<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>

<div>This report presents the findings of a study conducted in the upper Darling River floodplain, aimed at improving optical and interferometric synthetic aperture radar (InSAR) remote sensing products for groundwater dependant vegetation (GDV) characterisation. The research was part of the Upper Darling Floodplain (UDF) groundwater study, funded by the Exploring for the Future program.</div><div>This work tests the suitability of two novel remote sensing methods for characterising ecosystems with a range of likely groundwater dependence: combined wetness and greenness indices derived from Landsat products available through Geoscience Australia’s Digital Earth Australia platform, and an InSAR derived index of vegetation structure (known as SARGDE), which has been so far tested only in northern Australia. In addition, the relationship between the Normalised Difference Vegetation Index (NDVI), a remotely sensed proxy for vegetation condition, and water availability from surface water flows, rainfall, and groundwater was tested for sites with a range of low to high likely groundwater dependence.&nbsp;</div><div>The key findings of this work, and potential implications, are:</div><div>• A multiple lines of evidence approach, drawing on persistence of wetness/greenness and vegetation structure, and correlation between inferred vegetation condition and groundwater levels, gives high confidence in the groundwater dependence of parts of the floodplain, particularly within the riparian zone. These indices require calibration with ground condition data to be applied in different regions, but a combined index could provide a high confidence measure of groundwater dependence.</div><div>• Combined greenness and wetness, SARGDE, and the relationship between NDVI and groundwater levels all showed areas classified as ‘moderate’ likelihood of groundwater dependence having signatures comparable to areas classified as high likelihood. This could address a shortcoming of the groundwater dependence classification methodology, which, when groundwater level information is missing, classifies some vegetation types as moderate.</div><div>• A combined index taking into account both greenness and wetness was able to better delineate vegetation types with a range of groundwater dependence previously not achievable using remote sensing products.&nbsp;</div><div>This work has provided improved methodology for applying remote sensing products to groundwater dependent vegetation characterisation in the study area. The methods are likely to be applicable to other regions with groundwater dependent vegetation. The results add to the evidence that it is necessary to better integrate surface and groundwater resources in water sharing plans at a basin scale. Further work is required to quantify the frequency and magnitude of flow events required to replenish alluvial groundwater sufficiently to maintain existing groundwater dependent ecosystems.&nbsp;&nbsp;</div><div><br></div><div><br></div>