60 second video announcing Digital Earth Australia - a world first analysis platform for satellite imagery and other Earth observations.

This report provides a preliminary assessment of the utility of a satellite remote sensing approach for the identification and characterisation of coastal habitats that are critical for threatened and migratory species in northern Australia. This work is part of the Habitats research theme in the A12 Northern Seascapes Scoping Project. The Australian Landsat archive in the Digital Earth Australia (DEA) analysis platform for satellite imagery was utilised to demonstrate its potential for mapping intertidal areas and mangrove extent, and changes over time in the extent of coastal landforms and habitats. Seven estuaries were examined, Darwin Harbour and the Keep, Daly, Roper, Macarthur, Flinders and Gilbert River estuaries. The estuaries were selected by the A12 Project team because they are known to provide important areas for the species of interest. Features of importance to shorebird populations were a focus. The focus of this scoping work was to utilise the DEA Landsat archive to build understanding of the effects of tidal dynamics on intertidal habitats across this region of large and complex tides, examine approaches to mapping the extent of key coastal habitats, and test the potential of the archive to detect coastal habitat change, in particular mangrove. In northern Australia, cloud interference can make it difficult to obtain clear satellite imagery. To avoid this issue, the geometric median of surface reflectance values was used to produce crisp, cloud-free composite images that depict the maximum observed tidal extent in the seven estuaries. Tide-tagging of satellite imagery was also successfully employed to allow any tide induced change to be removed from change-detection analyses and clearly depict the intertidal extent. Application of the Intertidal Extent Model in the DEA enabled the extent and morphology of estuarine intertidal environments to be mapped. The DEA also enabled habitat change change detection using the fully processed, high density, three decade long Landsat time series. The results clearly depict the dynamic nature of some areas, including large-scale rapid island growth and mangrove expansion (e.g. Keep River and Gilbert River estuaries), gradual long-term expansion of mangrove (Flinders River and McArthur River estuaries), and estuaries with areas of rapid recent die back of mangrove (Roper River and Flinders estuaries). This information is important for the management of key species as well decisions around coastal developments. With Landsat and new satellite data streams (e.g. Sentinal 2) continually being added to the DEA, this time-series analysis approach could be developed into an effective habitat extent and condition monitoring tool for northern Australia. The image products and analysis tools employed in this study demonstrate the potential utility of DEA for mapping the extent and dynamics of key coastal and estuarine habitats utilised by threatened and migratory species. To better inform the management of these species, a key next step in this approach is to utilise ground-validation data to enable these habitats to be robustly classified and quantified using the Landsat archive. This analysis should provide important baseline information and enable the extent and condition of key habitats to be monitored. <b>Preferred Citation:</b> <i>Phillips, C., Lymburner, L. & Brooke, B. (2018). Characterising northern estuaries using Digital Earth Australia.</i> Report to the National Environmental Science Programme, Marine Biodiversity Hub. <i>Geoscience Australia.</i>

The National Spectral Database (NSD) houses data from Australian remote sensing scientists. The database includes spectra covering targets as diverse as mineralogy, soils, plants, water bodies and various land surfaces. Currently the database holds spectral information from multiple locations across the country and as the collection grows in spatial / temporal coverage, the NSD will service continental scale validation requirements of the Earth observation community for satellite-based measurements of surface reflectance. <b>Value:</b> Curated spectral data provides a wealth of knowledge to remote sensing scientists. For other parties interested in calibration and validation (Cal/Val) of surface reflectance products, the Geoscience Australia (GA) Cal/Val dataset provides a useful resource of ground-truth data to compare to reflectance captured by Landsat 8 and Sentinel 2 satellites. The Aquatic Library is a robust collection of Australian datasets from 1994 to present time, primarily of end-member and substratum measurements. The University of Wollongong collection represents immense value in end-member studies, both terrestrial and aquatic. <b>Scope:</b> The NSD covers Australian data including historical datasets as old as 1994. Physical study sites encompass locations around Australia, with spectra captured in every state. <b>Data types:</b> - Spectral data: raw digital numbers (DN), radiance and reflectance.  - From spectral bands VIS-NIR, SWIR1 & SWIR2: wavelengths 350nm - 2500nm collected with instruments in the field or lab setting. Contact for further information: NSDB_manager@ga.gov.au

Australia has a vast and highly dynamic coastline of over 30,000 kilometres with many unique environments: sandy beaches, rocky cliffs, muddy tidal flats, and mangroves. Until recently, this scale and complexity has meant that many of Australia's coastal environments have been poorly and inconsistently mapped, particularly in dynamic or remote regions where accurate survey data can be extremely challenging and costly to obtain. In recent years, however, satellites orbiting our planet have provided a new and powerful source of information about Australia's coast and how it has changed over recent decades. Digital Earth Australia is a government platform that prepares these vast volumes of satellite data and makes it available to governments and industry for easy use. This talk will showcase how new and innovative analysis techniques can be applied to petabytes of DEA satellite data to better understand and monitor Australia's vast coastal zone from space: from using the rise and fall of the tide to map the 3D shape of Australia's coast, to track how our coastline has shifted and changed over the past three decades in unprecedented scale and detail. We will demonstrate how these freely available coastal products and tools developed by Digital Earth Australia can be used by scientists, managers, policymakers and the general public to provide new information to help maintain and protect Australia's iconic shores for future generations.

Factsheet for DEA with information relevant to stakeholders from the earth observation iand other related industries.

Digital Earth Australia (DEA) is a world-class digital infrastructure that uses satellite data, in the form of images and information, to detect physical changes across Australia in unprecedented detail. It identifies soil and coastal erosion, crop growth, water quality and changes to cities and regions. DEA provides government, industry, and individuals with the high-quality data and tools required for policy and investment decision-making. DEA will support industry productivity and innovation and the development of new digital products and services. These capabilities will improve decision-making, increase business efficiency, bolster profits and create jobs. For more information visit www.ga.gov.au/dea

The National Spectral Database (NSD) houses data taken by Australian remote sensing scientists. The database includes spectra covering targets as diverse as mineralogy, soils, plants, water bodies and various land surfaces.<br /> Currently the database holds spectral information from multiple locations across the country and as the collection grows in spatial / temporal coverage, the NSD will service continental scale validation requirements of the Earth observation community for satellite-based measurements of surface reflectance. The NSD is accessed with information provided at the NSD Geoscience Australia Content Management Interface (CMI) web page: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database <b>Value:</b> Curated spectral data provides a wealth of knowledge to remote sensing scientists. For other parties interested in calibration and validation (Cal/Val) of surface reflectance products, the Geoscience Australia (GA) Cal/Val dataset provides a useful resource of ground-truth data to compare to reflectance captured by Landsat 8 and Sentinel 2 satellites. The Aquatic Library is a robust collection of Australian datasets from 1994 to present time, primarily of end-member and substratum measurements. The University of Wollongong collection represents immense value in end-member studies, both terrestrial and aquatic. <b>Scope:</b> The NSD covers Australian data including historical datasets as old as 1994. Physical study sites encompass locations around Australia, with spectra captured in every state. <b>Data types:</b> - Spectral data: raw digital numbers (DN), radiance and reflectance.  - From spectral bands VIS-NIR, SWIR1 & SWIR2: wavelengths 350nm - 2500nm collected with instruments in the field or lab setting. Contact for further information: NSDB_manager@ga.gov.au <b>To view the entire collection click on the keyword "HVC 144490" in the below Keyword listing <b>

Analysis Ready Data (ARD) are satellite data that have been pre-processed for immediate analysis with minimal user effort. The generation of Surface Reflectance (SR) from optical satellite data, involves a series of corrections to standardise the data and enable meaningful comparison of data from multiple sensors and across time. Surface reflectance data are foundational for time-series analyses and rapid generation of other information products. Field based validation of surface reflectance data is therefore critical to determine its fitness for purpose, and applicability for downstream product development. In this paper, an approach for continental scale validation of the surface reflectance data from Landsat-8 and Sentinel-2 satellites, using field-based measurements that are near-synchronous to the satellite observations over multiple sites across Australia is presented. Good practice measurement protocols governing the acquisition of field data, including field instrument calibration, sampling strategy and approach for post-collection processing and management of field spectral data are outlined. This study has been a nationally coordinated, collaborative field data collection campaign across Australia. Permanent field sites, to support validation efforts within the broader Earth Observation (EO) community for continental scale products were also identified. The approach is expected to serve as a model for coordinated ongoing validation of ARD products at continental to global scales. Presented at the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

<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>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>