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Area of Science:

  • Geosciences
  • Environmental Science
  • Spatial Analysis

Background:

  • Geostatistics is widely applied to coastal habitat characterization.
  • Standard geostatistical methods face challenges with complex coastal geometries and compositional data.
  • Existing techniques may not accurately predict sediment distribution due to these limitations.

Purpose of the Study:

  • To develop and illustrate tailored geostatistical approaches for coastal habitat characterization.
  • To address the limitations of traditional methods when dealing with complex coastal geometries and compositional variables.
  • To improve the accuracy of sediment texture prediction in coastal environments.

Main Methods:

  • Multidimensional scaling was used to transform non-Euclidean over-water distances into a Euclidean space for variogram modeling and kriging.
  • Compositional data analysis involved transforming sediment textural fractions into log-ratios for geostatistical analysis.
  • Residual kriging and machine learning were employed to build a spatial trend model using geomorphometric variables.

Main Results:

  • The application of non-Euclidean distances and log-ratio transformations improved the accuracy of geostatistical predictions.
  • Coherent sediment texture maps were generated using the compositional data analysis approach.
  • The study demonstrated that tailored geostatistical methods yield more reliable results for coastal habitat characterization.

Conclusions:

  • Adapting geostatistical tools to site-specific characteristics, such as complex geometries and compositional data, is crucial for effective coastal habitat analysis.
  • Nonlinear proximity measures and log-ratio transformations offer robust solutions to common geostatistical challenges in coastal settings.
  • This integrated approach enhances the accuracy and reliability of spatial predictions for coastal sediment distribution.