Groundwater GridsThis web service provides access to gridded data produced by Geoscience Australia from studies of Australian groundwater and hydrogeological systems.eftfgroundwaterhydrogeologyremote sensinglandsattasselled capgroundwater dependent ecosystemgridsurn:ogc:service:wcs2.0.11.1.11.1.0http://www.opengis.net/spec/WCS/2.0/conf/corehttp://www.opengis.net/spec/WCS_protocol-binding_get-kvp/1.0.1http://www.opengis.net/spec/WCS_protocol-binding_post-xml/1.0http://www.opengis.net/spec/WCS_service-extension_crs/1.0/conf/crs-gridded-coveragehttp://www.opengis.net/spec/GMLCOV/1.0/conf/gml-coveragehttp://www.opengis.net/spec/GMLCOV/1.0/conf/special-formathttp://www.opengis.net/spec/GMLCOV/1.0/conf/multiparthttp://www.opengis.net/spec/WCS_service-extension_scaling/1.0/conf/scalinghttp://www.opengis.net/spec/WCS_service-extension_crs/1.0/conf/crshttp://www.opengis.net/spec/WCS_service-extension_interpolation/1.0/conf/interpolationhttp://www.opengis.net/spec/WCS_service-extension_interpolation/1.0/conf/interpolation-per-axishttp://www.opengis.net/spec/WCS_service-extension_interpolation/1.0/conf/nearest-neighborhttp://www.opengis.net/spec/WCS_service-extension_interpolation/1.0/conf/linearhttp://www.opengis.net/spec/WCS_service-extension_interpolation/1.0/conf/cubichttp://www.opengis.net/spec/WCS_service-extension_range-subsetting/1.0/conf/record-subsettingNONE© Commonwealth of Australia (Geoscience Australia) 2024. This product is released under the Creative Commons Attribution 4.0 International Licence. http://creativecommons.org/licenses/by/4.0/legalcodeGeoscience AustraliaClient Services+61 2 6249 9111+61 2 6249 9999GPO Box 378CanberraACT2601Australiaclientservices@ga.gov.auXMLapplication/gml+xmlimage/jpegimage/pngimage/tiffimage/tiff;application=geotifftext/plainhttp://www.opengis.net/def/crs/EPSG/0/3577http://www.opengis.net/def/crs/EPSG/0/4283http://www.opengis.net/def/crs/EPSG/0/4326http://www.opengis.net/def/crs/EPSG/0/3857http://www.opengis.net/def/crs/EPSG/0/20248http://www.opengis.net/def/crs/EPSG/0/20249http://www.opengis.net/def/crs/EPSG/0/20250http://www.opengis.net/def/crs/EPSG/0/20251http://www.opengis.net/def/crs/EPSG/0/20252http://www.opengis.net/def/crs/EPSG/0/20253http://www.opengis.net/def/crs/EPSG/0/20254http://www.opengis.net/def/crs/EPSG/0/20255http://www.opengis.net/def/crs/EPSG/0/20256http://www.opengis.net/def/crs/EPSG/0/20257http://www.opengis.net/def/crs/EPSG/0/20258http://www.opengis.net/def/crs/EPSG/0/28348http://www.opengis.net/def/crs/EPSG/0/28349http://www.opengis.net/def/crs/EPSG/0/28350http://www.opengis.net/def/crs/EPSG/0/28351http://www.opengis.net/def/crs/EPSG/0/28352http://www.opengis.net/def/crs/EPSG/0/28353http://www.opengis.net/def/crs/EPSG/0/28354http://www.opengis.net/def/crs/EPSG/0/28355http://www.opengis.net/def/crs/EPSG/0/28356http://www.opengis.net/def/crs/EPSG/0/28357http://www.opengis.net/def/crs/EPSG/0/28358http://www.opengis.net/def/crs/EPSG/0/32748http://www.opengis.net/def/crs/EPSG/0/32749http://www.opengis.net/def/crs/EPSG/0/32750http://www.opengis.net/def/crs/EPSG/0/32751http://www.opengis.net/def/crs/EPSG/0/32752http://www.opengis.net/def/crs/EPSG/0/32753http://www.opengis.net/def/crs/EPSG/0/32754http://www.opengis.net/def/crs/EPSG/0/32755http://www.opengis.net/def/crs/EPSG/0/32756http://www.opengis.net/def/crs/EPSG/0/32757http://www.opengis.net/def/crs/EPSG/0/32758http://www.opengis.net/def/crs/EPSG/0/3110http://www.opengis.net/def/crs/EPSG/0/3111http://www.opengis.net/def/crs/EPSG/0/3112http://www.opengis.net/def/crs/EPSG/0/3031http://www.opengis.net/def/crs/EPSG/0/3032http://www.opengis.net/def/crs/EPSG/0/3308http://www.opengis.net/def/crs/EPSG/0/3107http://www.opengis.net/def/interpolation/OGC/1/nearest-neighborhttp://www.opengis.net/def/interpolation/OGC/1/linearhttp://www.opengis.net/def/interpolation/OGC/1/cubicEast Kimberley 2017 DEM 5mEast Kimberley LiDAR Digital Elevation Model (DEM) mosaic raster at 5 m spatial resolution was resampled from orthometric 1m mosaic DEM to 5m using cubic convolution value assigning methodin ArcGIS. LiDAR data was acquired by AAM Pty Ltd in June 2017 using Optech Galaxy LiDAR unit. Geoscience Australia Elevation team carried out post processing, classification, production, and delivery of all released datasets.LiDARDigital Elevation ModelDEMek-raster__KimberleyEast2017_DEM_fst_5mRectifiedGridCoverage428000.0 8232000.0544000.0 8358000.0128.32709482346954 -15.991538034748825129.41122623677128 -14.851417499978732East Kimberley 2017 DEM 5m HillshadeEast Kimberley LiDAR Digital Elevation Model (DEM) shaded relief raster, representing ground surface illuminated at 45 degree altitude with North West azimuth, and 10 times vertical exaggeration at 5m spatial resolution. LiDAR data was acquired by AAM Pty Ltd in June 2017 using Optech Galaxy LiDAR unit. Geoscience Australia Elevation team carried out post processing, classification, production, and delivery of all released datasets.LiDARDigital Elevation ModelDEMhillshadeshaded reliefek-raster__KimberleyEast2017_DEM_fst_5m_HillshadeRectifiedGridCoverage428000.0 8232000.0544000.0 8358000.0128.32709482346954 -15.991538034748825129.41122623677128 -14.851417499978732McBride Basalt Inferred BaseThis is a raster representing the base surface (mAHD) of the McBride Basalt Province. The surface has been inferred from sparse available data, dominated by private water bore records. This interpretation is based on bore strata and drilling logs, the ground surface elevation at the edges of the province, and surface topography. Iterative processes were used to produce an interpretation with positive thickness within areas of outcropping basalt, and to reflect likely topographic shapes. The resultant model is just one possible base of basalt, based on sparse input data (no uncertainty has been presented); other models are possible. This model has been produced for visualisation purposes and should not be considered a definitive depth prediction dataset.McBride BasaltUpper BurdekinVolcanic Provincebase surfaceExploring for the FutureEFTFub-gw-rasters__McBride_Basalt_Inferred_BaseRectifiedGridCoverage136551.38072519534 7912675.305456456313476.38072519534 8038050.305456456143.551623720566 -18.869539520229885145.2408769558591 -17.714871046442877McBride Basalt Potential RechargeInferred relative groundwater recharge potential within the McBride Basalt Province. Recharge potential categories are derived from weightings assigned to qualitative estimates of relative permeability based on mapped soil type and surface geology.McBride BasaltUpper BurdekinVolcanic ProvinceGroundwaterPermeabilityPotential Rechargeub-gw-rasters__McBride_Basalt_Potential_RechargeRectifiedGridCoverage127847.01293745029 7906994.19068552321538.5215252886 8045114.210831818143.46807744485864 -18.92157406266374145.31748222331646 -17.64969341029561McBride NDVI Combined ClassificationNormalised Difference Vegetation Index (NDVI) was used to map vegetation with potential access to groundwater in the McBride basalt province in the Upper Burdekin. The vegetation classifications in this layer are a product of the median dry and wet NDVI calculated for the end of the dry season and end of the wet season for all available cloud-free Landsat imagery between 1987 and 2018. Dry NDVI values were subtracted from wet NDVI values to show the difference over time. Both dry NDVI and NDVI difference were classified and the classified datasets were then combined with the first number of the two digit code corresponding to the dry NDVI class and the second digit corresponding to the NDVI difference class. Links to the ‘Exploring for the Future – Hydrogeological summary of the McBride and Nulla basalt provinces, North Queensland’ report containing further information on how the data were classified, and a file download for the full dataset used to generate this classification, are available at http://pid.geoscience.gov.au/dataset/ga/135648.Upper BurdekinGroundwaterGroundwater Dependent VegetationNormalised Difference Vegetation IndexNDVIvegetation densityvegetation vigourub-gw-rasters__UB_McBride_NDVIRectifiedGridCoverage135090.0 7903380.0327300.0 8038950.0143.53605111755618 -18.95471603088902145.37128225383205 -17.706512843260963NDVI 20th Percentile Band 1East Kimberley NDVI 20th percentile for dry season (April to October) 1987-2018. NDVI was calculated for each Landsat surface reflectance observation and the 20th percentile was calculated for each pixel.satellite imageryNDVINormalised Difference Vegetation IndexLandsatek-raster__NDVI_20thPercentile_Band_1RectifiedGridCoverage324120.5794645462 8233637.824867175626141.5255939632 8442635.01960618127.35657142134511 -15.976611671046943130.17875939103305 -14.081650941532367NDVI 50th Percentile Band 2East Kimberley NDVI 50th percentile for 1987-2018. NDVI was calculated for each Landsat surface reflectance observation within the temporal and spatial query and the 20th, 50th and 80th percentile were calculated for each pixel.satellite imageryNDVINormalised Difference Vegetation IndexLandsatek-raster__NDVI_50thPercentile_Band_2RectifiedGridCoverage324120.5794645462 8233637.824867175626141.5255939632 8442635.01960618127.35657142134511 -15.976611671046943130.17875939103305 -14.081650941532367NDVI 80th Percentile Band 3The 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, 2019). NDVI was calculated for each Landsat surface reflectance observation and the 20th, 50th and 80th percentiles were calculated for each pixel. East Kimberley NDVI 80th percentile for dry season (April to October) 1987-2018. NDVI was calculated for each Landsat surface reflectance observation and the 80th percentile was calculated for each pixel. Geoscience Australia, 2019. Earth Observation Archive. Geoscience Australia, Canberra. http://dx.doi.org/10.4225/25/57D9DCA3910CD.satellite imageryNDVINormalised Difference Vegetation IndexLandsatek-raster__NDVI_80thPercentile_Band_3RectifiedGridCoverage324120.5794645462 8233637.824867175626141.5255939632 8442635.01960618127.35657142134511 -15.976611671046943130.17875939103305 -14.081650941532367Nulla Basalt Inferred BaseThis is a raster representing the base surface (mAHD) of the Nulla Basalt Province. The surface has been inferred from sparse available data, dominated by private water bore records. This interpretation is based on bore strata and drilling logs, the ground surface elevation at the edges of the province, and surface topography. Iterative processes were used to produce an interpretation with positive thickness within areas of outcropping basalt, and to reflect likely topographic shapes. The resultant model is just one possible base of basalt, based on sparse input data (no uncertainty has been presented); other models are possible. This model is for visualisation purposes and should not be considered a definitive depth prediction dataset.Nulla BasaltUpper BurdekinVolcanic Provincebase surfaceExploring for the FutureEFTFub-gw-rasters__Nulla_Basalt_Inferred_BaseRectifiedGridCoverage241408.7408415913 7762092.10202975425208.74084159127 7861917.10202975144.52504292433662 -20.237344745026782146.28796952367028 -19.320084133010877Nulla Basalt Potential RechargeInferred relative groundwater recharge potential within the Nulla Basalt Province. Recharge potential categories are derived from weightings assigned to qualitative estimates of relative permeability based on mapped soil type and surface geology.Nulla BasaltUpper BurdekinVolcanic ProvinceGroundwaterPermeabilityPotential Rechargeub-gw-rasters__Nulla_Basalt_Potential_RechargeRectifiedGridCoverage229656.0912511199 7731173.222804923437332.7384568744 7879249.10122926144.40798634037088 -20.517156836534138146.40395388616722 -19.162062517030808Nulla NDVI Combined ClassificationNormalised Difference Vegetation Index (NDVI) was used to map vegetation with potential access to groundwater in the McBride basalt province in the Upper Burdekin. The vegetation classifications in this layer are a product of the median dry and wet NDVI calculated for the end of the dry season and end of the wet season for all available cloud-free Landsat imagery between 1987 and 2018. Dry NDVI values were subtracted from wet NDVI values to show the difference over time. Both dry NDVI and NDVI difference were classified and the classified datasets were then combined with the first number of the two digit code corresponding to the dry NDVI class and the second digit corresponding to the NDVI difference class. Links to the ‘Exploring for the Future – Hydrogeological summary of the McBride and Nulla basalt provinces, North Queensland’ report containing further information on how the data were classified, and a file download for the full dataset used to generate this classification, are available at http://pid.geoscience.gov.au/dataset/ga/135648.Upper BurdekinGroundwaterGroundwater Dependent VegetationNormalised Difference Vegetation IndexNDVIvegetation densityvegetation vigourub-gw-rasters__UB_Nulla_NDVIRectifiedGridCoverage237270.0 7764390.0426870.0 7865850.0144.4857931098585 -20.216645623237348146.30393469855963 -19.28403939636536Sentinel-2 NDMINormalised Difference Moisture Index (NDMI) is used to detect vegetation moisture stress or waterlogging. NDMI is calculated as (NIR – SWIR)/(NIR + SWIR). Multi-colour ramp image with linear histogram stretched values, showing distribution of the vegetation and irrigated crops leaf moisture from low: -1 to high: 1.Surface MoistureSentinel-2Normalised Difference Moisture IndexNDMIek-raster__EK_Sen2_NDMIRectifiedGridCoverage-423860.0 -1704490.0-247220.0 -1564930.0128.062357207346 -16.007460383189255129.72593624192984 -14.697936751223999Sentinel-2 NDMI ClassifiedNormalised Difference Moisture Index (NDMI) is used to detect vegetation moisture stress or waterlogging. NDMI raster with 20 discrete colours for each defined class interval of 0.1 is showing distribution of the surface or leaf moisture in vegetation and irrigated crops as values between -1 (dry) to 1 (water). Waterlogged areas correspond to class values from 0.2 to 0.4.Surface MoistureSentinel-2Normalised Difference Moisture IndexNDMIek-raster__EK_Sen2_NDMI_ClassifiedRectifiedGridCoverage-423860.0 -1704490.0-247220.0 -1564930.0128.062357207346 -16.007460383189255129.72593624192984 -14.697936751223999Sentinel-2 NDWINormalised Difference Water Index (NDWI) is used to detect surface moisture and delineate water bodies. NDWI is calculated as (Green-NIR) / (Green+NIR). Multi-colour ramp image with the standard deviation histogram stretched values, showing distribution of the surface moisture from low: -1 to high: 1. Positive values indicate waterlogged areas.Surface MoistureSentinel-2Normalised Difference Water IndexNDWIek-raster__EK_Sen2_NDWIRectifiedGridCoverage-423860.0 -1704490.0-247220.0 -1564930.0128.062357207346 -16.007460383189255129.72593624192984 -14.697936751223999Sentinel-2 NDWI ClassifiedNormalised Difference Water Index (NDWI) is used to detect surface moisture and delineate water bodies. NDWI is calculated as (Green-NIR) / (Green+NIR). NDWI raster with 20 discrete colours for each defined class interval of 0.1 is showing distribution of the surface moisture as values from -1 (dry) to 1 (water). Positive values indicate waterlogged areas. Water bodies usually get values higher than 0.2.Surface MoistureSentinel-2Normalised Difference Water IndexNDWIek-raster__EK_Sen2_NDWI_ClassifiedRectifiedGridCoverage-423860.0 -1704490.0-247220.0 -1564930.0128.062357207346 -16.007460383189255129.72593624192984 -14.697936751223999South Nicholson - Georgina Basin Tasselled Cap Combined Greenness and Wetness 10th PercentilesThe Tasselled Cap greenness and wetness 10th percentiles were extracted from Digital Earth Australia to help identify and characterise groundwater dependent ecosystems (GDE) in the South Nicholson-Georgina basins study area. The Tasselled Cap greenness and wetness 10th percentiles represent the lowest 10% of values and is considered to represent the least green/driest period for the timeframe between 1987 and 2022. These datasets were each classified into nine classes and combined where the first number of the resulting two-digit code represents greenness and the second value represents wetness. A potential GDE is most likely when both digits are above four. E.g., a code of 89 represents an area that is well above average in both greenness and wetness and has a higher probability of being a GDE than a code of 63, which is only above average in greenness (but is still a potential GDE). More information and source data can be obtained from https://pid.geoscience.gov.au/dataset/ga/149293.EFTFSouth Nicholson BasinGeorgina BasinRemote SensingLandsatTasselled CapGround Water Dependent EcosystemGDENorthern TerritoryNTQueenslandQLDsng_gde_grids__GSN_Tasselled_Cap_1987_2022_CombinedGreenWetRectifiedGridCoverage-156060.0 -2743260.01014180.0 -1473360.0130.43129114522927 -25.356530021367018142.1734501810812 -13.583790289980561South Nicholson - Georgina Basin Tasselled Cap Greenness 10th PercentileThe Tasselled Cap greenness 10th percentiles were extracted from Digital Earth Australia to help identify and characterise groundwater dependent ecosystems (GDE) in the South Nicholson-Georgina basins study area. The Tasselled Cap greenness 10th percentile represent the lowest 10% of values and is considered to represent the least green period for the timeframe between 1987 and 2022. These datasets were each classified into nine classes where a code of 9 represents an area that is well above average in greenness. More information and source data can be obtained from https://pid.geoscience.gov.au/dataset/ga/149293.EFTFSouth Nicholson BasinGeorgina BasinRemote SensingLandsatTasselled CapGround Water Dependent EcosystemGDENorthern TerritoryNTQueenslandQLDsng_gde_grids__GSN_Tasselled_Cap_1987_2022_greennessRectifiedGridCoverage-156060.0 -2743260.01014180.0 -1473360.0130.43129114522927 -25.356530021367018142.1734501810812 -13.583790289980561South Nicholson - Georgina Basin Tasselled Cap Greenness 50th Percentile Coefficient of VariationThe Tasselled Cap greenness 50th percentile Coefficient of Variation (CV) dataset was created using data extracted from Digital Earth Australia to help identify and characterise groundwater dependent ecosystems (GDE) in the South Nicholson-Georgina basins study area. The Tasselled Cap greenness 50th CV represents the amount of variation between a wet and dry period with low CV values indicating a potential GDE, for the timeframe between 1987 and 2022. However, a low CV value could also indicate a permanent waterbody or bare ground, so should be used in conjunction with other datasets. More information and source data can be obtained from https://pid.geoscience.gov.au/dataset/ga/149293.EFTFSouth Nicholson BasinGeorgina BasinRemote SensingLandsatTasselled CapGround Water Dependent EcosystemGDENorthern TerritoryNTQueenslandQLDsng_gde_grids__GSN_Tasselled_Cap_1987_2022_CVgreen50pcRectifiedGridCoverage-156060.0 -2743260.01014180.0 -1473360.0130.43129114522927 -25.356530021367018142.1734501810812 -13.583790289980561South Nicholson - Georgina Basin Tasselled Cap Wetness 10th PercentileThe Tasselled Cap wetness 10th percentiles were extracted from Digital Earth Australia to help identify and characterise groundwater dependent ecosystems (GDE) in the Upper Darling region of NSW. The Tasselled Cap wetness 10th percentiles represent the lowest 10% of values and is considered to represent the driest period for the timeframe between 1987 and 2022. These datasets were each classified into nine classes where a code of 9 represents an area that is well above average in wetness. More information and source data can be obtained from https://pid.geoscience.gov.au/dataset/ga/149293.EFTFSouth Nicholson BasinGeorgina BasinRemote SensingLandsatTasselled CapGround Water Dependent EcosystemGDENorthern TerritoryNTQueenslandQLDsng_gde_grids__GSN_Tasselled_Cap_1987_2022_wetnessRectifiedGridCoverage-156060.0 -2743260.01014180.0 -1473360.0130.43129114522927 -25.356530021367018142.1734501810812 -13.583790289980561TC Wetness Brightness Greenness Exceedances SummaryThe Tasselled Cap brightness, greenness and wetness statistics are linear combinations of Landsat bands and were calculated using the coefficients presented in Crist (1985). This dataset shows the percentage of observations that each pixel exceeded a particular threshold of brightness, greenness and wetness as well as the mean and standard deviation of these metrics. These surface reflectance products can be used to highlight vegetation characteristics such as wetness and greenness, and land cover. Tasselled Cap Exceedance Summary include brightness, greenness and wetness as a composite image and were derived from the Landsat 5, 7 and 8 data stored in Digital Earth Australia (see Geoscience Australia, 2019). Tasselled Cap brightness, greenness and wetness coefficients (see Crist, 1985) were calculated for period 1987 to 2018. Brightness, greenness and wetness exceedance was identified where the respective coefficient exceeded a particular threshold. The mean and standard deviation statistics of brightness, greenness and wetness were calculated on pixel basis for the available time series.satellite imageryLandsatTassel Capwetnessbrightnessgreennessek-raster__TC_Exceedances_SummaryRectifiedGridCoverage324120.5794645462 8233637.824867175626141.5255939632 8442635.01960618127.35657142134511 -15.976611671046943130.17875939103305 -14.081650941532367Upper Darling Tasselled Cap Combined Greenness and Wetness 10th PercentilesThe Tasselled Cap greenness and wetness 10th percentiles were extracted from Digital Earth Australia to help identify and characterise groundwater dependent ecosystems (GDE) in the Upper Darling region of NSW. The Tasselled Cap greenness and wetness 10th percentiles represent the lowest 10% of values and is considered to represent the least green/driest period for the timeframe between 1987 and 2022. These datasets were each classified into nine classes and combined where the first number of the resulting two-digit code represents greenness and the second value represents wetness. A potential GDE is most likely when both digits are above four. E.g., a code of 89 represents an area that is well above average in both greenness and wetness and has a higher probability of being a GDE than a code of 63, which is only above average in greenness (but is still a potential GDE).EFTFUpper DarlingRemote SensingLandsatTasselled CapGround Water Dependent EcosystemGDENew South WalesNSWgw_grids__UD_CombinedGreenWet_pc10_1987_2022RectifiedGridCoverage1038630.0 -3531660.01497750.0 -3242880.0142.841939844437 -31.992542877195405147.9681839177344 -29.001546790334466Upper Darling Tasselled Cap Greenness 50th Percentile Coefficient of VariationThe Tasselled Cap greenness 50th percentile Coefficient of Variation (CV) dataset was created using data extracted from Digital Earth Australia to help identify and characterise groundwater dependent ecosystems (GDE) in the Upper Darling region of NSW. The Tasselled Cap greenness 50th CV represents the amount of variation between a wet and dry period with low CV values indicating a potential GDE, for the timeframe between 1987 and 2022. However, a low CV value could also indicate a permanent waterbody or bare ground, so should be used in conjunction with other datasets.remote sensingLandsatTasselled CapGround Water Dependent EcosystemGDEFloodplainNew South WalesNSWEFTFUpper Darlinggw_grids__UD_CV_Green_pc50_1987_2022RectifiedGridCoverage1038630.0 -3531660.01497750.0 -3242880.0142.841939844437 -31.992542877195405147.9681839177344 -29.001546790334466Upper Darling Tasselled Cap Scaled Greeness and Wetness 10th PercentilesThe Tasselled Cap greenness and wetness 10th percentiles were extracted from Digital Earth Australia to help identify and characterise groundwater dependent ecosystems (GDE) in the Upper Darling region of NSW. The Tasselled Cap greenness and wetness 10th percentiles represent the lowest 10% of values and is considered to represent the least green/driest period for the timeframe between 1987 and 2022. These datasets were scaled and then added together, with the higher values correlating to areas of above average greenness and wetness, indicating potential GDEs.EFTFUpper DarlingRemote SensingLandsatTasselled CapGroundwater Dependent EcosystemGDEUpper DarlingFloodplainNew South WalesNSWGroundwatergw_grids__UD_ScaledGreenWet_pc10_1987_2022RectifiedGridCoverage1038630.0 -3531660.01497750.0 -3242880.0142.841939844437 -31.992542877195405147.9681839177344 -29.001546790334466Vegetation Structure Stratum ClassesThe Vegetation Structure Stratum 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 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).Vegetation Structure StratumLiDARvegetation heightcanopy densityek-raster__VegetationStructure_StratumRectifiedGridCoverage427999.9999999993 8232000.0543999.9999999993 8358000.0128.32709482346954 -15.991503998602402129.41122623677128 -14.851417499978732Vegetation Structure SubStratum ClassesThe Vegetation Structure SubStratum 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 classification include 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).Vegetation Structure SubStratumLiDARvegetation heightcanopy densityek-raster__VegetationStructure_SubStratumRectifiedGridCoverage427999.9999999993 8232000.0543999.9999999993 8358000.0128.32709482346954 -15.991538034748825129.41122623677128 -14.851417499978732