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Landsat

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  • This record provides an overview of the scope and research output of NESP Marine Biodiversity Hub Project C3 - "Change detection and monitoring of key marine and coastal environments – application of the Australian Geoscience Data Cube". For specific data outputs from this project, please see child records associated with this metadata. -------------------- This project aims at leveraging the extensive time-series of earth observation image data in the Australian Geoscience Data Cube (AGDC) by developing change detection algorithms to analyse key environmental parameters in the coastal and marine zone. Spatial information produced by this project can inform management decisions, and assist in evaluating management action outcomes, by providing a quantifiable measure of historical change and ongoing monitoring and change detection capabilities. In Phase 1 of this project we aim to demonstrate the capability of using the AGDC through the development of an inter-tidal zone change detection algorithm and data set, with a view to developing and implementing an expanded range of stakeholder targeted algorithms to inform decision making processes in Phase 2. Planned Outputs • Progress Report • Demonstrator summary Report • Data Products (GIS maps and data, delivered from the GA website as a web service) • Marine Biodiversity Hub article • Pesentation at the Australian Marine Science Association Conference.

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    Giant kelp (Macrocystis pyrifera) forests are a foundational habitat that dominates many nearshore rocky coastlines in temperate and cold-water regions worldwide. Macrocystis forests can extend as much as 40 m to the surface and form closed canopies that alter the light, current, and sedimentation environment beneath them. They play an ecosystem-structuring function and provide habitat for a diverse range of fish and invertebrate species. The coastal waters of Tasmania represent the most extensive giant kelp habitat in Australia. Dense forests have historically covered large areas of the nearshore reef habitat, but significant declines have been recorded in eastern Tasmania in recent decades. These losses are thought to be linked to environmental stressors such as extended periods of high water movement, warming sea temperatures, and nutrient depletion. Eastern Tasmania has experienced some of the most pronounced declines, leading to the 2012 listing of the giant kelp community as an ‘endangered marine community type’ under Australia’s Federal Environment Protection and Biodiversity Conservation Act. This dataset uses Landsat satellite imagery to quantifiy the spatial extent and temporal variability of giant kelp surface canopies along the Tasmanian coastline to address gaps in long-term monitoring. The data collection is divided into two components: 1) A statewide analysis that maps the extent of Macrocystis pyrifera canopies across the whole Tasmanian coastline, grouped into nine three-year bins spanning 1987–2015. This long-term dataset is intended for assessing long-term (decadal) changes at a broad spatial scale. 2) A higher temporal-resolution analysis of 24 specific sites along the Tasmanian coastline. This dataset includes canopy coverage from all cloud-free Landsat imagery captured over the period 1986-2015 to enable detailed analysis of seasonal and interannual fluctuations in kelp canopy extent and understand localised population dynamics.

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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. https://doi.org/10.3390/rs14143365