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

Mangroves are a globally important ecosystem subject to significant anthropogenic and climate impacts. Tidally submerged forests and those that occur in arid and semi-arid regions are particularly susceptible to sea level rise or are growing at the margins of their their ecophysiological limits. The spatial extent of these types of mangroves over broad scales are typically poorly documented as their structural and environmental characteristics make them difficult to detect using remote sensing models. This study utilised the entire Landsat 8 satellite collection between January 2014 and June 2021. A new cloud-based time-series method was used that accounts for tidal variance in detecting mangrove areas that are periodically inundated and have historically been difficult to detect with traditional remote sensing methods. A habitat area model was derived for remote North-western Australia and detected an additional 32% (76,048 hectares) of mangroves that were previously undocumented. The accuracy of the model was assessed within the distinct geomorphic zones of the region through visual validation from high-resolution imagery. See accompanying report for full methodology: Hickey, S.M.; Radford, B. Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing. Remote Sens. 2022, 14, x. https://doi.org/10.3390/rs14143365

Digitised habitat layers for the southern section of the Solitary Islands Marine Park and Solitary Islands Marine Reserve, NSW. Includes reef and unconsolidated seafloor types as interpreted from a series of high resolution bathymetry and backscatter data obtained during swath surveys 2005-2012.

Digitised habitat layers for the New South Wales continental shelf predominantly to 3NM. The shape file contains polygons of areas of 1) reef and 2) unconsolidated seafloor types as interpreted from a number of remote sensing methods predominantly mulitbeam, LIDAR (LADS) and Aerial Imagery obtained in surveys across 2005-2013.