WMS 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. satellite imagery Landsat NDVI Normalised Difference Vegetation Index Tasselled Cap Exceedance Summary LiDAR DEM Sentinel-2 Vegetation Structure Stratum Classes Geoscience Australia Client Services Postal

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This product is released under the Creative Commons Attribution 4.0 International Licence. http://creativecommons.org/licenses/by/4.0/legalcode text/xml image/png application/atom+xml application/geopackage+sqlite3 application/json;type=utfgrid application/pdf application/rss+xml application/vnd.google-earth.kml+xml application/vnd.google-earth.kml+xml;mode=networklink application/vnd.google-earth.kmz image/geotiff image/geotiff8 image/gif image/jpeg image/png; mode=8bit image/svg+xml image/tiff image/tiff8 image/vnd.jpeg-png image/vnd.jpeg-png8 text/html; subtype=openlayers text/html; subtype=openlayers2 text/html; subtype=openlayers3 text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml; subtype=gml/3.1.1 text/html application/json XML INIMAGE BLANK JSON 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. EPSG:28352 EPSG:7852 EPSG:32752 EPSG:3857 EPSG:4283 EPSG:7844 EPSG:4326 EPSG:3577 CRS:84 127.35631114229716 130.17894612754455 -16.047001324058282 -14.081650941532367 This layer group 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. EPSG:28352 127.35631114229716 130.17894612754455 -16.008484521672735 -14.081650945471928 Landsat derived products highlighting vegetation characteristics such as wetness and greenness, and land cover. Tasselled Cap Exceedance Summary includes wetness, brightness, and greenness as a composite image. Normalised Difference Vegetation Index (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. NDVI percentiles are for dry seasons 1987 to 2018. satellite imagery EPSG:28352 127.3565714213451 130.17875939103303 -15.976611676730265 -14.081650945471928 text/html NDVI_20thPercentile_Band_1 East 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 imagery NDVI Normalised Difference Vegetation Index Landsat EPSG:28352 CRS:84 127.35657142134511 130.17875939103305 -15.976611671046943 -14.081650941532367 text/html NDVI_50thPercentile_Band_2 East 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 imagery NDVI Normalised Difference Vegetation Index Landsat EPSG:28352 CRS:84 127.35657142134511 130.17875939103305 -15.976611671046943 -14.081650941532367 text/html NDVI_80thPercentile_Band_3 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, 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 imagery NDVI Normalised Difference Vegetation Index Landsat EPSG:28352 CRS:84 127.35657142134511 130.17875939103305 -15.976611671046943 -14.081650941532367 text/html TC_Exceedances_Summary The 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 imagery Landsat Tassel Cap wetness brightness greenness EPSG:28352 CRS:84 127.35657142134511 130.17875939103305 -15.976611671046943 -14.081650941532367 text/html East Kimberley LiDAR Products contains Vegetation Structure classes derived from Vegetation Height Model (VHM) and Fractional Cover Model (FCM) LiDAR datasets, and Elevation mosaic raster with shaded relief image at 5m spatial resolution. EPSG:28352 128.32709482346954 129.41122623677128 -15.991538040447248 -14.851417504585068 text/html text/html VegetationStructure_Stratum The 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 Stratum LiDAR vegetation height canopy density EPSG:28352 CRS:84 128.32709482346954 129.41122623677128 -15.991503998602402 -14.851417499978732 text/html VegetationStructure_SubStratum The 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 SubStratum LiDAR vegetation height canopy density EPSG:28352 CRS:84 128.32709482346954 129.41122623677128 -15.991538034748825 -14.851417499978732 text/html KimberleyEast2017_DEM_fst_5m East 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. LiDAR Digital Elevation Model DEM EPSG:28352 CRS:84 128.32709482346954 129.41122623677128 -15.991538034748825 -14.851417499978732 text/html KimberleyEast2017_DEM_fst_5m_Hillshade East 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. LiDAR Digital Elevation Model DEM hillshade shaded relief EPSG:28352 CRS:84 128.32709482346954 129.41122623677128 -15.991538034748825 -14.851417499978732 text/html The East Kimberley Surface Moisture products group layer contains classified and visually enhanced derivatives of the Sentinel-2 satellite sensor ‘geo-median’ dataset. A weighted geometric median statistic has been applied to Sentinel-2 dataset to derive median reflectance dataset from time series data collected from 2015 to 2018 (Roberts et al., 2017). The Normalised Difference Moisture Index (NDMI; Gao, 1996) and Normalised Difference Water Index (NDWI; McFeeters, 1996) were calculated from the median reflectance. These datasets have been classified and visually enhanced to detect vegetation moisture stress or water-logging and show distribution of moisture. NDMI is used to detect vegetation moisture stress or waterlogging, as NDWI is used to detect surface/soil moisture and delineate water bodies. Layers of classified indices between -1 to 1, and multi-colour ramp stretch images show distribution of moisture from low: -1 to high: 1. 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. Surface Moisture Sentinel-2 Normalised Difference Moisture Index NDMI Normalised Difference Water Index NDWI EPSG:3577 128.02120156427134 129.7494686455228 -16.047001324058282 -14.657818167147704 text/html text/html EK_Sen2_NDMI Normalised 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 Moisture Sentinel-2 Normalised Difference Moisture Index NDMI EPSG:3577 CRS:84 128.062357207346 129.72593624192984 -16.007460383189255 -14.697936751223999 text/html EK_Sen2_NDMI_Classified Normalised 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 Moisture Sentinel-2 Normalised Difference Moisture Index NDMI EPSG:3577 CRS:84 128.062357207346 129.72593624192984 -16.007460383189255 -14.697936751223999 text/html EK_Sen2_NDWI Normalised 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 Moisture Sentinel-2 Normalised Difference Water Index NDWI EPSG:3577 CRS:84 128.062357207346 129.72593624192984 -16.007460383189255 -14.697936751223999 text/html EK_Sen2_NDWI_Classified Normalised 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 Moisture Sentinel-2 Normalised Difference Water Index NDWI EPSG:3577 CRS:84 128.062357207346 129.72593624192984 -16.007460383189255 -14.697936751223999 text/html