This resource includes bathymetry data acquired during the Southern Depths of the Great Barrier Reef survey using Kongsberg EM302 and EM710 multibeam sonar systems. The Southern Great Barrier Reef Shelf Bathymetry survey (FK201122/GA4867); also known as Ice Age Geology of the Great Barrier Reef survey; was led by Queensland University of Technology aboard the Schmidt Ocean Institute's research vessel Falkor from the 22nd of November to the 21st of December 2020. The primary objective of the expedition was to explore ancient undersea features that formed during the last Ice Age, when sea level was around 125 m lower than it is today. While once an exposed part of the Australian coast, these shelf areas were submerged as Earth’s glaciers and ice sheets melted and sea level rose, flooding Australia’s continental shelf. Another objective was to find the southern extent of an older limestone platform that may represent the approximately 20 million-year-old base upon which the present Great Barrier Reef has grown. This V1 dataset contains two 64m resolution 32-bit floating point geotiff files of the Southern Great Barrier Reef Shelf Bathymetry survey area, derived from the processed EM302 and EM710 bathymetry data, using CARIS HIPS and SIPS software. This dataset is not to be used for navigational purposes. This dataset is published with the permission of the CEO, Geoscience Australia.

Geoscience Australia carried out a marine survey on Carnarvon shelf (WA) in 2008 (SOL4769) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments and infauna, observation of benthic habitats using underwater towed video and stills photography, and measurement of ocean tides and wavegenerated currents. Data and samples were acquired using the Australian Institute of Marine Science (AIMS) Research Vessel Solander. Bathymetric mapping, sampling and video transects were completed in three survey areas that extended seaward from Ningaloo Reef to the shelf edge, including: Mandu Creek (80 sq km); Point Cloates (281 sq km), and; Gnaraloo (321 sq km). Additional bathymetric mapping (but no sampling or video) was completed between Mandu creek and Point Cloates, covering 277 sq km and north of Mandu Creek, covering 79 sq km. Two oceanographic moorings were deployed in the Point Cloates survey area. The survey also mapped and sampled an area to the northeast of the Muiron Islands covering 52 sq km. cloates_3m is an ArcINFO grid of Point Cloates of Carnarvon Shelf survey area produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

This dataset contains species identifications of molluscs from shell grit and sediments collected during survey SOL5463 (R.V. Southern Surveyor, 3-31 May 2012). Sediments were collected with a Smith Mac grab and processed in the GA laboratory. Sediment samples from two grabs contained many mollusc shells, some intact, and these were lodged at the Museum and Art Gallery of the Northern Territory (MAGNT). Species-level identifications were undertaken by Dr Richard Willan at the MAGNT and were delivered to Geoscience Australia on the 19 September 2012. See GA Record 2012/66 for further details on survey methods and specimen acquisition. Data is presented here exactly as delivered by the taxonomist, and Geoscience Australia is unable to verify the accuracy of the taxonomic identifications. Note that all specimens were identified from dead material and shell fragments. Specimens that were alive upon collection were processed separately with infaunal samples.

An interpretation of SIMS O isotope analysis and LA-MC-ICP-MS Lu-Hf analyses of zircons from the Rum Jungle Complex, northern Australia

22-1/F50-16/7 Vertical scale: 100

Groundwater dependent ecosystems (GDEs) are an important feature of the Australia landscape and as need to be incorporated into water management to maintain their persistence. However the first step to ensuring sustainable management of GDEs is the identification of such communities. With recent technological advances in remote sensing, identification of GDEs is becoming more commonly achieved through temporal analysis of biophysical properties detected from satellite imagery, for example as used in the National Atlas of Groundwater Dependent Ecosystems. However, many of these remote sensing studies only concentrate on surface processes and fail to integrate the spatial and temporal dynamics of these communities with subsurface processes such as depth to watertable, groundwater quality, groundwater flow paths and recharge zones. In this study, LiDAR canopy digital elevation model and foliage projected cover data were combined with Landsat imagery in order to characterise the spatial and temporal behaviour of woody vegetation in the Lower Darling Floodplain, New South Wales. This multi-temporal data was then combined with hydrogeological, hydrogeochemical and hydrogeophysical data to assess the relative importance of hydrological processes and groundwater characteristics. Central to the approach was the use of airborne electromagnetics which provided a 3-dimensional context to otherwise point-based borehole data. Through these multiple lines of evidence, two types of groundwater dependent vegetation communities were identified. In both classes vegetation was concluded to be utilising groundwater within the shallow unconfined aquifer, however the distinction was the degree of connectivity with underlying aquifers through either an absence of the regional aquitard or connection via faults. This study highlights the importance of integrating remote sensing with both surface and subsurface data to gain an improved understanding of vegetation dynamics and groundwater dependency. These findings are being used to assess the suitability of proposed groundwater-development options in the study area, and have implications for riparian vegetation management more broadly.

22-1/F53-2/4 Vertical scale: 400

22-1/F51-14/6 Vertical scale: 500

22-1/H53-1/4 Vertical scale: 400

In the literature of remote sensing image analysis, an endmember is defined as a pixel containing only one land cover substance. However, with the varying resolutions of available sensors, in most cases a single pixel in a satellite image contains more than one type of land cover substance. One challenge is to decompose a pixel with mixed spectral readings into a set of endmembers, and estimate the corresponding abundance fractions. The linear spectral unmixing model assumes that spectral reading of a single pixel is a linear combination of spectral readings from a set of endmembers. Most linear spectral unmixing algorithms rely on spectral signatures from endmembers in pre-defined libraries obtained from previous on-ground studies. Therefore, the applications of these algorithms are restricted to images whose extent and acquisition time coincide with those of the endmember library. We propose a linear spectral unmixing algorithm which is able to identify a set of endmembers from the actual image of the studied area. Existing spectral libraries are used as training sets to infer a model which determines the class labels of the derived image based endmembers. The advantage of such approach is that it is capable of performing consistent spectral unmixing in areas with no established endmember libraries. Testing has been conducted on a Landsat7 ETM+ image subset of the Gwydir region acquired on Jun 22, 2008. Three types of land cover classes: bare soil, green vegetation and non-photosynthetic are specified for this test. A set consisting of 150 endmember samples and a number of ground abundance observations were obtained from a corresponding field trip. The study successfully identified an endmember set from the image for the specified land cover classes. For most test points, the spectral unmixing and estimation of the corresponding abundance are consistent with the ground validation data.