This seagrass habitat map was produced by the ACEAS Seagrass working group as part of the seagrass habitat risk modelling effort. The map identified seagrass presence based on 1) on the NISB (National Intertidal-Subtidal Benthic) Habitat Map created by the University of Tasmania for a partnership between the Department of Climate Change and the National Land and Water Resources Audit, 2) UNEP WCMC Seagrass map 2005, 3) publications/reports and 4) expert knowledge/personal observation. This nationally aggregated seagrass presence/absence habitat map was produced from individual state-based habitat maps (see child records) for the purpose of publication to the AODN Data Portal.

The seagrass dataset has been compiled by UNEP-WCMC in collaboration with Dr Frederick T. Short, University of New Hampshire, USA to show the global distribution of seagrass species. This dataset has been created from multiple sources and was used in the creation of the “World Atlas of Seagrasses”(2003). This polygon feature dataset is an update of the data used in the Atlas and is a unique data holding about the state of the world’s seagrasses. For a complete overview of global seagrass distribution this dataset should be displayed together with the associated point dataset. Both polygon and point datasets are available to download from this record.

The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100 m**2) scales. However, landscape scale (> 100 km**2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142 km**2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.