This map shows the boundary of the Maritime Security Zones for each port for the purpose of the Maritime Transport & Office Security Act 2003. 1 Sheet (Colour) June 2010 Not for sale or public distribution Contact Manager LOSAMBA project, PMD

This map shows the boundary of the Maritime Security Zones for each port for the purpose of the Maritime Transport & Office Security Act 2003. 9 Sheets (Colour) March 2010 Not for sale or public distribution Contact Manager LOSAMBA project, PMD

Improving techniques for mapping land surface composition at regional- to continental-scale is the next step in delivering the benefits of remote sensing technology to Australia. New methodologies and collaborative efforts have been made as part of a multi-agency project to facilitate uptake of these techniques. Calibration of ASTER data with HyMAP has been very promising, and following an program in Queensland, a mosaic has been made for the Gawler-Curnamona region in South Australia. These programs, undertaken by Geoscience Australia, CSIRO, and state and industry partners, aims to refine and standardise processing and to make them easily integrated with other datasets in a GIS.

Data gathered in the field during the sample collection phase of the National Geochemical Survey of Australia (NGSA) has been used to compile the Preliminary Soil pH map of Australia. The map, which was completed in late 2009, offers a first-order estimate of where acid or alkaline soil conditions are likely to be expected. It provides fundamental datasets that can be used for mineral exploration and resource potential evaluation, environmental monitoring, landuse policy development, and geomedical studies into the health of humans, animals and plants.

The global ocean absorbs 30% of anthropogenic CO2 emissions each year, which changes the seawater chemistry. The absorbed CO2 lowers the pH of seawater and thus causes ocean acidification. The pH of the global ocean has decreased by approximately 0.1 pH units since the Industrial Revolution, decreasing the concentration of carbonate ions. This has been shown to reduce the rate of biological carbonate production and to increase the solubility of carbonate minerals. As more CO2 is emitted and absorbed by the oceans, it is expected that there will be continuing reduction in carbonate production coupled with dissolution of carbonate sediments. This study was undertaken as part of a program to collect baseline data from Australia's seabed environments and to assess the likely impacts of ocean acidification on continental shelf sediments. Over 250 samples from four continental shelf areas of northern Australia (Capricorn Reef, Great Barrier Reef Lagoon, Torres Strait, Joseph Bonaparte Gulf) were analysed to characterise the surface sediment mineral and geochemical composition. Of particular importance was the quantification of carbonate minerals (calcite, aragonite, high-magnesium calcite) and the magnesium content in high-magnesium calcite. The latter determines the solubility of high-magnesium calcite, which is most soluble of all common carbonate minerals. The thermodynamic stability of carbonate minerals as referred to the state of saturation was calculated using the current and predicted equatorial ocean water composition [1]. Northern Australian continental shelf sediments are largely dominated by carbonate. High-magnesium calcite had the highest abundance of all carbonate minerals followed by aragonite in all areas. The average mol% MgCO3 in high-magnesium calcite varied from 13.6 to 15.5 mol% for the different areas, which is in agreement with the global average magnesium concentration in high-magnesium calcite in tropical and subtropical regions [2].

Severe wind has major impacts on exposed human settlements and infrastructure, while climate change is expected to increase the severe wind hazard in many regions of Australia. The Risk and Impact Analysis Group (RIAG) in Geoscience Australia (GA) has developed a series of techniques to analyse the impact of severe wind imposed on the residential buildings under current and future climate. The process includes four components: hazard, exposure, vulnerability and risk. Severe wind hazard represents site specific wind speed values for different return periods (e.g. 500-year, 2000-year return periods), which may be derived by the wind loading standard (AS/NZS 1170.2), or be a result of modelling for current or future climates. GA has developed a National Exposure Information System (NEXIS), a repository of spatial and structural information of infrastructure exposed and vulnerable to natural hazards. NEXIS has also been extended to consider the number of future residential structures by utilising simple spatial relationships. Using an expert evaluation process, GA has developed a series of fragility curves which relate wind speed to the expected level of damage to residential buildings (measured as a percentage of the total replacement cost) in specific regions in Australia. These curves include consideration of factors such as building location, age, roof material, wall material, and so on. Given a certain intensity of severe wind imposed on a certain type of residential building in a specific region, the physical impact to a community can be determined in terms of the economic loss and casualties. By applying above concepts and procedures, based on sample data from the selected cities, we have integrated these three components (hazard, residential buildings exposure and vulnerability) within a computational framework to derive severe wind risk under both current climate and for a range of climate scenarios. These processes will be utilised for the assessment of climate change adaptation strategies concerning structural wind loading.

Preliminary zircon data and tectonic framework for the Thomson Orogen, northwestern NSW

The term "Smartline" refers to a GIS line map format which can allow rapid capture of diverse coastal data into a single consistently classified map, which in turn can be readily analysed for many purposes. This format has been used to create a detailed nationally-consistent coastal geomorphic map of Australia, which is currently being used for the National Coastal Vulnerability Assessment (NCVA) as part of the underpinning information for understanding the vulnerability to sea level rise and other climate change influenced hazards such as storm surge. The utility of the Smartline format results from application of a number of key principles. A hierarchical form- and fabric-based (rather than morpho-dynamic) geomorphic classification is used to classify coastal landforms in shore-parallel tidal zones relating to but not necessarily co-incident with the GIS line itself. Together with the use of broad but geomorphically-meaningful classes, this allows Smartline to readily import coastal data from a diversity of differently-classified prior sources into one consistent map. The resulting map can be as spatially detailed as the available data sources allow, and can be used in at least two key ways: Firstly, Smartline can work as a source of consistently classified information which has been distilled out of a diversity of data sources and presented in a simple format from which required information can be rapidly extracted using queries. Given the practical difficulty many coastal planners and managers face in accessing and using the vast amount of primary coastal data now available in Australia, Smartline can provide the means to assimilate and synthesise all this data into more usable forms.

A key component of Geoscience Australia's marine program involves developing products that contain spatial information about the seabed for Australia's marine jurisdiction. This spatial information is derived from sparse or unevenly distributed samples collected over a number of years using many different sampling methods. Spatial interpolation methods are used for generating spatially continuous information from the point samples. These methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Machine learning methods, like random forest (RF) and support vector machine (SVM), have proven to be among the most accurate methods in disciplines such as bioinformatics and terrestrial ecology. However, they have been rarely previously applied to the spatial interpolation of environmental variables using point samples. To improve the accuracy of spatial interpolations to better represent the seabed environment for a variety of applications, including prediction of biodiversity and surrogacy research, Geoscience Australia has conducted two simulation experiments to compare the performance of 14 mathematical and statistical methods to predict seabed mud content for three regions (i.e., Southwest, North, Northeast) of Australia's marine jurisdiction Since 2008. This study confirms the effectiveness of applying machine learning methods to spatial data interpolation, especially in combination with OK or IDS, and also confirms the effectiveness of averaging the predictions of these combined methods. Moreover, an alternative source of methods for spatial interpolation of both marine and terrestrial environmental properties using point survey samples has been identified, with associated improvements in accuracy over commonly used methods.

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