Data

Benthic and substrate cover data derived from field photo-transect surveys for the Mackay to Capricorn management region of the Great Barrier Reef (GBR), May/June 2019

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=info:doi10.1594/PANGAEA.946749&rft.title=Benthic and substrate cover data derived from field photo-transect surveys for the Mackay to Capricorn management region of the Great Barrier Reef (GBR), May/June 2019&rft.identifier=10.1594/PANGAEA.946749&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 training set 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 WGS-84.&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.creator=Roelfsema, Christiaan&rft.date=2022&rft_rights= https://creativecommons.org/licenses/by/4.0/deed.en&rft_subject=eng&rft_subject=Great Barrier Reef&rft_subject=Mackay&rft_subject=Capricorn&rft_subject=field photo-transect surveys&rft_subject=ENVIRONMENTAL SCIENCE AND MANAGEMENT&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=ENVIRONMENTAL SCIENCES&rft.type=dataset&rft.language=English Access the data
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Contact Information

c.roelfsema@uq.edu.au

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 training set 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 WGS-84.

Issued: 2022

Data time period: 21 05 2019 to 27 05 2019

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Subjects
Capricorn | Environmental Science and Management | Environmental Sciences | Environmental Sciences | Great Barrier Reef | Mackay | eng | field photo-transect surveys |

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local : UQ:289097

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