This web service provides access to gridded data produced by Geoscience Australia from studies of Australian groundwater and hydrogeological systems.

This service includes world bathymetry, elevation (hillshade), and satellite imagery data, and ocean, country, population and natural features. The information was derived from various sources, including Natural Earth and Landsat Imagery. It is a cached service with a Web Mercator Projection. The service contains layer scale dependencies.

This web service provides access to gridded data produced by Geoscience Australia from studies of Australian groundwater and hydrogeological systems.

This service includes world bathymetry, elevation (hillshade), and satellite imagery data, and ocean, country, population and natural features. The information was derived from various sources, including Natural Earth and Landsat Imagery. It is a cached service with a Web Mercator Projection. The service contains layer scale dependencies.

<div>In recent years Geoscience Australia has undertaken a successful continental scale validation program, targeting Landsat and Sentinel analysis ready data surface reflectance products. The field validation model used for this program successfully built on earlier studies and the measurement uncertainties associated with these protocols have been quantified and published. As a consequence, the Australian earth observation community was well-placed to respond to the United States Geological Survey (USGS) call for collaborators with the 2021 Landsat 8 (L8) and Landsat 9 (L9) 6 underfly. Despite a number of challenges, seven validation datasets were captured across five sites. As there was only a single 100% overlap transit across Australia and with the country in the midst of a strong La Niña climate cycle, it was decided to deploy teams to the two available overpasses with only 15% side lap. The validation sites encompassed rangelands, chenopod scrublands and a large inland lake. Apart from instrument problems at one site, good weather enabled the capture of high quality field data allowing for meaningful comparisons between the radiometric performance of L8 and L9, as well as the USGS and Australian Landsat analysis ready data processing models. Duplicate (cross calibration) spectral sampling at different sites provides evidence of the field protocol reliability, while the off-nadir view of L9 over the water site has been used to better compare the performance of different water and atmospheric correction (ATCOR) processing models.&nbsp;</div> <b>Citation: </b>Byrne, G.; Broomhall, M.; Walsh, A.J.; Thankappan, M.; Hay, E.; Li, F.; McAtee, B.; Garcia, R.; Anstee, J.; Kerrisk, G.; et al. Validating Digital Earth Australia NBART for the Landsat 9 Underfly of Landsat 8. <i>Remote Sens.</i> <b>2024</b>, 16, 1233. https://doi.org/10.3390/rs16071233

<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>The Groundwater Dependent Ecosystem (GDE) Atlas (Bureau of Meteorology, 2019) is a well-known national product that has been utilised for a wide range of applications including environmental impact statements, water planning and research. A complementary GDE dataset, Groundwater Dependent Waterbodies (GDW), has been produced from Digital Earth Australia (DEA) national data products. This new GDW ArcGIS dataset is spatially aligned with Landsat satellite-derived products, enabling ready integration with other spatial data to map and characterise GDEs across the continent.</div><div><br></div><div>The DEA Water Observations Multi Year Statistics (Mueller et al. 2016; DEA 2019) and the DEA Waterbodies (version 2) data product (Kraus et al., 2021; DEA Waterbodies, 2022) have been combined with the national GDE Atlas to produce the GDW dataset which delineates surface waterbodies that are known and/or high potential aquatic GDEs. The potential of a GDE relates to the confidence that the mapped feature is a GDE, where known GDEs have been mapped from regional studies and high potential GDEs identified from regional or national studies (Nation et al., 2017). The GDW dataset are aquatic GDE waterbodies, including springs, rivers, lakes and wetlands, which rely on a surface expression of groundwater to meet some or all of their water requirements. </div><div><br></div><div>The DEA Water Observation Multi Year Statistics, based on Collection 3 Landsat satellite imagery, shows the percentage of wet observations in the landscape relative to the total number of clear observations since 1986. DEA Waterbodies identifies the locations of waterbodies across Australia that are present for greater than 10% of the time and are larger than 2700m2 (3 Landsat pixels) in size. These waterbodies include GDEs and non-GDEs (e.g. surface water features not reliant on groundwater, such as dams). Where known/high potential GDEs in the GDE Atlas intersected a DEA waterbody, the entire waterbody polygon was assigned as a potential GDW, resulting in 55,799 waterbodies in the GDW dataset. Conversely, any GDEs not classified as known/high potential GDEs in the Atlas, due to a lack of data, are not included in the GDW product. Even though this method should remove dams from the GDW dataset (assuming they have been assigned appropriately in the GDE Atlas), due to spatial misalignment some may still be included that are not potential GDEs. Furthermore, surface water features that are too small to be detected by Landsat satellite data will be excluded from the GDW dataset.</div><div><br></div><div>The GDW polygons were attributed with the spatial summary of maximum, median, mean and minimum percentages for pixels within each GDW, derived from the DEA Water Observation Multi Year Statistics i.e. maximum/minimum pixel value or median/mean across all pixels in the GDW. This attribute enables comparison between GDWs of the proportion of time they have surface water observed. An additional attribute was added to the GDW dataset to indicate amount of overlap between waterbodies and aquatic GDEs in the GDE Atlas.&nbsp;</div><div><br></div><div>An ESRI dataset, AquaticGDW.gdb, and a variety of national ArcGIS layer files have been produced using the spatial summary statistics in the GDW dataset.</div><div>These provide a first-pass representation of known/high potential aquatic GDEs and their surface water persistence, derived consistently from Landsat satellite imagery across Australia.</div><div><br></div><div><strong>References:</strong></div><div> </div><div>Bureau of Meteorology, 2019. <em>Groundwater Dependent Ecosystems Atlas</em>. http://www.bom.gov.au/water/groundwater/gde/index.shtml </div><div>&nbsp;</div><div>DEA Water Observations Statistics, 2019. https://cmi.ga.gov.au/data-products/dea/686/dea-water-observations-statistics-landsat</div><div><br></div><div>DEA Waterbodies, 2022. https://www.dea.ga.gov.au/products/dea-waterbodies</div><div><br></div><div>Krause, C.E., Newey, V., Alger, M.J., and Lymburner, L., 2021. Mapping and Monitoring the Multi-Decadal Dynamics of Australia’s Open Waterbodies Using Landsat, <em>Remote Sensing</em>, 13(8), 1437. https://doi.org/10.3390/rs13081437</div><div><br></div><div>Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S. and Ip, A., 2016. Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. <em>Remote Sensing of Environment</em>, 174, 341-352, ISSN 0034-4257.</div><div><br></div><div>Nation, E.R., Elsum, L., Glanville, K., Carrara, E. and Elmahdi, A., 2017. Updating the Atlas of Groundwater Dependent Ecosystems in response to user demand, 22nd International Congress on Modelling and Simulation, Hobart, Tasmania, mssanz.org.au/modsim2017</div>

This web service provides access to gridded data produced by Geoscience Australia from studies of Australian groundwater and hydrogeological systems.

<div>Tasseled Cap percentiles provide an annual summary of how the environment has varied through a year. The Tasseled Cap percentiles provide the upper, lower and middle conditions as described by the 90th, 10th and 50th percentiles respectively, of greenness, wetness and brightness across the landscape.</div><div><br></div><div>These percentiles are intended for use as inputs into classification algorithms to identify such environmental features as wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms.</div><div><br></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>