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.

<div>The A1 poster incorporates 4 images of Australia taken from space by Earth observing satellites. The accompanying text briefly introduces sensors and the bands within the electromagnetic spectrum. The images include examples of both true and false colour and the diverse range of applications of satellite images such as tracking visible changes to the Earth’s surface like crop growth, bushfires, coastal changes and floods. Scientists, land and emergency managers use satellite images to analyse vegetation, surface water or human activities as well as evaluate natural&nbsp;hazards.</div>

<div>This document steps teachers and students through accessing and using satellite data on the Digital Earth Australia (DEA) Portal, with a particular focus on bushfires and flood events. The document is intended to be followed with the DEA portal open so teachers and students can explore the data using the links provided in the guide. The document also provides brief background information on how spectral satellites operate and how various bands of the electromagnetic spectrum can deliver useful data.</div>

This report was prepared by Geoscience Australia for the Bushfire CRC. It is intended that this report be used as part of the background material for the reports prepared for the Royal Commission into the Victorian Bushfires 2009. This report contains a demographic analysis of some of the areas directly affected by the bushfires. The areas included in this report (with alternative fire names in brackets) are: Churchill (Churchill - Jeeralang) Bunyip (Bunyip SP - Bunyip Ridge Trk) Bendigo (Mainden Gully/Eaglehawk - Bracewell St) Kilmore (Kilmore East - Murrindindi Complex South) Murrindindi/Yea (Kilmore East - Murrindindi Complex North) Beechworth Horsham Narre Warren

<div>This spatial product shows nationally aggregated, harmonised, and standardised accumulating 3-hourly snapshots of bushfire and prescribed burn boundaries, consistent across all jurisdictions who have the technical ability or appropriate licence conditions to provide this information. This dataset currently contains information from every Australian state/territory except the Northern Territory. This dataset is derived from the <em>Bushfire Boundaries Near-Real-Time </em>product.&nbsp;&nbsp;</div><div><br></div><div>The Bushfire Boundaries - 3-Hourly Accumulation dataset can be accessed through Digital Atlas of Australia.

<div>This spatial product shows nationally aggregated, harmonised, and standardised ‘near real-time’ bushfire and prescribed burn boundaries for all jurisdictions who have the technical ability or appropriate licence conditions to provide this information. This dataset currently contains information from every Australian state/territory except the Northern Territory.</div><div><br></div><div>The Bushfire Boundaries - Near Real-Time dataset can be accessed through Digital Atlas of Australia.

<div>A package of deliverables for the Australian Research Data Commons (ARDC), Bushfire History Data Project, Work Package 5. If you require further information or access, please contact earth.observation@ga.gov.au</div><div><br></div><div>Outputs generated for this Project are interim and represent a snapshot of work to date, as of September 2023. Deliverables are developmental in nature and are under further advancement. Datasets or visualisations should not be treated as endorsed, authoritative, or quality assured; and should not be used for anything other than a minimal viable product, especially not for safety of life decisions. The eventual purpose of this information is for strategic decisions, rather than tactical decisions. For local data, updates and alerts, please refer to your State or Territory emergency or fire service.</div><div><br></div><div>The purpose of this Project (WP5) was to generate fire history products from Earth observation (EO) data available from the Landsat and Sentinel-2 satellites. WP5 aimed to implement a suite of automated EO-based algorithms currently in use by State and Territory agencies, to produce National-scale data products describing the timing, location, and extent of bushfires across Australia. WP5 outputs are published here as a “deliverable package”, listed as documents, datasets and Jupyter notebooks.&nbsp;</div><div><br></div><div>Burnt area data demonstrators were produced to a Minimum Viable Product level. Four burnt area detection methods were investigated: </div><div>* BurnCube (Geoscience Australia, ANU, (Renzullo et al. 2019)),</div><div>* Burnt Area Characteristics (Geoscience Australia, unpublished methodology),</div><div>* A version of the Victoria’s Random Forest (Victorian, Tasmanian and New South Wales Governments). Based on method as described in Collins et al. (2018), and</div><div>* Queensland’s RapidFire (Queensland Government, (Van den Berg et al. 2021). Please note that demonstrator burnt area data from the Queensland method was only investigated for the Queensland location. Data were sourced from Terrestrial Ecosystem Research Network (TERN) infrastructure, which is enabled by the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS). </div><div><br></div><div>In addition demonstrator products that examine the use of Near Real Time satellite data to map burnt area, data quality and data uncertainty were delivered. </div><div><br></div><div>The algorithms were tested on several study sites:</div><div>* Eastern Victoria,</div><div>* Cooktown QLD,</div><div>* Kangaroo Island SA,</div><div>* Port Hedland WA, and</div><div>* Esperance WA.</div><div><br></div><div>The BurnCube (Renzullo et al. 2019) method was implemented at a national-scale using the Historic Burnt Area Processing Pipeline documented below “GA-ARDC-DataProcessingPipeline.pdf”. Continental-scale interim summary results were generated for both 2020 Calendar Year and 2020 Financial Year. Results were based upon both Landsat 8 and Sentinel-2 (combined 2a and 2b) satellite outputs, producing four separate interim products:&nbsp;</div><div>* Landsat 8, 2020 Calendar Year, BurnCube Summary (ga_ls8c_nbart_bc_cyear_3),</div><div>* Landsat 8, 2020 Financial Year, BurnCube Summary (ga_ls8c_nbart_bc_fyear_3),</div><div>* Sentinel 2a and 2b, 2020 Calendar Year, BurnCube Summary (ga_s2_ard_bc_cyear_3),</div><div>* Sentinel 2a and 2b, 2020 Financial Year, BurnCube Summary (ga_s2_ard_bc_fyear_3).</div><div>&nbsp;</div><div>The other methods have sample products for the study sites, as discussed in the "lineage" section. </div><div><br></div><div>The Earth observation approach has several limitations, leading to errors of omission and commission (ie under estimation and over estimation of the burnt area). Omission errors can result from: lack of visibility due to clouds; small or patchy fires; rapid vegetation regrowth between fire and satellite observation; cool understorey burns being hidden by the vegetation canopy. Commission errors can result from: incorrect cloud or cloud-shadow masking; high-intensity land-use changes (such as cropping); areas of inundation. In addition cloud and shadow masking lead to differences in elapsed time between reference imagery and observations of change resulting in differences in burn area detection. For more information on data caveats please see Section 7.6 of DRAFT-ARDC-WP5-HistoricBurntArea.</div><div><br></div><div>The official Project title is: The Australian Research Data Commons (ARDC), Bushfire Data Challenges Program; Project Stream 1: the ARDC Bushfire History Data Project; Work Package 5 (WP5): National burnt area products analysed from Landsat and Sentinel 2 satellite imagery.</div><div><br></div><div>We thank the Mindaroo Foundation and ARDC for their support in this work.</div>

Modelling of the risk posed by the impacts of extreme weather events requires knowledge of the vulnerability, or performance, of building assets. Furthermore, to assess the benefits of mitigation an ability to quantitatively model the change in vulnerability associated with mitigation actions is required. In Australia past efforts at establishing vulnerability relationships between building damage and severe wind have centred on empirical techniques, using data from damage surveys or insurance losses, and heuristic techniques. Neither of these methods permits the change in vulnerability afforded by mitigation work to be quantitatively modelled. The Bushfire and Natural Hazards CRC project “Improving the Resilience of Existing Housing to Severe Wind Events” is developing a software tool, Vulnerability and Adaption to Wind Simulation (VAWS), to provide a quantitative vulnerability model for Australian house types. It is based on the premise that overall building damage is strongly related to the failure of key connections. The software uses a Monte Carlo approach whereby numerous realisations of a single generic house type are subjected to an increasing gust wind speed and the loss at each wind speed is calculated. Each realisation of the house varies from others as many key building parameters, such as connection strength, are sampled from probability distributions. For each instance, at each wind speed, the number and type of failed connections are related to damage states and extents of damage which permits the repair cost to be calculated. The repair cost is adjusted for the repair of debris impact damage and water ingress damage. The modelling of mitigation is easily accomplished by rerunning a house modelled with the probability distribution of an upgraded connection’s strength substituted. The software tool provides quantitative measures of reduced vulnerability that can be used in assessing the incremental effectiveness of a range of mitigation strategies in economic terms. Abstract submitted to/presented at AMOS-ICSHMO 2018 (https://www.ametsoc.org/index.cfm/ams/meetings-events/ams-meetings/amos-icshmo-2018/)

This atlas-style report presents a spatial demographic analysis for Victoria including measures of population vulnerability. It updates the 2016 report which relied on data from the ABS 2011 census of population and housing. This version uses information from the 2016 census along with other updated population data. Key findings include: • Fire is a natural part of the Australian landscape but its incidence and impact can be increased by the presence of people. • Measures of vulnerability are indicative. They do not predict how a particular individual will respond to a specific event. Nevertheless, research studies have shown that some characteristics are associated with an individual’s level of vulnerability before, during, or after a disaster. • Population vulnerabilities have a geographical distribution. Some communities will have a greater measure of vulnerability than others, and some locations may display multiple types of vulnerability. • The vulnerability level of a household will be determined by its weakest rather than its strongest member. KEY FINDINGS • Population characteristics change over time. Hence patterns of vulnerability can also change over time. Sometimes changing characteristics occur because people move into or out of a community. Other changes occur within a population. Children may be born, increasing the number of infants in a community, or people may age in place, causing an increse in numbers of older people. • In Melbourne’s fringe and peri-urban areas, this pattern of ageing in place is likely to cause a significant increase in numbers of older people. • Most population measures are based on where people usually live or work, yet people can be highly mobile. • People may have more than one residence. This can include: holiday homes; weekenders; or for regional populations, a townhouse in the city. • Population mobility presents particular challenges for risk assessment and emergency management. Towns may vary in population size by a factor of four or five during particular seasons of the year. • Popular visitor and holiday locations such as the Dandenong Ranges and Great Ocean Road have particularly high fire risk. Planning for fire therefore requires an understanding of both permanent and part-time populations.

This dataset presents a three hourly accumulation of bushfire boundaries throughout the 2021/22 bushfire season (1/9/2021 - 30/4/22) for all Australian jurisdictions who have the technical ability and/or appropriate licence conditions to provide this information. The accuracy and completeness of the data attributes within this webservice is reliant on each jurisdictional source and the information they elect to publish into their historical bushfire boundary data products. The update frequency of the underlying data varies across the jurisdictions and therefore data may not be present in some areas. No filtering has been performed where source data overlaps.