In the past two decades, multibeam sonar systems have become the preferred seabed mapping tool. Many users have assumed that multibeam bathymetry data is highly accurate in spatial position. In reality, both vertical and horizontal uncertainties exist in every data point. These uncertainties are often represented as one single measure of Total Propagated Uncertainty (TPU). TPU is important to understand because it affects the quality of products generated from multibeam bathymetry data. To account for the magnitude and spatial distribution of this influence, an objective uncertainty analysis is required. Randomisation is the key process in such an uncertainty analysis. This study compared two randomisation methods, restricted spatial randomness (RSR) and complete spatial randomness (CSR), in an uncertainty analysis of a slope gradient dataset derived from multibeam bathymetry data. CSR regards data error in every grid cell as independent and assumes that the data error varies within a known statistical distribution without any neighbourhood effect. RSR assumes spatial structure and thus spatial auto-correlation in the data. We present a case study from a survey of the Oceanic Shoals Commonwealth Marine Reserve in the Timor Sea, conducted in 2012 by the Marine Biodiversity Hub through the Australian Government National Environmental Research Program. The survey area is characterised by steep-sided carbonate banks and terraces with abrupt breaks in slope of limited spatial extent. As habitats, the carbonate banks and terraces are important because they provide hardground for diverse epibenthic assemblages of sponges and corals, with their steep sides marking the environmental transition to deeper water, soft sediment habitats. In this analysis, the data errors in the multibeam bathymetry data were assumed to follow a Gaussian distribution with a mean of zero and a standard deviation represented by the TPU. The CSR and RSR methods were each implemented using a Monte Carlo procedure with 500 iterations. After about 300 iterations, the Monte Carlo procedure converged for both methods. Results for the study area are compared against pre-processed slope data (Figure 1a). The averaged slope gradient from the CSR method is 4.5 degree greater than the original slope layer, whereas for the RSR method this value is 0.03 degree. Moreover, the slope layer from the CSR method resolves noticeably less detail than the original slope layer and is an over-simplification of the true bathymetry (Figure 1b). In contrast, the RSR method maintained the spatial pattern and detail observed in the original slope layer (Figure 1c). This study demonstrates that although the uncertainty in multibeam bathymetry data should not be ignored, its impact on the subsequent derivative analysis may be limited. The selection of appropriate randomisation method is important for the uncertainty analysis. When the data errors exhibit spatial structure, we recommend using the RSR method.

This PowerPoint presentation gives an overview of Australia's mineral and energy resources. It was used by Andy Barnicoat to brief the Diplomatic Corps at the Department of Foreign Affairs and Trade on 8 April 2014.

Within Australian communities there is a wide range of building types. These vary in many attributes that include floor area, number of storeys, age, architectural style, fit out quality, construction material types and the level of maintenance. For mitigation research it is necessary to take this variety of building types and spatial distribution and discretise it into building classes or categories of similar, if not identical, vulnerability. This 'pigeon holing' strategy makes research on impact, risk and mitigation more tractable in that vulnerabilities can be assigned to each class with the reduced variability within the class captured in the uncertainty of the model. Available exposure information can also be mapped to the schema along with building types that can benefit from retrofit interventions. This report presents the preliminary building schema proposed for the Cost Effective Mitigation Strategy Development for Flood Prone Buildings BNHCRC project. The report discusses the utility of a building schema and which building attributes are important for distinguishing between houses of differing vulnerabilities in the Australian building stock. The proposed schema divides each building into foundations, bottom floor, upper floors (if any) and roof to describe its vulnerability. Through this arrangement it is made possible to assess vulnerability of structures with different construction material used in different floors and also to assess vulnerability of tall structures where only bottom floors are supposed to be inundated. The schema classifies each floor system based on the following attributes: - Construction period - Fit-out quality - Storey height - Bottom floor system - Internal wall material - External wall material Allowing for combinations that are invalid in an Australian context, the draft schema defines 60 discrete vulnerability classes based on the above mentioned attributes. Furthermore, the schema proposed 6 roof types based on material and pitch of the roof. This proposed schema is the initial categorisation of residential structures as to vulnerability class for this project. It is expected to change and be refined as the project is taken forward and the specific building types for retrofit research are identified. The concept of 'nestability' may be subsequently used where mitigation research focuses on several building types that fall within a single broader category and become sub-classes. The draft schema has been developed in recognition of the current and projected ability to define national building exposure and of the parallel BNHCRC mitigation projects examining vulnerability to earthquake and severe wind. While vulnerability schema are hazard specific, alignment has been sought with the schemas for other hazards where possible.

An animation of false colour (R:G:B = SWIR band, NIR band, Green band) Landsat imagery for Trentham and surrounds for the period from 1998 to 2012. The animation consists of gap-filled Landsat data and shows approximately 'a month per second' in the animation.

An animation of fractional cover data(R:G:B =bare soil, green vegetation, non-photosynthetic vegetation) from Landat for Towoomba for the period from 1998 to 2012. The animation consists of gap-filled fractional cover data and shows approximately 'a month per second' in the animation.

An animation of false colour (R:G:B = SWIR band, NIR band, Green band) Landsat imagery for Towoomba for the period from 1998 to 2012. The animation consists of gap-filled Landsat data and shows approximately 'a month per second' in the animation.

AAM was engaged by DPIPWE to acquire LiDAR data over several coastal areas of Tasmania during March and April 2014. Granville Harbour comprises approximately 3.8 km2

It is a video to accompany the GA Achievements Document.

AAM was engaged by DPIPWE to acquire LiDAR data over several coastal areas of Tasmania during March and April 2014. South Port comprises approximately 5.07 km²

This Record presents data collected as part of the ongoing NTGS-GA geochronology project between July 2013 and June 2014 under the National Geoscience Agreement (NGA). In total, 6 new U-Pb SHRIMP zircon and monazite geochronological results derived from 6 samples from the Arunta Region in the Northern Territory are presented herein (Table 1; Figure 1). One igneous sample was collected from LAKE MACKAY1 in the western Arunta Region, and a second igneous sample from the Central Arunta Region (ALCOOTA). Another 4 samples are from JERVOIS RANGE in HUCKITTA in the Eastern Arunta, and comprise metasedimentary and metaigneous rocks.