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    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.

  • This dataset has been superseded by (Victorian Statewide Marine Habitat Map 2023). The Victorian seabed habitat map documents the distribution of broad benthic habitat types in Victorian Coastal Waters to the State’s 3 nautical mile jurisdictional limit. The map was created using a top-down modelling process whereby habitat descriptors were assigned using seafloor structure and biological information derived from multibeam sonar (Victorian Marine Habitat Mapping Project), bathymetric LiDAR (Future Coasts program) and observations from underwater video. Identification of benthic biota, to the lowest discernible taxonomic level, and substrate characteristics were recorded according to the Victorian Towed Video Classification scheme (Ierodiaconou et al. 2007).