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Biotic taxonomic identification

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    This is a high resolution survey (1:5,000 scale dataset) describing mapped vegetation communities for the Ludmilla Creek catchment area (within the City of Darwin, Northern Territory). (Vegetation Unit - A reasonably homogeneous part of the land surface, distinct from surrounding terrain with constant properties in landform and vegetation) This vegetation survey was undertaken in 1996. This dataset represents the vegetation on the ground at the time of the survey and reflects the data and methods used to create the mapped polygons. The dataset has been revised in 2015 to include community descriptions but has not been updated to reflect the vegetation on the ground. The Darwin Remnant Vegetation Survey is considered a more up-to-date representation of vegetation for this area.

  • This polygon spatial dataset describes the benthic habitats likely to occur in marine environments around Gunn Point Peninsula and informs the desktop assessment of the marine biodiversity values for the region. The benthic habitats are inferred from existing information but in most cases require further data collection to validate and refine. Habitat modelling forms the basis of benthic community value assessment for Shoal Bay (Galaiduk et al. 2019). The accompanying report Marine and Coastal Biodiversity Assessment of the Gunn Point Area (2020) forms part of the Mapping the Future Project - Gunn Point. This project has identified land capability, water availability and biodiversity values to support land planning and inform development potential over the Gunn Point area. Reports and maps can be viewed from the webpage: denr.nt.gov.au/land-resource-management/development-opportunities - Development Potential of the Gunn Point Area (2020) - Biodiversity Assessment of the Gunn Point Area (2020) - Groundwater Resources of the Gunn Point Area (2020) - Marine and Coastal Biodiversity Assessment of the Gunn Point Area (2020) - Soil and Land Resources of the Gunn Point Area (2020) - Soil and Land Suitability Assessment for Irrigated Agriculture in the Gunn Point Area (2017) - Vegetation Communities of the Gunn Point Area (2020)

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    This polygon spatial dataset describes the mangrove communities for Bynoe Harbour and its environs. The original field survey was undertaken in 1998 at a mapping scale of 1:25,000. The associated report notes an area of approximately 24,000 hectares of mangroves in Bynoe Harbour and describes ten main mangrove associations or communities. The data provides a basis for identifying the extent and distribution of mangrove communities for Bynoe Harbour and its environs. Mangrove mapping was updated over this area in a 2016 survey. See metadata record for Mangrove Community Mapping - Charles Point to Gunn Point. www.ntlis.nt.gov.au/metadata/export_data?type=html&metadata_id=87F656131848A609E050CD9B214417E1

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    This is a collection of polygons outlining signficant seagrass meadows around Arnhem Land, Kakadu and Gulf of Carpentaria coastal waters for the Northern Australia Marine Biodiversity Survey (NAMBS) project between 2003 and 2005. Data was collected from imagery and ground truthed by observers from helicopter and insitu sample grabs.

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    This polygon spatial dataset describes the mangrove communities for Darwin Harbour and its environs and was surveyed at a mapping scale of 1:25 000. The associated report notes an area of approximately 20,400 hectares of mangroves in Darwin Harbour and describes ten main mangrove associations or communities. The data provides a basis for identifying the extent and distribution of mangrove communities for Darwin Harbour and its environs.

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    This spatial polygon dataset is derived from the 2001 mapping of mud crab (Scylla serrata) habitats along the NT and Queensland coastal wetlands. The polygons were derived from Landsat imagery and verified with field work and is suitable for use at scale 1:100,000. The information can be used in many ways to assist the management of these habitats. It provides a suitable baseline dataset for monitoring broad scale changes in area and composition of coastal wetland communities. Additionally, it provides information required by managers for choosing representative habitats for protection.

  • Spatially referenced underwater video transect data for Tasmanian coastal waters from the LWM (Low water mark) to 80 metres in depth or 1.5 kms from shore.

  • Point data collected from video drops identifying benthic habitats such as seagrass, macroalgae and reef, collected during field work in 2007 to 2011. Used to support the Benthic Habitat Mapping project undertaken by DENR to map the nearshore benthic habitats of South Australia

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    This dataset contains habitat mapping and outer boundaries for estuaries of South Australia. These estuaries were identified in the draft 'Estuaries Policy and Action Plan.' Used to identify the estuarine habitats within South Australia for use in natural resource management and conservaton planning. Description of attributes in related Inventory and Water Quality tables can be found in report: 'Working Towards a State-wide Inventory of Estuaries 2009 - Advancing the inventory of estuaries in five Natural Resource Management Regions of South Australia', DEH and Adelaide Mounty Lofty Ranges NRM Board.

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    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.