The Surface Hydrology Points (Regional) dataset provides a set of related features classes to be used as the basis of the production of consistent hydrological information. This dataset contains a geometric representation of major hydrographic point elements - both natural and artificial. This dataset is the best available data supplied by Jurisdictions and aggregated by Geoscience Australia it is intended for defining hydrological features.

APPEA extended abstract

The Sun's gravitational field deflects the apparent positions of close objects in accordance with the formulae of general relativity. Optical astrometry is used to test the prediction, but only with the stars close to the Sun and only during total Solar eclipses. Nowadays, more advanced technique, geodetic Very Long Baseline Interferometry (VLBI) is applied for testing of general relativity with precision about 0.01 percent. The geodetic VLBI is capable of measuring the gravitational delay based on the differential Shapiro's delay. By reason, the gravitational delay is equivalent to the deflection of the light from distant radio sources and could be measured at any time and across the whole sky. In accordance with the theory, all celestial objects display annual circular motion with the magnitude proportional to their ecliptic latitude due to the Earth orbital motion. In particular, the objects near the ecliptic pole draw an annual circle with magnitude of 4 millisecond of arc. In contrast to a single optical telescope, a single ground-based VLBI interferometer is made of two radio telescopes separated by several thousand kilometers. This provides an additional advantage to detect a secondary light deflection angle caused by the parallactic shift of the Sun as observed from both ends of the interferometer. This effect is proportional to the baseline length and is about 0".01 for grazing light at baseline of 8000 km. It could be used in future space interplanetary VLBI missions with baseline length of one billion kilometers (comparable to the Jupiter orbit size) for direct detection of invisible mass from extragalactic objects.

Back-up SDE Dev, Test and Prod data should NOT be utilised - only for back-up as a failsafe for upgrading of SDE Dev, Test and Prod. Archive Only

Abstract for the Floodplain Management Association conference 19-22 May 2015

Digital elevation models (DEMs) provide basic information about the shape of the earth. This information is fundamental for understanding and modelling natural hazards and their consequences. The low resolution and accuracy of the current generation of freely available global DEMs means inappropriate use can lead to dangerously misinformed disaster management decisions being made, particularly at the local level. Investing in high resolution, high accuracy global DEMs and making them freely available to the global community would have many benefits to society, one of which would be better informed disaster management decision making.

Geoscience Australia conducted a marine survey in the Caswell Sub-basin of the Browse Basin, offshore Western Australia, in late 2014 to investigate containment questions relating to the potential long-term geological storage of CO2. The survey aimed to identify and characterise indicators of natural hydrocarbon or fluid seepage that may suggest the presence of deep plumbing systems which could compromise seal integrity. Prior to the survey, 2D and 3D seismic data were used to map fault networks connecting the Aptian regional seal to the sea floor and any associated amplitude anomalies. This mapping informed survey site selection aimed at testing seal integrity over Maastrichtian, Campanian, Valanginian and Barremian submarine fans in the Caswell Sub-basin, and up-dip migration and leakage of hydrocarbons, via channels and basin margin faults, such as the Heywood Fault, into shallow marine sands on the eastern shelf margin. Vessel and Autonomous Underwater Vehicle (AUV) multibeam bathymetry and sub-bottom profiler systems confirmed the presence of recently active faults in the area, some with significant seafloor surface expression (up to 40m offset). A subset of these faults was visually inspected with a Remotely Operated Vehicle (ROV) which also confirmed the presence of diverse biological communities. Indications of shallow gas were observed on sub-bottom profiles, including amplitude anomalies, cross-cutting reflectors and zones of signal starvation. Water column observations including sidescan sonar, single-beam and multibeam echosounders, underwater video and photography did not conclusively identify hydrocarbon or other fluid seepage. Strong currents encountered during parts of the survey may have interfered with the direct detection of seeps in the water column. However, headspace gas and high-molecular weight hydrocarbon analysis from shallow cores also provided no evidence for migrated thermogenic gas or oil. While no active signs of seepage were observed, the geochemical and biological sampling undertaken will aid in baseline environmental investigations for this region.

We propose a surface cover change detection system based on the Australian Geoscience Data Cube (AGDC). The AGDC is a common analytical framework for large volumes of regularly gridded geoscientific data initially developed by Geoscience Australia (GA). AGDC effectively links geoscience data sets from various sources by spatial and temporal stamps associated with the data. Therefore, AGDC enables analysis of generations of consistent remote sensing time series data across Australia. The Australian Reflectance Grid 25m is one of the remote sensing data sets in the AGDC. The data is currently hosted at the high performance computational cloud at the National Computational Infrastructure. The proposed change detection system takes advantage of temporally rich data in the AGDC, applying time series analysis to identify changes in surface cover. The proposed system consists of various modules, which are independent of each other. The modules include: - a pixel quality mask and time series noise detection mask, which detects and filters out noise in data; - spectral classification modules based on random forests algorithm, which classifies pixels into specific objects using spectral information; - training modules which create classification modules using known surface cover data; - time series analysis modules, which models and transforms time series data into coefficients relevant to change detection targets; - temporal and spatial classification modules, which classify pixels into predefined land cover classes. A typical work flow for a change detection system includes sequential integration of the above mentioned modules. The system has been tested for applications in shallow water coastal zones and reforestation / deforestation detection, and displays a good potential for further development. This paper summarises development of the work flow and the initial results from example applications, such as reforestation / deforestation detection.

This dataset is the most current national compilation of catchment scale land use data for Australia (CLUM), as at March 2015. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. It has been compiled from vector land use datasets collected as part of state and territory mapping programs through the Australian Collaborative Land Use and Management Program (ACLUMP). Catchment scale land use data was produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field. The date of mapping (1997 to 2014) and scale of mapping (1:20 000 to 1:250 000) vary, reflecting the source data capture date and scale. This information is provided in a supporting polygon dataset.

This project consists of data that has been reprocessed by RPS and AAM for the purpose of creating an improved Victorian coastal DEM including contours based on the original data acquired in 2007. The purpose of this project is to reclassify the original level 2 classification LiDAR data into level 3 for input to a higher accuracy ICSM Level 3 classification (Level 3 DEM). LiDAR (Light Detection and Ranging) is an airborne remote sensing technique for rapid collection of terrain data. The sensor used for this LiDAR project collected XYZ and Intensity data for first and last return by bouncing a pulse from the aircraft to the surface that enables the height and intensity values to be calculated. From the raw LiDAR data, a suite of elevation products was generated including DEM and Contours. Project Products: DEM, Contours, raw LiDAR.