether it is possible to predict s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4657885?pdf=render |
| _version_ | 1819113632123846656 |
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| author | David Stephens Markus Diesing |
| author_facet | David Stephens Markus Diesing |
| author_sort | David Stephens |
| collection | DOAJ |
| description | There is a need for fit-for-purpose maps for accurately depicting the types of seabed substrate and habitat and the properties of the seabed for the benefits of research, resource management, conservation and spatial planning. The aim of this study is to determine whether it is possible to predict substrate composition across a large area of seabed using legacy grain-size data and environmental predictors. The study area includes the North Sea up to approximately 58.44°N and the United Kingdom's parts of the English Channel and the Celtic Seas. The analysis combines outputs from hydrodynamic models as well as optical remote sensing data from satellite platforms and bathymetric variables, which are mainly derived from acoustic remote sensing. We build a statistical regression model to make quantitative predictions of sediment composition (fractions of mud, sand and gravel) using the random forest algorithm. The compositional data is analysed on the additive log-ratio scale. An independent test set indicates that approximately 66% and 71% of the variability of the two log-ratio variables are explained by the predictive models. A EUNIS substrate model, derived from the predicted sediment composition, achieved an overall accuracy of 83% and a kappa coefficient of 0.60. We demonstrate that it is feasible to spatially predict the seabed sediment composition across a large area of continental shelf in a repeatable and validated way. We also highlight the potential for further improvements to the method. |
| first_indexed | 2024-12-22T04:32:29Z |
| format | Article |
| id | doaj.art-613a5f6d614a46af88748028e58b1e72 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T04:32:29Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-613a5f6d614a46af88748028e58b1e722022-12-21T18:39:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-011011e014250210.1371/journal.pone.0142502Towards Quantitative Spatial Models of Seabed Sediment Composition.David StephensMarkus DiesingThere is a need for fit-for-purpose maps for accurately depicting the types of seabed substrate and habitat and the properties of the seabed for the benefits of research, resource management, conservation and spatial planning. The aim of this study is to determine whether it is possible to predict substrate composition across a large area of seabed using legacy grain-size data and environmental predictors. The study area includes the North Sea up to approximately 58.44°N and the United Kingdom's parts of the English Channel and the Celtic Seas. The analysis combines outputs from hydrodynamic models as well as optical remote sensing data from satellite platforms and bathymetric variables, which are mainly derived from acoustic remote sensing. We build a statistical regression model to make quantitative predictions of sediment composition (fractions of mud, sand and gravel) using the random forest algorithm. The compositional data is analysed on the additive log-ratio scale. An independent test set indicates that approximately 66% and 71% of the variability of the two log-ratio variables are explained by the predictive models. A EUNIS substrate model, derived from the predicted sediment composition, achieved an overall accuracy of 83% and a kappa coefficient of 0.60. We demonstrate that it is feasible to spatially predict the seabed sediment composition across a large area of continental shelf in a repeatable and validated way. We also highlight the potential for further improvements to the method.http://europepmc.org/articles/PMC4657885?pdf=render |
| spellingShingle | David Stephens Markus Diesing Towards Quantitative Spatial Models of Seabed Sediment Composition. PLoS ONE |
| title | Towards Quantitative Spatial Models of Seabed Sediment Composition. |
| title_full | Towards Quantitative Spatial Models of Seabed Sediment Composition. |
| title_fullStr | Towards Quantitative Spatial Models of Seabed Sediment Composition. |
| title_full_unstemmed | Towards Quantitative Spatial Models of Seabed Sediment Composition. |
| title_short | Towards Quantitative Spatial Models of Seabed Sediment Composition. |
| title_sort | towards quantitative spatial models of seabed sediment composition |
| url | http://europepmc.org/articles/PMC4657885?pdf=render |
| work_keys_str_mv | AT davidstephens towardsquantitativespatialmodelsofseabedsedimentcomposition AT markusdiesing towardsquantitativespatialmodelsofseabedsedimentcomposition |