<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>

On 8 March 2014, the Boeing 777-200ER aircraft registered as Malaysia Airlines 9M-MRO and operating as flight MH370 (MH370) disappeared from air traffic control radar after taking off from Kuala Lumpur, Malaysia on a scheduled passenger service to Beijing, China with 227 passengers and 12 crew on board. After analysis of satellite data it was discovered that MH370 continued to fly for over six hours after contact was lost. All the available data indicates the aircraft entered the sea close to a long but narrow arc of the southern Indian Ocean. On 31 March 2014, following an extensive sea and air search, the Malaysian Government accepted the Australian Government’s offer to take the lead in the search and recovery operation in the southern Indian Ocean in support of the Malaysian accident investigation. On behalf of Australia, the Australian Transport Safety Bureau (ATSB) coordinated and led the search operations for MH370 in the southern Indian Ocean. Geoscience Australia (GA) provided advice, expertise and support to the ATSB in sea floor mapping (bathymetric survey) and the underwater search. In March 2017 GA was subsequently asked by the ATSB to provide advice and scientific expertise in the analysis of satellite imagery (PLEIADES 1A) (Source: French Military Intelligence Service © CNES) for the detection of possible non-natural objects. All enquiries regarding the overall search should be directed to the ATSB (atsbinfo@atsb.gov.au).

The Australian Geoscience Data Cube has won the 2016 Content Platform of the Year category at the Geospatial World Leadership Awards. The awards recognise significant contributions made by champions of change within the global geospatial industry and were presented during the 2017 Geospatial World Forum held in Hyderabad, India. The Data Cube was developed by Geoscience Australia in partnership with the CSIRO and the National Computational Infrastructure at the Australian National University, and is a world-leading data analysis system for satellite and other Earth observation data. Visit www.datacube.org.au to find out more including the technical specifications, and learn how you can develop your own Data Cube and become part of the collective.

Petascale archives of Earth observations from space (EOS) have the potential to characterise water resources at continental scales. For this data to be useful, it needs to be organised, converted from individual scenes as acquired by multiple sensors, converted into ‘analysis ready data’ and made available through high performance computing platforms. Moreover, converting this data into insights requires integration of non-EOS datasets that can provide biophysical and climatic context for EOS. Digital Earth Australia has demonstrated its ability to link EOS to rainfall and stream gauge data to provide insight into surface water dynamics during the hydrological extremes of flood and drought. This information is supporting the characterisation of groundwater resources across Australia’s north and could potentially be used to gain an understanding of the vulnerability of transport infrastructure to floods in remote, sparsely gauged regions of northern and central Australia.

<div>The Digital Earth Africa notebooks and tools repository hosts Jupyter Notebooks, Python scripts and use cases/workflows for analysing Digital Earth Africa data products and services. The repository is intended to provide a guide to using the Digital Earth Africa Sandbox environment, and to showcase a wide range of geospatial analyses relevant to Digital Earth Africa's thematic areas of interest.</div><div><br></div><div>The Digital Earth Africa notebooks repository is a live and dynamic GitHub project and is regularly updated. The project wiki and readme document contains more detailed information.</div>

<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>

<div>The United States Geological Survey's (USGS) Landsat satellite program has been capturing images of the Australian continent for more than 30 years. This data is highly useful for land and coastal mapping studies.</div><div><br></div><div>In particular, the light reflected from the Earth’s surface (surface reflectance) is important for monitoring environmental resources – such as agricultural production and mining activities – over time.</div><div><br></div><div>We make accurate comparisons of imagery acquired at different times, seasons and geographic locations. However, inconsistencies can arise due to variations in atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect. These are reduced or removed to ensure the data is consistent and can be compared over time.</div><div><br></div><div>The Geoscience Australia Landsat 9 OLI TIRS Analysis Ready Data Collection 3 contains three sub-products that provide corrections or attribution information:</div><div>- DEA Surface Reflectance NBAR* (Landsat 9)</div><div>- DEA Surface Reflectance NBART** (Landsat 9)</div><div>- DEA Surface Reflectance OA*** (Landsat 9)</div><div><br></div><div>Note: DEA produces NBAR as part of the Landsat ARD, this is available in the National Computing Infrastructure environment only and is not available in the DEA cloud environments.</div><div><br></div><div>The resolution is a 30 m grid based on the USGS Landsat Collection 2 archive, or 15 m for the panchromatic band. This data forms part of the DEA Collection 3 archive. </div><div><br></div><div>* Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR)</div><div>** Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance with terrain illumination correction (NBART)</div><div>*** Observation Attributes (OA)</div>

<div>This report gives an overview of the activities of the Geoscience Australia International VLBI Service (IVS) Analysis Centre during 2021-2022.</div><div><br></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>

Chapter 13 "Bathymetry" was provided by Kim Picard for Volume 3B of the 'Earth Observation Series' published by Australia and New Zealand CRC for Spatial Information. The final volume introduces the Australian environment in terms of geography, climate, biota, and resource management, then covers a broad range of application areas reliant on EO data. Specific case studies are included to demonstrate individual applications. Source - https://www.eoa.org.au/earth-observation-textbooks Recommended Chapter Citation: PIcard, K., Anstee, J.M., and Harrison, B.A. (2021). Bathymetry. Ch 13 in Earth Observation: Data, Processing and Applications. Volume 3B—Surface Waters. CRCSI, Melbourne. pp. 223–241. ISBN 978-0-6482278-5-4 Recommended Citation for Volume 3B: CRCSI (2020). Earth Observation: Data, Processing and Applications. Volume 3B: Applications—Surface Waters. (Eds. Harrison, B.A., Anstee, J.M., Dekker, A.G., King, E.A., Griffin, D.A., Mueller, N., Phinn, S.R., Kovacs, E., and Byrne, G.) CRCSI, Melbourne.