Deep Substrate Model of the Pacific Canadian Shelf

A deep water substrate bottom type raster model aids habitat modeling and complements nearshore data. It classifies the seafloor into four categories: Rock, Mixed, Sand, and Mud using random forest classification on bathymetric derivatives and observations. The model covers the Pacific Canadian shelf area.

Use Cases

• Train a geospatial model to predict substrate categories like Rock or Mud from bathymetric derivative layers.
• Analyze the spatial distribution of the four substrate classes (Rock, Mixed, Sand, Mud) for marine habitat mapping.
• Integrate the substrate classification raster with other oceanographic data layers for ecological modeling.

Strengths

• Model provides a categorical classification of the seafloor into four distinct substrate types.
• Created using random forest classification, a established machine learning method for spatial prediction.
• Produced by Fisheries and Oceans Canada, a national authority on marine data.

Limitations

• The underlying sample size of bottom type observations used for model training is not specified.
• Model accuracy may vary in areas with sparse observation data or complex geology.
• Data is provided as a raster, requiring GIS software for detailed spatial analysis.

Provenance

Source

Fisheries and Oceans Canada

Collection Method

Created using random forest classification on a combination of bathymetrically-derived layers and bottom type observations.

Freshness

Last updated March 2026.

Geography

Pacific Canadian shelf