East Kimberley remotely sensed datasets

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

East Kimberley vegetation structure

The Vegetation Structure classes dataset was derived from Vegetation Height Model (VHM) and Fractional Cover Model (FCM) LiDAR products. The National Vegetation Information System framework was used to classify vegetation height and canopy/cover density into (sub-)stratum, growth forms, and structural formation classes. The classifications contain descriptions and spatial extents of the vegetation types for the East Kimberley LiDAR survey area. The displayed classifications include 19 dominant structural formation classes, and 43 dominant sub-structural formation classes for lower-, mid-, and upper stratum. High resolution LiDAR imagery, including Digital Elevation Model (DEM), Canopy Height Model (CHM), Vegetation Height Model (VHM), Vegetation Cover Model (VCM) and Fractional Cover Model (FCM) surfaces were acquired for the East Kimberley area in June 2017. All the data were released in 2019 (Geoscience Australia, 2019). For the purposes of vegetation structure mapping, the two input datasets were resampled, classified and combined to produce a vegetation structure map for the East Kimberley area. The methods are described by Lawrie et al. (2012), with the following differences: • resampling used Focal Statistic Min in ArcGIS as it more accurately represented vegetation extent • VHM was used instead of CHM as CHM did not include low vegetation (i.e ground cover). • VHM and FCM were classified into height and foliage cover classes using the Australian Vegetation Attribute Manual (NVIS Technical Working Group, 2017). Authors acknowledge the tremendous work of the Geoscience Australia Elevation team who carried out post processing, classification, production, quality assurance and delivery of all released LiDAR data products (see Geoscience Australia, 2019). In particular, the authors thank Graham Hammond, Kevin Kennedy, Jonathan Weales, Grahaem Chiles, Robert Kay, Shane Crossman, and Simon Costelloe. Geoscience Australia, 2019. Kimberley East - LiDAR data. Geoscience Australia, Canberra. C7FDA017-80B2-4F98-8147-4D3E4DF595A2 https://pid.geoscience.gov.au/dataset/ga/129985 Lawrie, K.C., Brodie, R.S., Tan, K.P., Gibson, D., Magee, J., Clarke, J.D.A., Halas, L., Gow, L., Somerville, P., Apps, H.E., Christensen, N.B., Brodie, R.C., Abraham, J., Smith, M., Page, D., Dillon, P., Vanderzalm, J., Miotlinski, K., Hostetler, S., Davis, A., Ley-Cooper, A.Y., Schoning, G., Barry, K. and Levett, K. 2012. BHMAR Project: Data Acquisition, processing, analysis and interpretation methods. Geoscience Australia Record 2012/11. 826p. NVIS Technical Working Group. 2017 Chapter 4.0 NVIS attributes listed and described in detail. In: Australian Vegetation Attribute Manual: National. Vegetation Information System, Version 7.0. Department of the Environment and Energy, Canberra. Prep by Bolton, M.P., deLacey, C. and Bossard, K.B. (Eds).

P1289 East Kimberley Airborne Gravity Survey 2016

<div>Sander Geophysics Limited (SGL) conducted a fixed-wing high resolution gravimetric survey in the East Kimberley area of the state of Western Australia for Geoscience Australia. A total of 37,806 line kilometres of airborne gravity data were acquired using </div><div>SGL’s airborne gravity system, Airborne Inertially Referenced Gravimeter (AIRGrav). The survey was funded by the Department of Mines, Industry Regulation and Safety (DMIRS) Western Australia, and managed by Geoscience Australia.</div><div><br></div><div>The data were peer reviewed by airborne gravity expert Dr Mark Dransfield contracted by Geoscience Australia.</div><div><br></div><div>The data from this survey were released by the Geological Survey of Western Australia and can be downloaded from MAGIX under reference 71156.</div><div><br></div><div><strong>Survey details</strong></div><div>Survey Name: East Kimberley Airborne Gravity Survey 2016</div><div>State/Territory: Western Australia</div><div>Datasets Acquired: Airborne gravity, </div><div>Geoscience Australia Project Number: P1289</div><div>Acquisition Start Date: 08 October 2016</div><div>Acquisition End Date: 03 December 2016</div><div>Flight line spacing: 2,500m</div><div>Flight line direction: East-West</div><div>Tie line spacing: 25,000m</div><div>Tie line direction: North-South</div><div>Total distance flown: 37,806 line km</div><div>Nominal terrain clearance (above ground level): 160m</div><div>Aircraft type: Fixed wing Cessna Grand Caravan 208B</div><div>Data Acquisition: Sander Geophysics Limited</div><div>Project Management: Geoscience Australia</div><div>Quality Control: Geoscience Australia</div><div>Dataset Ownership: Western Australia Department of Mines, Industry Regulation and Safety, and Geoscience Australia</div><div><br></div><div><strong>Files included in this download</strong></div><div><br></div><div><strong>1. Point-located data / line data</strong> </div><div>Data in ASEG GDF2 format, with accompanying description and definition files </div><div>P1289_grav.dat – survey data</div><div>P1289_grav.dfn – survey data definition</div><div>P1289_grav.met – survey metadata</div><div><br></div><div><strong>2. Grids - 7 grid files</strong></div><div>Datum:      GDA94</div><div>Projection:  MGA 52 </div><div>Grid cell size: 500m</div><div>Formats:     General eXchange Format (.gxf) and ERMapper (.ers) format.</div><div><br></div><div>Grid name Units Description</div><div>- GRVFAL2500M      µms-2      Free air gravity, 2500m half-wavelength spatial filter</div><div>- FVDFAL2500M      Eotvos      First vertical derivative of free air gravity, 2500m half-wavelength spatial filter</div><div>- GRVBGL2500M_267  µms-2      Full Bouguer gravity, 2500m half-wavelength spatial filter, 2670 kg/m3 density</div><div>- FVDBGL2500M_267  Eotvos      First vertical derivative of full Bouguer gravity, 2500m half-wavelength spatial filter, 2670 kg/m3 density</div><div>- GRVISO2500M_267   µms-2      Isostatic residual gravity, 2500m half-wavelength spatial filter, 2670 kg/m3 density</div><div>- FVDISO2500M_267   Eotvos      First vertical derivative of isostatic residual gravity, 2500m half-wavelength spatial filter, 2670 kg/m3 density</div><div>- BAREEARTH       m         Bare earth terrain (above GDA94 Ellipsoid)</div><div><br></div><div><strong>3. Reports</strong> </div><div>- Delivery Information from contractor: Delivery Information - East Kimberley AIRGrav.pdf</div><div>- Final technical report from the contractor: East Kimberley 2016 TR-842-002.pdf </div><div>- Quality control report: East Kimberley 2016 QC report.pdf </div><div><br></div>

Probabilistic modelling of groundwater salinity using borehole and airborne electromagnetics (AEM) data

Groundwater is a critical resource for supporting human consumption, stock water, agricultural use, and mineral or energy extraction as well as the environment. However, the quality of groundwater varies enormously from potable to hyper-saline, particularly in the Australian context. To evaluate the suitability of a groundwater resource, the spatial distribution of salinity within an aquifer is typically estimated by measuring the electrical conductivity (EC) of groundwater samples from within boreholes. However, drilling is a logistically and economically challenging task, and hydrogeologists are usually left with a sparse set of measurements from which to infer groundwater salinity over large spatial extents. Airborne electromagnetic (AEM) surveying is a geophysical technique for estimating the bulk electrical conductivity of the near-surface. Where AEM bulk conductivity are well correlated with groundwater salinity in aquifers, AEM is a useful tool for modelling salinity in the data sparse areas between the boreholes. We present here a probabilistic method for modelling salinity and a case study from the Keep River Plains in the Northern Territory. Co-located probabilistic AEM inversions and EC measurements on pore fluids at coincident locations were fused to calculate an empirical joint probability density function. This function allowed us to estimate salinity away from the bores by sampling the ensemble of AEM conductivities. Unlike deterministic methods that provide a single estimate of salinity, our method generates an ensemble of estimates, which can be used to quantify predictive uncertainty. The results provided by our method can feed into decision making while accounting for uncertainty, enabling remote communities to manage their land and water resources more responsibly.