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    Coastal features within and adjacent to the Great Barrier Reef World Heritage area (such as the Queensland Mainland and Islands, Indicative Reef boundaries and Rocks.). Major coral cay features,major and other coral reef structures (as defined by the reef shoal edge) and tidal, drying or emergent reef areas within the Great Barrier Reef World Heritage Area.

  • The GBR10 benthic habitat type map is the output of a modelling process that combines satellite imagery and other environmental attributes like water depth, slope and wave climate, along with known occurrences of benthic habitat type. The occurrences of benthic habitat type were derived through machine learning applied to geolocated photos of the benthos (>100,000 photos) that were collected along reef flats and reef slopes at various offshore shallow reefs (~100 reefs). The modelling process involves taking those occurrences of benthic habitat at known locations (training data) and using a machine learning model to build a relationship between benthic habitat type and the underlying data layers (imagery, depth, slope, waves). Because the data layers cover the whole Great Barrier Reef, a prediction is then able to be made for benthic habitat across the whole Great Barrier Reef as well. Contextual editing was then used to make changes to the map, based on a set of geomorphology- and ecology-based rules, such as what environment a class can occur in and what classes are able to neighbour each other (object-based rulesets). This map covers the “offshore” or “mid and outer-shelf” reefs of the Great Barrier Reef Marine Park. Some of the occurrences of benthic habitat type are withheld from the process, and used to check how well the mapping performed at the end (validation). The mapping was carried out by the Remote Sensing Research Centre at the University of Queensland. The scientific method for generating the benthic habitat map can be briefly described as: 1. Ingestion of Sentinel-2 satellite image data, bathymetry and wave climate data derived from Sentinel-2 image data, and various additional derived environmental covariates into Google Earth Engine 2. Stacking of the input data sources into a model-ready environment 3. Running a segmentation routine to create image objects 4. Fitting a supervised machine learning model (e.g. random forest) to known occurrences in order to classify segments into benthic classes 5. Application of object based rules using a range of colour, shape texture and relationship rules to modify the class attribution 6. Validation of mapping accuracy and performance This is an snapshot of the GBR10 benthic dataset taken on Jan 2023 for the Seamap Australia project from the GBRMP Reef Knowledge System (https://reefiq.gbrmpa.gov.au/ReefKnowledgeSystem), see also https://gbrmpa.maps.arcgis.com/home/item.html?id=492a87d95e8243728486718e7aed02a8. An updated version of the data may be available from the source provider.