Data

National Supratidal and Coastal Lowland Forest extent map of Australia

The University of Newcastle
Christopher Owers (Aggregated by) Jeffrey Kelleway (Aggregated by) Rafael Carvalho (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=1959.13/29755589.v3&rft.title=National Supratidal and Coastal Lowland Forest extent map of Australia&rft.identifier=1959.13/29755589.v3&rft.publisher=The University of Newcastle&rft.description=National Supratidal and Coastal Lowland Forest (SCLF) extent map (v1_release)This is the suite of spatial data products detailing the national supratidal and coastal lowland forest extent map.This is a set of four (4) spatial data layers which are described belowThese include the final national supratidal and coastal lowland forest extent map and the three models that generate this final map, including an elevation model, connectivity model, and vegetation model.# SCL_elevation_model.tifInfo: This dataset shows the confidence of an area being considered supratidal and coastal lowland (SCL) based on elevation within the coastal landscape.Data: Raster dataset of continuous values between 0 and 1, where 0 indicates very limited confidence of being with the expected elevation range, and 1 indicates very high confidence.Data year: 2020# SCL_connectivity_model.tifInfo: This dataset shows the confidence of an area being considered supratidal and coastal lowland (SCL) based on connectivity within the coastal landscape. Here we use a least cost path approach to determine confidence of connectivity to intertidal landscapes.Data: Raster dataset of continuous values between 0 and 1, where 0 indicates very limited confidence of being connected to intertidal landscapes, and 1 indicates very high confidence.Data year: 2020# SCL_vegetation_model.tifInfo: This dataset shows the confidence of an area being considered forest that is within the supratidal and coastal lowland (SCL) area.Data: Raster dataset of continuous values between 0 and 1, where 0 indicates very limited confidence of being forest, and 1 indicates very high confidence.Data year: 2020# SCLF_model.tifInfo: his dataset shows the confidence of an area being considered supratidal and coastal lowland forest (SCLF).Data: Raster dataset of continuous values between 0 and 1, where 0 indicates very limited confidence of being supratidal and coastal lowland forest, and 1 indicates very high confidence. Note: the vegetation model woody cover fraction dataset has been threshold at 0.2 for this dataset, meaning that any pixels with a woody cover fraction 20% canopy cover.Data year: 2020# for more information on the creation of these datasets, please refer to the landing page of the GitHub for this project (https://github.com/christopherowers/supratidal_forests/)# Creators: Chris Owers, Rafael Carvalho, Jeff Kelleway# Contact: Chris Owers, University of Newcastle ([email protected])# Date created: June 2025&rft.creator=Christopher Owers&rft.creator=Jeffrey Kelleway&rft.creator=Rafael Carvalho&rft.date=2025&rft_rights=CC-BY-4.0&rft_subject=blue carbon&rft_subject=coastal wetlands&rft_subject=ecosystem services&rft_subject=ecosystem accounts&rft_subject=supratidal forests&rft_subject=Assessment and management of coastal and estuarine ecosystems&rft.type=dataset&rft.language=English Access the data

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Full description

National Supratidal and Coastal Lowland Forest (SCLF) extent map (v1_release)

  • This is the suite of spatial data products detailing the national supratidal and coastal lowland forest extent map.
  • This is a set of four (4) spatial data layers which are described below
  • These include the final national supratidal and coastal lowland forest extent map and the three models that generate this final map, including an elevation model, connectivity model, and vegetation model.


# SCL_elevation_model.tif

  • Info: This dataset shows the confidence of an area being considered supratidal and coastal lowland (SCL) based on elevation within the coastal landscape.
  • Data: Raster dataset of continuous values between 0 and 1, where 0 indicates very limited confidence of being with the expected elevation range, and 1 indicates very high confidence.
  • Data year: 2020


# SCL_connectivity_model.tif

  • Info: This dataset shows the confidence of an area being considered supratidal and coastal lowland (SCL) based on connectivity within the coastal landscape. Here we use a least cost path approach to determine confidence of connectivity to intertidal landscapes.
  • Data: Raster dataset of continuous values between 0 and 1, where 0 indicates very limited confidence of being connected to intertidal landscapes, and 1 indicates very high confidence.
  • Data year: 2020


# SCL_vegetation_model.tif

  • Info: This dataset shows the confidence of an area being considered forest that is within the supratidal and coastal lowland (SCL) area.
  • Data: Raster dataset of continuous values between 0 and 1, where 0 indicates very limited confidence of being forest, and 1 indicates very high confidence.
  • Data year: 2020


# SCLF_model.tif

  • Info: his dataset shows the confidence of an area being considered supratidal and coastal lowland forest (SCLF).
  • Data: Raster dataset of continuous values between 0 and 1, where 0 indicates very limited confidence of being supratidal and coastal lowland forest, and 1 indicates very high confidence. Note: the vegetation model woody cover fraction dataset has been threshold at 0.2 for this dataset, meaning that any pixels with a woody cover fraction <0.2 are not considered forest. This align with international (FAO) and national (ABARES NFI) definitions of forest, considered where >20% canopy cover.
  • Data year: 2020

# for more information on the creation of these datasets, please refer to the landing page of the GitHub for this project (https://github.com/christopherowers/supratidal_forests/)


# Creators: Chris Owers, Rafael Carvalho, Jeff Kelleway

# Contact: Chris Owers, University of Newcastle ([email protected])

# Date created: June 2025

Issued: 2025-01-01

Created: 2025-08-10

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