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    The aim of this study was to create a seagrass presence/absence map for the optically complex waters of Moreton Bay. The capability to map seagrass meadows in waters of varying clarity using a consistent and repeatable method is an invaluable resource for conservation and management of seagrass regionally and globally. The map was created using an adaptation of a Google Earth Engine (GEE) cloud processing and machine learning algorithm which for seagrass, utilized citizen science spot check field data, Landsat 8 OLI imagery pulled directly from GEE, a bathymetry layer (30 m), slope derived from depth and a coral mask. This dataset consists of a shapefile that shows seagrass presence (≥ 25 % cover) and substrate mapped simultaneously for the turbid waters of the Western Bay coastline and the optically clear waters of the Eastern Banks, Moreton Bay, Queensland, Australia. This record contains a snapshot of the data taken for use in Seamap Australia (a national benthic habitat map; https://seamapaustralia.org). View the original record at: https://doi.pangaea.de/10.1594/PANGAEA.937501

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    A supervised classification was applied to a Landsat TM5 image. This image was acquired on the 8th August 2004, 15 minutes after low tide. The image classification was applied on areas of clear waters up to three metres depth and for exposed regions of Moreton Bay. Field validation data was collected at 2800 survey sites by UQ, 18 Seagrass-Watch sites and 60 Port of Brisbane Corporation survey sites. GPS referenced field data were used as training areas for the image classification process. For this training the substrate DN signatures were extracted from the Landsat 5 TM image for field survey locations of known substrate cover, enabling a characteristic "spectral reflectance signature" to be defined for each target. The Landsat TM image, containing only those pixels in water < 3.0m deep, was then subject to minimum distance to means algorithm to group pixels with similar DN signatures (assumed to correspond to the different substrata). This process enabled each pixel to be assigned a label of either seagrass cover (0, 1-25 %, 25-50 %, 50-75 % and 75-100 %). The resulting raster data was then converted into a vector polygon file. Species information was added based on the field data and expert knowledge. Both polygon files were joined by overlaying features of remote sensing files with the EHMP field data to produce an output theme that contains the attributes and full extent of both themes. If polygons of remote sensing were within polygons of field data the assumption was made that the remote sensing polygon was showing more detail and the underlying field polygon was deleted.

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    A supervised classification was applied to a Landsat TM5 image. This image was acquired 9:40 am, on the 27th July 2011 (5.14 am low tide at Brisbane Bar). The image classification was applied on areas of clear waters up to three metres depth and for exposed regions of Moreton Bay. Field validation data was collected at 4797 survey sites by UQ. GPS referenced field data were used as training areas for the image classification process. For this training the substrate DN signatures were extracted from the Landsat 5 TM image for field survey locations of known substrate cover, enabling a characteristic "spectral reflectance signature" to be defined for each target. The Landsat TM image, containing only those pixels in water < 3.0m deep, was then subject to minimum distance to means algorithm to group pixels with similar DN signatures (assumed to correspond to the different substrata). This process enabled each pixel to be assigned a label of either seagrass cover (0, 1-25 %, 25-50 %, 50-75 % and 75-100 %). The resulting raster data was then converted into a vector polygon file. Species information was added based on the field data and expert knowledge. Both polygon files were joined by overlaying features of remote sensing files with the EHMP field data to produce an output theme that contains the attributes and full extent of both themes. If polygons of remote sensing were within polygons of field data the assumption was made that the remote sensing polygon was showing more detail and the underlying field polygon was deleted.