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 greater than 25m by 25m 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. Areas depicted in the dataset have been exaggerated to enable visibility.

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 greater than 25m by 25m 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. Areas depicted in the dataset have been exaggerated to enable visibility.

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.