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.

The Digital Earth Australia (DEA) Program Roadmap describes the high level work plan to be undertaken by the DEA Program in order to achieve its objectives and deliver benefits to the Australian Government and industry.

Analysis Ready Data (ARD) takes medium resolution satellite 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. This product is a single, cohesive ARD package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. ARD consists of sub products, including : 1) NBAR Surface Reflectance which produces standardised optical surface reflectance data using robust physical models which correct for variations and inconsistencies in image radiance values. Corrections are performed using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR). 2) NBART Surface Reflectance which performs the same function as NBAR Surface Reflectance, but also applies terrain illumination correction. 3) OA Observation Attributes product which provides accurate and reliable contextual information about the data. This 'data provenance' provides a chain of information which allows the data to be replicated or utilised by derivative applications. It takes a number of different forms, including satellite, solar and surface geometry and classification attribution labels. ARD enables generation of Derivative Data and information products that represent biophysical parameters, either summarised as statistics, or as observations, which underpin an understanding of environmental dynamics. The development of derivative products to monitor land, inland waterways and coastal features, such as: - urban growth - coastal habitats - mining activities - agricultural activity (e.g. pastoral, irrigated cropping, rain-fed cropping) - water extent Derivative products include: - Water Observations from Space (WOfS) - National Intertidal Digital Elevation Model (NIDEM) - Fractional Cover (FC) - Geomedian ARD and Derivative products are reproduced through a period collection upgrade process for each sensor platform. This process applied improvements to the algorithms and techniques and benefits from improvements applied to the baseline data that feeds into the ARD production processes. <b>Value: </b>These data are used to understand distributions of and changes in surface character, environmental systems, land use. <b>Scope: </b>Australian mainland and some part of adjacent nations. Access data via the DEA web page - <a href="https://www.dea.ga.gov.au/products/baseline-data">https://www.dea.ga.gov.au/products/baseline-data</a>

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

Groundwater-dependent ecosystems (GDEs) rely on access to groundwater on a permanent or intermittent basis for some or all of their water requirements (Queensland Government, 2018). Remotely sensed data from Digital Earth Australia (DEA) (Geoscience Australia, 2018) were used to map potential aquatic and other GDEs and enhance understanding of surface water – groundwater interactions in the Upper Burdekin region. Two Landsat TM satellite products (Water Observations from Space (WOfS; Mueller et al. 2016) summary statistic and Tasselled Cap Index (TCI) wetness summary)) were used to investigate the persistence of surface water and soil moisture in the landscape to identify perennial streams, springs and other parts of the landscape that may rely on groundwater discharge. The WOfS summary statistic represents, for each pixel, the percentage of time that water is detected at the surface relative to the total number of clear observations. Due to the 25-m by 25-m pixel size of Landsat data, only features at least 25 m wide are detected and only features covering multiple pixels are consistently detected. The WOfS summary statistic was produced over the McBride and Nulla Basalt provinces for the entire period of available data (1987 to 2018). Pixels were polygonised and classified in order to visually enhance key data in the imagery, such as the identification of standing water for at least 80% of the time. The TCI is a method of reducing six surface reflectance bands of satellite data to three bands (Brightness, Greenness, Wetness) using a Principal Components Analysis (PCA) and Procrustes' Rotation (Roberts et al., 2018). The published coefficients of Crist (1985) are applied to DEA's Landsat data to generate a TCI composite. The resulting Tasselled Cap bands are a linear combination of the original surface reflectance bands that correlate with the Brightness (bare earth), Greenness and Wetness of the landscape. The TCI wetness summary (or Tasselled Cap Wetness (TCW) percentage exceedance composite), derived from the Wetness band, represents the behaviour of water in the landscape, as defined by the presence of water, moist soil or wet vegetation at each pixel through time. The summary shows the percentage of observed scenes where the Wetness layer of the Tasselled Cap transform is above the threshold, i.e. where each pixel has been observed as ‘wet’ according to the TCI. Areas that retain surface water or wetness in the landscape during the dry season are potential areas of groundwater discharge and associated GDEs. The TCW threshold is set at -600 to calculate the percentage exceedance. This threshold is based on scientific judgment and is currently in the research/testing phase. It is based on Australian conditions and conservative in nature. The dry season, when surface runoff to streams and rainfall are minimal, is particularly useful for identifying and mapping groundwater-fed streams, springs and other ecosystems that rely on access to groundwater during periods of limited rainfall. The Upper Burdekin region was especially dry between May and October 2013, with low rainfall totals in the months preceding this dry season and overall below-average rainfall conditions (i.e. decline in rainfall residual mass). The TCW exceedance composite was classified into percentage intervals to distinguish areas that were wet for different proportions of time during the 2013 dry season. Field validation of the remote sensing data products would be required to confirm the preliminary identification of parts of the landscape where groundwater discharges to the surface and potentially supports GDEs. This release includes the classified WOfS summary statistic and classified TCW percentage exceedance composite (May-October 2013) data products for the McBride and Nulla basalt provinces in the Upper Burdekin region, North Queensland. <b>References: </b> Crist EP (1985) A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment 17(3), 301–306. Doi: 10.1016/0034-4257(85)90102-6. Geoscience Australia (2018) Digital Earth Australia. Geoscience Australia, http://www.ga.gov.au/dea. 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. Remote Sensing of Environment 174, 341-352, ISSN 0034-4257. Queensland Government (2018) Groundwater dependent ecosystems, WetlandInfo 2014. Queensland Government, Brisbane, https://wetlandinfo.des.qld.gov.au/wetlands/ecology/aquatic-ecosystems-natural/groundwater-dependent/. Roberts D, Dunn B and Mueller N (2018) Open Data Cube Products Using High-Dimensional Statistics of Time Series. International Geoscience and Remote Sensing Symposium. Valencia, Spain: IEEE Geoscience and Remote Sensing Society.

The Tasselled Cap Wetness (TCW) percentage exceedance composite represents the behaviour of water in the landscape, as defined by the presence of water, moist soil or wet vegetation at each pixel through time. The summary shows the percentage of observed scenes where the Wetness layer of the Tasselled Cap transform is above the threshold, i.e. where each pixel has been observed as ‘wet’. Areas that retain surface water or wetness in the landscape during the dry season are potential areas of groundwater discharge and associated GDEs. The TCW exceedance composite was classified into percentage intervals to distinguish areas that were wet for different proportions of time during the 2013 dry season. Areas depicted in the dataset have been exaggerated to enable visibility.

Factsheet for DEA with information relevant to stakeholders from the Australian Government

<div>These two videos provide tutorials on how to use the Digital Earth Australia (DEA) portal in the classroom. They include guides for basic navigation, how to load a data set (DEA Landcover) and how to compare different dates within a data set. Additionally, they also show how to share your data via either a share link, image or as an interactive ‘Story’ of saved scenes.</div><div>Videos included:</div><div>-&nbsp;Introduction to using the Digital Earth Australia (DEA) portal</div><div>- Exploring land cover data using the Digital Earth Australia (DEA) portal</div>

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 Tasselled Cap Wetness (TCW) percentage exceedance composite represents the behaviour of water in the landscape, as defined by the presence of water, moist soil or wet vegetation at each pixel through time. The summary shows the percentage of observed scenes where the Wetness layer of the Tasselled Cap transform is above the threshold, i.e. where each pixel has been observed as ‘wet’. Areas that retain surface water or wetness in the landscape during the dry season are potential areas of groundwater discharge and associated GDEs. The TCW exceedance composite was classified into percentage intervals to distinguish areas that were wet for different proportions of time during the 2013 dry season. Areas depicted in the dataset have been exaggerated to enable visibility.