IMAS Metadata Catalogue
Seagrass meadow extents derived from field to spaceborne earth observation at Yule Point, Great Barrier Reef, October 2017 to July 2020
Seagrass meadow extent and meadow-scape was mapped using four alternative approaches at Yule Point, a coastal clear water habitat, in the Cairns section of the Great Barrier Reef, between October 2017 and July 2020. Approach 1 included mapping meadow boundaries and meadow-scape during low spring tides on foot using a handheld 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 imagery collected during low spring tides with a UAV at an altitude of 30 m with a resolution of 0.2cm/pixel. Approach 4 used PlanetScope Dove imagery captured on 05 September 2017 and 09 August 2019 coinciding as close as possible to the field-surveys in 2017 and 2019, 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 4 (PlanetScope imagery). View the original metadata record at https://doi.pangaea.de/10.1594/PANGAEA.946604 for the full data collection.
Simple
Identification info
- Alternate title
- YP_seagrass_by_spaceborne_20190809
- Alternate title
- YP_seagrass_by_spaceborne_20170905
- Date (Creation)
- 2022-05-26
- Date (Publication)
- 2023-02-21
Principal investigator
- Status
- Completed
Point of contact
- Spatial representation type
- Vector
- Topic category
-
- Biota
Extent
))
Temporal extent
- Time period
- 2017-10-17 2020-07-30
Resource format
- Title
- ESRI Shapefile
- Date
- Edition
- -
- Global Change Master Directory (GCMD) Earth Science Keywords Version 8.0
- Keywords (Place)
-
- Great Barrier Reef
- Keywords (Theme)
-
- Benthic habitat
- reef
- machine learning
- deep learning
- coastal
Resource specific usage
- Specific usage
- Data supplied for use by the Seamap Australia Project.
Resource constraints
- Classification
- Unclassified
Resource constraints
- Linkage
-
http://i.creativecommons.org/l/by/4.0/88x31.png
License Graphic
- Title
- Creative Commons Attribution 4.0 International License
- Website
-
http://creativecommons.org/licenses/by/4.0/
License Text
- Other constraints
- Langlois, Lucas; McKenzie, Len J (2022): Seagrass meadows derived from field to spaceborne earth observation at Yule Point, a coastal habitat in the Cairns section of the Great Barrier Reef, October 2017 to July 2020. PANGAEA, https://doi.org/10.1594/PANGAEA.946604
- Other constraints
- This dataset is a reduced version of the full dataset, available at https://doi.pangaea.de/10.1594/PANGAEA.946604, 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.
- Language
- English
- Character encoding
- UTF8
Content Information
- Content type
- Physical measurement
- Description
- Appended to original data for styling purposes for the Seamap Australia Project
- Name
-
Benthic habitat
- Name
-
SM_HAB_CLS
Distribution Information
- Distribution format
-
-
ESRI Shapefile
-
ESRI Shapefile
- OnLine resource
-
SHAPE-ZIP
DATA ACCESS - This OGC WFS service returns the data (Yule Point seagrass) in Shapefile format.
- OnLine resource
-
seamap:SeamapAus_QLD_YulePoint_seagrass_2019
MAP - Yule Point seagrass
- OnLine resource
-
Original metadata record [PANGAEA catalogue]
Resource lineage
- 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. For Approach 4, we created spatially explicit seagrass maps from PlanetScope Dove imagery, and conducted the classification using a machine-learning model (Random Forest) coupled with a Boot-strapping process (100 iterations). Due to the lower seagrass density and occurrence of morphologically smaller species, the classes used were: (1) bare sediment, (2) low seagrass cover (>0 ≤25%), AND (3) high seagrass cover (>25%). The final raster was 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 05 September 2017 and 09 August 2019 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 98.8±0.0002% and 93.69±0.0009% for 2017 and 2019, respectively.
- Hierarchy level
- Dataset
- Hierarchy level
- Dataset
Platform
Identifier
- Code
- human
- Hierarchy level
- Dataset
Platform
Identifier
- Code
- diver
Metadata
- Metadata identifier
-
dbc3c0e6-0597-4c57-b9ee-ff2de41d9e83
- Language
- English
- Character encoding
- UTF8
Point of contact
- Parent metadata
Type of resource
- Resource scope
- Dataset
- Metadata linkage
-
https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/dbc3c0e6-0597-4c57-b9ee-ff2de41d9e83
Point of truth URL of this metadata record
- Date info (Creation)
- 2020-09-23T12:36:21
- Date info (Revision)
- 2020-09-23T12:36:21
Metadata standard
- Title
- ISO 19115-3:2018
Overviews
Spatial extent
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