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.

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.

Light detection and ranging (LiDAR) systems measure surface properties at high resolution, including ground surface elevation, and vegetation height and density. As well as having routine application in studies of surface hydrology, vegetation, ecology, infrastructure and hazard assessments, LiDAR is important in groundwater studies as it can help characterise and inform hydrogeological architecture, recharge and discharge processes, surface water–groundwater connectivity, and groundwater-dependent ecosystems. LiDAR-based high-resolution elevation data support surface and subsurface mapping, borehole data analysis, and the processing, calibration and interpretation of geophysics and remote sensing. Here, we describe several applications of airborne LiDAR to understanding groundwater systems in two case study areas in northern Australia: the East Kimberley area in the Northern Territory and Western Australia, and the Upper Burdekin area in Queensland. The East Kimberley LiDAR data were critical to mapping geomorphology and near-surface hydrostratigraphy, which informed our understanding of recharge processes. The Upper Burdekin LiDAR data enabled the mapping of key surface features such as lava flows and rootless cones, which can act as recharge pathways. <b>Citation:</b> Halas, L., Kilgour, P., Gow, L. and Haiblen, A., 2020. Application of high-resolution LiDAR data for hydrogeological investigations. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

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).

Normalised Difference Vegetation Index (NDVI) was used to map vegetation with potential access to groundwater in the basalt provinces in the Upper Burdekin. NDVI is widely used to infer vegetation density and/or vigour. Several studies (e.g. Barron et al., 2014; Gou et al., 2015; Lv et al., 2013) have used NDVI to identify groundwater-dependent vegetation (GDV) based on the hypothesis that during dry seasons or extended dry periods, soil moisture progressively becomes depleted. Under these conditions, GDV are expected to exhibit minimal or no reduction in condition relative to vegetation subject to the same conditions that do not have access to groundwater.<br> <b>References: </b><br> Barron OV, Emelyanova I, Van Niel TG, Pollock D and Hodgson G (2014) Mapping groundwater-dependent ecosystems using remote sensing measures of vegetation and moisture dynamics. Hydrological processes 28(2), 372-385. Doi: 10.1002/hyp.9609; Gou S, Gonzales S and Miller GR (2015) Mapping Potential Groundwater-Dependent Ecosystems for Sustainable Management. Ground Water 53(1), 99-110. Doi: 10.1111/gwat.12169; Lv J, Wang X-S, Zhou Y, Qian K, Wan L, Eamus D and Tao Z (2013) Groundwater-dependent distribution of vegetation in Hailiutu River catchment, a semi-arid region in China. Ecohydrology 6(1), 142-149. Doi: 10.1002/eco.1254.