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.

the broad geological blocks from Archaean in the west, through Proterozoic in the centre, to Palaeozoic-Cainozoic in the east, are well presented in the 3-D electrical conductivity model as simple lower conductivity structures. In addition, the model shows conductivity contrast in the western craton, characteristic of enhanced conductivity structures which separate the cratonic blocks, and enhanced conductivity anomalies presented in eastern Australia.

Extensive historical (anecdotal) information covering the past 3 decades indicated that the remote and pristine Nadgee lake estuary in southern NSW had a benthic dominated ecology. All descriptions indicated that it had oligotrophic waters with dense cover of benthic macropyhtes and associated avifauna. When we arrived at Nadgee in late 2008 for the first scientific aquatic survey (ever) it looked nothing like this. The lake was dominated by an intense microalgal bloom and no macrophytes were present. Why? Entrance opening and closure are the major disturbances in an intermittent estuary like Nadgee, but there are no records of past entrance behaviour for such a remote site. This paper describes the use of Geoscience Australia's recent compilation and rectification of Landsat images (the Australian Geoscience Data Cube), along with the application of a consistent water detection tool for all pixels in that compilation, to determine opening and closing regimes. The output of the analyses provides an indication of whether a pixel was wet or dry (or not able to be determined) for all images over the entire 27 year's worth of data. Water level records measured by OEH since 2009 were used to ground-truth the remote sensed data. We can now determine when, over the past 27 years, the Lake opened and how long the water level remained low. This information, along with an understanding of the ecology of the primary macrophytes has been used to provide some possible models that explain when and why the fundamental shift from benthic to pelagic may have occurred.

Diagram produced for the Department of Industry and Science to depict those areas of water adjacent to SA that fall under the OPGGS Act, Petroeum (Seas and Submerged Lands) Act 1982 (SA) and Petroleum and Geothermal Energy Act 2000 (SA).

Exploration geologists are increasingly being inundated by a large volume and variety of digital spatial data. Unsupervised clustering algorithms, such as Self-Organizing Maps (SOM), provide an opportunity to gain insight into complex geological phenomena not evident from a single dataset. Unsupervised clustering algorithms are able to efficiently integrate and recognize patterns within 'Big Data' into manageable and interpretable outputs. This study demonstrates data fusion for mineral exploration and highlights the potential for data-driven clustering analysis to assist geoscientists in gaining robust understanding of the geological controls on mineralization in regolith dominated terrains. We interpret the nature of Uranium mineralization across the Australian continent by integrating remotely sensed, continental-scale geophysical and mineralogical data using SOM. We combine the outputs of our cluster analysis with Uranium mineral occurrence data (n = 1138) to construct prospectivity maps of regional Uranium mineralization for the Australian continent. Furthermore, we divide prospective areas into several unique groups. These groups represent subtle but significant differences in regolith and bedrock geophysical and mineralogical characteristics of Uranium mineralization targets. A total 11.94% of the samples input into the SOM analysis are likely to be prospective for Uranium mineralization. The resulting Uranium prospectivity map identifies the location of Uranium mines (operating and historic) with an accuracy 88.89% (n = 119). By interrogating the unique geophysical and mineralogical characteristics of Uranium prospectivity groups we can distinguish regions of: older landscapes with subdued topography dominated by arid climatic conditions and mechanical weathering processes; and relatively young landscapes over thin crust exhibiting moist climatic conditions and deeply weathered regolith profiles. These broad groups can be further subdivided into areas likely to represent magmatic-hydrothermal, unconformity and calcrete-hosted paleochannel Uranium deposits. The clustering analysis methodology presented here can be applied to the analysis of other bedrock and regolith associated mineral commodities and at local- and/or prospect-scales. Our techniques provide additional tools for the exploration geologist to develop a robust understanding of likely geological context of target mineralization. In turn, this will help to define the geological controls on mineralization and will contribute significantly to developing appropriate exploration strategies.

The National Flood Risk Informaiton Project (NFRIp) has produced a flyer for the Floodplain Management Association Conference on 19-22 May 2015 where the Australian Flood Risk Information Portal (AFRIP) will be promoted at a Geoscience Australia booth. NFRIP funded the revision of the guidelines as part of a $12m funding initiative by the Australia Government. The flyer promotes the three core activities of NFRIP; the Australian Flood Risk Information Portal, revision of Australian Rainfall and Runoff guidelines and Water Observations from Space (WOfS).

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.

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.

Seabed hardness is an important character of seabed substrate as it may influence the nature of attachment of an organism to the seabed. Hence spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is usually inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage or directly measured at limited locations. It can also be predicted based on two-class hardness data using environmental predictors, but no study has been undertaken for predicting multiple-class hardness data. In this study, we classified the seabed hardness into four classes based on underwater video images that were extracted from the underwater video footage. We developed an optimal predictive model to predict the spatial distribution of seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam bathymetry, backscatter and other derived predictors. A novel model selection method that is the averaged variable importance (AVI) was used based on predictive accuracy that was acquired from averaging the results of 100 times replication of 10-fold cross validation. Finally, the spatial predictions generated using the most accurate model was visually examined and analyzed in comparison with previously published predictions based on two-class hardness data. This study confirmed that: 1) seabed hardness of four classes can be predicted into a spatially continuous layer with a high degree of accuracy; 2) model selection for RF is essential for identifying an optimal predictive model in environmental sciences and AVI select the most accurate predictive model(s) instead of the most parsimonious ones, and is recommended for future studies; 3) the typical approach used in pre-selecting predictors by excluding correlated variables (i.e. r 0.95 or the inflation factor 20) needs to be re-examined for identifying predictive models using machine learning methods, at least for the application of random forest in marine environmental sciences; 4) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to `small p and large n problems in the environmental sciences; and 5) the spatial predictions for four-class hardness data were similar with the predictions based on two hardness classes, with a high match rates. RF and AVI are recommended for generating spatially continuous predictions of categorical variables in future studies. In summary, AVI shows its effectiveness in searching for the most accurate predictive models and are recommended for future studies. This study further confirms the superior performance of RF in marine environmental sciences. RF is an effective modelling method with high predictive accuracy not only for presence/absence data and but also for multi-level categorical data. RF and AVI are recommended for generating spatially continuous predictions of categorical variables in future studies.

Satellite Earth observation data presents unique opportunities for society to respond to major challenges like climate change, food security and sustainable development. But significant technical challenges, including to enable different data streams to be integrated and the sheer volume of the data, are preventing that full value from being realised. The explosion in free, highresolution, global data from next-generation satellites, linked with the potential of new highperformance ICT infrastructure and architectures, positions us to meet this challenge. As the 2016 CEOS Chair, and as a sophisticated user of multiple EO satellite data streams, Australia is proposing that CEOS explore how these new technologies can ensure CEOS agency satellite data can be 'unlocked and put to work'.