This web service provides access to satellite imagery products for the identification of potential groundwater dependent ecosystems (GDEs) in the South Nicholson - Georgina region.

<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>

This web service contains a selection of remotely sensed raster products used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Selected products were derived from LiDAR, Landsat (5, 7, and 8), and Sentinel-2 data. Datasets include: 1) mosaic 5 m digital elevation model (DEM) with shaded relief; 2) vegetation structure stratum and substratum classes; 3) Normalised Difference Vegetation Index (NDVI) 20th, 50th, and 80th percentiles; 4) Tasselled Cap exceedance summaries; 5) Normalised Difference Moisture Index (NDMI) and Normalised Difference Wetness Index (NDWI). Landsat spectral reflectance products can be used to highlight land cover characteristics such as brightness, greenness and wetness, and vegetation condition; Sentinel-2 datasets help to detect vegetation moisture stress or waterlogging; LiDAR datasets providing a five meter DEM and vegetation structure stratum classes for detailed analysis of vegetation and relief.

This web service contains a selection of remotely sensed raster products used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Selected products were derived from LiDAR, Landsat (5, 7, and 8), and Sentinel-2 data. Datasets include: 1) mosaic 5 m digital elevation model (DEM) with shaded relief; 2) vegetation structure stratum and substratum classes; 3) Normalised Difference Vegetation Index (NDVI) 20th, 50th, and 80th percentiles; 4) Tasselled Cap exceedance summaries; 5) Normalised Difference Moisture Index (NDMI) and Normalised Difference Wetness Index (NDWI). Landsat spectral reflectance products can be used to highlight land cover characteristics such as brightness, greenness and wetness, and vegetation condition; Sentinel-2 datasets help to detect vegetation moisture stress or waterlogging; LiDAR datasets providing a five meter DEM and vegetation structure stratum classes for detailed analysis of vegetation and relief.

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.

This web service provides access to satellite imagery products for the identification of potential groundwater dependent ecosystems (GDEs) in the South Nicholson - Georgina region.

<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">Tasselled Cap percentile products</a> created by Digital Earth Australia (2023) were used to identify potential GDEs for the upper Darling River floodplain 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 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 Tasselled Cap greenness (TCG) and Tasselled Cap wetness (TCW) products were used as the basis for the assessment of GDEs for the upper Darling River floodplain study area. </div><div><br></div><div>This data release is an ESRI geodatabase, with layer files, including:</div><div><br></div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;original greenness and wetness datasets extracted; </div><div><br></div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;classified 10th percentile greenness and wetness datasets (used as input for the combined dataset); </div><div><br></div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;combined scaled 10th percentile greenness and wetness dataset (useful for a quick glance to identify potential groundwater dependent vegetation (GDV) that have high greenness and wetness e.g. river red gums)</div><div><br></div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;combined classified 10th percentile greenness and wetness dataset (useful to identify potential GDV/GDE and differentiate between vegetation types)</div><div><br></div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;coefficient of variation of 50th percentile greenness dataset (useful when used in conjunction with the scaled/combined products to help identify GDEs)</div><div><br></div><div>For more information and detail on these products, refer to <a href="https://dx.doi.org/10.26186/148545">https://dx.doi.org/10.26186/148545</a>.</div><div><br></div><div><strong>References</strong></div><div>Digital Earth Australia (2023). <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. <em>Australian groundwater-dependent ecosystem toolbox part 1: Assessment framework.</em> Waterlines Report 69. Canberra, Australia: Waterlines.</div>

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.

This web service provides access to satellite imagery products for the identification of potential groundwater dependent ecosystems (GDEs) in the South Nicholson - Georgina region.

This web service contains a selection of remotely sensed raster products used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Selected products were derived from LiDAR, Landsat (5, 7, and 8), and Sentinel-2 data. Datasets include: 1) mosaic 5 m digital elevation model (DEM) with shaded relief; 2) vegetation structure stratum and substratum classes; 3) Normalised Difference Vegetation Index (NDVI) 20th, 50th, and 80th percentiles; 4) Tasselled Cap exceedance summaries; 5) Normalised Difference Moisture Index (NDMI) and Normalised Difference Wetness Index (NDWI). Landsat spectral reflectance products can be used to highlight land cover characteristics such as brightness, greenness and wetness, and vegetation condition; Sentinel-2 datasets help to detect vegetation moisture stress or waterlogging; LiDAR datasets providing a five meter DEM and vegetation structure stratum classes for detailed analysis of vegetation and relief.