This dataset provides the spatially continuous data of seabed gravel (sediment fraction >2000 µm), mud (sediment fraction < 63 µm) and sand content (sediment fraction 63-2000 µm) expressed as a weight percentage ranging from 0 to 100%, presented in 0.0025 decimal degree (dd) resolution raster grids format and ascii text file. The dataset covers the Petrel sub-basin in the Australian continental EEZ. This dataset supersedes previous predictions of sediment gravel, mud and sand content for the basin with demonstrated improvements in accuracy. Accuracy of predictions varies based on density of underlying data and level of seabed complexity. Artefacts occur in this dataset as a result of insufficient samples in relevant regions. This dataset is intended for use at the basin scale. The dataset may not be appropriate for use at smaller scales in areas where sample density is insufficient to detect local variation in sediment properties. To obtain the most accurate interpretation of sediment distribution in these areas, it is recommended that additional samples be collected and interpolations updated.

Understanding surface water resources is important for communities, agriculture and the environment, especially in water-limited environments. In 2014 Geoscience Australia released the Water Observations from Space (WOfS) product, providing information on the presence of surface water across the Australian continent from 27 years of Landsat satellite imagery. WOfS was created to provide insight into the extent of flooding anywhere in Australia, but broader applications are emerging in the areas of wetland behaviour, river system mapping, groundwater surface water interaction, and water body perenniality. Understanding the characteristics of inundation for every waterbody across a county, over a period of time, gives a greater knowledge of perenniality and helps support decision making for a wide range of users including aquatic ecological community and water resource management. WOfS provides a consistent tool to locate and characterise water bodies at the continental scale.

Data package containing an ESRI shapefile and associated comma-separated value table (.csv) of the Pacific islands, including the countries of Cook Islands, Federated States of Micronesia, Fiji, Kiribati, Nauru, Niue, Palau, Papua New Guinea, Republic of Marshall Islands, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu and Vanuatu. The ESRI shapefile contains polygons of the islands and has been adapted from the World Vector Shoreline dataset, with original scale suitability of 1:250,000 (reference: Soluri, E.A. and Woodson, V.A. 1990. World Vector Shoreline. International Hydrographic Review LXVII(1)). See lineage for more information. The .csv file contains tabular data associated with the island polygons. The file has been adapted to suit the purposes of the companion report by Dixon-Jain et al. (2014). The island polygon shapefile and .csv file can be joined using the common UniqueID field. The attribute fields within the .csv file include island hydrogeological and physical characteristics. Relative ratings for component of the potential vulnerability framework are included for the two projection periods (2035-2064 and 2070-2099), for each climate hazard (low rainfall periods and mean sea-level rise). See the field list within lineage in the Data Dictionary for more information on the source of each attribute. The full bibliographic reference for the companion report (catalogue number 79066) is: Dixon-Jain, P., Norman, R., Stewart, G., Fontaine, K., Walker, K., Sundaram, B., Flannery, E., Riddell, A., Wallace, L. 2014. Pacific Island Groundwater and Future Climates: First-Pass Regional Vulnerability Assessment. Record 2014/43. Geoscience Australia, Canberra. http://dx.doi.org/10.11636/Record.2014.043

Geoscience Australia's entry to the ASC2014 SPECTRUM science-art exhibition Title: Seeing Water Through Time Author: Norman Mueller Type: Science Communication image Description: The WOfS, Water Observations from Space, image is a colour-scale of how many times water was detected from the Landsat 5 and 7 satellites over central Australia from 1998 to 2012. The colours range from very low number of times (red) to very high number of times (blue), using a standard rainbow colour scheme (red-orange-yellow-green-blue). This means that red areas are hardly ever wet while blue areas are more permanent water features like lakes. The area covered includes Lake Eyre (at left) Cooper Creek (right of centre) to the Paroo River (bottom right).

This video explains the concept behind Geoscience Australia's Data Cube, a new way of organising, analysing and managing the large amounts of data collected from Earth Observation Satellites (EOS) studies over time. The Data Cube facilitates efficient data analysis and enables users to interrogate Australia's EOS data from the past and present. It is hoped that the Data Cube will become a useful tool used by remote sensing scientists and data analysts to extract information to support for informing future decision-making and policy development within Australia.

This dataset provides the spatially continuous data of seabed gravel (sediment fraction >2000 µm), mud (sediment fraction < 63 µm) and sand content (sediment fraction 63-2000 µm) expressed as a weight percentage ranging from 0 to 100%, presented in 0.0025 decimal degree (dd) resolution raster grids format and ascii text file. The dataset covers the Browse region in the Australian continental EEZ. This dataset supersedes previous predictions of sediment gravel, mud and sand content for the basin with demonstrated improvements in accuracy. Accuracy of predictions varies based on density of underlying data and level of seabed complexity. Artefacts occur in this dataset as a result of insufficient samples in relevant regions. This dataset is intended for use at the basin scale. The dataset may not be appropriate for use at smaller scales in areas where sample density is insufficient to detect local variation in sediment properties. To obtain the most accurate interpretation of sediment distribution in these areas, it is recommended that additional samples be collected and predictions updated.

To date, a range of methods have been developed and applied to the processing and analysis of underwater video and imagery, in part driven by different requirements. For example, in Australia, the marine science community who are partnered by the National Environmental Research Program (NERP) and funded by the Marine Biodiversity Hub, has developed a national CATAMI (Collaborative and Automated Tools for Analysis of Marine Imagery and video) scheme. Technological advances in recent years have improved the usability and output quality of underwater video and still images used to identify and monitor underwater habitats and structures and as a result, these techniques are more frequently applied to marine studies. So far, a comprehensive review of underwater video and still imagery processing/analysis methods has not been completed, although the number of studies utilising underwater stills and video has increased dramatically. Difficulties in diver limitation and stringent regulations applied to the collection of diver-based imagery and video data from underwater benthic habitats. Therefore, remote sensing methods such as underwater video and still imagery are becoming increasingly pivotal for ground-truthing benthic biological and physical habitats in shallow and deep marine and freshwater habitats and are also providing a permanent archive for future analyses. This review focuses on post-processing observational methods used for underwater video and still image habitat classification and quantification. We summarise the main applications, advantages and disadvantages of video and still imagery scoring methods, and illustrate recent advances in this topic.

This presentation will provide an overview of geological storage projects and research in Australia.

In this study, we aim to identify the most accurate methods for spatial prediction of seabed gravel content in the northwest Australian Exclusive Economic Zone. We experimentally examined: 1) whether input secondary variables affect the performance of RFOK and RFIDW, 2) whether the performances of RF, SIMs and their hybrid methods are data-specific, and 3) whether model averaging improves predictive accuracy of these methods in the study region. For RF and the hybrid methods, up to 21 variables were used as predictors. The predictive accuracy was assessed in terms of relative mean absolute error and relative root mean squared error based on the average of 100 iterations of 10-fold cross validation. In this study, the following important findings were achieved: - the predictive errors fluctuate with the input secondary variables; - the existence of correlated variables can alter the results of model selection, leading to different models; - the set of initial input variables affects the model selected; - the most accurate model can be missed out during the model selection; - RF, RFOK and RFIDW prove to be the most accurate methods in this study, with RFOK preferred; and these methods are not data-specific, but their models are, so best model needs to be identified; and - Model averaging is clearly data-specific. In conclusion, model selection is essential for RF and the hybrid methods. RF and the hybrid methods are not data-specific, but their models are. RFOK is the most accurate method. Model averaging is also data-specific. Hence best model needs to be identified for individual studies and application of model averaging should also be examined accordingly. RF and the hybrid methods have displayed substantial potentials for predicting environmental properties and are recommended for further test for spatial predictions in environmental sciences and other relevant disciplines in the future. This study provides suggestions and guidelines for improving the spatial predictions of biophysical variables in both marine and terrestrial environments.

This report provides background information about the Ginninderra controlled release Experiment 2 including a description of the environmental and weather conditions during the experiment, the groundwater levels and a brief description of all the monitoring techniques that were trialled during the experiment. Release of CO2 began 26 October 2012 at 2:25 PM and stopped 21 December 2012 at 1:30 PM. The total CO2 release rate during Experiment 2 was 218 kg/d CO2. The aim of the second Ginninderra controlled release was to artificially simulate the leakage of CO2 along a line source, to represent leakage along a fault. Multiple methods and techniques were then trialled in order to assess their abilities to: - detect that a leak was present - pinpoint the location of the leak - identify the strength of the leak - monitor how the CO2 behaves in the sub-surface - assess the effects it may have on plant health Several monitoring and assessment techniques were trialled for their effectiveness to quantify and qualify the CO2 that was release. This experiment had a focus on plant health indicators to assess the aims listed above, in order to evaluate the effectiveness of monitoring plant health and the use of geophysical methods to identify that a CO2 leak may be present. The methods are described in this report and include: - soil gas - airborne hyperspectral surveys - plant health (PhenoMobile) - soil CO2 flux - electromagnetic (EM-31) - electromagnetic (EM-38) - ground penetrating radar (GPR) This report is a reference guide to describe the Ginninderra Experiment 2 details. Only methods are described in this report with the results of the study published in conference papers and future journal articles.