Bayside LiDAR 2007

These datasets cover approximately 600 sq km in the southern and eastern sectors of the Somerset Regional Council and are part of the 2009 South East Queensland LiDAR capture project. This project, undertaken by AAM Hatch Pty Ltd on behalf of the Queensland Government captured highly accurate elevation data using LiDAR technology. Available dataset formats (in 1 kilometre tiles) are: - Classified las (LiDAR Data Exchange Format where strikes are classified as ground, non-ground or building) - 1 metre Digital Elevation Model (DEM) in ASCII xyz - 1 metre Digital Elevation Model (DEM) in ESRI ASCII grid - 0.25 metre contours in ESRI Shape

Exploring for the Future (EFTF) is an eight year, $225 million Australian Government funded program which commenced in 2016. The program is delivering new geoscience data, knowledge and decision support tools to support increased industry investment and sustainable economic development across Australia. Further detail is available at http://www.ga.gov.au/eftf. The program’s objective over the four years from 2016-2020 was to provide a holistic picture of the potential mineral, energy and groundwater resources in northern Australia. Groundwater is a critical resource that accounts for most water used across northern Australia. The groundwater component of the EFTF program focused on addressing groundwater resource knowledge gaps, to support future opportunities for economic development via irrigated agriculture, extractive industries and increased security of community water supplies. Through collaboration with State and Territory partners, the program undertook targeted regional investigations of groundwater systems and assessments of groundwater potential more broadly across the region. The program's activities, implemented by Geoscience Australia, involved application of innovative geoscience tools to collect, integrate and analyse a range of data. It includes geological and hydrogeological data, airborne and ground-based geophysical and hydrogeochemical surveys, remote sensing data as well as stratigraphic drilling. The new data and better understanding of groundwater systems also helps inform decision making about groundwater use to protect environmental and cultural assets. These outcomes strengthen investor confidence in resources and agricultural projects by de-risking groundwater in northern Australia. Surface nuclear magnetic resonance (SNMR) is an electrical, geophysical technique that was adapted from magnetic resonance imaging techniques used in the medical field. This technique is gaining prominence in groundwater studies as it can be used to detect the presence of water and estimate hydraulic properties in the top 100m of subsurface. SNMR data can be acquired rapidly, cheaply and non-invasively. This is advantageous in Australian groundwater studies where drilling is often expensive and logistically challenging due to land access issues and environmental regulations. For the reasons described above SNMR has been one of the most important groundwater datasets acquired as part of the EFTF program. The derived estimates of water content have been used for several applications including; estimating hydraulic conductivity, mapping the water table surface, and defining aquifer architecture. The purpose of this document is to provide a description of the SNMR method and how the data are acquired, processed and inverted as part of the EFTF program.

Subtitle: Behind the Scenes of Geofabric Version 3 Pilot & the Future of Geospatial Surface Water Information The Bureau of Meteorology's Australian Hydrological Geospatial Fabric (Geofabric) was established in 2008 as the spatial information database to support water accounting and resource assessment mandated under the Water Act 2007. Foundation layers for Geofabric versions 1 and 2 were developed from 1:250K streamline data and the 9 second resolution national DEM. The uses of the Geofabric data have expanded to new disciplines and have resulted in increased demand for finer national resolution. Version 3 of the Geofabric is now under development in a collaborative project between Geoscience Australia, CSIRO, Australian National University (ANU) and the Bureau of Meteorology. The foundation inputs for Geofabric version 3 are based on the integrated national surface hydrology dataset which uses the best available scale data from the jurisdictions and the 1 second resolution SRTM DEM. This significant enhancement presents both challenges and opportunities. This presentation at the Surveying & Spatial Sciences Institute (SSSI) ACT Region conference on 16 August 2013 aims to show the work being undertaken in the pilot areas of the Namoi and Murrumbidgee River Regions.

Publicly available baseline surface water data are compiled to provide a common information base for resource development and regulatory decisions in the Galilee Basin region. This data guide captures existing knowledge of the catchments and watercourses overlying the Galilee Basin, including streamflow quality and quantity, inundation, and climatological data. The Galilee Basin straddles the Great Dividing Range and encompasses the headwaters of 9 major river basins, with the largest area underlying Cooper Creek, Diamantina River and Flinders River catchments. The Galilee Basin geological boundary also intersects parts of the catchment of the Burdekin River, Fitzroy River, Warrego River, Bulloo River, Paroo River and Condamine-Balonne rivers. The data on the catchments overlying the Galilee Basin have been summarised at a point in time to inform decisions on resource development activities. Key data sources are the Water Monitoring Information Portal (Queensland Government), Water Data Online (Bureau of Meteorology), DEA Water Observations (Geoscience Australia) and Terrestrial Ecosystem Research Network.

Weathering is an important process of the Earth’s surface that has a major influence on the chemical and physical properties of rock and soil. The intensity of this process largely controls the degree to which primary minerals are altered to secondary components, including clay and oxide minerals. The degree of surface weathering is particularly important in Australia, where variations in weathering intensity correspond to differences in the nature and distribution of regolith (weathered bedrock and sediments), which mantles approximately 80% of the Australian continent. Here, I use a random forest decision tree machine learning algorithm to first establish a relationship between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. I then apply this relationship to generate an improved national model of surface to near-surface weathering intensity. Covariates include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. The model performs very well, with an r-squared correlation of 0.85 based on 5 K-fold cross-validation on the mean and standard deviation of 300 random forest models. This new weathering intensity model has broad utility for mineral exploration in variably weathered landscapes, agricultural mapping of chemical and physical soil attributes, ecology, and advancing the understanding of weathering processes within the upper regolith. <b>Citation:</b> Wilford, J., 2020. Revised weathering intensity model of Australia. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

Australia has a significant number of surface sediment geochemical surveys that have been undertaken by industry and government over the past 50 years. These surveys represent a vast investment and have up to now only been able to be used in isolation, independently from one another. The key to maximising the full potential of these data and the information they provide for mineral exploration, environmental management and agricultural purposes is using all the surveys together, seamlessly. These disparate geochemical surveys not only sampled various landscape elements and analysed a range of size fractions, but also used multiple analytical techniques, instrument types and laboratories. The geochemical data from these surveys require levelling to eliminate, as much as possible, non-geological variation. Using a variety of methodologies, including reanalysis of both international standards and small subsets of samples from previous surveys, we have created a seamless surface geochemical map for northern Australia, from nine surveys with 15,605 samples. We tested our approach using two surveys from the southern Thomson Orogen, which demonstrated the successful removal of inter-laboratory and other analytical variation. Creation of the new combined and levelled northern Australian dataset paves the way for the application of statistical and data analytics techniques, such as principal component analysis and machine learning, thereby maximising the value of these legacy data holdings. The methodology documented here can be applied to additional geochemical datasets as they become available.

These datasets cover approximately 514 sq km over the Towns of Esk, Kilcoy and Toogoolawah and over Lockyer Creek Gap in the Somerset Regional Council and are part of the 2011 Inland Towns Stage 3 LiDAR capture project. This section of the project, undertaken by AAM Pty Ltd on behalf of the Queensland Government captured highly accurate elevation data using LiDAR technology. Available dataset formats (in 1 kilometre tiles) are: - Classified las (LiDAR Data Exchange Format where strikes are classified as ground, non-ground, vegetation or building) - 1 metre Digital Elevation Model (DEM) in ASCII xyz - 1 metre Digital Elevation Model (DEM) in ESRI ASCII grid - 0.25 metre contours in ESRI Shape

<p>Dataset "Detailed surface geology – Upper Burdekin basalt provinces", downloaded from the Queensland Spatial Catalogue in April 2017 and clipped to the Upper Burdekin basalt provinces. <p>The polygons in this dataset are a digital representation of the distribution or extent of geological units within the area. Polygons have a range of attributes including unit name, age, lithological description and an abbreviated symbol for use in labelling the polygons. These have been extracted from the Rock Units Table held in Department of Natural Resources and Mines MERLIN Database. <p>© State of Queensland (Department of Natural Resources and Mines) 2017 Creative Commons Attribution

These datasets cover all of Ipswich City and are part of the 2009 South East Queensland LiDAR capture project. This project, undertaken by AAM Hatch Pty Ltd on behalf of the Queensland Government captured highly accurate elevation data using LiDAR technology. Available dataset formats (in 1 kilometre tiles) are: - Classified las (LiDAR Data Exchange Format where strikes are classified as ground, non-ground or building) - 1 metre Digital Elevation Model (DEM) in ASCII xyz - 1 metre Digital Elevation Model (DEM) in ESRI ASCII grid - 0.25 metre contours in ESRI Shape