The Historical Bushfire Boundaries service represents the aggregation of jurisdictional supplied burnt areas polygons stemming from the early 1900's through to 2022 (excluding the Northern Territory). The burnt area data represents curated jurisdictional owned polygons of both bushfires and prescribed (planned) burns. To ensure the dataset adhered to the nationally approved and agreed data dictionary for fire history Geoscience Australia had to modify some of the attributes presented. The information provided within this service is reflective only of data supplied by participating authoritative agencies and may or may not represent all fire history within a state.

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 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.

Geoscience Australia undertook a marine survey of the Vlaming Sub-basin in March and April 2012 to provide seabed and shallow geological information to support an assessment of the CO2 storage potential of this sedimentary basin. The survey was undertaken under the Australian Government's National CO2 Infrastructure Plan (NCIP) to help identify sites suitable for the long term storage of CO2 within reasonable distances of major sources of CO2 emissions. The Vlaming Sub-basin is located offshore from Perth, Western Australia, and was previously identified by the Carbon Storage Taskforce (2009) as potentially highly suitable for CO2 storage. The principal aim of the Vlaming Sub-basin marine survey (GA survey number GA334) was to look for evidence of any past or current gas or fluid seepage at the seabed, and to determine whether these features are related to structures (e.g. faults) in the Vlaming Sub-basin that may extend up to the seabed. The survey also mapped seabed habitats and biota in the areas of interest to provide information on communities and biophysical features that may be associated with seepage. This research addresses key questions on the potential for containment of CO2 in the Early Cretaceous Gage Sandstone (the basin's proposed CO2 storage unit) and the regional integrity of the South Perth Shale (the seal unit that overlies the Gage Sandstone). This dataset comprises sidescan grids.

Geoscience Australia defines a borehole as the generalized term for any narrow shaft drilled in the ground, either vertically or horizontally, and would include Mineral Drillholes, Petroleum Wells and Water Bores along with a variety of others types, but does not include Costean, Trench or Pit. For the purposes of a Water Well as defined by Groundwater ML v1.0, the dataset has been restricted to onshore Australian boreholes only, and bores that have the potential to support assessment of groundwater resources, within a Bioregional Assessment.

The Aerial Survey Photography Records consist of more than 11,000 film negatives as well as derivative contract prints and diapositives. These records of the Australian landscape were created by Geoscience Australia and its predecessor agencies such as the Australian Surveying and Land Information Group, the Australian Survey Office and the Division of National Mapping. The records were captured during the period c.1928-1993 and have been used as the basis for the Commonwealth government's topographic map production as well as providing an opportunity to track environmental changes in the landscape over an extensive period of time. Antarctic films are also included in the collection. The entire collection was transferred to National Archives Australia in December 2010.

Wildfires are one of the major natural hazards facing the Australian continent. Chen (2004) rated wildfires as the third largest cause of building damage in Australia during the 20th Century. Most of this damage was due to a few extreme wildfire events. For a vast country like Australia with its sparse network of weather observation sites and short temporal length of records, it is important to employ a range of modelling techniques that involve both observed and modelled data in order to produce fire hazard and risk information/products with utility. This presentation details the use of statistical and deterministic modelling of both observations and synthetic climate model output (downscaled gridded reanalysis information) in the development of extreme fire weather potential maps. Fire danger indices such as the McArthur Fire Forest Danger Index (FFDI) are widely used by fire management agencies to assess fire weather conditions and issue public warnings. FFDI is regularly calculated at weather stations using measurements of weather variables and fuel information. As it has been shown that relatively few extreme events cause most of the impacts, the ability to derive the spatial distribution of the return period of extreme FFDI values contributes important information to the understanding of how potential risk is distributed across the continent. The long-term spatial tendency FFDI has been assessed by calculating the return period of its extreme values from point-based observational data. The frequency and intensity as well as the spatial distribution of FFDI extremes were obtained by applying an advanced spatial interpolation algorithm to the recording stations' measurements. As an illustration maps of 50 and 100-year return-period (RP) of FFDI under current climate conditions are presented (based on both observations and reanalysis climate model output). MODSIM 2013 Conference

Imagine you are an incident controller viewing a computer screen which depicts the likely spread of a bushfire that's just started. The display shows houses and other structures in the fire's path, and even the demographics of the people living in the area, such as the number of people, their age spread, whether households have independent transport, and whether English is their second language. In addition, imagine that you can quantify and display the uncertainty in both the fire weather and also the type and state of the vegetation, visualising the sensitivity of the expected fire spread and impact to these uncertainties. It will be possible to consider 'what if' scenarios as the event unfolds, and reject those scenarios that are no longer plausible. The advantages of such a simulation system in making speedy, well-informed decisions has been considered by a group of Bushfire CRC researchers who have collaborated to produce a 'proof of concept' for such a system, demonstrated initially on three case studies. The 'proof of concept' system has the working name FireDST (Fire Impact and Risk Evaluation Decision Support Tool). FireDST links various databases and models, including the Phoenix RapidFire fire prediction model and building vulnerability assessment models, as well as infrastructure and demographic databases. The information is assembled into an integrated simulation framework through a geographical information system (GIS) interface. Pre-processed information, such as factors that determine the local and regional wind, and also the typical response of buildings to fire, are linked through a database, along with census-derived social and economic information. This presentation provides an overview of the FireDST simulation 'proof of concept' tool and walks through a sample probabilistic simulation constructed using the tool. Handbook MODSIM2013 Conference

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.

Geoscience Australia defines a borehole as the generalized term for any narrow shaft drilled in the ground, either vertically or horizontally, and would include Mineral Drillholes, Petroleum Wells and Water Bores along with a variety of others types, but does not include Costean, Trench or Pit. For the purpose of a borehole as defined by GeoSciML Borehole 3.0, the dataset has been restricted to onshore and offshore Australian boreholes, and bores that have the potential to support geological investigations and assessment of a variety of resources.