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Towards Quantitative Spatial Models of Seabed Sediment Composition.

David Stephens1, Markus Diesing1

  • 1Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, Suffolk, United Kingdom.

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Summary
This summary is machine-generated.

This study demonstrates a repeatable method for predicting seabed sediment composition across large marine areas. The approach uses existing data and environmental factors, achieving high accuracy for habitat mapping and resource management.

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Area of Science:

  • Marine geology
  • Seafloor mapping
  • Environmental modeling

Background:

  • Accurate seabed substrate and habitat maps are crucial for marine research, resource management, conservation, and spatial planning.
  • Existing methods often lack the spatial resolution or accuracy needed for comprehensive assessments.
  • There is a need for fit-for-purpose mapping solutions to understand seabed properties.

Purpose of the Study:

  • To determine the feasibility of predicting seabed substrate composition over large areas using legacy grain-size data and environmental predictors.
  • To develop and validate a quantitative model for predicting sediment fractions (mud, sand, gravel).
  • To assess the accuracy of a derived EUNIS substrate model.

Main Methods:

  • Combines hydrodynamic model outputs, satellite optical remote sensing data, and bathymetric variables (from acoustic remote sensing).
  • Utilizes a statistical regression model with the random forest algorithm to predict sediment composition.
  • Analyzes compositional data on the additive log-ratio scale.

Main Results:

  • Predictive models explained approximately 66% and 71% of the variability in two log-ratio variables for sediment composition.
  • A derived EUNIS substrate model achieved 83% overall accuracy and a kappa coefficient of 0.60.
  • Demonstrated the feasibility of spatially predicting seabed sediment composition in a repeatable and validated manner.

Conclusions:

  • It is feasible to predict seabed sediment composition across large continental shelf areas using integrated data and statistical modeling.
  • The developed method offers a repeatable and validated approach for creating fit-for-purpose seabed maps.
  • Potential exists for further methodological improvements to enhance prediction accuracy and spatial coverage.