<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>

<b>This record was retired 02/03/2023 with approval from M. Wilson as it has been superseded by <a href="https://dx.doi.org/10.26186/146552">eCat 146552 </a>& <a href="https://dx.doi.org/10.26186/146551">eCat 146551</a></b> Surface Reflectance product has been corrected to account for variations caused by atmospheric properties, sun position and sensor view angle at time of image capture. These corrections have been applied to all satellite imagery in the Sentinel-2 archive. This is undertaken to allow comparison of imagery acquired at different times, in different seasons and in different geographic locations. These products also indicate where the imagery has been affected by cloud or cloud shadow, contains missing data or has been affected in other ways. The Surface Reflectance products are useful as a fundamental starting point for any further analysis, and underpinall other optical derivedDigital Earth Australiaproducts.

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 146261 DEA Geometric Median and Median Absolute Deviation (Landsat)</b> This product provides ‘second order’ statistical techniques that follow from the geometric median, which is useful for environmental characterisation and change detection. The Median Absolute Deviation (MAD) is a generalisation of the classic one-dimensional statistic for multidimensional applications, and is a measure of variance in a dataset through comparison to the median. It is similar in concept to the way that the standard deviation in statistics can be used to understand variance compared to the mean.

<b>This record has been superseded by eCat 148920 DEA Waterbodies v3.0 (Landsat) with approval from N.Mueller on 01/02/2024 This record was retired 15/09/2022 with approval from S.Oliver as it has been superseded by eCat 146197 DEA Waterbodies (Landsat) </b> <p>Up to date information about the extent and location of surface water provides all Australians with a common understanding of this valuable and increasingly scarce resource. <p>Digital Earth Australia Waterbodies shows the wet surface area of waterbodies as estimated from satellites. It does not show depth, volume, purpose of the waterbody, nor the source of the water. <p>Digital Earth Australia Waterbodies uses Geoscience Australia’s archive of over 30 years of Landsat satellite imagery to identify where almost 300,000 waterbodies are in the Australian landscape and tells us the wet surface area within those waterbodies. <p>It supports users to understand and manage water across Australia. For example, users can gain insights into the severity and spatial distribution of drought, or identify potential water sources for aerial firefighting during bushfires. <p>The tool uses a water classification for every available Landsat satellite image and maps the locations of waterbodies across Australia. It provides a timeseries of wet surface area for waterbodies that are present more than 10% of the time and are larger than 3125m2 (5 Landsat pixels). <p>The tool indicates changes in the wet surface area of waterbodies. This can be used to identify when waterbodies are increasing or decreasing in wet surface area.

<p>This mangrove canopy cover product provides valuable information about the extent and canopy density of mangroves for each year between 1987 and 2018 for the entire Australian coastline. </p> <p>The canopy cover classes are 20-50% (pale green), 50-80% (mid green), 80-100% (dark green). The product consists of a sequence (one per year) of 25-metre resolution maps that are generated by analysing the Landsat fractional cover developed by the Joint Remote Sensing Research Program (https://doi.org/10.6084/m9.figshare.94250.v1) and the Global Mangrove Watch layers developed by the Japanese Aerospace Exploration Agency (https://doi.org/10.1071/MF13177). </p> <p>This product can be cited as Lymburner, L., Bunting, P., Lucas, R., Scarth, P., Alam, I., Phillips, C., Ticehurst, C. and Held, A. (2018). Mapping the multi-decadal mangrove dynamics of the Australian coastline. See https://www.sciencedirect.com/science/article/pii/S0034425719301890. </p>

Up to date information about the extent and location of surface water provides all Australians with a common understanding of this valuable and increasingly scarce resource. Digital Earth Australia (DEA) Waterbodies shows the wet surface area of waterbodies as estimated from satellites. It does not show depth, volume, purpose of the waterbody, nor the source of the water. DEA Waterbodies uses Geoscience Australia’s archive of over 30 years of Landsat satellite imagery to identify where over 300,000 waterbodies are in the Australian landscape and tells us the wet surface area within those waterbodies. It supports users to understand and manage water across Australia. For example, users can gain insights into the severity and spatial distribution of drought or identify potential water sources for aerial firefighting. The tool uses a water classification for every available Landsat satellite image and maps the locations of waterbodies across Australia. It provides a timeseries of wet surface area for waterbodies that are present more than 10% of the time and are larger than 2700m2 (3 Landsat pixels). The tool indicates changes in the wet surface area of waterbodies. This can be used to identify when waterbodies are increasing or decreasing in wet surface area. More information on using this dataset can be accessed on the DEA Knowledge Hub at <a href="https://docs.dea.ga.gov.au/data/product/dea-waterbodies-landsat/?tab=overview">https://docs.dea.ga.gov.au/data/product/dea-waterbodies-landsat/?tab=overview</a>. Refer to the research paper Krause et al. 2021 for additional details: <a href="https://doi.org/10.3390/rs13081437">https://doi.org/10.3390/rs13081437</a> The update from version 2 to version 3.0 of the DEA Waterbodies product and service was created through a collaboration between Geoscience Australia, the National Aerial Firefighting Centre, Natural Hazards Research Australia, and FrontierSI to make the product more useful in hazard applications. Geoscience Australia, the National Aerial Firefighting Centre, Natural Hazards Research Australia, and FrontierSI advise that the information published by this service comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, FrontierSI, Geoscience Australia, the National Aerial Firefighting Centre and Natural Hazards Research Australia (including its employees and consultants) are excluded from all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

<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>

<b>This record was retired 02/03/2023 with approval from M. Wilson as it has been superseded by <a href="https://dx.doi.org/10.26186/146552">eCat 146552 </a>& <a href="https://dx.doi.org/10.26186/146551">eCat 146551</a></b> The Surface Reflectance product has been corrected to account for variations caused by atmospheric properties, sun position and sensor view angle at time of image capture. These corrections have been applied to all satellite imagery in the Sentinel-2 archive. This is undertaken to allow comparison of imagery acquired at different times,in different seasons and in different geographic locations. These products also indicate where the imagery has been affected by cloud or cloud shadow, contains missing data or has been affected in other ways. The Surface Reflectance products are useful as a fundamental starting point for any further analysis, and underpinall other optical derivedDigital Earth Australiaproducts.

<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>

<b>BACKGROUND</b> <p> <p>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. <p>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. <p>We need to 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 need to be reduced or removed to ensure the data is consistent and can be compared over time. <p>&nbsp</p> <b>WHAT THIS PRODUCT OFFERS</b> <p> <p>GA Landsat 8 OLI/TIRS Analysis Ready Data Collection 3 takes Landsat 8 imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. <p> <p>The imagery is captured using the Operational Land Imager (OLI) and Thermal Infra-Red Scanner (TIRS) sensors aboard Landsat 8. <p> <p>This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. <p> <p>It contains three sub-products that provide corrections or attribution information: <p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1) GA Landsat 8 OLI/TIRS NBAR Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2) GA Landsat 8 OLI/TIRS NBART Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3) GA Landsat 8 OLI/TIRS OA Collection 3 <p> <p>The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.