<div>Groundwater dependent ecosystems (GDEs) rely on access to groundwater on a permanent or intermittent basis to meet some or all of their water requirements (Richardson et al., 2011). The <a href="https://explorer-aws.dea.ga.gov.au/products/ga_ls_tc_pc_cyear_3">Tasseled Cap percentile products</a> created by Digital Earth Australia (2023) were used to identify potential GDEs for the South Nicholson-Georgina basins study area. These percentile products provide statistical summaries (10th, 50th, 90th percentiles) of landscape brightness, greenness and wetness in imagery acquired between 1987 and present day. The 10th percentile greenness and wetness represent the lowest 10% of values for the time period evaluated, e.g. 10th percentile greenness represents the least green period. In arid regions, areas that are depicted as persistently green and/or wet at the 10th percentile have the greatest potential to be GDEs. For this reason, and due to accessibility of the data, the 10th percentile Tasseled Cap greenness (TCG) and Tasseled Cap wetness (TCW) products were used as the basis for the assessment of GDEs for the South Nicholson-Georgina region. The 50th percentile greenness was utilised to create the coefficient of variance (CV) dataset. This data release is an ESRI geodatabase, with layer files, including: - combined classified 10th percentile greenness and wetness dataset (useful to identify potential groundwater dependent vegetation/other GDEs and differentiate between vegetation types) - CV of 50th percentile greenness dataset (useful when used in conjunction with the combined product to help identify groundwater dependent vegetation) For more information and detail on these products, refer to associated <a href="https://dx.doi.org/10.26186/149377">report</a>. </div><div><br></div><div><strong>References</strong></div><div>Digital Earth Australia (2023).&nbsp;<em><a href="https://docs.dea.ga.gov.au/">Digital Earth Australia User Guide.</a></em></div><div>Richardson, S., E. Irvine, R. Froend, P. Boon, S. Barber, and B. Bonneville. 2011a.&nbsp;<em>Australian groundwater-dependent ecosystem toolbox part 1: Assessment framework.</em>&nbsp;Waterlines Report 69. Canberra, Australia: Waterlines.</div><div><br></div>

This compilation data release is a selection of remotely sensed imagery used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Datasets include: • Mosaic 5 m digital elevation model (DEM) with shaded relief • Normalised Difference Vegetation Index (NDVI) percentiles • Tasselled Cap exceedance summaries • Normalised Difference Moisture Index (NDMI) • Normalised Difference Wetness Index (NDWI) The 5m spatial resolution digital elevation model with associated shaded relief image were derived from the East Kimberley 2017 LiDAR survey (Geoscience Australia, 2019b). The Normalised Difference Vegetation Index (NDVI) percentiles include 20th, 50th, and 80th for dry seasons (April to October) 1987 to 2018 and were derived from the Landsat 5,7 and 8 data stored in Digital Earth Australia (see Geoscience Australia, 2019a). Tasselled Cap Exceedance Summary include brightness, greenness and wetness as a composite image and were also derived from the Landsat data. These surface reflectance products can be used to highlight vegetation characteristics such as wetness and greenness, and land cover. The Normalised Difference Moisture Index (NDMI) and Normalised Difference Water Index (NDWI) were derived from the Sentinel-2 satellite imagery. These datasets have been classified and visually enhanced to detect vegetation moisture stress or water-logging and show distribution of moisture. For example, positive NDWI values indicate waterlogged areas while waterbodies typically correspond with values greater than 0.2. Waterlogged areas also correspond to NDMI values of 0.2 to 0.4. Geoscience Australia, 2019a. Earth Observation Archive. Geoscience Australia, Canberra. http://dx.doi.org/10.4225/25/57D9DCA3910CD Geoscience Australia, 2019b. Kimberley East - LiDAR data. Geoscience Australia, Canberra. C7FDA017-80B2-4F98-8147-4D3E4DF595A2 https://pid.geoscience.gov.au/dataset/ga/129985