Data

Seagrass meadows extents derived from field to spaceborne earth observation at Midge Point, Great Barrier Reef, September/October 2017

University of Tasmania, Australia
Langlois, Lucas ; McKenzie, Len
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/afc2b7ad-c872-4768-8636-6ab577542ba7&rft.title=Seagrass meadows extents derived from field to spaceborne earth observation at Midge Point, Great Barrier Reef, September/October 2017&rft.identifier=https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/afc2b7ad-c872-4768-8636-6ab577542ba7&rft.description=Seagrass meadow extent and meadow-scape was mapped using three alternative approaches at Midge Point, a coastal turbid water habitat, in the central section of the Great Barrier Reef, in September/October 2017. Approach 1 included mapping meadow boundaries and meadow-scape during low spring tides on foot using a handheld Garmin GPS. Approach 2 was where the meadows were surveyed at low tide with observations from a helicopter, with observational spot-checks conducted at a number haphazardly scattered points. Approach 3 used PlanetScope Dove imagery captured on 09 October 2017 coinciding as close as possible to the field-surveys, with 3.7 m x 3.7 m pixels (nadir viewing) acquired from the PlanetScope archive. This record describes meadow extent data collected using Approach 3 (PlanetScope imagery). View the original metadata record at https://doi.org/10.1594/PANGAEA.946606 for the full data collection.Statement: Spatially explicit seagrass maps were created from PlanetScope Dove imagery, and classified using a machine-learning model (Random Forest) coupled with a Boot-strapping process (100 iterations). The final model predictions were then gathered into separate rasters, based on Bootstrap Probability thresholds of 60% and 100%. The final rasters were cleaned using a majority filter algorithm, to eliminate stray pixel predictions using a moving window between 3 and 9 pixels depending on the size of the imagery. All meadows captured on the 09 October 2017 are represented. Seagrass distribution and abundance can change seasonally and between years, and users should ensure that they make appropriate enquires to determine whether new information is available on the particular subject matter. Model accuracy is 98.7±0.0002%&rft.creator=Langlois, Lucas &rft.creator=McKenzie, Len &rft.date=2020&rft.coverage=westlimit=148.688; southlimit=-20.644; eastlimit=148.725; northlimit=-20.625&rft.coverage=westlimit=148.688; southlimit=-20.644; eastlimit=148.725; northlimit=-20.625&rft_rights= http://creativecommons.org/licenses/by/4.0/&rft_rights=http://i.creativecommons.org/l/by/4.0/88x31.png&rft_rights=WWW:LINK-1.0-http--related&rft_rights=License Graphic&rft_rights=Creative Commons Attribution 4.0 International License&rft_rights=http://creativecommons.org/international/&rft_rights=WWW:LINK-1.0-http--related&rft_rights=WWW:LINK-1.0-http--related&rft_rights=License Text&rft_rights=Langlois, Lucas; McKenzie, Len J (2022): Seagrass meadows derived from field to spaceborne earth observation at Midge Point, a coastal habitat in the central section of the Great Barrier Reef, September/October 2017. PANGAEA, https://doi.org/10.1594/PANGAEA.946606&rft_rights=This dataset is a reduced version of the full dataset, available at https://doi.pangaea.de/10.1594/PANGAEA.946606, and is hosted by the Institute for Marine and Antarctic Studies (IMAS), University of Tasmania, on behalf of James Cook University (JCU) for the purposes of the Seamap Australia collaborative project.&rft_rights=Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0&rft_subject=biota&rft_subject=reef&rft_subject=coastal&rft_subject=Great Barrier Reef&rft_subject=machine learning&rft_subject=MARINE HABITAT&rft_subject=EARTH SCIENCE&rft_subject=BIOSPHERE&rft_subject=AQUATIC ECOSYSTEMS&rft_subject=SEAGRASS&rft_subject=BIOLOGICAL CLASSIFICATION&rft_subject=PLANTS&rft_subject=ANGIOSPERMS (FLOWERING PLANTS)&rft_subject=MONOCOTS&rft_subject=Environmental Sciences not elsewhere classified&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=OTHER ENVIRONMENTAL SCIENCES&rft_subject=Environmental Management&rft_subject=ENVIRONMENTAL SCIENCE AND MANAGEMENT&rft_subject=Marine and Estuarine Ecology (incl. Marine Ichthyology)&rft_subject=BIOLOGICAL SCIENCES&rft_subject=ECOLOGY&rft_subject=human&rft_subject=orbiting satellite&rft_subject=helicopter&rft_subject=Abundance of biota&rft_subject=Benthic habitat&rft.type=dataset&rft.language=English Access the data
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License Text

Langlois, Lucas; McKenzie, Len J (2022): Seagrass meadows derived from field to spaceborne earth observation at Midge Point, a coastal habitat in the central section of the Great Barrier Reef, September/October 2017. PANGAEA, https://doi.org/10.1594/PANGAEA.946606

This dataset is a reduced version of the full dataset, available at https://doi.pangaea.de/10.1594/PANGAEA.946606, and is hosted by the Institute for Marine and Antarctic Studies (IMAS), University of Tasmania, on behalf of James Cook University (JCU) for the purposes of the Seamap Australia collaborative project.

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Brief description

Seagrass meadow extent and meadow-scape was mapped using three alternative approaches at Midge Point, a coastal turbid water habitat, in the central section of the Great Barrier Reef, in September/October 2017. Approach 1 included mapping meadow boundaries and meadow-scape during low spring tides on foot using a handheld Garmin GPS. Approach 2 was where the meadows were surveyed at low tide with observations from a helicopter, with observational spot-checks conducted at a number haphazardly scattered points. Approach 3 used PlanetScope Dove imagery captured on 09 October 2017 coinciding as close as possible to the field-surveys, with 3.7 m x 3.7 m pixels (nadir viewing) acquired from the PlanetScope archive. This record describes meadow extent data collected using Approach 3 (PlanetScope imagery). View the original metadata record at https://doi.org/10.1594/PANGAEA.946606 for the full data collection.

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Statement: Spatially explicit seagrass maps were created from PlanetScope Dove imagery, and classified using a machine-learning model (Random Forest) coupled with a Boot-strapping process (100 iterations). The final model predictions were then gathered into separate rasters, based on Bootstrap Probability thresholds of 60% and 100%. The final rasters were cleaned using a majority filter algorithm, to eliminate stray pixel predictions using a moving window between 3 and 9 pixels depending on the size of the imagery. All meadows captured on the 09 October 2017 are represented. Seagrass distribution and abundance can change seasonally and between years, and users should ensure that they make appropriate enquires to determine whether new information is available on the particular subject matter. Model accuracy is 98.7±0.0002%

Data time period: 2017-09-17 to 2017-10-17

This dataset is part of a larger collection

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148.725,-20.625 148.725,-20.644 148.688,-20.644 148.688,-20.625 148.725,-20.625

148.7065,-20.6345

text: westlimit=148.688; southlimit=-20.644; eastlimit=148.725; northlimit=-20.625

Subjects
ANGIOSPERMS (FLOWERING PLANTS) | AQUATIC ECOSYSTEMS | Abundance of biota | BIOLOGICAL CLASSIFICATION | Biological Sciences | BIOSPHERE | Benthic habitat | EARTH SCIENCE | Ecology | Environmental Science and Management | Environmental Sciences | Environmental Management | Environmental Sciences Not Elsewhere Classified | Great Barrier Reef | MARINE HABITAT | MONOCOTS | Marine and Estuarine Ecology (Incl. Marine Ichthyology) | Other Environmental Sciences | PLANTS | SEAGRASS | biota | coastal | helicopter | human | machine learning | orbiting satellite | reef |

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Other Information
(Original metadata record [PANGAEA catalogue])

doi : https://doi.org/10.1594/PANGAEA.946606

global : 4739e4b0-4dba-4ec5-b658-02c09f27ab9a

Identifiers
  • global : afc2b7ad-c872-4768-8636-6ab577542ba7