Data

Benthic and substrate cover data derived from field photo-transect surveys for the Cairns to Cooktown management region of the Great Barrier Reef (GBR), January and April/May 2017

The University of Queensland
Associate Professor Chris Roelfsema (Aggregated by) Associate Professor Chris Roelfsema (Aggregated by) Dr Eva Kovacs (Aggregated by) Dr Eva Kovacs (Aggregated by) Miss Kathryn Markey (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=https://espace.library.uq.edu.au/view/UQ:734230&rft.title=Benthic and substrate cover data derived from field photo-transect surveys for the Cairns to Cooktown management region of the Great Barrier Reef (GBR), January and April/May 2017&rft.publisher=The University of Queensland&rft.description=A subset of photoquadrats were uploaded to the CoralNet machine learning interface (https://coralnet.ucsd.edu/) and manually labelled for coral, algae or substrate type using 50 points per quadrat. Follow training of the machine, this enabled automatic annotation of all unclassified field images: the remaining field photos were uploaded to the database and 50 annotation points were overlaid on each of the images. Every point was assigned a benthic cover category from a label list automatically by the program. The resulting benthic cover data of each photo was linked to gps coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 55 South.&rft.creator=Associate Professor Chris Roelfsema&rft.creator=Associate Professor Chris Roelfsema&rft.creator=Dr Eva Kovacs&rft.creator=Dr Eva Kovacs&rft.creator=Miss Kathryn Markey&rft.creator=Miss Kathryn Markey&rft.creator=Professor Stuart Phinn&rft.creator=Professor Stuart Phinn&rft.date=2018&rft.coverage=143.91135,-13.672065 148.1301,-13.672065 148.1301,-18.567559 143.91135,-18.567559 143.91135,-13.672065&rft_rights=2018, The University of Queensland&rft_rights= http://creativecommons.org/licenses/by/3.0/deed.en_US&rft_subject=eng&rft_subject=Physical Geography and Environmental Geoscience not elsewhere classified&rft_subject=EARTH SCIENCES&rft_subject=PHYSICAL GEOGRAPHY AND ENVIRONMENTAL GEOSCIENCE&rft_subject=Environmental Management&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=ENVIRONMENTAL SCIENCE AND MANAGEMENT&rft_subject=Environmental Monitoring&rft_subject=Environmental Science and Management not elsewhere classified&rft_subject=Marine and Estuarine Ecology (incl. Marine Ichthyology)&rft_subject=BIOLOGICAL SCIENCES&rft_subject=ECOLOGY&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Open Licence view details
CC-BY

http://creativecommons.org/licenses/by/3.0/deed.en_US

2018, The University of Queensland

Access:

Open

Contact Information

[email protected]

Full description

A subset of photoquadrats were uploaded to the CoralNet machine learning interface (https://coralnet.ucsd.edu/) and manually labelled for coral, algae or substrate type using 50 points per quadrat. Follow training of the machine, this enabled automatic annotation of all unclassified field images: the remaining field photos were uploaded to the database and 50 annotation points were overlaid on each of the images. Every point was assigned a benthic cover category from a label list automatically by the program. The resulting benthic cover data of each photo was linked to gps coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 55 South.

Issued: 2018

Data time period: 2017 to 31 05 2017

This dataset is part of a larger collection

Click to explore relationships graph

143.91135,-13.67207 148.1301,-13.67207 148.1301,-18.56756 143.91135,-18.56756 143.91135,-13.67207

146.020725,-16.119812