• Arctic SDI catalogue
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Deep substrate model (100m) of the Pacific Canadian shelf

This deep water substrate bottom type model was created to aid in habitat modeling, and to complement the nearshore bottom patches. It was created from a combination of bathymetrically-derived layers in addition to bottom type observations. Using random forest classification, the relationship between observed substrates and bathymetric derivatives was estimated across the entire area of interest.

The raster is categorized into: 1) Rock, 2) Mixed, 3) Sand, 4) Mud

Simple

Date ( RI_367 )
2018-09-17
Date ( RI_366 )
2018-03-30
Date ( RI_368 )
2025-03-13
RI_413
  Government of Canada; Fisheries and Oceans Canada; Ecosystems and Oceans Science/Pacific Science/Stock Assessment and Research Division - Dana Haggarty ( Inshore Rockfish and Lingcod Program Head )
Pacific Biological Station 3190 Hammond Bay Road , Nanaimo , British Columbia , V9T 6N7 , Canada
250-756-7386
RI_415
  Government of Canada; Fisheries and Oceans Canada; Marine Spatial Ecology and Analytics Section (MSEA) - Joanne Lessard ( Research Biologist )
Pacific Biological Station 3190 Hammond Bay Road , Nanaimo , British Columbia , V9T 6N7 , Canada
250-729-8364
Credit
Du Preez, C. 2015. A new arc–chord ratio (ACR) rugosity index for quantifying three-dimensional landscape structural complexity. Landscape Ecology 30:181-192. https://link.springer.com/article/10.1007/s10980-014-0118-8.
Credit
Gregr EJ, Haggarty DR, Davies SC, Fields C, Lessard J (2021) Comprehensive marine substrate classification applied to Canada’s Pacific shelf. PLoS ONE 16(10): e0259156. https://doi.org/10.1371/journal.pone.0259156.
Credit
Masson, D., and I. Fine (2012), Modeling seasonal to interannual ocean variability of coastal British Columbia, J. Geophys. Res., 117, C10019. doi:10.1029/2012JC008151.
Credit
Walbridge, S.; Slocum, N.; Pobuda, M.; Wright, D.J. Unified Geomorphological Analysis Workflows with Benthic Terrain Modeler. Geosciences 2018, 8, 94. doi:10.3390/geosciences8030094.
Credit
Package ‘ranger’. January 10, 2020. https://cran.r-project.org/web/packages/ranger/ranger.pdf.
Status
completed; complété RI_593
Maintenance and update frequency
irregular; irrégulier RI_541
Keywords
  • Rugosity
  • Seabed
  • Sea bed
  • Substrate
Government of Canada Core Subject Thesaurus Thésaurus des sujets de base du gouvernement du Canada ( RI_528 )
  • Ocean floor
  • Sediments
Classification
unclassified; nonClassifié RI_484
Use limitation
Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada)
Access constraints
license; licence RI_606
Use constraints
license; licence RI_606
Spatial representation type
grid; grille RI_636
Metadata language
eng; CAN
Character set
utf8; utf8 RI_458
Topic category
  • Oceans
Begin date
1984-01-01
End date
2018-03-30
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Supplemental Information

Substrate observations were assembled from the following sources: Canadian Hydrographic Service (CHS), Dive survey data, CHS marsh data, Natural Resources Canada (NRCan), and Remotely Operated Vehicle (ROV) surveys. These observations were mapped to common bottom type classes: rock, mixed, sand, or mud. Using ArcGIS, the points were classified into training (2/3) or testing (1/3) data. The training subset is fully withheld from the model-building process and is used to evaluate the model’s performance. The random partitioning of the data into training and test subsets does not address the issue of spatial autocorrelation between observations.

The code to create the model follows the following steps:

A raster stack of environmental predictors is created. Point observations are overlayed onto the raster stack, and the values from the predictors are extracted to the points. Records with invalid BType4 values or with NA values from the predictors are removed. The training data are weighted according to their prevalence and are used to fit a random forest model using the ranger package (which supports case weighting). The fitted model is used to predict to the extent of the input environmental raster stack, classifying the entire area into rock, mixed, sand, or mud. The predicted surface is exported as a GeoTIFF raster file in the same resolution as the raster stack predictors (in this case, 100 metre resolution) and with the same projection. Evaluation statistics including Kappa, and accuracy by predicted class are generated using the withheld test data.

Manual steps after the model has been generated:

1) Reproject layer to EPSG: 3005 (R does not support writing the top-level EPSG code to the coordinate reference system information)

2) Create a raster attribute table with SUBSTRATE field for text classes.

3) Add a field for prevalence in the raster attribute table.

Predictor Layers:

Using GDAL, the source bathymetry layers (NOAA and BCMCA) were resampled and mosaicked to produce a 100 m bathymetry raster layer. Where bathymetry rasters overlapped in space (the majority of the region except for Dixon Entrance, Chatham Sound and Portland Canal areas) the NOAA bathymetry layer overwrote the BCMCA layer. The derivatives of bathymetry were created using the Arcpy module in python (see Scripts section in the metadata). The bathymetry layer was smoothed using a focal mean prior to generating the following derivatives: slope -> standard deviation of slope, and curvature. This step was required as a method to reduce artefacts found in the derivatives. During development, these artefacts from the non-smoothed bathymetric derivatives carried through to the predicted substrate models. The non-smoothed bathymetry was used to generate the standardized BPI layers because this processing already involves processing with a neighbourhood of cells. A categorical layer for rugosity was used from the B.C. Marine Conservation Atlas (BCMCA). It was converted to a 100 m raster grid to align with the other predictor layers. It was decided to use this layer because it had been derived from bathymetry and then manually edited. See the methods described in the data sources link. As input to the random forest model, the original non-smoothed bathymetry was used. Ocean energy layers were also included – mean bottom ocean currents (Masson and Fine 2012), and mean tidal speed on the bottom (Masson and Fine 2012). The original data for the Regional Ocean Modeling System (ROMS) has a 3 by 3 km grid resolution. These data were interpolated using Spline with Barriers (ESRI) and resampled to 100 m resolution rasters. See the scripts section for a link to the ROMS data processing. The bathymetry was primarily sourced from NOAA, with a small portion mosaicked to the northwest from SciTech bathymetry (see Data Sources for links). The bathymetric position index (BPI) layers were created using the Benthic Terrain Modeler toolbox and were standardized after being calculated. It is important to note that when calculating BPI, the tool expects bathymetric data to have negative values associated with depths rather than positive.

Predictor Layers:

1: Bathymetry

2: Slope (bathymetric derivative) - degrees

3: Standard Deviation of Slope (bathymetric derivative)

4: Broad Bathymetric Position Index (bathymetric derivative) - Inner Radius: 25 - Outer Radius: 250

5: Medium Bathymetric Position Index (bathymetric derivative) - Inner Radius: 10 - Outer Radius: 100

6: Fine Bathymetric Position Index (bathymetric derivative) - Inner Radius: 3 - Outer Radius: 25

7: Curvature (bathymetric derivative; slope of slope)

8: Rugosity (BCMCA)

9: Circulation

10: Tidal

Reference system identifier
https://epsg.io / EPSG:4326 /
Distribution format
  • TIFF ( Unknown )

RI_412
  Government of Canada; Fisheries and Oceans Canada; Marine Spatial Ecology and Analytics Section (MSEA) - Joanne Lessard ( Research Biologist )
Pacific Biological Station 3190 Hammond Bay Road , Nanaimo , British Columbia , V9T 6N7 , Canada
250-729-8364
OnLine resource
Deep substrate model (100m) of the Pacific Canadian shelf - Tiff ( HTTPS )

Dataset;TIFF;eng,fra

OnLine resource
References ( HTTPS )

Supporting Document;PDF;eng,fra

OnLine resource
Data Dictionary ( HTTPS )

Supporting Document;CSV;eng

OnLine resource
Data Dictionary ( HTTPS )

Supporting Document;CSV;fra

OnLine resource
Deep substrate model (100m) of the Pacific Canadian shelf - GIS Hub metadata ( HTTPS )

Supporting Document;PDF;eng

OnLine resource
Deep substrate model (100m) of the Pacific Canadian shelf - GIS Hub metadata ( HTTPS )

Supporting Document;PDF;fra

OnLine resource
Deep substrate model (100m) of the Pacific Canadian shelf ( ESRI REST: Map Server )

Web Service;ESRI REST;eng

OnLine resource
Deep substrate model (100m) of the Pacific Canadian shelf ( ESRI REST: Map Server )

Web Service;ESRI REST;fra

File identifier
55818c6c-2ba2-46a4-bb6d-635f9960b828 XML
Metadata language
eng; CAN
Character set
utf8; utf8 RI_458
Hierarchy level
dataset; jeuDonnées RI_622
Date stamp
2025-03-13T18:21:22.634Z
Metadata standard name
North American Profile of ISO 19115:2003 - Geographic information - Metadata
Metadata standard version
CAN/CGSB-171.100-2009
RI_414
  Government of Canada; Fisheries and Oceans Canada; Ecosystems and Ocean Science/Pacific Science/Ecosystem Science Division/Marine Spatial Ecology and Analysis Section - Marine Spatial Ecology and Analysis Section - Data Stewardship Unit ( )
Institute of Ocean Sciences 9860 West Saanich Road P.O. Box 6000 , Sidney , British Columbia , V8L4B2 , Canada
 
 

Overviews

overview
DeepSubstrate_thumbnail.jpg

Spatial extent

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Keywords


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