Digital Earth Australia - OGC Web Services Digital Earth Australia OGC Web Services time-series mangrove WOfS landsat australia burn characteristics HLTC geomedian fractional-cover NIDEM bare-earth OGC WMTS 1.0.0 none none Geoscience Australia Digital Earth Australia +61 2 6249 9111 GPO Box 378 Canberra ACT 2609 Australia earth.observation@ga.gov.au KVP KVP KVP DEA Surface Reflectance (Landsat and Sentinel-2) Geoscience Australia Landsat and Sentinel-2 Analysis Ready Data Collection 3 This product takes Sentinel-2 and Landsat imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. Where Sentinel-2 and Landsat data are both available, the higher resolution Sentinel-2 data is used. This product combines: * Sentinel-2A https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2a-msi/ * Sentinel-2B https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2b-msi/ * Sentinel-2C https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2c-msi/ * Landsat-5 https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-5-tm/ * Landsat-7 https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-7-etm/ * Landsat-8 https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-8-oli-tirs/ * Landsat-9 https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-9-oli-tirs/ For service status information, see https://status.dea.ga.gov.au 70.0896133050788 -56.9991011905099 169.900466171221 -6.78664175448088 s2_ls_combined image/png application/json Time Date 1986-08-16 1986-08-17 1986-08-18 1986-08-19 1986-08-20 1986-08-21 1986-08-22 1986-08-23 1986-08-24 1986-08-25 1986-08-26 1986-08-27 1986-09-01 1986-09-12 1986-09-21 1987-05-19 1987-05-20 1987-05-21 1987-05-22 1987-05-23 1987-05-24 1987-05-25 1987-05-26 1987-05-27 1987-05-28 1987-05-29 1987-05-30 1987-05-31 1987-06-01 1987-07-13 1987-07-14 1987-07-15 1987-07-16 1987-07-17 1987-07-18 1987-07-20 1987-07-21 1987-07-27 1987-07-28 1987-07-31 1987-08-17 1987-08-18 1987-08-22 1987-08-23 1987-08-25 1987-09-01 1987-09-02 1987-09-03 1987-09-04 1987-09-05 1987-09-06 1987-09-07 1987-09-08 1987-09-09 1987-09-10 1987-09-11 1987-09-12 1987-09-13 1987-09-14 1987-09-15 1987-09-16 1987-09-17 1987-09-18 1987-09-19 1987-09-20 1987-09-21 1987-09-22 1987-09-23 1987-09-24 1987-09-25 1987-09-26 1987-09-27 1987-09-28 1987-09-29 1987-09-30 1987-10-01 1987-10-02 1987-10-03 1987-10-04 1987-10-05 1987-10-06 1987-10-07 1987-10-08 1987-10-09 1987-10-10 1987-10-11 1987-10-12 1987-10-13 1987-10-14 1987-10-15 1987-10-16 1987-10-17 1987-10-18 1987-10-19 1987-10-20 1987-10-21 1987-10-22 1987-10-23 1987-10-24 1987-10-25 1987-10-26 1987-10-27 1987-10-29 1987-10-30 1987-10-31 1987-11-01 1987-11-02 1987-11-03 1987-11-04 1987-11-05 1987-11-06 1987-11-07 1987-11-08 1987-11-09 1987-11-10 1987-11-11 1987-11-12 1987-11-13 1987-11-14 1987-11-15 1987-11-16 1987-11-17 1987-11-18 1987-11-19 1987-11-20 1987-11-21 1987-11-22 1987-11-23 1987-11-24 1987-11-25 1987-11-26 1987-11-27 1987-11-28 1987-11-29 1987-11-30 1987-12-01 1987-12-02 1987-12-03 1987-12-04 1987-12-05 1987-12-06 1987-12-07 1987-12-08 1987-12-09 1987-12-10 1987-12-11 1987-12-13 1987-12-14 1987-12-15 1987-12-16 1987-12-17 1987-12-18 1987-12-19 1987-12-20 1987-12-21 1987-12-22 1987-12-23 1987-12-24 1987-12-25 1987-12-26 1987-12-27 1987-12-28 1987-12-29 1987-12-30 1987-12-31 1988-01-02 1988-01-03 1988-01-04 1988-01-05 1988-01-06 1988-01-07 1988-01-08 1988-01-09 1988-01-10 1988-01-11 1988-01-12 1988-01-13 1988-01-14 1988-01-15 1988-01-16 1988-01-17 1988-01-18 1988-01-19 1988-01-20 1988-01-21 1988-01-22 1988-01-23 1988-01-24 1988-01-25 1988-01-26 1988-01-27 1988-01-28 1988-01-29 1988-01-30 1988-01-31 1988-02-01 1988-02-02 1988-02-05 1988-02-06 1988-02-07 1988-02-08 1988-02-09 1988-02-10 1988-02-11 1988-02-12 1988-02-13 1988-02-14 1988-02-15 1988-02-16 1988-02-17 1988-02-18 1988-02-19 1988-02-20 1988-02-21 1988-02-22 1988-02-23 1988-02-24 1988-02-25 1988-02-26 1988-02-27 1988-02-28 1988-02-29 1988-03-01 1988-03-02 1988-03-03 1988-03-04 1988-03-05 1988-03-06 1988-03-07 1988-03-08 1988-03-09 1988-03-10 1988-03-11 1988-03-12 1988-03-13 1988-03-14 1988-03-15 1988-03-16 1988-03-17 1988-03-18 1988-03-19 1988-03-20 1988-03-21 1988-03-22 1988-03-23 1988-03-24 1988-03-25 1988-03-26 1988-03-27 1988-03-28 1988-03-29 1988-03-30 1988-03-31 1988-04-01 1988-04-02 1988-04-03 1988-04-04 1988-04-05 1988-04-06 1988-04-07 1988-04-08 1988-04-09 1988-04-10 1988-04-11 1988-04-12 1988-04-13 1988-04-14 1988-04-15 1988-04-16 1988-04-17 1988-04-18 1988-04-19 1988-04-20 1988-04-21 1988-04-22 1988-04-23 1988-04-24 1988-04-26 1988-04-27 1988-04-28 1988-04-29 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2025-12-20 2025-12-21 2025-12-22 2025-12-23 2025-12-24 2025-12-25 2025-12-26 2025-12-27 2025-12-28 2025-12-29 2025-12-30 2025-12-31 2026-01-01 2026-01-02 2026-01-03 2026-01-04 2026-01-05 2026-01-06 2026-01-07 2026-01-08 2026-01-09 2026-01-10 2026-01-11 2026-01-12 2026-01-13 2026-01-14 2026-01-15 2026-01-16 2026-01-17 2026-01-18 2026-01-19 2026-01-20 2026-01-21 2026-01-22 2026-01-23 2026-01-24 2026-01-25 2026-01-26 2026-01-27 2026-01-28 2026-01-29 2026-01-30 2026-01-31 2026-02-01 2026-02-02 2026-02-03 2026-02-04 2026-02-05 2026-02-06 2026-02-07 2026-02-08 2026-02-09 2026-02-10 2026-02-11 2026-02-12 2026-02-13 2026-02-14 2026-02-15 2026-02-16 2026-02-17 2026-02-18 2026-02-19 2026-02-20 2026-02-21 2026-02-22 2026-02-23 2026-02-24 2026-02-25 2026-02-26 2026-02-27 2026-02-28 2026-03-01 2026-03-02 2026-03-03 2026-03-04 2026-03-05 2026-03-06 2026-03-07 2026-03-08 2026-03-09 2026-03-10 2026-03-11 2026-03-12 2026-03-13 2026-03-14 2026-03-15 2026-03-16 2026-03-17 2026-03-18 2026-03-19 2026-03-20 2026-03-21 2026-03-22 2026-03-23 2026-03-24 2026-03-25 2026-03-26 2026-03-27 2026-03-28 2026-03-29 2026-03-30 2026-03-31 2026-04-01 2026-04-02 2026-04-03 2026-04-04 2026-04-05 2026-04-06 2026-04-07 2026-04-08 2026-04-09 2026-04-10 2026-04-11 2026-04-12 2026-04-13 2026-04-14 2026-04-15 2026-04-16 2026-04-17 2026-04-18 2026-04-19 2026-04-20 2026-04-20 WholeWorld_WebMercator EPSG:3111 DEA Surface Reflectance (Landsat) Geoscience Australia Landsat Analysis Ready Data Collection 3 This product takes Landsat imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. This product combines: Landsat 9 imagery https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-9-oli-tirs/, Landsat 8 imagery https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-8-oli-tirs/, Landsat 7 imagery https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-7-etm/ and Landsat 5 Imagery https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-5-tm/ For service status information, see https://status.dea.ga.gov.au 70.0896133050788 -56.9991011905099 169.900466171221 -6.78664175448088 ga_ls_ard_3 image/png application/json Time Date 1986-08-16 1986-08-17 1986-08-18 1986-08-19 1986-08-20 1986-08-21 1986-08-22 1986-08-23 1986-08-24 1986-08-25 1986-08-26 1986-08-27 1986-09-01 1986-09-12 1986-09-21 1987-05-19 1987-05-20 1987-05-21 1987-05-22 1987-05-23 1987-05-24 1987-05-25 1987-05-26 1987-05-27 1987-05-28 1987-05-29 1987-05-30 1987-05-31 1987-06-01 1987-07-13 1987-07-14 1987-07-15 1987-07-16 1987-07-17 1987-07-18 1987-07-20 1987-07-21 1987-07-27 1987-07-28 1987-07-31 1987-08-17 1987-08-18 1987-08-22 1987-08-23 1987-08-25 1987-09-01 1987-09-02 1987-09-03 1987-09-04 1987-09-05 1987-09-06 1987-09-07 1987-09-08 1987-09-09 1987-09-10 1987-09-11 1987-09-12 1987-09-13 1987-09-14 1987-09-15 1987-09-16 1987-09-17 1987-09-18 1987-09-19 1987-09-20 1987-09-21 1987-09-22 1987-09-23 1987-09-24 1987-09-25 1987-09-26 1987-09-27 1987-09-28 1987-09-29 1987-09-30 1987-10-01 1987-10-02 1987-10-03 1987-10-04 1987-10-05 1987-10-06 1987-10-07 1987-10-08 1987-10-09 1987-10-10 1987-10-11 1987-10-12 1987-10-13 1987-10-14 1987-10-15 1987-10-16 1987-10-17 1987-10-18 1987-10-19 1987-10-20 1987-10-21 1987-10-22 1987-10-23 1987-10-24 1987-10-25 1987-10-26 1987-10-27 1987-10-29 1987-10-30 1987-10-31 1987-11-01 1987-11-02 1987-11-03 1987-11-04 1987-11-05 1987-11-06 1987-11-07 1987-11-08 1987-11-09 1987-11-10 1987-11-11 1987-11-12 1987-11-13 1987-11-14 1987-11-15 1987-11-16 1987-11-17 1987-11-18 1987-11-19 1987-11-20 1987-11-21 1987-11-22 1987-11-23 1987-11-24 1987-11-25 1987-11-26 1987-11-27 1987-11-28 1987-11-29 1987-11-30 1987-12-01 1987-12-02 1987-12-03 1987-12-04 1987-12-05 1987-12-06 1987-12-07 1987-12-08 1987-12-09 1987-12-10 1987-12-11 1987-12-13 1987-12-14 1987-12-15 1987-12-16 1987-12-17 1987-12-18 1987-12-19 1987-12-20 1987-12-21 1987-12-22 1987-12-23 1987-12-24 1987-12-25 1987-12-26 1987-12-27 1987-12-28 1987-12-29 1987-12-30 1987-12-31 1988-01-02 1988-01-03 1988-01-04 1988-01-05 1988-01-06 1988-01-07 1988-01-08 1988-01-09 1988-01-10 1988-01-11 1988-01-12 1988-01-13 1988-01-14 1988-01-15 1988-01-16 1988-01-17 1988-01-18 1988-01-19 1988-01-20 1988-01-21 1988-01-22 1988-01-23 1988-01-24 1988-01-25 1988-01-26 1988-01-27 1988-01-28 1988-01-29 1988-01-30 1988-01-31 1988-02-01 1988-02-02 1988-02-05 1988-02-06 1988-02-07 1988-02-08 1988-02-09 1988-02-10 1988-02-11 1988-02-12 1988-02-13 1988-02-14 1988-02-15 1988-02-16 1988-02-17 1988-02-18 1988-02-19 1988-02-20 1988-02-21 1988-02-22 1988-02-23 1988-02-24 1988-02-25 1988-02-26 1988-02-27 1988-02-28 1988-02-29 1988-03-01 1988-03-02 1988-03-03 1988-03-04 1988-03-05 1988-03-06 1988-03-07 1988-03-08 1988-03-09 1988-03-10 1988-03-11 1988-03-12 1988-03-13 1988-03-14 1988-03-15 1988-03-16 1988-03-17 1988-03-18 1988-03-19 1988-03-20 1988-03-21 1988-03-22 1988-03-23 1988-03-24 1988-03-25 1988-03-26 1988-03-27 1988-03-28 1988-03-29 1988-03-30 1988-03-31 1988-04-01 1988-04-02 1988-04-03 1988-04-04 1988-04-05 1988-04-06 1988-04-07 1988-04-08 1988-04-09 1988-04-10 1988-04-11 1988-04-12 1988-04-13 1988-04-14 1988-04-15 1988-04-16 1988-04-17 1988-04-18 1988-04-19 1988-04-20 1988-04-21 1988-04-22 1988-04-23 1988-04-24 1988-04-26 1988-04-27 1988-04-28 1988-04-29 1988-04-30 1988-05-01 1988-05-02 1988-05-03 1988-05-04 1988-05-05 1988-05-06 1988-05-07 1988-05-08 1988-05-09 1988-05-10 1988-05-13 1988-05-14 1988-05-15 1988-05-16 1988-05-17 1988-05-18 1988-05-19 1988-05-20 1988-05-21 1988-05-22 1988-05-23 1988-05-24 1988-05-25 1988-05-26 1988-05-27 1988-05-29 1988-05-30 1988-05-31 1988-06-01 1988-06-02 1988-06-03 1988-06-04 1988-06-05 1988-06-06 1988-06-07 1988-06-08 1988-06-09 1988-06-10 1988-06-11 1988-06-13 1988-06-14 1988-06-15 1988-06-16 1988-06-17 1988-06-18 1988-06-19 1988-06-20 1988-06-21 1988-06-22 1988-06-23 1988-06-24 1988-06-25 1988-06-26 1988-06-27 1988-06-28 1988-06-30 1988-07-01 1988-07-03 1988-07-04 1988-07-05 1988-07-06 1988-07-07 1988-07-08 1988-07-09 1988-07-10 1988-07-11 1988-07-12 1988-07-13 1988-07-14 1988-07-15 1988-07-16 1988-07-17 1988-07-18 1988-07-19 1988-07-20 1988-07-21 1988-07-22 1988-07-23 1988-07-24 1988-07-25 1988-07-26 1988-07-27 1988-07-28 1988-07-29 1988-07-30 1988-07-31 1988-08-01 1988-08-02 1988-08-03 1988-08-04 1988-08-05 1988-08-06 1988-08-07 1988-08-08 1988-08-09 1988-08-10 1988-08-11 1988-08-12 1988-08-13 1988-08-14 1988-08-15 1988-08-16 1988-08-19 1988-08-21 1988-08-22 1988-08-23 1988-08-24 1988-08-25 1988-08-26 1988-08-27 1988-08-28 1988-08-29 1988-08-30 1988-08-31 1988-09-01 1988-09-02 1988-09-03 1988-09-04 1988-09-05 1988-09-06 1988-09-07 1988-09-08 1988-09-09 1988-09-10 1988-09-11 1988-09-12 1988-09-13 1988-09-14 1988-09-15 1988-09-17 1988-09-18 1988-09-19 1988-09-20 1988-09-21 1988-09-22 1988-09-23 1988-09-24 1988-09-25 1988-09-26 1988-09-27 1988-09-28 1988-09-29 1988-09-30 1988-10-01 1988-10-02 1988-10-03 1988-10-04 1988-10-05 1988-10-06 1988-10-07 1988-10-08 1988-10-09 1988-10-10 1988-10-11 1988-10-12 1988-10-13 1988-10-14 1988-10-15 1988-10-16 1988-10-17 1988-10-18 1988-10-20 1988-10-21 1988-10-22 1988-10-23 1988-10-24 1988-10-25 1988-10-26 1988-10-27 1988-10-28 1988-10-29 1988-10-30 1988-10-31 1988-11-01 1988-11-02 1988-11-03 1988-11-04 1988-11-05 1988-11-06 1988-11-07 1988-11-08 1988-11-09 1988-11-10 1988-11-11 1988-11-12 1988-11-13 1988-11-14 1988-11-15 1988-11-18 1988-11-19 1988-11-20 1988-11-21 1988-11-22 1988-11-23 1988-11-24 1988-11-25 1988-11-26 1988-11-27 1988-11-28 1988-11-29 1988-11-30 1988-12-01 1988-12-02 1988-12-03 1988-12-04 1988-12-05 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2026-04-15 2026-04-16 2026-04-17 2026-04-18 2026-04-19 2026-04-20 2026-04-20 WholeWorld_WebMercator EPSG:3111 DEA Surface Reflectance (Sentinel-2) Geoscience Australia Sentinel-2 Analysis Ready Data Collection 3 This product takes Sentinel-2 imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2A, Sentinel-2B and Sentinel-2C. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections. The resolution is a 10/20/60 m grid based on the ESA Level 1C archive. Full Sentinel-2A product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2a-msi/ Full Sentinel-2B product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2b-msi/ Full Sentinel-2C product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2c-msi/ For service status information, see https://status.dea.ga.gov.au 71.9335801182649 -56.033068214974 169.065352168893 -8.12502254507701 ga_s2m_ard_3 image/png application/json Time Date 2015-07-12 2015-07-25 2015-07-31 2015-08-01 2015-08-04 2015-08-05 2015-08-10 2015-08-11 2015-08-12 2015-08-14 2015-08-15 2015-08-18 2015-08-20 2015-08-21 2015-08-22 2015-08-23 2015-08-25 2015-08-26 2015-08-27 2015-08-28 2015-08-29 2015-08-30 2015-08-31 2015-09-02 2015-09-03 2015-09-04 2015-09-05 2015-09-08 2015-09-09 2015-09-11 2015-09-12 2015-09-13 2015-09-14 2015-09-15 2015-09-19 2015-09-22 2015-09-23 2015-09-29 2015-09-30 2015-10-01 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2024-02-24 2024-02-25 2024-02-26 2024-02-27 2024-02-28 2024-02-29 2024-03-01 2024-03-02 2024-03-03 2024-03-04 2024-03-05 2024-03-06 2024-03-07 2024-03-08 2024-03-09 2024-03-10 2024-03-11 2024-03-12 2024-03-13 2024-03-14 2024-03-15 2024-03-16 2024-03-17 2024-03-18 2024-03-19 2024-03-20 2024-03-21 2024-03-22 2024-03-23 2024-03-24 2024-03-25 2024-03-26 2024-03-27 2024-03-28 2024-03-29 2024-03-30 2024-03-31 2024-04-01 2024-04-02 2024-04-03 2024-04-04 2024-04-05 2024-04-06 2024-04-07 2024-04-08 2024-04-09 2024-04-10 2024-04-11 2024-04-12 2024-04-13 2024-04-14 2024-04-15 2024-04-16 2024-04-17 2024-04-18 2024-04-19 2024-04-20 2024-04-21 2024-04-22 2024-04-23 2024-04-24 2024-04-25 2024-04-26 2024-04-27 2024-04-28 2024-04-29 2024-04-30 2024-05-01 2024-05-02 2024-05-03 2024-05-04 2024-05-05 2024-05-06 2024-05-07 2024-05-08 2024-05-09 2024-05-10 2024-05-11 2024-05-12 2024-05-13 2024-05-14 2024-05-15 2024-05-16 2024-05-17 2024-05-18 2024-05-19 2024-05-20 2024-05-21 2024-05-22 2024-05-23 2024-05-24 2024-05-25 2024-05-26 2024-05-27 2024-05-28 2024-05-29 2024-05-30 2024-05-31 2024-06-01 2024-06-02 2024-06-03 2024-06-04 2024-06-05 2024-06-06 2024-06-07 2024-06-08 2024-06-09 2024-06-10 2024-06-11 2024-06-12 2024-06-13 2024-06-14 2024-06-15 2024-06-16 2024-06-17 2024-06-18 2024-06-19 2024-06-20 2024-06-21 2024-06-22 2024-06-23 2024-06-24 2024-06-25 2024-06-26 2024-06-27 2024-06-28 2024-06-29 2024-06-30 2024-07-01 2024-07-02 2024-07-03 2024-07-04 2024-07-05 2024-07-06 2024-07-07 2024-07-08 2024-07-09 2024-07-10 2024-07-11 2024-07-12 2024-07-13 2024-07-14 2024-07-15 2024-07-16 2024-07-17 2024-07-18 2024-07-19 2024-07-20 2024-07-21 2024-07-22 2024-07-23 2024-07-24 2024-07-25 2024-07-26 2024-07-27 2024-07-28 2024-07-29 2024-07-30 2024-07-31 2024-08-01 2024-08-02 2024-08-03 2024-08-04 2024-08-05 2024-08-06 2024-08-07 2024-08-08 2024-08-09 2024-08-10 2024-08-11 2024-08-12 2024-08-13 2024-08-14 2024-08-15 2024-08-16 2024-08-17 2024-08-18 2024-08-19 2024-08-20 2024-08-21 2024-08-22 2024-08-23 2024-08-24 2024-08-25 2024-08-26 2024-08-27 2024-08-28 2024-08-29 2024-08-30 2024-08-31 2024-09-01 2024-09-02 2024-09-03 2024-09-04 2024-09-05 2024-09-06 2024-09-07 2024-09-08 2024-09-09 2024-09-10 2024-09-11 2024-09-12 2024-09-13 2024-09-14 2024-09-15 2024-09-16 2024-09-17 2024-09-18 2024-09-19 2024-09-20 2024-09-21 2024-09-22 2024-09-23 2024-09-24 2024-09-25 2024-09-26 2024-09-27 2024-09-28 2024-09-29 2024-09-30 2024-10-01 2024-10-02 2024-10-03 2024-10-04 2024-10-05 2024-10-06 2024-10-07 2024-10-08 2024-10-09 2024-10-10 2024-10-11 2024-10-12 2024-10-13 2024-10-14 2024-10-15 2024-10-16 2024-10-17 2024-10-18 2024-10-19 2024-10-20 2024-10-21 2024-10-22 2024-10-23 2024-10-24 2024-10-25 2024-10-26 2024-10-27 2024-10-28 2024-10-29 2024-10-30 2024-10-31 2024-11-01 2024-11-02 2024-11-03 2024-11-04 2024-11-05 2024-11-06 2024-11-07 2024-11-08 2024-11-09 2024-11-10 2024-11-11 2024-11-12 2024-11-13 2024-11-14 2024-11-15 2024-11-16 2024-11-17 2024-11-18 2024-11-19 2024-11-20 2024-11-21 2024-11-22 2024-11-23 2024-11-24 2024-11-25 2024-11-26 2024-11-27 2024-11-28 2024-11-29 2024-11-30 2024-12-01 2024-12-02 2024-12-03 2024-12-04 2024-12-05 2024-12-06 2024-12-07 2024-12-08 2024-12-09 2024-12-10 2024-12-11 2024-12-12 2024-12-13 2024-12-14 2024-12-15 2024-12-16 2024-12-17 2024-12-18 2024-12-19 2024-12-20 2024-12-21 2024-12-22 2024-12-23 2024-12-24 2024-12-25 2024-12-26 2024-12-27 2024-12-28 2024-12-29 2024-12-30 2024-12-31 2025-01-01 2025-01-02 2025-01-03 2025-01-04 2025-01-05 2025-01-06 2025-01-07 2025-01-08 2025-01-09 2025-01-10 2025-01-11 2025-01-12 2025-01-13 2025-01-14 2025-01-15 2025-01-16 2025-01-17 2025-01-18 2025-01-19 2025-01-20 2025-01-21 2025-01-22 2025-01-23 2025-01-24 2025-01-25 2025-01-26 2025-01-27 2025-01-28 2025-01-29 2025-01-30 2025-01-31 2025-02-01 2025-02-02 2025-02-03 2025-02-04 2025-02-05 2025-02-06 2025-02-07 2025-02-08 2025-02-09 2025-02-10 2025-02-11 2025-02-12 2025-02-13 2025-02-14 2025-02-15 2025-02-16 2025-02-17 2025-02-18 2025-02-19 2025-02-20 2025-02-21 2025-02-22 2025-02-23 2025-02-24 2025-02-25 2025-02-26 2025-02-27 2025-02-28 2025-03-01 2025-03-02 2025-03-03 2025-03-04 2025-03-05 2025-03-06 2025-03-07 2025-03-08 2025-03-09 2025-03-10 2025-03-11 2025-03-12 2025-03-13 2025-03-14 2025-03-15 2025-03-16 2025-03-17 2025-03-18 2025-03-19 2025-03-20 2025-03-21 2025-03-22 2025-03-23 2025-03-24 2025-03-25 2025-03-26 2025-03-27 2025-03-28 2025-03-29 2025-03-30 2025-03-31 2025-04-01 2025-04-02 2025-04-03 2025-04-04 2025-04-05 2025-04-06 2025-04-07 2025-04-08 2025-04-09 2025-04-10 2025-04-11 2025-04-12 2025-04-13 2025-04-14 2025-04-15 2025-04-16 2025-04-17 2025-04-18 2025-04-19 2025-04-20 2025-04-21 2025-04-22 2025-04-23 2025-04-24 2025-04-25 2025-04-26 2025-04-27 2025-04-28 2025-04-29 2025-04-30 2025-05-01 2025-05-02 2025-05-03 2025-05-04 2025-05-05 2025-05-06 2025-05-07 2025-05-08 2025-05-09 2025-05-10 2025-05-11 2025-05-12 2025-05-13 2025-05-14 2025-05-15 2025-05-16 2025-05-17 2025-05-18 2025-05-19 2025-05-20 2025-05-21 2025-05-22 2025-05-23 2025-05-24 2025-05-25 2025-05-26 2025-05-27 2025-05-28 2025-05-29 2025-05-30 2025-05-31 2025-06-01 2025-06-02 2025-06-03 2025-06-04 2025-06-05 2025-06-06 2025-06-07 2025-06-08 2025-06-09 2025-06-10 2025-06-11 2025-06-12 2025-06-13 2025-06-14 2025-06-15 2025-06-16 2025-06-17 2025-06-18 2025-06-19 2025-06-20 2025-06-21 2025-06-22 2025-06-23 2025-06-24 2025-06-25 2025-06-26 2025-06-27 2025-06-28 2025-06-29 2025-06-30 2025-07-01 2025-07-02 2025-07-03 2025-07-04 2025-07-05 2025-07-06 2025-07-07 2025-07-08 2025-07-09 2025-07-10 2025-07-11 2025-07-12 2025-07-13 2025-07-14 2025-07-15 2025-07-16 2025-07-17 2025-07-18 2025-07-19 2025-07-20 2025-07-21 2025-07-22 2025-07-23 2025-07-24 2025-07-25 2025-07-26 2025-07-27 2025-07-28 2025-07-29 2025-07-30 2025-07-31 2025-08-01 2025-08-02 2025-08-03 2025-08-04 2025-08-05 2025-08-06 2025-08-07 2025-08-08 2025-08-09 2025-08-10 2025-08-11 2025-08-12 2025-08-13 2025-08-14 2025-08-15 2025-08-16 2025-08-17 2025-08-18 2025-08-19 2025-08-20 2025-08-21 2025-08-22 2025-08-23 2025-08-24 2025-08-25 2025-08-26 2025-08-27 2025-08-28 2025-08-29 2025-08-30 2025-08-31 2025-09-01 2025-09-02 2025-09-03 2025-09-04 2025-09-05 2025-09-06 2025-09-07 2025-09-08 2025-09-09 2025-09-10 2025-09-11 2025-09-12 2025-09-13 2025-09-14 2025-09-15 2025-09-16 2025-09-17 2025-09-18 2025-09-19 2025-09-20 2025-09-21 2025-09-22 2025-09-23 2025-09-24 2025-09-25 2025-09-26 2025-09-27 2025-09-28 2025-09-29 2025-09-30 2025-10-01 2025-10-02 2025-10-03 2025-10-04 2025-10-05 2025-10-06 2025-10-07 2025-10-08 2025-10-09 2025-10-10 2025-10-11 2025-10-12 2025-10-13 2025-10-14 2025-10-15 2025-10-16 2025-10-17 2025-10-18 2025-10-19 2025-10-20 2025-10-21 2025-10-22 2025-10-23 2025-10-24 2025-10-25 2025-10-26 2025-10-27 2025-10-28 2025-10-29 2025-10-30 2025-10-31 2025-11-01 2025-11-02 2025-11-03 2025-11-04 2025-11-05 2025-11-06 2025-11-07 2025-11-08 2025-11-09 2025-11-10 2025-11-11 2025-11-12 2025-11-13 2025-11-14 2025-11-15 2025-11-16 2025-11-17 2025-11-18 2025-11-19 2025-11-20 2025-11-21 2025-11-22 2025-11-23 2025-11-24 2025-11-25 2025-11-26 2025-11-27 2025-11-28 2025-11-29 2025-11-30 2025-12-01 2025-12-02 2025-12-03 2025-12-04 2025-12-05 2025-12-06 2025-12-07 2025-12-08 2025-12-09 2025-12-10 2025-12-11 2025-12-12 2025-12-13 2025-12-14 2025-12-15 2025-12-16 2025-12-17 2025-12-18 2025-12-19 2025-12-20 2025-12-21 2025-12-22 2025-12-23 2025-12-24 2025-12-25 2025-12-26 2025-12-27 2025-12-28 2025-12-29 2025-12-30 2025-12-31 2026-01-01 2026-01-02 2026-01-03 2026-01-04 2026-01-05 2026-01-06 2026-01-07 2026-01-08 2026-01-09 2026-01-10 2026-01-11 2026-01-12 2026-01-13 2026-01-14 2026-01-15 2026-01-16 2026-01-17 2026-01-18 2026-01-19 2026-01-20 2026-01-21 2026-01-22 2026-01-23 2026-01-24 2026-01-25 2026-01-26 2026-01-27 2026-01-28 2026-01-29 2026-01-30 2026-01-31 2026-02-01 2026-02-02 2026-02-03 2026-02-04 2026-02-05 2026-02-06 2026-02-07 2026-02-08 2026-02-09 2026-02-10 2026-02-11 2026-02-12 2026-02-13 2026-02-14 2026-02-15 2026-02-16 2026-02-17 2026-02-18 2026-02-19 2026-02-20 2026-02-21 2026-02-22 2026-02-23 2026-02-24 2026-02-25 2026-02-26 2026-02-27 2026-02-28 2026-03-01 2026-03-02 2026-03-03 2026-03-04 2026-03-05 2026-03-06 2026-03-07 2026-03-08 2026-03-09 2026-03-10 2026-03-11 2026-03-12 2026-03-13 2026-03-14 2026-03-15 2026-03-16 2026-03-17 2026-03-18 2026-03-19 2026-03-20 2026-03-21 2026-03-22 2026-03-23 2026-03-24 2026-03-25 2026-03-26 2026-03-27 2026-03-28 2026-03-29 2026-03-30 2026-03-31 2026-04-01 2026-04-02 2026-04-03 2026-04-04 2026-04-05 2026-04-06 2026-04-07 2026-04-08 2026-04-09 2026-04-10 2026-04-11 2026-04-12 2026-04-13 2026-04-14 2026-04-15 2026-04-16 2026-04-17 2026-04-18 2026-04-19 2026-04-20 2026-04-20 WholeWorld_WebMercator EPSG:3111 DEA GeoMAD (Landsat 8 & 9 OLI-TIRS) Geoscience Australia Landsat 8 and 9 Geometric Median and Median Absolute Deviation Collection 3 This product provides statistical tools to exploit the time series of Earth Observation data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year. The geomedian part of the product provides an "average" cloud-free image over the given year. The geomedian image is calculated with a multi-dimensional median, using all the spectral measurements from the satellite imagery at the same time in order to maintain the relationships among the measurements. The median absolute deviation part of the product uses three measures of variance, each of which provides a 'second order' high dimensional statistical composite for the given year. The three variance measures show much an area varies from the "average" in terms of 'distance' based on factors such as brightness and spectra: Euclidean distance (EMAD) Cosine (spectral) distance (SMAD) Bray Curtis dissimilarity (BCMAD) Together, they provide information on variance in the landscape over the given year and are useful for change detection applications. v4.0.0 Changelog: Cloud masks have been cleaned using a 3-pixel morphological opening on clouds and a 6-pixel dilation on cloud and shadows. The south-west origin point of the DEA Summary Product Grid has been shifted 18 tiles west and 15 tiles south in this version. A single combined product for Landsat 8 and 9 is provided. A standalone Landsat 8 product will no longer be provided from calendar year 2022 and onwards. Band measurement names were updated for consistency with DEA's ARD products by adding the 'nbart' prefix. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/ For service status information, see https://status.dea.ga.gov.au 111.533088723661 -44.3403132062909 155.073704691127 -8.51120628432547 ga_ls8cls9c_gm_cyear_3 image/png application/json Time Date 2013-01-01 2014-01-01 2015-01-01 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2021-01-01 2022-01-01 2023-01-01 2024-01-01 2025-01-01 2025-01-01 WholeWorld_WebMercator EPSG:3111 DEA GeoMAD (Landsat 7 ETM+) Geoscience Australia Landsat 7 Geometric Median and Median Absolute Deviation Collection 3 This product provides statistical tools to exploit the time series of Earth Observation data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year. The geomedian part of the product provides an "average" cloud-free image over the given year. The geomedian image is calculated with a multi-dimensional median, using all the spectral measurements from the satellite imagery at the same time in order to maintain the relationships among the measurements. The median absolute deviation part of the product uses three measures of variance, each of which provides a 'second order' high dimensional statistical composite for the given year. The three variance measures show much an area varies from the "average" in terms of 'distance' based on factors such as brightness and spectra: Euclidean distance (EMAD) Cosine (spectral) distance (SMAD) Bray Curtis dissimilarity (BCMAD) Together, they provide information on variance in the landscape over the given year and are useful for change detection applications. v4.0.0 Changelog: Cloud masks have been cleaned using a 3-pixel morphological opening on clouds and a 6-pixel dilation on cloud and shadows. The south-west origin point of the DEA Summary Product Grid has been shifted 18 tiles west and 15 tiles south in this version. A single combined product for Landsat 8 and 9 is provided. A standalone Landsat 8 product will no longer be provided from calendar year 2022 and onwards. Band measurement names were updated for consistency with DEA's ARD products by adding the 'nbart' prefix. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/ For service status information, see https://status.dea.ga.gov.au 111.533088723661 -44.3403132062909 155.073704691127 -8.51120628432547 ga_ls7e_gm_cyear_3 image/png application/json Time Date 1999-01-01 2000-01-01 2001-01-01 2002-01-01 2003-01-01 2004-01-01 2005-01-01 2006-01-01 2007-01-01 2008-01-01 2009-01-01 2010-01-01 2011-01-01 2012-01-01 2013-01-01 2014-01-01 2015-01-01 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2021-01-01 2021-01-01 WholeWorld_WebMercator EPSG:3111 DEA GeoMAD (Landsat 5 TM) Geoscience Australia Landsat 5 Geometric Median and Median Absolute Deviation Collection 3 This product provides statistical tools to exploit the time series of Earth Observation data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year. The geomedian part of the product provides an "average" cloud-free image over the given year. The geomedian image is calculated with a multi-dimensional median, using all the spectral measurements from the satellite imagery at the same time in order to maintain the relationships among the measurements. The median absolute deviation part of the product uses three measures of variance, each of which provides a 'second order' high dimensional statistical composite for the given year. The three variance measures show much an area varies from the "average" in terms of 'distance' based on factors such as brightness and spectra: Euclidean distance (EMAD) Cosine (spectral) distance (SMAD) Bray Curtis dissimilarity (BCMAD) Together, they provide information on variance in the landscape over the given year and are useful for change detection applications. v4.0.0 Changelog: Cloud masks have been cleaned using a 3-pixel morphological opening on clouds and a 6-pixel dilation on cloud and shadows. The south-west origin point of the DEA Summary Product Grid has been shifted 18 tiles west and 15 tiles south in this version. A single combined product for Landsat 8 and 9 is provided. A standalone Landsat 8 product will no longer be provided from calendar year 2022 and onwards. Band measurement names were updated for consistency with DEA's ARD products by adding the 'nbart' prefix. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/ For service status information, see https://status.dea.ga.gov.au 111.533088723661 -44.3403132062909 155.073704691127 -8.51120628432547 ga_ls5t_gm_cyear_3 image/png application/json Time Date 1986-01-01 1987-01-01 1988-01-01 1989-01-01 1990-01-01 1991-01-01 1992-01-01 1993-01-01 1994-01-01 1995-01-01 1996-01-01 1997-01-01 1998-01-01 1999-01-01 2000-01-01 2003-01-01 2004-01-01 2005-01-01 2006-01-01 2007-01-01 2008-01-01 2009-01-01 2010-01-01 2011-01-01 2011-01-01 WholeWorld_WebMercator EPSG:3111 DEA Surface Reflectance (Landsat 9 OLI-TIRS) Geoscience Australia Landsat 9 OLI-TIRS Analysis Ready Data Collection 3 This product takes Landsat 9 imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Operational Land Imager (OLI) and Thermal Infra-Red Scanner (TIRS) sensors aboard Landsat 9. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. It contains three sub-products that provide corrections or attribution information: DEA Surface Reflectance NBAR (Landsat 9 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbar-landsat-9-oli-tirs/ DEA Surface Reflectance NBART (Landsat 9 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-landsat-9-oli-tirs/ DEA Surface Reflectance OA (Landsat 9 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-landsat-9-oli-tirs/ The resolution is a 30 m grid based on the USGS Landsat Collection 2 archive. https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-9-oli-tirs/ For service status information, see https://status.dea.ga.gov.au 70.0896133050788 -55.5920096844518 169.900466171221 -7.63119259520273 ga_ls9c_ard_3 image/png application/json Time Date 2021-10-31 2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09 2021-11-10 2021-11-11 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The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Operational Land Imager (OLI) and Thermal Infra-Red Scanner (TIRS) sensors aboard Landsat 8. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. It contains three sub-products that provide corrections or attribution information: DEA Surface Reflectance NBAR (Landsat 8 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbar-landsat-8-oli-tirs/ DEA Surface Reflectance NBART (Landsat 8 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-landsat-8-oli-tirs/ DEA Surface Reflectance OA (Landsat 8 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-landsat-8-oli-tirs/ The resolution is a 30 m grid based on the USGS Landsat Collection 1 and 2 archive. https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-8-oli-tirs/ For service status information, see https://status.dea.ga.gov.au 70.2593444675981 -56.9991011905099 169.58619462215 -7.63017175317532 ga_ls8c_ard_3 image/png application/json Time Date 2013-03-19 2013-03-20 2013-03-21 2013-03-22 2013-03-23 2013-03-24 2013-03-25 2013-03-26 2013-03-27 2013-03-28 2013-03-29 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2026-01-11 2026-01-12 2026-01-13 2026-01-14 2026-01-15 2026-01-16 2026-01-17 2026-01-18 2026-01-19 2026-01-20 2026-01-21 2026-01-22 2026-01-23 2026-01-24 2026-01-25 2026-01-26 2026-01-27 2026-01-28 2026-01-29 2026-01-30 2026-01-31 2026-02-01 2026-02-02 2026-02-03 2026-02-04 2026-02-05 2026-02-06 2026-02-07 2026-02-08 2026-02-09 2026-02-10 2026-02-11 2026-02-12 2026-02-13 2026-02-14 2026-02-15 2026-02-16 2026-02-17 2026-02-18 2026-02-19 2026-02-20 2026-02-21 2026-02-22 2026-02-23 2026-02-24 2026-02-25 2026-02-26 2026-02-27 2026-02-28 2026-03-01 2026-03-02 2026-03-03 2026-03-04 2026-03-05 2026-03-06 2026-03-07 2026-03-08 2026-03-09 2026-03-10 2026-03-11 2026-03-12 2026-03-13 2026-03-14 2026-03-15 2026-03-16 2026-03-17 2026-03-18 2026-03-19 2026-03-20 2026-03-21 2026-03-22 2026-03-23 2026-03-24 2026-03-25 2026-03-26 2026-03-27 2026-03-28 2026-03-29 2026-03-30 2026-03-31 2026-04-01 2026-04-02 2026-04-03 2026-04-04 2026-04-05 2026-04-06 2026-04-07 2026-04-08 2026-04-09 2026-04-10 2026-04-11 2026-04-12 2026-04-13 2026-04-14 2026-04-15 2026-04-16 2026-04-17 2026-04-18 2026-04-19 2026-04-20 2026-04-20 WholeWorld_WebMercator EPSG:3111 DEA Surface Reflectance (Landsat 7 ETM+) Geoscience Australia Landsat 7 ETM+ Analysis Ready Data Collection 3 This product takes Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. It contains three sub-products that provide corrections or attribution information: DEA Surface Reflectance NBAR (Landsat 7 ETM+) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbar-landsat-7-etm/ DEA Surface Reflectance NBART (Landsat 7 ETM+) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-landsat-7-etm/ DEA Surface Reflectance OA (Landsat 7 ETM+) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-landsat-7-etm/ The resolution is a 30 m grid based on the USGS Landsat Collection 1 and 2 archive. https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-7-etm/ For service status information, see https://status.dea.ga.gov.au 110.696007613984 -44.1889410289207 155.711647298981 -9.15270092381057 ga_ls7e_ard_3 image/png application/json Time Date 1999-05-28 1999-06-30 1999-07-02 1999-07-04 1999-07-06 1999-07-08 1999-07-09 1999-07-10 1999-07-11 1999-07-12 1999-07-13 1999-07-14 1999-07-15 1999-07-16 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Data Collection 3 This product takes Landsat 5 Thematic Mapper (TM) imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. It contains three sub-products that provide corrections or attribution information: DEA Surface Reflectance NBAR (Landsat 5 TM) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbar-landsat-5-tm/ DEA Surface Reflectance NBART (Landsat 5 TM) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-landsat-5-tm/ DEA Surface Reflectance OA (Landsat 5 TM) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-landsat-5-tm/ The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive. https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-5-tm/ For service status information, see https://status.dea.ga.gov.au 110.68734388386 -44.2624681575318 156.365649522865 -6.78664175448088 ga_ls5t_ard_3 image/png application/json Time Date 1986-08-16 1986-08-17 1986-08-18 1986-08-19 1986-08-20 1986-08-21 1986-08-22 1986-08-23 1986-08-24 1986-08-25 1986-08-26 1986-08-27 1986-09-01 1986-09-12 1986-09-21 1987-05-19 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2011-11-17 WholeWorld_WebMercator EPSG:3111 DEA Surface Reflectance (Sentinel-2A MSI) Geoscience Australia Sentinel-2A MSI Analysis Ready Data Collection 3 This product takes Sentinel-2A imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2A. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections. It contains two sub-products that provide corrections or attribution information: * DEA Surface Reflectance NBART (Sentinel-2A MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-sentinel-2a-msi/ * DEA Surface Reflectance OA (Sentinel-2A MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-sentinel-2a-msi/ The resolution is a 10/20/60 m grid based on the ESA Level 1C archive. Full product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2a-msi/ For service status information, see https://status.dea.ga.gov.au 71.9335801182649 -55.5538798507226 169.04717701964 -8.12502254507701 ga_s2am_ard_3 image/png application/json Time Date 2015-07-12 2015-07-25 2015-07-31 2015-08-01 2015-08-04 2015-08-05 2015-08-10 2015-08-11 2015-08-12 2015-08-14 2015-08-15 2015-08-18 2015-08-20 2015-08-21 2015-08-22 2015-08-23 2015-08-25 2015-08-26 2015-08-27 2015-08-28 2015-08-29 2015-08-30 2015-08-31 2015-09-02 2015-09-03 2015-09-04 2015-09-05 2015-09-08 2015-09-09 2015-09-11 2015-09-12 2015-09-13 2015-09-14 2015-09-15 2015-09-19 2015-09-22 2015-09-23 2015-09-29 2015-09-30 2015-10-01 2015-10-02 2015-10-03 2015-10-04 2015-10-05 2015-10-06 2015-10-07 2015-10-08 2015-10-20 2015-10-21 2015-10-22 2015-10-23 2015-10-24 2015-10-28 2015-11-12 2015-11-13 2015-11-14 2015-11-15 2015-11-17 2015-11-18 2015-11-20 2015-11-21 2015-11-22 2015-11-23 2015-11-24 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Reflectance (Sentinel-2B MSI) Geoscience Australia Sentinel-2B MSI Analysis Ready Data Collection 3 This product takes Sentinel-2B imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2B. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections. It contains two sub-products that provide corrections or attribution information: * DEA Surface Reflectance NBART (Sentinel-2B MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-sentinel-2b-msi/ * DEA Surface Reflectance OA (Sentinel-2B MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-sentinel-2b-msi/ The resolution is a 10/20/60 m grid based on the ESA Level 1C archive. Full product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2b-msi/ For service status information, see https://status.dea.ga.gov.au 71.9335801182649 -56.033068214974 169.065352168893 -8.12502254507701 ga_s2bm_ard_3 image/png application/json Time Date 2017-06-30 2017-07-01 2017-07-02 2017-07-03 2017-07-04 2017-07-05 2017-07-06 2017-07-07 2017-07-08 2017-07-09 2017-07-10 2017-07-11 2017-07-12 2017-07-13 2017-07-14 2017-07-15 2017-07-16 2017-07-17 2017-07-18 2017-07-19 2017-07-20 2017-07-21 2017-07-22 2017-07-23 2017-07-24 2017-07-25 2017-07-26 2017-07-27 2017-07-28 2017-07-29 2017-07-30 2017-07-31 2017-08-01 2017-08-02 2017-08-03 2017-08-04 2017-08-05 2017-08-06 2017-08-07 2017-08-08 2017-08-09 2017-08-10 2017-08-11 2017-08-12 2017-08-13 2017-08-14 2017-08-15 2017-08-16 2017-08-17 2017-08-18 2017-08-19 2017-08-20 2017-08-21 2017-08-22 2017-08-23 2017-08-24 2017-08-25 2017-08-26 2017-08-27 2017-08-28 2017-08-29 2017-08-30 2017-08-31 2017-09-01 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2026-03-08 2026-03-09 2026-03-10 2026-03-11 2026-03-12 2026-03-13 2026-03-14 2026-03-15 2026-03-16 2026-03-17 2026-03-18 2026-03-19 2026-03-20 2026-03-21 2026-03-22 2026-03-23 2026-03-24 2026-03-25 2026-03-26 2026-03-27 2026-03-28 2026-03-29 2026-03-30 2026-04-01 2026-04-02 2026-04-03 2026-04-04 2026-04-05 2026-04-06 2026-04-07 2026-04-08 2026-04-09 2026-04-10 2026-04-11 2026-04-12 2026-04-13 2026-04-14 2026-04-15 2026-04-16 2026-04-17 2026-04-18 2026-04-19 2026-04-20 2026-04-20 WholeWorld_WebMercator EPSG:3111 DEA Surface Reflectance (Sentinel-2C MSI) Geoscience Australia Sentinel-2C MSI Analysis Ready Data Collection 3 This product takes Sentinel-2C imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2C. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections. It contains two sub-products that provide corrections or attribution information: * DEA Surface Reflectance NBART (Sentinel-2C MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-sentinel-2c-msi/ * DEA Surface Reflectance OA (Sentinel-2C MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-sentinel-2c-msi/ The resolution is a 10/20/60 m grid based on the ESA Level 1C archive. Full product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2c-msi/ For service status information, see https://status.dea.ga.gov.au 71.9335801182649 -55.486729409112 169.047022959767 -8.12502254507701 ga_s2cm_ard_3 image/png application/json Time Date 2025-01-22 2025-01-23 2025-01-24 2025-01-25 2025-01-26 2025-01-27 2025-01-28 2025-01-29 2025-01-30 2025-01-31 2025-02-01 2025-02-02 2025-02-03 2025-02-04 2025-02-05 2025-02-06 2025-02-07 2025-02-08 2025-02-09 2025-02-10 2025-02-11 2025-02-12 2025-02-13 2025-02-14 2025-02-15 2025-02-16 2025-02-17 2025-02-18 2025-02-19 2025-02-21 2025-02-22 2025-02-23 2025-02-24 2025-02-25 2025-02-26 2025-02-27 2025-02-28 2025-03-01 2025-03-02 2025-03-03 2025-03-04 2025-03-05 2025-03-06 2025-03-07 2025-03-08 2025-03-09 2025-03-10 2025-03-11 2025-03-12 2025-03-13 2025-03-14 2025-03-15 2025-03-16 2025-03-17 2025-03-18 2025-03-19 2025-03-20 2025-03-21 2025-03-22 2025-03-23 2025-03-24 2025-03-25 2025-03-26 2025-03-27 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2025-09-27 2025-09-28 2025-09-29 2025-09-30 2025-10-01 2025-10-02 2025-10-03 2025-10-04 2025-10-05 2025-10-06 2025-10-07 2025-10-08 2025-10-09 2025-10-10 2025-10-11 2025-10-12 2025-10-13 2025-10-14 2025-10-15 2025-10-16 2025-10-17 2025-10-18 2025-10-19 2025-10-20 2025-10-21 2025-10-22 2025-10-23 2025-10-24 2025-10-25 2025-10-26 2025-10-27 2025-10-28 2025-10-29 2025-10-30 2025-10-31 2025-11-01 2025-11-02 2025-11-03 2025-11-04 2025-11-05 2025-11-07 2025-11-08 2025-11-09 2025-11-10 2025-11-11 2025-11-12 2025-11-13 2025-11-14 2025-11-15 2025-11-16 2025-11-17 2025-11-18 2025-11-19 2025-11-20 2025-11-21 2025-11-22 2025-11-23 2025-11-24 2025-11-25 2025-11-26 2025-11-27 2025-11-28 2025-11-29 2025-11-30 2025-12-01 2025-12-02 2025-12-03 2025-12-04 2025-12-05 2025-12-06 2025-12-07 2025-12-08 2025-12-09 2025-12-10 2025-12-11 2025-12-12 2025-12-13 2025-12-14 2025-12-15 2025-12-16 2025-12-17 2025-12-18 2025-12-19 2025-12-20 2025-12-21 2025-12-22 2025-12-23 2025-12-24 2025-12-25 2025-12-26 2025-12-27 2025-12-28 2025-12-29 2025-12-30 2025-12-31 2026-01-01 2026-01-02 2026-01-03 2026-01-04 2026-01-05 2026-01-06 2026-01-07 2026-01-08 2026-01-09 2026-01-10 2026-01-11 2026-01-12 2026-01-13 2026-01-14 2026-01-15 2026-01-16 2026-01-17 2026-01-18 2026-01-19 2026-01-20 2026-01-21 2026-01-22 2026-01-23 2026-01-24 2026-01-25 2026-01-26 2026-01-27 2026-01-28 2026-01-29 2026-01-30 2026-01-31 2026-02-01 2026-02-02 2026-02-03 2026-02-04 2026-02-05 2026-02-06 2026-02-07 2026-02-08 2026-02-09 2026-02-10 2026-02-11 2026-02-12 2026-02-13 2026-02-14 2026-02-15 2026-02-16 2026-02-17 2026-02-18 2026-02-19 2026-02-20 2026-02-21 2026-02-22 2026-02-23 2026-02-24 2026-02-25 2026-02-26 2026-02-27 2026-02-28 2026-03-01 2026-03-02 2026-03-03 2026-03-04 2026-03-05 2026-03-06 2026-03-07 2026-03-08 2026-03-09 2026-03-10 2026-03-11 2026-03-12 2026-03-13 2026-03-14 2026-03-15 2026-03-16 2026-03-17 2026-03-18 2026-03-19 2026-03-20 2026-03-21 2026-03-22 2026-03-23 2026-03-25 2026-03-26 2026-03-27 2026-03-28 2026-03-29 2026-03-30 2026-03-31 2026-04-01 2026-04-02 2026-04-03 2026-04-04 2026-04-05 2026-04-06 2026-04-07 2026-04-08 2026-04-09 2026-04-10 2026-04-11 2026-04-12 2026-04-13 2026-04-14 2026-04-15 2026-04-16 2026-04-17 2026-04-18 2026-04-19 2026-04-20 2026-04-20 WholeWorld_WebMercator EPSG:3111 DEA Land Cover Calendar Year (Landsat) DEA Land Cover Calendar Year (Landsat) Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps the understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time allows the observation of the state of land cover at specific times and therefore the way that land cover changes. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/ For service status information, see https://status.dea.ga.gov.au 112.731828633068 -44.2342184871416 154.267421772154 -9.93509678400509 ga_ls_landcover image/png application/json Time Date 1988-01-01 1989-01-01 1990-01-01 1991-01-01 1992-01-01 1993-01-01 1994-01-01 1995-01-01 1996-01-01 1997-01-01 1998-01-01 1999-01-01 2000-01-01 2001-01-01 2002-01-01 2003-01-01 2004-01-01 2005-01-01 2006-01-01 2007-01-01 2008-01-01 2009-01-01 2010-01-01 2011-01-01 2012-01-01 2013-01-01 2014-01-01 2015-01-01 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2020-01-01 WholeWorld_WebMercator EPSG:3111 DEA Land Cover Environmental Descriptors DEA Land Cover Environmental Descriptors Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps the understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time allows the observation of the state of land cover at specific times and therefore the way that land cover changes. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/ For service status information, see https://status.dea.ga.gov.au 112.731828633068 -44.2342184871416 154.267421772154 -9.93509678400509 ga_ls_landcover_descriptors image/png application/json Time Date 1988-01-01 1989-01-01 1990-01-01 1991-01-01 1992-01-01 1993-01-01 1994-01-01 1995-01-01 1996-01-01 1997-01-01 1998-01-01 1999-01-01 2000-01-01 2001-01-01 2002-01-01 2003-01-01 2004-01-01 2005-01-01 2006-01-01 2007-01-01 2008-01-01 2009-01-01 2010-01-01 2011-01-01 2012-01-01 2013-01-01 2014-01-01 2015-01-01 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2020-01-01 WholeWorld_WebMercator EPSG:3111 DEA Land Cover Collection 3 Calendar Year (Landsat) DEA Land Cover Collection 3 Calendar Year (Landsat) Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps the understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time allows the observation of the state of land cover at specific times and therefore the way that land cover changes. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/ For service status information, see https://status.dea.ga.gov.au 112.343354889631 -44.3403132062909 155.073704691127 -9.40358670690844 ga_ls_landcover_c3 image/png application/json Time Date 1988-01-01 1989-01-01 1990-01-01 1991-01-01 1992-01-01 1993-01-01 1994-01-01 1995-01-01 1996-01-01 1997-01-01 1998-01-01 1999-01-01 2000-01-01 2001-01-01 2002-01-01 2003-01-01 2004-01-01 2005-01-01 2006-01-01 2007-01-01 2008-01-01 2009-01-01 2010-01-01 2011-01-01 2012-01-01 2013-01-01 2014-01-01 2015-01-01 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2021-01-01 2022-01-01 2023-01-01 2024-01-01 2025-01-01 2025-01-01 WholeWorld_WebMercator EPSG:3111 DEA Land Cover Environmental Descriptors DEA Land Cover Environmental Descriptors Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps the understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time allows the observation of the state of land cover at specific times and therefore the way that land cover changes. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/ For service status information, see https://status.dea.ga.gov.au 112.343354889631 -44.3403132062909 155.073704691127 -9.40358670690844 ga_ls_landcover_c3_descriptors image/png application/json Time Date 1988-01-01 1989-01-01 1990-01-01 1991-01-01 1992-01-01 1993-01-01 1994-01-01 1995-01-01 1996-01-01 1997-01-01 1998-01-01 1999-01-01 2000-01-01 2001-01-01 2002-01-01 2003-01-01 2004-01-01 2005-01-01 2006-01-01 2007-01-01 2008-01-01 2009-01-01 2010-01-01 2011-01-01 2012-01-01 2013-01-01 2014-01-01 2015-01-01 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2021-01-01 2022-01-01 2023-01-01 2024-01-01 2025-01-01 2025-01-01 WholeWorld_WebMercator EPSG:3111 DEA Fractional Cover (Landsat) Geoscience Australia Landsat Fractional Cover Collection 3 Fractional Cover (FC), developed by the Joint Remote Sensing Research Program, is a measurement that splits the landscape into three parts, or fractions: green (leaves, grass, and growing crops) brown (branches, dry grass or hay, and dead leaf litter) bare ground (soil or rock) DEA uses Fractional Cover to characterise every 30 m square of Australia for any point in time from 1987 to today. https://knowledge.dea.ga.gov.au/data/product/dea-fractional-cover-landsat/ For service status information, see https://status.dea.ga.gov.au 70.0896133050788 -55.5931015527544 169.900466171221 -7.63017175317532 ga_ls_fc_3 image/png application/json Time Date 1986-08-16 1986-08-17 1986-08-18 1986-08-19 1986-08-20 1986-08-21 1986-08-22 1986-08-23 1986-08-24 1986-08-25 1986-08-26 1986-08-27 1986-09-01 1986-09-12 1986-09-21 1987-05-19 1987-05-20 1987-05-21 1987-05-22 1987-05-23 1987-05-24 1987-05-25 1987-05-26 1987-05-27 1987-05-28 1987-05-29 1987-05-30 1987-05-31 1987-06-01 1987-07-13 1987-07-14 1987-07-15 1987-07-16 1987-07-17 1987-07-18 1987-07-20 1987-07-21 1987-07-27 1987-07-28 1987-07-31 1987-08-17 1987-08-18 1987-08-22 1987-08-23 1987-08-25 1987-09-01 1987-09-02 1987-09-03 1987-09-04 1987-09-05 1987-09-06 1987-09-07 1987-09-08 1987-09-09 1987-09-10 1987-09-11 1987-09-12 1987-09-13 1987-09-14 1987-09-15 1987-09-16 1987-09-17 1987-09-18 1987-09-19 1987-09-20 1987-09-21 1987-09-22 1987-09-23 1987-09-24 1987-09-25 1987-09-26 1987-09-27 1987-09-28 1987-09-29 1987-09-30 1987-10-01 1987-10-02 1987-10-03 1987-10-04 1987-10-05 1987-10-06 1987-10-07 1987-10-08 1987-10-09 1987-10-10 1987-10-11 1987-10-12 1987-10-13 1987-10-14 1987-10-15 1987-10-16 1987-10-17 1987-10-18 1987-10-19 1987-10-20 1987-10-21 1987-10-22 1987-10-23 1987-10-24 1987-10-25 1987-10-26 1987-10-27 1987-10-29 1987-10-30 1987-10-31 1987-11-01 1987-11-02 1987-11-03 1987-11-04 1987-11-05 1987-11-06 1987-11-07 1987-11-08 1987-11-09 1987-11-10 1987-11-11 1987-11-12 1987-11-13 1987-11-14 1987-11-15 1987-11-16 1987-11-17 1987-11-18 1987-11-19 1987-11-20 1987-11-21 1987-11-22 1987-11-23 1987-11-24 1987-11-25 1987-11-26 1987-11-27 1987-11-28 1987-11-29 1987-11-30 1987-12-01 1987-12-02 1987-12-03 1987-12-04 1987-12-05 1987-12-06 1987-12-07 1987-12-08 1987-12-09 1987-12-10 1987-12-11 1987-12-13 1987-12-14 1987-12-15 1987-12-16 1987-12-17 1987-12-18 1987-12-19 1987-12-20 1987-12-21 1987-12-22 1987-12-23 1987-12-24 1987-12-25 1987-12-26 1987-12-27 1987-12-28 1987-12-29 1987-12-30 1987-12-31 1988-01-02 1988-01-03 1988-01-04 1988-01-05 1988-01-06 1988-01-07 1988-01-08 1988-01-09 1988-01-10 1988-01-11 1988-01-12 1988-01-13 1988-01-14 1988-01-15 1988-01-16 1988-01-17 1988-01-18 1988-01-19 1988-01-20 1988-01-21 1988-01-22 1988-01-23 1988-01-24 1988-01-25 1988-01-26 1988-01-27 1988-01-28 1988-01-29 1988-01-30 1988-01-31 1988-02-01 1988-02-02 1988-02-05 1988-02-06 1988-02-07 1988-02-08 1988-02-09 1988-02-10 1988-02-11 1988-02-12 1988-02-13 1988-02-14 1988-02-15 1988-02-16 1988-02-17 1988-02-18 1988-02-19 1988-02-20 1988-02-21 1988-02-22 1988-02-23 1988-02-24 1988-02-25 1988-02-26 1988-02-27 1988-02-28 1988-02-29 1988-03-01 1988-03-02 1988-03-03 1988-03-04 1988-03-05 1988-03-06 1988-03-07 1988-03-08 1988-03-09 1988-03-10 1988-03-11 1988-03-12 1988-03-13 1988-03-14 1988-03-15 1988-03-16 1988-03-17 1988-03-18 1988-03-19 1988-03-20 1988-03-21 1988-03-22 1988-03-23 1988-03-24 1988-03-25 1988-03-26 1988-03-27 1988-03-28 1988-03-29 1988-03-30 1988-03-31 1988-04-01 1988-04-02 1988-04-03 1988-04-04 1988-04-05 1988-04-06 1988-04-07 1988-04-08 1988-04-09 1988-04-10 1988-04-11 1988-04-12 1988-04-13 1988-04-14 1988-04-15 1988-04-16 1988-04-17 1988-04-18 1988-04-19 1988-04-20 1988-04-21 1988-04-22 1988-04-23 1988-04-24 1988-04-26 1988-04-27 1988-04-28 1988-04-29 1988-04-30 1988-05-01 1988-05-02 1988-05-03 1988-05-04 1988-05-05 1988-05-06 1988-05-07 1988-05-08 1988-05-09 1988-05-10 1988-05-13 1988-05-14 1988-05-15 1988-05-16 1988-05-17 1988-05-18 1988-05-19 1988-05-20 1988-05-21 1988-05-22 1988-05-23 1988-05-24 1988-05-25 1988-05-26 1988-05-27 1988-05-29 1988-05-30 1988-05-31 1988-06-01 1988-06-02 1988-06-03 1988-06-04 1988-06-05 1988-06-06 1988-06-07 1988-06-08 1988-06-09 1988-06-10 1988-06-11 1988-06-13 1988-06-14 1988-06-15 1988-06-16 1988-06-17 1988-06-18 1988-06-19 1988-06-20 1988-06-21 1988-06-22 1988-06-23 1988-06-24 1988-06-25 1988-06-26 1988-06-27 1988-06-28 1988-06-30 1988-07-01 1988-07-03 1988-07-04 1988-07-05 1988-07-06 1988-07-07 1988-07-08 1988-07-09 1988-07-10 1988-07-11 1988-07-12 1988-07-13 1988-07-14 1988-07-15 1988-07-16 1988-07-17 1988-07-18 1988-07-19 1988-07-20 1988-07-21 1988-07-22 1988-07-23 1988-07-24 1988-07-25 1988-07-26 1988-07-27 1988-07-28 1988-07-29 1988-07-30 1988-07-31 1988-08-01 1988-08-02 1988-08-03 1988-08-04 1988-08-05 1988-08-06 1988-08-07 1988-08-08 1988-08-09 1988-08-10 1988-08-11 1988-08-12 1988-08-13 1988-08-14 1988-08-15 1988-08-16 1988-08-19 1988-08-21 1988-08-22 1988-08-23 1988-08-24 1988-08-25 1988-08-26 1988-08-27 1988-08-28 1988-08-29 1988-08-30 1988-08-31 1988-09-01 1988-09-02 1988-09-03 1988-09-04 1988-09-05 1988-09-06 1988-09-07 1988-09-08 1988-09-09 1988-09-10 1988-09-11 1988-09-12 1988-09-13 1988-09-14 1988-09-15 1988-09-17 1988-09-18 1988-09-19 1988-09-20 1988-09-21 1988-09-22 1988-09-23 1988-09-24 1988-09-25 1988-09-26 1988-09-27 1988-09-28 1988-09-29 1988-09-30 1988-10-01 1988-10-02 1988-10-03 1988-10-04 1988-10-05 1988-10-06 1988-10-07 1988-10-08 1988-10-09 1988-10-10 1988-10-11 1988-10-12 1988-10-13 1988-10-14 1988-10-15 1988-10-16 1988-10-17 1988-10-18 1988-10-20 1988-10-21 1988-10-22 1988-10-23 1988-10-24 1988-10-25 1988-10-26 1988-10-27 1988-10-28 1988-10-29 1988-10-30 1988-10-31 1988-11-01 1988-11-02 1988-11-03 1988-11-04 1988-11-05 1988-11-06 1988-11-07 1988-11-08 1988-11-09 1988-11-10 1988-11-11 1988-11-12 1988-11-13 1988-11-14 1988-11-15 1988-11-18 1988-11-19 1988-11-20 1988-11-21 1988-11-22 1988-11-23 1988-11-24 1988-11-25 1988-11-26 1988-11-27 1988-11-28 1988-11-29 1988-11-30 1988-12-01 1988-12-02 1988-12-03 1988-12-04 1988-12-05 1988-12-06 1988-12-07 1988-12-08 1988-12-09 1988-12-10 1988-12-11 1988-12-12 1988-12-13 1988-12-14 1988-12-15 1988-12-16 1988-12-17 1988-12-18 1988-12-19 1988-12-20 1988-12-21 1988-12-22 1988-12-23 1988-12-24 1988-12-25 1988-12-26 1988-12-27 1988-12-28 1988-12-29 1988-12-30 1988-12-31 1989-01-02 1989-01-03 1989-01-04 1989-01-05 1989-01-06 1989-01-07 1989-01-08 1989-01-09 1989-01-10 1989-01-11 1989-01-12 1989-01-13 1989-01-14 1989-01-15 1989-01-16 1989-01-17 1989-01-18 1989-01-19 1989-01-20 1989-01-21 1989-01-22 1989-01-23 1989-01-24 1989-01-25 1989-01-26 1989-01-27 1989-01-28 1989-01-29 1989-01-30 1989-01-31 1989-02-01 1989-02-02 1989-02-03 1989-02-04 1989-02-05 1989-02-06 1989-02-07 1989-02-08 1989-02-09 1989-02-10 1989-02-11 1989-02-12 1989-02-13 1989-02-15 1989-02-16 1989-02-17 1989-02-18 1989-02-19 1989-02-20 1989-02-21 1989-02-22 1989-02-23 1989-02-24 1989-02-25 1989-02-26 1989-02-27 1989-02-28 1989-03-01 1989-03-02 1989-03-03 1989-03-04 1989-03-05 1989-03-06 1989-03-07 1989-03-08 1989-03-09 1989-03-10 1989-03-11 1989-03-12 1989-03-13 1989-03-14 1989-03-15 1989-03-16 1989-03-17 1989-03-18 1989-03-19 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2025-08-24 2025-08-25 2025-08-26 2025-08-27 2025-08-28 2025-08-29 2025-08-30 2025-08-31 2025-09-01 2025-09-02 2025-09-03 2025-09-04 2025-09-05 2025-09-06 2025-09-07 2025-09-08 2025-09-09 2025-09-10 2025-09-11 2025-09-12 2025-09-13 2025-09-14 2025-09-15 2025-09-16 2025-09-17 2025-09-18 2025-09-19 2025-09-20 2025-09-21 2025-09-22 2025-09-23 2025-09-24 2025-09-25 2025-09-26 2025-09-27 2025-09-28 2025-09-29 2025-09-30 2025-10-01 2025-10-02 2025-10-03 2025-10-04 2025-10-05 2025-10-06 2025-10-07 2025-10-08 2025-10-09 2025-10-10 2025-10-11 2025-10-12 2025-10-13 2025-10-14 2025-10-15 2025-10-16 2025-10-17 2025-10-18 2025-10-19 2025-10-20 2025-10-21 2025-10-22 2025-10-23 2025-10-24 2025-10-25 2025-10-26 2025-10-27 2025-10-28 2025-10-29 2025-10-30 2025-10-31 2025-11-01 2025-11-02 2025-11-03 2025-11-04 2025-11-05 2025-11-06 2025-11-07 2025-11-08 2025-11-09 2025-11-10 2025-11-11 2025-11-12 2025-11-13 2025-11-14 2025-11-15 2025-11-16 2025-11-17 2025-11-18 2025-11-19 2025-11-20 2025-11-21 2025-11-22 2025-11-23 2025-11-24 2025-11-25 2025-11-26 2025-11-27 2025-11-28 2025-11-29 2025-11-30 2025-12-01 2025-12-02 2025-12-03 2025-12-04 2025-12-05 2025-12-06 2025-12-07 2025-12-08 2025-12-09 2025-12-10 2025-12-11 2025-12-12 2025-12-13 2025-12-14 2025-12-15 2025-12-16 2025-12-17 2025-12-18 2025-12-19 2025-12-20 2025-12-21 2025-12-22 2025-12-23 2025-12-24 2025-12-25 2025-12-26 2025-12-27 2025-12-28 2025-12-29 2025-12-30 2025-12-31 2026-01-01 2026-01-02 2026-01-03 2026-01-04 2026-01-05 2026-01-06 2026-01-07 2026-01-08 2026-01-09 2026-01-10 2026-01-11 2026-01-12 2026-01-13 2026-01-14 2026-01-15 2026-01-16 2026-01-17 2026-01-18 2026-01-19 2026-01-20 2026-01-21 2026-01-22 2026-01-23 2026-01-24 2026-01-25 2026-01-26 2026-01-27 2026-01-28 2026-01-29 2026-01-30 2026-01-31 2026-02-01 2026-02-02 2026-02-03 2026-02-04 2026-02-05 2026-02-06 2026-02-07 2026-02-08 2026-02-09 2026-02-10 2026-02-11 2026-02-12 2026-02-13 2026-02-14 2026-02-15 2026-02-16 2026-02-17 2026-02-18 2026-02-19 2026-02-20 2026-02-21 2026-02-22 2026-02-23 2026-02-24 2026-02-25 2026-02-26 2026-02-27 2026-02-28 2026-03-01 2026-03-02 2026-03-03 2026-03-04 2026-03-05 2026-03-06 2026-03-07 2026-03-08 2026-03-09 2026-03-10 2026-03-11 2026-03-12 2026-03-13 2026-03-14 2026-03-15 2026-03-16 2026-03-17 2026-03-18 2026-03-18 WholeWorld_WebMercator EPSG:3111 DEA Fractional Cover Percentiles Calendar Year (Landsat) Fractional Cover 30m Percentiles 3.0.0 (Landsat, Annual) Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 30m x 30m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program. This contains the percentage of green vegetation, non-green vegetation and bare soil per pixel at the 10th, 50th (median) and 90th percentiles respectively for observations acquired in each full calendar year (1st of January - 31st December) from 1987 to the most recent full calendar year. Fractional Cover products use Water Observations (WO) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 3 clear observations over the year. https://knowledge.dea.ga.gov.au/data/product/dea-fractional-cover-percentiles-landsat/ For service status information, see https://status.dea.ga.gov.au 111.533088723661 -44.3403132062909 155.073704691127 -8.51120628432547 ga_ls_fc_pc_cyear_3 image/png application/json Time Date 1987-01-01 1988-01-01 1989-01-01 1990-01-01 1991-01-01 1992-01-01 1993-01-01 1994-01-01 1995-01-01 1996-01-01 1997-01-01 1998-01-01 1999-01-01 2000-01-01 2001-01-01 2002-01-01 2003-01-01 2004-01-01 2005-01-01 2006-01-01 2007-01-01 2008-01-01 2009-01-01 2010-01-01 2011-01-01 2012-01-01 2013-01-01 2014-01-01 2015-01-01 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2021-01-01 2022-01-01 2023-01-01 2024-01-01 2025-01-01 2025-01-01 WholeWorld_WebMercator EPSG:3111 DEA Mangroves (Landsat) Mangrove Canopy Cover 30m (Landsat, Collection 3) Mangrove canopy cover version, 30 metre. Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. The mangrove canopy cover product provides valuable information about the extent and canopy density of mangroves for each full year between 1987 and the present for the entire Australian coastline. The canopy cover classes are: 20-50% (pale green), 50-80% (mid green), 80-100% (dark green). The product consists of a sequence (one per year) of 30 meter resolution maps that are generated by analysing the Landsat fractional cover (https://doi.org/10.6084/m9.figshare.94250.v1) developed by the Joint Remote Sensing Research Program and the Global Mangrove Watch layers (https://doi.org/10.1071/MF13177) developed by the Japanese Aerospace Exploration Agency. https://knowledge.dea.ga.gov.au/data/product/dea-mangrove-canopy-cover-landsat/ For service status information, see https://status.dea.ga.gov.au 112.492257439061 -39.1292216144938 154.264053741666 -9.56989631398544 ga_ls_mangrove_cover_cyear_3 image/png application/json Time Date 1987-01-01 1988-01-01 1989-01-01 1990-01-01 1991-01-01 1992-01-01 1993-01-01 1994-01-01 1995-01-01 1996-01-01 1997-01-01 1998-01-01 1999-01-01 2000-01-01 2001-01-01 2002-01-01 2003-01-01 2004-01-01 2005-01-01 2006-01-01 2007-01-01 2008-01-01 2009-01-01 2010-01-01 2011-01-01 2012-01-01 2013-01-01 2014-01-01 2015-01-01 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2021-01-01 2022-01-01 2023-01-01 2024-01-01 2025-01-01 2025-01-01 WholeWorld_WebMercator EPSG:3111 GA Barest Earth (Sentinel-2) GA Barest Earth (Sentinel-2) Abstract The Sentinel-2 Bare Earth thematic product provides the first national scale mosaic of the Australian continent to support improved mapping of soil and geology. The bare earth algorithm using all available Sentinel-2 A and Sentinel-2 B observations up to September 2020 preferentially weights bare pixels through time to significantly reduce the effect of seasonal vegetation in the imagery. The result are image pixels that are more likely to reflect the mineralogy and/or geochemistry of soil and bedrock. The algorithm uses a high-dimensional weighted geometric median approach that maintains the spectral relationships across all Sentinel-2 bands. A similar bare earth algorithm has been applied to Geoscience Australia’s deeper Landsat time series archive (please search for "Landsat barest Earth". Both bare earth products have spectral bands in the visible near infrared and shortwave infrared region of the electromagnetic spectrum. However, the main visible and near-infrared Sentinel-2 bands have a spatial resolution of 10 meters compared to 30m for the Landsat TM equivalents. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. Not all the sentinel-2 bands have been processed - we have excluded atmospheric bands including 1, 9 and 10. The remaining bands have been re-number 1-10 and these bands correlate to the original bands in brackets below: 1 = blue (2) , 2 = green (3) , 3 = red (4), 4 = vegetation red edge (5), 5 = vegetation red edge (6), 6= vegetation red edge (7), 7 = NIR(8), 8 = Narrow NIR (8a), 9 = SWIR1 (11) and 10 = SWIR2(12). All 10 bands have been resampled to 10 meters to facilitate band integration and use in machine learning. Pixel quality and the degree of barest recorded on the ground will largely depend on the number of ‘clean’ cloud free bare earth observation. We have noticed some image artefacts near the vicinity of Lake Eyre where highly reflective surface materials are not being well separated by the cloud filter – these artefacts will be address in a future improved version of the bare earth model. Lineage Statement Large-scale image composites are increasingly important for a variety of applications such as land cover mapping, soil and bedrock mapping, change detection, and the generation of high-quality data to parameterise and validate bio-physical and geophysical models. A number of compositing methodologies are being used in remote sensing in general, however challenges such as maintaining the spectral relationship between bands, mitigating against boundary artifacts due to mosaicking scenes from different epochs, and ensuring spatial regularity across the mosaic image still exist. The creation of good composite images is a particularly important technology since the opening of the Landsat archive by the United States Geological Survey. The greater availability of satellite imagery has resulted in demand to provide large regional mosaics that are representative of conditions over specific time periods while also being free of clouds and other unwanted image noise. One approach is the stitching together of a number of clear images. Another is the creation of mosaics where pixels from different epochs are combined based on some algorithm from a time series of observations. This ‘pixel composite’ approach to mosaic generation provides a more consistent result compared with stitching clear images due to the improved color balance created by the combining of one-by-one pixel representative images. Another strength of pixel-based composites is their ability to be automated for application to very large data collections and time series such as national satellite data archives. The Bare Earth pixel composite mosaic (BE-PCM) provides an approach that leverages high-dimensional statistical theory to deliver a spectrally consistent, artefact-free pixel composite product that is representative of the barest (i.e., least vegetation) state at each pixel over the specific time period. The BE-PCM is derived from Sentinel-2 A and Sentinel-2 B observations from 2014 to September 2020 corrected to measurements of NBAR surface reflectance (e.g., SR-N_25_2.0.0 or SR-NT_25_2.0.0). The data are masked for cloud, shadows and other image artefacts using the pixel quality product (PQ_25_2.0.0) to help provide as clear a set of observations as possible from which to calculate the BE-PCM. The BE-PCM methodology and algorithm is given in Roberts, Wilford, Ghattas (2018). The technology builds on the earlier work of Roberts et al. (2017) where a method for producing cloud-free pixel composite mosaics using ‘geometric medians’ was proposed. Note: The constituent pixels in the BE-PCM pixel composite mosaics are synthetic, meaning that the pixels have not been physically observed by the satellite. Rather they are the computed high-dimensional median of a time series of pixels which gives a robust estimate of the median state of the Earth at its barest (i.e., least vegetation). References Roberts, D., Wilford, J., Ghattas, O. (2018). Revealing the Australian Continent at its Barest. Submitted and under review. Roberts, D., Mueller, N., Mcintyre, A. (2017). High-dimensional pixel composites from earth observation time series. IEEE Transactions on Geoscience and Remote Sensing 55 (11), 6254-6264 For service status information, see https://status.dea.ga.gov.au 112.324372771065 -43.9381826788341 154.70510751296 -8.82186564540388 s2_barest_earth image/png application/json Time Date 2017-01-01 2017-01-01 WholeWorld_WebMercator EPSG:3111 GA Barest Earth (Landsat 8 OLI/TIRS) GA Barest Earth (Landsat 8 OLI/TIRS) A `weighted geometric median’ approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 OLI observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted. Mosaics are available for the following years: Landsat 8: 2013 to 2017; For service status information, see https://status.dea.ga.gov.au 111.492400120054 -44.3357065215098 155.066563941708 -8.83515695199938 ls8_barest_earth_mosaic image/png application/json Time Date 2013-01-01 2013-01-01 WholeWorld_WebMercator EPSG:3111 GA Barest Earth (Landsat) GA Barest Earth (Landsat) An estimate of the spectra of the barest state (i.e., least vegetation) observed from imagery of the Australian continent collected by the Landsat 5, 7, and 8 satellites over a period of more than 30 years (1983 - 2018). The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. This product complements the Landsat-8 Barest Earth which is based on the same algorithm but just uses Landsat8 satellite imagery from 2013-2108. Landsat-8's OLI sensor provides improved signal-to-noise radiometric (SNR) performance quantised over a 12-bit dynamic range compared to the 8-bit dynamic range of Landsat-5 and Landsat-7 data. However the Landsat 30+ Barest Earth has a greater capacity to find the barest ground due to the greater temporal depth. Reference: Roberts, D., Wilford, J., Ghattas, O. (2019). Exposed Soil and Mineral Map of the Australian Continent Revealing the Land at its Barest. Nature Communications. Mosaics are available for the following years: Landsat 5 / Landsat 7 / Landsat 8 - 1983 to 2018; For service status information, see https://status.dea.ga.gov.au 111.033686003887 -44.4285210281062 155.790571411147 -8.49453875182811 landsat_barest_earth image/png application/json Time Date 1999-01-01 1999-01-01 WholeWorld_WebMercator EPSG:3111 DEA Wetness Percentiles (Landsat) Tasseled Cap Wetness Percentiles 25m 2.0.0 The Tasseled Cap Wetness Percentiles provide a multi-decadal summary of landscape wetness that can be used to identify wetlands and groundwater ecosystems. They provide statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap wetness index from 1987 to 2017. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. Geoscience Australia Landsat Collection 2 Tasseled Cap Wetness Percentiles 1986-2018, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Areas that are partially covered in water, or where water is mixed with vegetation when viewed from above, provide habitat for a wide range of aquatic organisms. The ability to map partial inundation is also crucial to understand patterns of human water use. We need to be able to identify potential wetlands and groundwater dependent ecosystems on the Australian continent so that they can be monitored and managed. The Tasseled Cap Wetness Percentiles provide a multi-decadal summary of landscape wetness that can be used to identify wetlands and groundwater ecosystems. They provide statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap wetness index from 1987 to 2017. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. This product provides valuable discrimination for characterising: - vegetated wetlands, - salt flats, - salt lakes, - coastal land cover classes The Tasseled Cap wetness transform translates the six spectral bands of Landsat into a single wetness index. The wetness index can be used to identify areas in the landscape that are potentially wetlands or groundwater dependent ecosystems. The Tasseled Cap Wetness Percentiles capture how the wetness index behaves over time. The percentiles are well suited to characterising wetlands, salt flats/salt lakes and coastal ecosystems. However, care should be applied when analysing the wetness index, as soil colour and fire scars can cause misleading results. In areas of high relief caused by cliffs or steep terrain, terrain shadows can cause false positives (a falsely high wetness index). The 10th, 50th and 90th percentiles of the Tasseled Cap wetness index are intended to capture the extreme (10th and 90th percentile) values and long-term average (50th percentile) values of the wetness index. Percentiles are used in preference to minimum, maximum and mean, as the min/max/mean statistical measures are more sensitive to undetected cloud/cloud shadow, and can be misleading for non-normally distributed data. The Tasseled Cap Wetness Percentiles are intended to complement the Water Observations from Space (WOfS) algorithm. WOfS is designed to discriminate open water, but the Tasseled Cap wetness index identifies areas of water and areas where water and vegetation are mixed together; i.e. mangroves and palustrine wetlands. If you are interested in terrestrial vegetation (where water in the pixel is not a factor), use the Fractional Cover product, which provides a better biophysical characterisation of green vegetation fraction, dry vegetation fraction and bare soil vegetation fraction. In terms of limitations, caution should be used, especially with the Tasseled Cap wetness index results in areas where residual terrain shadow, or dark soils can cause high 'wetness' index values. One of the limitations of using the Tasseled Cap wetness index is that it will identify all 'wet' things, including potential wetlands, groundwater dependent ecosystems, irrigated crops/pasture, man-made water storages and sewerage treatment, and does not discriminate between these. As such it should be used in conjunction with other contextual data to ensure that features identified using the Tasseled Cap Wetness Percentiles are features of interest rather than false positives. We used the Tasseled Cap transforms described in Crist et al. (1985). Crist, E. P. (1985). A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17(3), 301–306. https://doi.org/10.1016/0034-4257(85)90102-6 For service status information, see https://status.dea.ga.gov.au 112.501524524947 -44.315077785668 154.340852639902 -9.07349125191761 ga_ls_tcw_percentiles_2 image/png application/json Time Date 1987-01-01 1987-01-01 WholeWorld_WebMercator EPSG:3111 DEA Tasseled Cap Indices Percentiles Calendar Year (Landsat) Tasseled Cap Indices Percentiles 30m 1.0.0 The Tasseled Cap Indices Percentiles provide an annual summary of landscape wetness, greenness and brightness indices that can be used to identify wetlands and groundwater ecosystems. They provide annual statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap indices. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. Geoscience Australia Landsat Collection 3 Tasseled Cap Indices Percentiles, 30 metre, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Areas that are partially covered in water, or where water is mixed with vegetation when viewed from above, provide habitat for a wide range of aquatic organisms. The ability to map partial inundation is also crucial to understand patterns of human water use. We need to be able to identify potential wetlands and groundwater dependent ecosystems on the Australian continent so that they can be monitored and managed. The Tasseled Cap Wetness Percentiles provide a multi-decadal summary of landscape wetness that can be used to identify wetlands and groundwater ecosystems. They provide statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap wetness index from 1987 to 2017. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. This product provides valuable discrimination for characterising: - vegetated wetlands, - salt flats, - salt lakes, - coastal land cover classes The Tasseled Cap indices transform translates the six spectral bands of Landsat into a single wetness index. The wetness index can be used to identify areas in the landscape that are potentially wetlands or groundwater dependent ecosystems. The Tasseled Cap Indices Percentiles capture how the wetness, greeness and brightness index behaves over time. The percentiles are well suited to characterising wetlands, salt flats/salt lakes and coastal ecosystems. However, care should be applied when analysing the wetness index, as soil colour and fire scars can cause misleading results. In areas of high relief caused by cliffs or steep terrain, terrain shadows can cause false positives (a falsely high wetness index). The 10th, 50th and 90th percentiles of the Tasseled Cap Indices are intended to capture the extreme (10th and 90th percentile) values and long-term average (50th percentile) values of the indices. Percentiles are used in preference to minimum, maximum and mean, as the min/max/mean statistical measures are more sensitive to undetected cloud/cloud shadow, and can be misleading for non-normally distributed data. The Tasseled Cap Indices Percentiles are intended to complement the Water Observations (WO) algorithm. WO is designed to discriminate open water, but the Tasseled Cap wetness index identifies areas of water and areas where water and vegetation are mixed together; i.e. mangroves and palustrine wetlands. If you are interested in terrestrial vegetation (where water in the pixel is not a factor), use the Fractional Cover product, which provides a better biophysical characterisation of green vegetation fraction, dry vegetation fraction and bare soil vegetation fraction. In terms of limitations, caution should be used, especially with the Tasseled Cap Indices results in areas where residual terrain shadow, or dark soils can cause high 'wetness' index values. One of the limitations of using the Tasseled Cap wetness index is that it will identify all 'wet' things, including potential wetlands, groundwater dependent ecosystems, irrigated crops/pasture, man-made water storages and sewerage treatment, and does not discriminate between these. As such it should be used in conjunction with other contextual data to ensure that features identified using the Tasseled Cap Wetness Percentiles are features of interest rather than false positives. We used the Tasseled Cap transforms described in Crist et al. (1985). Crist, E. P. (1985). A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17(3), 301–306. https://doi.org/10.1016/0034-4257(85)90102-6 For service status information, see https://status.dea.ga.gov.au 111.533088723661 -44.3403132062909 155.073704691127 -8.51120628432547 ga_ls_tc_pc_cyear_3 image/png application/json Time Date 1987-01-01 1988-01-01 1989-01-01 1990-01-01 1991-01-01 1992-01-01 1993-01-01 1994-01-01 1995-01-01 1996-01-01 1997-01-01 1998-01-01 1999-01-01 2000-01-01 2001-01-01 2002-01-01 2003-01-01 2004-01-01 2005-01-01 2006-01-01 2007-01-01 2008-01-01 2009-01-01 2010-01-01 2011-01-01 2012-01-01 2013-01-01 2014-01-01 2015-01-01 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2021-01-01 2022-01-01 2023-01-01 2024-01-01 2025-01-01 2025-01-01 WholeWorld_WebMercator EPSG:3111 DEA Water Observations (Landsat) **Geoscience Australia Water Observations (Landsat, Collection 3, 30 m, Individual Observations, 2.0.0).** Water Observations are the principal Digital Earth Australia (DEA) Water product (previously known as Water Observations from Space (WOfS)). This product shows where surface water was observed within each individual Landsat (5, 7 and 8) satellite image on each particular day since mid 1986. These daily data layers are termed Water Observations (WOs). WOs show the extent of water in a corresponding Landsat scene, along with the degree to which the scene was obscured by clouds, shadows or where sensor problems cause parts of a scene to not be observable. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-landsat/ For service status information, see https://status.dea.ga.gov.au 95.6231557755943 -45.6734414490927 169.58619462215 -7.63027349493903 ga_ls_wo_3 image/png application/json Time Date 1986-08-16 1986-08-17 1986-08-18 1986-08-19 1986-08-20 1986-08-21 1986-08-22 1986-08-23 1986-08-24 1986-08-25 1986-08-26 1986-08-27 1986-09-01 1986-09-12 1986-09-21 1987-05-19 1987-05-20 1987-05-21 1987-05-22 1987-05-23 1987-05-24 1987-05-25 1987-05-26 1987-05-27 1987-05-28 1987-05-29 1987-05-30 1987-05-31 1987-06-01 1987-07-13 1987-07-14 1987-07-15 1987-07-16 1987-07-17 1987-07-18 1987-07-20 1987-07-21 1987-07-27 1987-07-28 1987-07-31 1987-08-17 1987-08-18 1987-08-22 1987-08-23 1987-08-25 1987-09-01 1987-09-02 1987-09-03 1987-09-04 1987-09-05 1987-09-06 1987-09-07 1987-09-08 1987-09-09 1987-09-10 1987-09-11 1987-09-12 1987-09-13 1987-09-14 1987-09-15 1987-09-16 1987-09-17 1987-09-18 1987-09-19 1987-09-20 1987-09-21 1987-09-22 1987-09-23 1987-09-24 1987-09-25 1987-09-26 1987-09-27 1987-09-28 1987-09-29 1987-09-30 1987-10-01 1987-10-02 1987-10-03 1987-10-04 1987-10-05 1987-10-06 1987-10-07 1987-10-08 1987-10-09 1987-10-10 1987-10-11 1987-10-12 1987-10-13 1987-10-14 1987-10-15 1987-10-16 1987-10-17 1987-10-18 1987-10-19 1987-10-20 1987-10-21 1987-10-22 1987-10-23 1987-10-24 1987-10-25 1987-10-26 1987-10-27 1987-10-29 1987-10-30 1987-10-31 1987-11-01 1987-11-02 1987-11-03 1987-11-04 1987-11-05 1987-11-06 1987-11-07 1987-11-08 1987-11-09 1987-11-10 1987-11-11 1987-11-12 1987-11-13 1987-11-14 1987-11-15 1987-11-16 1987-11-17 1987-11-18 1987-11-19 1987-11-20 1987-11-21 1987-11-22 1987-11-23 1987-11-24 1987-11-25 1987-11-26 1987-11-27 1987-11-28 1987-11-29 1987-11-30 1987-12-01 1987-12-02 1987-12-03 1987-12-04 1987-12-05 1987-12-06 1987-12-07 1987-12-08 1987-12-09 1987-12-10 1987-12-11 1987-12-13 1987-12-14 1987-12-15 1987-12-16 1987-12-17 1987-12-18 1987-12-19 1987-12-20 1987-12-21 1987-12-22 1987-12-23 1987-12-24 1987-12-25 1987-12-26 1987-12-27 1987-12-28 1987-12-29 1987-12-30 1987-12-31 1988-01-02 1988-01-03 1988-01-04 1988-01-05 1988-01-06 1988-01-07 1988-01-08 1988-01-09 1988-01-10 1988-01-11 1988-01-12 1988-01-13 1988-01-14 1988-01-15 1988-01-16 1988-01-17 1988-01-18 1988-01-19 1988-01-20 1988-01-21 1988-01-22 1988-01-23 1988-01-24 1988-01-25 1988-01-26 1988-01-27 1988-01-28 1988-01-29 1988-01-30 1988-01-31 1988-02-01 1988-02-02 1988-02-05 1988-02-06 1988-02-07 1988-02-08 1988-02-09 1988-02-10 1988-02-11 1988-02-12 1988-02-13 1988-02-14 1988-02-15 1988-02-16 1988-02-17 1988-02-18 1988-02-19 1988-02-20 1988-02-21 1988-02-22 1988-02-23 1988-02-24 1988-02-25 1988-02-26 1988-02-27 1988-02-28 1988-02-29 1988-03-01 1988-03-02 1988-03-03 1988-03-04 1988-03-05 1988-03-06 1988-03-07 1988-03-08 1988-03-09 1988-03-10 1988-03-11 1988-03-12 1988-03-13 1988-03-14 1988-03-15 1988-03-16 1988-03-17 1988-03-18 1988-03-19 1988-03-20 1988-03-21 1988-03-22 1988-03-23 1988-03-24 1988-03-25 1988-03-26 1988-03-27 1988-03-28 1988-03-29 1988-03-30 1988-03-31 1988-04-01 1988-04-02 1988-04-03 1988-04-04 1988-04-05 1988-04-06 1988-04-07 1988-04-08 1988-04-09 1988-04-10 1988-04-11 1988-04-12 1988-04-13 1988-04-14 1988-04-15 1988-04-16 1988-04-17 1988-04-18 1988-04-19 1988-04-20 1988-04-21 1988-04-22 1988-04-23 1988-04-24 1988-04-26 1988-04-27 1988-04-28 1988-04-29 1988-04-30 1988-05-02 1988-05-03 1988-05-04 1988-05-05 1988-05-06 1988-05-07 1988-05-08 1988-05-09 1988-05-10 1988-05-13 1988-05-14 1988-05-15 1988-05-16 1988-05-17 1988-05-18 1988-05-19 1988-05-20 1988-05-21 1988-05-22 1988-05-23 1988-05-24 1988-05-25 1988-05-26 1988-05-27 1988-05-29 1988-05-30 1988-05-31 1988-06-01 1988-06-02 1988-06-03 1988-06-04 1988-06-05 1988-06-06 1988-06-07 1988-06-08 1988-06-09 1988-06-10 1988-06-11 1988-06-13 1988-06-14 1988-06-15 1988-06-16 1988-06-17 1988-06-18 1988-06-19 1988-06-20 1988-06-21 1988-06-22 1988-06-23 1988-06-24 1988-06-25 1988-06-26 1988-06-27 1988-06-28 1988-06-30 1988-07-01 1988-07-03 1988-07-04 1988-07-05 1988-07-06 1988-07-07 1988-07-08 1988-07-09 1988-07-10 1988-07-11 1988-07-12 1988-07-13 1988-07-14 1988-07-15 1988-07-16 1988-07-17 1988-07-18 1988-07-19 1988-07-20 1988-07-21 1988-07-22 1988-07-23 1988-07-24 1988-07-25 1988-07-26 1988-07-27 1988-07-28 1988-07-29 1988-07-30 1988-07-31 1988-08-01 1988-08-02 1988-08-03 1988-08-04 1988-08-05 1988-08-06 1988-08-07 1988-08-08 1988-08-09 1988-08-10 1988-08-11 1988-08-12 1988-08-13 1988-08-14 1988-08-15 1988-08-16 1988-08-19 1988-08-21 1988-08-22 1988-08-23 1988-08-24 1988-08-25 1988-08-26 1988-08-27 1988-08-28 1988-08-29 1988-08-30 1988-08-31 1988-09-01 1988-09-02 1988-09-03 1988-09-04 1988-09-05 1988-09-06 1988-09-07 1988-09-08 1988-09-09 1988-09-10 1988-09-11 1988-09-12 1988-09-13 1988-09-14 1988-09-15 1988-09-17 1988-09-18 1988-09-19 1988-09-20 1988-09-21 1988-09-22 1988-09-23 1988-09-24 1988-09-25 1988-09-26 1988-09-27 1988-09-28 1988-09-29 1988-09-30 1988-10-01 1988-10-02 1988-10-03 1988-10-04 1988-10-05 1988-10-06 1988-10-07 1988-10-08 1988-10-09 1988-10-10 1988-10-11 1988-10-12 1988-10-13 1988-10-14 1988-10-15 1988-10-16 1988-10-17 1988-10-18 1988-10-20 1988-10-21 1988-10-22 1988-10-23 1988-10-24 1988-10-25 1988-10-26 1988-10-27 1988-10-28 1988-10-29 1988-10-30 1988-10-31 1988-11-01 1988-11-02 1988-11-03 1988-11-04 1988-11-05 1988-11-06 1988-11-07 1988-11-08 1988-11-09 1988-11-10 1988-11-11 1988-11-12 1988-11-13 1988-11-14 1988-11-15 1988-11-18 1988-11-19 1988-11-20 1988-11-21 1988-11-22 1988-11-23 1988-11-24 1988-11-25 1988-11-26 1988-11-27 1988-11-28 1988-11-29 1988-11-30 1988-12-01 1988-12-02 1988-12-03 1988-12-04 1988-12-05 1988-12-06 1988-12-07 1988-12-08 1988-12-09 1988-12-10 1988-12-11 1988-12-12 1988-12-13 1988-12-14 1988-12-15 1988-12-16 1988-12-17 1988-12-18 1988-12-19 1988-12-20 1988-12-21 1988-12-22 1988-12-23 1988-12-24 1988-12-25 1988-12-26 1988-12-27 1988-12-28 1988-12-29 1988-12-30 1988-12-31 1989-01-02 1989-01-03 1989-01-04 1989-01-05 1989-01-06 1989-01-07 1989-01-08 1989-01-09 1989-01-10 1989-01-11 1989-01-12 1989-01-13 1989-01-14 1989-01-15 1989-01-16 1989-01-17 1989-01-18 1989-01-19 1989-01-20 1989-01-21 1989-01-22 1989-01-23 1989-01-24 1989-01-25 1989-01-26 1989-01-27 1989-01-28 1989-01-29 1989-01-30 1989-01-31 1989-02-01 1989-02-02 1989-02-03 1989-02-04 1989-02-05 1989-02-06 1989-02-07 1989-02-08 1989-02-09 1989-02-10 1989-02-11 1989-02-12 1989-02-13 1989-02-15 1989-02-16 1989-02-17 1989-02-18 1989-02-19 1989-02-20 1989-02-21 1989-02-22 1989-02-23 1989-02-24 1989-02-25 1989-02-26 1989-02-27 1989-02-28 1989-03-01 1989-03-02 1989-03-03 1989-03-04 1989-03-05 1989-03-06 1989-03-07 1989-03-08 1989-03-09 1989-03-10 1989-03-11 1989-03-12 1989-03-13 1989-03-14 1989-03-15 1989-03-16 1989-03-17 1989-03-18 1989-03-19 1989-03-20 1989-03-21 1989-03-22 1989-03-23 1989-03-24 1989-03-25 1989-03-26 1989-03-27 1989-03-28 1989-03-31 1989-04-01 1989-04-02 1989-04-03 1989-04-04 1989-04-06 1989-04-09 1989-04-10 1989-04-11 1989-04-13 1989-04-14 1989-04-15 1989-04-16 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2026-03-16 2026-03-17 2026-03-18 2026-03-18 WholeWorld_WebMercator EPSG:3111 DEA Water Observations Provisional (Sentinel-2 NRT) **Prototype Geoscience Australia Sentinel 2 Near Real Time Water Observations Provisional Collection 3** This product is the implementation of the DEA Water Observations (DEA WO) product (previously known as Water Observations from Space, or WOfS) on the Geoscience Australia Sentinel-2 Near Real Time surface reflectance product. This is a rapid, provisional, product. It has not been validated and is of unknown accuracy. The Provisional Digital Earth Australia Water Observations (Sentinel-2) product shows where surface water was observed by the Sentinel 2A and Sentinel 2B satellites on any particular day over the most recent 3 months. The surface water observations are derived from Geoscience Australia Sentinel-2 Near Real Time surface reflectance imagery for all of Australia. The provisional, Near Real Time product is available for a rolling window of the most recent three months of data, and is produced within 24 hours of the satellite passing over an area. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-sentinel-2-nrt/ 111.958991614373 -44.3414673034495 155.219024052459 -9.02727104242042 ga_s2_wo_provisional_3 image/png application/json Time Date 2021-07-01 2023-03-14 2023-03-15 2023-03-16 2023-03-17 2023-03-18 2023-03-19 2023-03-20 2023-03-21 2023-03-22 2023-03-23 2023-03-24 2023-03-25 2023-03-26 2023-03-27 2023-03-28 2023-03-29 2023-03-30 2023-03-31 2023-04-01 2023-04-02 2023-04-03 2023-04-04 2023-04-05 2023-04-06 2023-04-07 2023-04-08 2023-04-09 2023-04-10 2023-04-11 2023-04-12 2023-04-13 2023-04-14 2023-04-15 2023-04-16 2023-04-17 2023-04-18 2023-04-19 2023-04-20 2023-04-21 2023-04-22 2023-04-23 2023-04-24 2023-04-25 2023-04-26 2023-04-27 2023-04-28 2023-04-29 2023-04-30 2023-05-01 2023-05-02 2023-05-03 2023-05-04 2023-05-05 2023-05-06 2023-05-07 2023-05-08 2023-05-09 2023-05-10 2023-05-11 2023-05-12 2023-05-13 2023-05-14 2023-05-15 2023-05-16 2023-05-17 2023-05-18 2023-05-19 2023-05-20 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2024-02-18 2024-02-19 2024-02-20 2024-02-21 2024-02-22 2024-02-23 2024-02-24 2024-02-25 2024-02-26 2024-02-27 2024-02-28 2024-02-29 2024-03-01 2024-03-02 2024-03-03 2024-03-04 2024-03-05 2024-03-06 2024-03-07 2024-03-08 2024-03-09 2024-03-10 2024-03-11 2024-03-12 2024-03-13 2024-03-14 2024-03-15 2024-03-16 2024-03-17 2024-03-18 2024-03-19 2024-03-20 2024-03-21 2024-03-22 2024-03-23 2024-03-24 2024-03-25 2024-03-26 2024-03-27 2024-03-28 2024-03-29 2024-03-30 2024-03-31 2024-04-01 2024-04-02 2024-04-03 2024-04-04 2024-04-05 2024-04-06 2024-04-07 2024-04-08 2024-04-09 2024-04-10 2024-04-11 2024-04-12 2024-04-13 2024-04-14 2024-04-15 2024-04-16 2024-04-17 2024-04-18 2024-04-19 2024-04-20 2024-04-21 2024-04-22 2024-04-23 2024-04-24 2024-04-25 2024-04-26 2024-04-27 2024-04-28 2024-04-29 2024-04-30 2024-05-01 2024-05-02 2024-05-03 2024-05-04 2024-05-05 2024-05-06 2024-05-07 2024-05-08 2024-05-09 2024-05-10 2024-05-11 2024-05-12 2024-05-13 2024-05-14 2024-05-15 2024-05-16 2024-05-17 2024-05-18 2024-05-19 2024-05-20 2024-05-21 2024-05-22 2024-05-23 2024-05-24 2024-05-25 2024-05-26 2024-05-27 2024-05-28 2024-05-29 2024-05-30 2024-05-31 2024-06-01 2024-06-02 2024-06-03 2024-06-04 2024-06-05 2024-06-06 2024-06-07 2024-06-08 2024-06-09 2024-06-10 2024-06-11 2024-06-12 2024-06-13 2024-06-14 2024-06-15 2024-06-16 2024-06-17 2024-06-18 2024-06-19 2024-06-20 2024-06-21 2024-06-22 2024-06-23 2024-06-24 2024-06-25 2024-06-26 2024-06-27 2024-06-28 2024-06-29 2024-06-30 2024-07-01 2024-07-02 2024-07-03 2024-07-04 2024-07-05 2024-07-06 2024-07-07 2024-07-08 2024-07-10 2024-07-11 2024-07-12 2024-07-13 2024-07-14 2024-07-15 2024-07-16 2024-07-17 2024-07-18 2024-07-19 2024-07-20 2024-07-21 2024-07-22 2024-07-23 2024-07-24 2024-07-25 2024-07-26 2024-07-28 2024-07-29 2024-07-30 2024-07-31 2024-08-01 2024-08-02 2024-08-03 2024-08-04 2024-08-05 2024-08-06 2024-08-07 2024-08-08 2024-08-09 2024-08-10 2024-08-11 2024-08-12 2024-08-13 2024-08-14 2024-08-15 2024-08-16 2024-08-17 2024-08-18 2024-08-19 2024-08-20 2024-08-21 2024-08-22 2024-08-23 2024-08-24 2024-08-25 2024-08-26 2024-08-27 2024-08-28 2024-08-29 2024-08-30 2024-08-31 2024-09-01 2024-09-02 2024-09-03 2024-09-04 2024-09-05 2024-09-06 2024-09-07 2024-09-08 2024-09-09 2024-09-10 2024-09-11 2024-09-12 2024-09-13 2024-09-14 2024-09-15 2024-09-16 2024-09-17 2024-09-18 2024-09-19 2024-09-20 2024-09-21 2024-09-22 2024-09-23 2024-09-24 2024-09-25 2024-09-26 2024-09-27 2024-09-28 2024-09-29 2024-09-30 2024-10-01 2024-10-02 2024-10-03 2024-10-04 2024-10-05 2024-10-06 2024-10-07 2024-10-08 2024-10-09 2024-10-10 2024-10-11 2024-10-12 2024-10-13 2024-10-14 2024-10-15 2024-10-16 2024-10-17 2024-10-18 2024-10-19 2024-10-20 2024-10-21 2024-10-22 2024-10-23 2024-10-24 2024-10-25 2024-10-26 2024-10-27 2024-10-28 2024-10-29 2024-10-30 2024-10-31 2024-11-01 2024-11-02 2024-11-03 2024-11-04 2024-11-05 2024-11-06 2024-11-07 2024-11-08 2024-11-09 2024-11-10 2024-11-11 2024-11-12 2024-11-13 2024-11-14 2024-11-15 2024-11-16 2024-11-17 2024-11-18 2024-11-19 2024-11-20 2024-11-21 2024-11-22 2024-11-23 2024-11-24 2024-11-25 2024-11-26 2024-11-27 2024-11-28 2024-11-29 2024-11-30 2024-12-01 2024-12-02 2024-12-03 2024-12-04 2024-12-05 2024-12-06 2024-12-07 2024-12-08 2024-12-09 2024-12-10 2024-12-11 2024-12-12 2024-12-13 2024-12-14 2024-12-15 2024-12-16 2024-12-17 2024-12-18 2024-12-19 2024-12-20 2024-12-21 2024-12-22 2024-12-23 2024-12-24 2024-12-25 2024-12-26 2024-12-27 2024-12-28 2024-12-29 2024-12-30 2024-12-31 2025-01-01 2025-01-02 2025-01-03 2025-01-04 2025-01-05 2025-01-06 2025-01-07 2025-01-08 2025-01-09 2025-01-10 2025-01-11 2025-01-12 2025-01-13 2025-01-14 2025-01-15 2025-01-16 2025-01-17 2025-01-18 2025-01-19 2025-01-20 2025-01-21 2025-01-22 2025-01-23 2025-01-24 2025-01-25 2025-01-26 2025-01-27 2025-01-28 2025-01-29 2025-01-30 2025-01-31 2025-02-01 2025-02-02 2025-02-03 2025-02-04 2025-02-05 2025-02-06 2025-02-07 2025-02-08 2025-02-09 2025-02-10 2025-02-11 2025-02-12 2025-02-13 2025-02-14 2025-02-16 2025-02-18 2025-02-19 2025-02-20 2025-02-21 2025-02-22 2025-02-23 2025-02-24 2025-02-25 2025-02-26 2025-02-27 2025-02-28 2025-03-01 2025-03-02 2025-03-03 2025-03-04 2025-03-05 2025-03-06 2025-03-07 2025-03-08 2025-03-09 2025-03-10 2025-03-11 2025-03-12 2025-03-13 2025-03-14 2025-03-15 2025-03-16 2025-03-17 2025-03-18 2025-03-19 2025-03-20 2025-03-21 2025-03-22 2025-03-23 2025-03-24 2025-03-25 2025-03-26 2025-03-27 2025-03-28 2025-03-29 2025-03-30 2025-03-31 2025-04-01 2025-04-02 2025-04-04 2025-04-05 2025-04-06 2025-04-07 2025-04-08 2025-04-09 2025-04-10 2025-04-11 2025-04-12 2025-04-13 2025-04-14 2025-04-15 2025-04-16 2025-04-17 2025-04-18 2025-04-19 2025-04-20 2025-04-21 2025-04-22 2025-04-23 2025-04-24 2025-04-25 2025-04-26 2025-04-27 2025-04-28 2025-04-29 2025-04-30 2025-05-01 2025-05-02 2025-05-03 2025-05-04 2025-05-05 2025-05-06 2025-05-07 2025-05-08 2025-05-09 2025-05-10 2025-05-11 2025-05-12 2025-05-13 2025-05-14 2025-05-15 2025-05-16 2025-05-17 2025-05-18 2025-05-19 2025-05-20 2025-05-21 2025-05-22 2025-05-23 2025-05-24 2025-05-25 2025-05-26 2025-05-27 2025-05-28 2025-05-29 2025-05-30 2025-05-31 2025-06-01 2025-06-02 2025-06-03 2025-06-04 2025-06-05 2025-06-06 2025-06-07 2025-06-08 2025-06-09 2025-06-10 2025-06-11 2025-06-12 2025-06-13 2025-06-14 2025-06-15 2025-06-16 2025-06-17 2025-06-18 2025-06-19 2025-06-20 2025-06-21 2025-06-22 2025-06-23 2025-06-24 2025-06-25 2025-06-26 2025-06-27 2025-06-28 2025-06-29 2025-06-30 2025-07-01 2025-07-02 2025-07-03 2025-07-04 2025-07-05 2025-07-06 2025-07-07 2025-07-08 2025-07-09 2025-07-10 2025-07-11 2025-07-12 2025-07-13 2025-07-14 2025-07-15 2025-07-16 2025-07-17 2025-07-18 2025-07-19 2025-07-20 2025-07-21 2025-07-22 2025-07-23 2025-07-24 2025-07-25 2025-07-26 2025-07-27 2025-07-28 2025-07-29 2025-07-30 2025-07-31 2025-08-01 2025-08-02 2025-08-03 2025-08-04 2025-08-05 2025-08-06 2025-08-07 2025-08-08 2025-08-09 2025-08-10 2025-08-11 2025-08-12 2025-08-13 2025-08-14 2025-08-15 2025-08-16 2025-08-17 2025-08-18 2025-08-19 2025-08-20 2025-08-21 2025-08-22 2025-08-23 2025-08-24 2025-08-25 2025-08-26 2025-08-27 2025-08-28 2025-08-29 2025-08-30 2025-08-31 2025-09-01 2025-09-02 2025-09-03 2025-09-04 2025-09-05 2025-09-06 2025-09-07 2025-09-08 2025-09-09 2025-09-10 2025-09-11 2025-09-12 2025-09-13 2025-09-14 2025-09-15 2025-09-16 2025-09-17 2025-09-18 2025-09-19 2025-09-20 2025-09-21 2025-09-22 2025-09-23 2025-09-24 2025-09-25 2025-09-26 2025-09-27 2025-09-28 2025-09-29 2025-09-30 2025-10-01 2025-10-03 2025-10-04 2025-10-05 2025-10-06 2025-10-07 2025-10-08 2025-10-09 2025-10-10 2025-10-11 2025-10-12 2025-10-13 2025-10-14 2025-10-15 2025-10-16 2025-10-17 2025-10-18 2025-10-19 2025-10-20 2025-10-21 2025-10-22 2025-10-23 2025-10-24 2025-10-25 2025-10-26 2025-10-27 2025-10-28 2025-10-29 2025-10-30 2025-10-31 2025-11-01 2025-11-02 2025-11-03 2025-11-04 2025-11-05 2025-11-06 2025-11-07 2025-11-08 2025-11-09 2025-11-10 2025-11-11 2025-11-12 2025-11-13 2025-11-14 2025-11-15 2025-11-16 2025-11-17 2025-11-18 2025-11-19 2025-11-20 2025-11-21 2025-11-22 2025-11-23 2025-11-24 2025-11-25 2025-11-26 2025-11-27 2025-11-28 2025-11-29 2025-11-30 2025-12-01 2025-12-02 2025-12-03 2025-12-04 2025-12-05 2025-12-06 2025-12-07 2025-12-08 2025-12-09 2025-12-10 2025-12-11 2025-12-12 2025-12-13 2025-12-14 2025-12-15 2025-12-16 2025-12-17 2025-12-18 2025-12-19 2025-12-20 2025-12-21 2025-12-22 2025-12-23 2025-12-24 2025-12-25 2025-12-26 2025-12-27 2025-12-28 2025-12-29 2025-12-30 2025-12-31 2026-01-01 2026-01-02 2026-01-03 2026-01-04 2026-01-05 2026-01-06 2026-01-07 2026-01-08 2026-01-09 2026-01-10 2026-01-11 2026-01-12 2026-01-13 2026-01-14 2026-01-15 2026-01-16 2026-01-17 2026-01-18 2026-01-19 2026-01-20 2026-01-21 2026-01-22 2026-01-23 2026-01-24 2026-01-25 2026-01-26 2026-01-27 2026-01-28 2026-01-29 2026-01-30 2026-01-31 2026-02-01 2026-02-02 2026-02-03 2026-02-04 2026-02-05 2026-02-06 2026-02-07 2026-02-08 2026-02-09 2026-02-10 2026-02-11 2026-02-12 2026-02-13 2026-02-14 2026-02-15 2026-02-16 2026-02-17 2026-02-18 2026-02-19 2026-02-20 2026-02-21 2026-02-22 2026-02-23 2026-02-24 2026-02-25 2026-02-26 2026-02-27 2026-02-28 2026-03-01 2026-03-02 2026-03-03 2026-03-04 2026-03-05 2026-03-06 2026-03-07 2026-03-08 2026-03-09 2026-03-10 2026-03-11 2026-03-12 2026-03-13 2026-03-14 2026-03-15 2026-03-16 2026-03-17 2026-03-18 2026-03-19 2026-03-20 2026-03-21 2026-03-22 2026-03-23 2026-03-24 2026-03-25 2026-03-26 2026-03-27 2026-03-28 2026-03-29 2026-03-30 2026-03-31 2026-04-01 2026-04-02 2026-04-03 2026-04-04 2026-04-05 2026-04-06 2026-04-07 2026-04-08 2026-04-09 2026-04-10 2026-04-11 2026-04-12 2026-04-13 2026-04-14 2026-04-15 2026-04-16 2026-04-17 2026-04-18 2026-04-19 2026-04-20 2026-04-20 WholeWorld_WebMercator EPSG:3111 DEA Water Observations Multi Year (Landsat) **Geoscience Australia Water Observations, Multi Year Frequency Statistics, 1986 to near present (Landsat, Collection 3, 30 m, WO-STATS, Frequency, 2.0.0).** The DEA Multi Year Water Observation Statistic is a statistical summary that combines all years (1986 to near present) of the DEA Water Observations product and helps the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations that were observed over the specifed time period in the landscape. **What this product offers** Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected in observations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/ For service status information, see https://status.dea.ga.gov.au 111.533088723661 -44.3403132062909 155.073704691127 -8.51120628432547 ga_ls_wo_fq_myear_3 image/png application/json Time Date 2005-07-02 2005-07-02 WholeWorld_WebMercator EPSG:3111 DEA Water Observations Calendar Year (Landsat) **Geoscience Australia Water Observations, Annual Frequency Statistics, Calendar Year (Landsat, Collection 3, 30 m, WO-STATS-ANNUAL, 2.0.0).** The DEA Annual Water Observation Statistic is a set of calendar year statistical summaries of the DEA Water Observations product that combines satellite observations, that occur within each calendar year from 1986 to present, into summary products that help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; and the percentage of wet observations that were observed over the specified time period in the landscape. **What this product offers** Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected in observations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/ For service status information, see https://status.dea.ga.gov.au 111.533088723661 -44.3403132062909 155.073704691127 -8.51120628432547 ga_ls_wo_fq_cyear_3 image/png application/json Time Date 1986-01-01 1987-01-01 1988-01-01 1989-01-01 1990-01-01 1991-01-01 1992-01-01 1993-01-01 1994-01-01 1995-01-01 1996-01-01 1997-01-01 1998-01-01 1999-01-01 2000-01-01 2001-01-01 2002-01-01 2003-01-01 2004-01-01 2005-01-01 2006-01-01 2007-01-01 2008-01-01 2009-01-01 2010-01-01 2011-01-01 2012-01-01 2013-01-01 2014-01-01 2015-01-01 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2021-01-01 2022-01-01 2023-01-01 2024-01-01 2025-01-01 2025-01-01 WholeWorld_WebMercator EPSG:3111 DEA Water Observations April to October (Landsat) **Geoscience Australia Water Observations, Seasonal Frequency Statistics, April to October (Landsat, Collection 3, 30 m, WO-STATS-APR-OCT, 2.0.0).** The DEA Seasonal Water Observation (April to October) Statistic is a set of seasonal statistical summaries of the DEA Water Observations product. The product combines satellite observations, that occur during April to October within each year, into summary products that help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; and the percentage of wet observations that were observed over the specified time period in the landscape. **What this product offers** Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected in observations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/ For service status information, see https://status.dea.ga.gov.au 111.533088723661 -44.3403132062909 155.073704691127 -8.51120628432547 ga_ls_wo_fq_apr_oct_3 image/png application/json Time Date 1986-04-01 1987-04-01 1988-04-01 1989-04-01 1990-04-01 1991-04-01 1992-04-01 1993-04-01 1994-04-01 1995-04-01 1996-04-01 1997-04-01 1998-04-01 1999-04-01 2000-04-01 2001-04-01 2002-04-01 2003-04-01 2004-04-01 2005-04-01 2006-04-01 2007-04-01 2008-04-01 2009-04-01 2010-04-01 2011-04-01 2012-04-01 2013-04-01 2014-04-01 2015-04-01 2016-04-01 2017-04-01 2018-04-01 2019-04-01 2020-04-01 2021-04-01 2022-04-01 2023-04-01 2024-04-01 2025-04-01 2025-04-01 WholeWorld_WebMercator EPSG:3111 DEA Water Observations November to March (Landsat) **Geoscience Australia Water Observations, Seasonal Frequency Statistics, November to March (Landsat, Collection 3, 30 m, WO-STATS-NOV-MAR, 2.0.0).** The DEA Seasonal Water Observation (November to March) Statistic is a set of seasonal statistical summaries of the DEA Water Observations product. The product combines satellite observations, that occur during November to March, into summary products that help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; and the percentage of wet observations that were observed over the specified time period in the landscape. **What this product offers** Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected in observations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/ For service status information, see https://status.dea.ga.gov.au 111.533088723661 -44.3403132062909 155.073704691127 -8.51120628432547 ga_ls_wo_fq_nov_mar_3 image/png application/json Time Date 1987-11-01 1988-11-01 1989-11-01 1990-11-01 1991-11-01 1992-11-01 1993-11-01 1994-11-01 1995-11-01 1996-11-01 1997-11-01 1998-11-01 1999-11-01 2000-11-01 2001-11-01 2002-11-01 2003-11-01 2004-11-01 2005-11-01 2006-11-01 2007-11-01 2008-11-01 2009-11-01 2010-11-01 2011-11-01 2012-11-01 2013-11-01 2014-11-01 2015-11-01 2016-11-01 2017-11-01 2018-11-01 2019-11-01 2020-11-01 2021-11-01 2022-11-01 2023-11-01 2024-11-01 2024-11-01 WholeWorld_WebMercator EPSG:3111 DEA Multi-Year Water Observation Frequency Filtered Statistics (Landsat, DEPRECATED) Water Observations from Space Filtered Statistics 25m 2.1.5 (Landsat, Filtered) Water Observations from Space (WOfS) Filtered Statistics helps provide the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out. WOfS Filtered Statistics consists of a Confidence layer that compares the WOfS Statistics water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the WOfS Statistics water summary where Confidence is low. This layer is Filtered Water Summary: A simplified version of the Water Summary, showing the frequency of water observations where the Confidence is above a cutoff level. No clear observations of water causes an area to appear transparent, few clear observations of water correlate with red and yellow colours, deep blue and purple correspond to an area being wet through 90%-100% of clear observations. The Filtered Water Summary layer is a noise-reduced view of surface water across Australia. Even though confidence filtering is applied to the Filtered Water Summary, some cloud and shadow, and sensor noise does persist. For more information please see: https://data.dea.ga.gov.au/?prefix=WOfS/filtered_summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au 111.859562290899 -44.9351287319957 155.169368098324 -9.00692244744483 wofs_filtered_summary image/png application/json Time Date 1970-01-01 1970-01-01 WholeWorld_WebMercator EPSG:3111 DEA Multi-Year Clear Observation Statistics (Landsat, DEPRECATED) Water Observations from Space Statistics 25m 2.1.5 (Landsat, Clear) Water Observations from Space (WOfS) Statistics is a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products which help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This layer contains Clear Count: how many times an area could be clearly seen (ie. not affected by clouds, shadows or other satellite observation problems). No clear observations causes an area to appear transparent, 1-300 total clear observations of water correlate with red and yellow colours, 400 clear observations correlates with light green, 800 clear observations and above correlates with dark green. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics Filtered Summary product, which provide a noise-reduced view of the water summary. For more information please see: https://data.dea.ga.gov.au/WOfS/summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au 112.99986111 -44.0008652894921 153.999861110328 -10.00013889 wofs_summary_clear image/png application/json Time Date 1970-01-01 1970-01-01 WholeWorld_WebMercator EPSG:3111 DEA Multi-Year Wet Observation Statistics (Landsat, DEPRECATED) Water Observations from Space Statistics 25m 2.1.5 (Landsat, Wet) Water Observations from Space (WOfS) Statistics is a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products which help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This layer contains Wet Count: how many times water was detected in observations that were clear. No clear observations of water causes an area to appear transparent, 1-50 total clear observations of water correlate with red and yellow colours, 100 clear observations of water correlate with green, 200 clear observations of water correlates with light blue, 300 clear observations of water correlates to deep blue and 400 and over observations of clear water correlate to purple. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics Filtered Summary product, which provide a noise-reduced view of the water summary. For more information please see: https://data.dea.ga.gov.au/WOfS/summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au 112.99986111 -44.0008652894921 153.999861110328 -10.00013889 wofs_summary_wet image/png application/json Time Date 1970-01-01 1970-01-01 WholeWorld_WebMercator EPSG:3111 DEA Multi-Year Water Observation Frequency Statistics (Landsat, DEPRECATED) Water Observations from Space Statistics 25m 2.1.5 (Landsat, Frequency) Water Observations from Space (WOfS) Statistics is a set of statistical summaries of the WOfS product which combines WOfS observations into summary products that help the understanding of surface water across Australia. WOfS Statistics is calculated from the full depth time series (1986 – 2018). The water detected for each location is summed through time and then compared to the number of clear observations of that location. The result is a percentage value of the number of times water was observed at the location. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time (water summary). This layer contains the Water Summary: the percentage of clear observations which were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations of water causes an area to appear transparent, few clear observations of water correlate with red and yellow colours, deep blue and purple correspond to an area being wet through 90%-100% of clear observations. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics Filtered Summary product, which provide a noise-reduced view of the water summary. For more information please see: https://data.dea.ga.gov.au/WOfS/summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au 112.99986111 -44.0008652894921 153.999861110328 -10.00013889 Water Observations from Space Statistics image/png application/json Time Date 1970-01-01 1970-01-01 WholeWorld_WebMercator EPSG:3111 DEA Multi-Year Water Observation Confidence Statistics (Landsat, DEPRECATED) Water Observations from Space Filtered Statistics 25m 2.1.5 (Landsat, Confidence) Water Observations from Space (WOfS) Filtered Statistics helps provide the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out. WOfS Filtered Statistics consists of a Confidence layer that compares the WOfS Statistics water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the WOfS Statistics water summary where Confidence is low. This layer is Confidence: the degree of agreement between water shown in the Water Summary and other national datasets. Areas where there is less than 1% confidence appears black, areas with confidence for between 1% 10% confidence are styled between black and red, areas with 25% confidence are styled yellow, areas with 75% confidence and above correspond to green. The Confidence layer provides understanding of whether the water shown in the Water Summary agrees with where water should exist in the landscape, such as due to sloping land or whether water has been detected in a location by other means. For more information please see: https://data.dea.ga.gov.au/WOfS/filtered_summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au 111.859562290899 -44.9351287319957 155.169368098324 -9.00692244744483 wofs_filtered_summary_confidence image/png application/json Time Date 1970-01-01 1970-01-01 WholeWorld_WebMercator EPSG:3111 DEA Intertidal (Sentinel-2, Landsat) Geoscience Australia Sentinel-2 Landsat Intertidal Calendar Year Collection 3 The DEA Intertidal product suite maps the changing extent, elevation and topography of Australia's exposed intertidal zone, the complex zone that defines the interface between land and sea. Incorporating both Sentinel-2 and Landsat data, the product suite provides an annual 10 m resolution elevation product for the intertidal zone, enabling users to better monitor and understand some of the most dynamic regions of Australia’s coastlines. Utilising an improved tidal modelling capability, the product suite includes a continental scale mapping of intertidal exposure over time, enabling scientists and managers to integrate the data into ecological and migratory species applications and modelling. Product documentation: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/ Service status information: https://status.dea.ga.gov.au 109.014497679694 -44.8348910259975 159.453586509182 -8.36289586889526 ga_s2ls_intertidal_cyear_3 image/png application/json Time Date 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2021-01-01 2022-01-01 2023-01-01 2024-01-01 2024-01-01 WholeWorld_WebMercator EPSG:3111 DEA Tidal Composites (Sentinel-2) Geoscience Australia Sentinel-2 Tidal Composites Calendar Year Collection 3 Digital Earth Australia (DEA) Tidal Composites are cloud-free imagery mosaics of Australia's coasts, estuaries and reefs at low and high tide. Calculated using a geometric median of Sentinel-2 imagery from the highest and lowest 15% of observed tides, DEA Tidal Composites deliver an annually updated snapshot of high and low tide Australian coastal environments. Product documentation: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/ Service status information: https://status.dea.ga.gov.au 109.014497679694 -44.8310113541374 161.2959867784 -7.52530343626107 ga_s2_tidal_composites_cyear_3 image/png application/json Time Date 2016-01-01 2017-01-01 2018-01-01 2019-01-01 2020-01-01 2021-01-01 2022-01-01 2023-01-01 2024-01-01 2024-01-01 WholeWorld_WebMercator EPSG:3111 DEA Coastal Ecosystems (Sentinel-2) Geoscience Australia Sentinel-2 Coastal Ecosystems Calendar Year Collection 3 The Digital Earth Australia Coastal Ecosystems product suite includes a categorical classification of four coastal ecosystems, supported by probability layers for individual ecosystems and selected quality assurance layers to support product interpretation. Product documentation: https://knowledge.dea.ga.gov.au/data/product/dea-coastal-ecosystems/ Service status information: https://status.dea.ga.gov.au 109.014497679694 -44.8345141037585 158.713321244241 -8.4563402174858 ga_s2_coastalecosystems_cyear_3_v1 image/png application/json Time Date 2021-01-01 2022-01-01 2022-01-01 WholeWorld_WebMercator EPSG:3111 Intertidal Extents Model (ITEM) Intertidal Extents Model 25m 2.0.0 (Extents) This product is deprecated; users are advised to transition to using DEA Intertidal instead: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/ For service status information, see https://status.dea.ga.gov.au 112.459622677896 -43.7203758299765 153.670408736335 -10.3096529183452 ITEM_V2.0.0 image/png application/json Time Date 1986-01-01 1986-01-01 WholeWorld_WebMercator EPSG:3111 Intertidal Extents Model (ITEM) confidence Intertidal Extents Model 25m 2.0.0 (Confidence) This product is deprecated; users are advised to transition to using DEA Intertidal instead: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/ For service status information, see https://status.dea.ga.gov.au 112.459622677896 -43.7203758299765 153.670408736335 -10.3096529183452 ITEM_V2.0.0_Conf image/png application/json Time Date 1986-01-01 1986-01-01 WholeWorld_WebMercator EPSG:3111 National Intertidal Digital Elevation Model (NIDEM) National Intertidal Digital Elevation Model 25m 1.0.0 This product is deprecated; users are advised to transition to using DEA Intertidal instead: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/ For service status information, see https://status.dea.ga.gov.au 112.223058990767 -43.8291965530654 154.080299801132 -10.2371048142508 NIDEM image/png application/json Time Date 1986-01-01 1986-01-01 WholeWorld_WebMercator EPSG:3111 High Tide Composite (HLTC) High Tide and Low Tide Composites 2.0.0 (Landsat, High Tide) This product is deprecated; users are advised to transition to using DEA Tidal Composites instead: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/ For service status information, see https://status.dea.ga.gov.au 112.223058990767 -43.8291965530654 154.080299801132 -10.2371048142508 high_tide_composite image/png application/json Time Date 2000-01-01 2000-01-01 WholeWorld_WebMercator EPSG:3111 Low Tide Composite (HLTC) High Tide and Low Tide Composites 2.0.0 (Landsat, Low Tide) This product is deprecated; users are advised to transition to using DEA Tidal Composites instead: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/ For service status information, see https://status.dea.ga.gov.au 112.223058990767 -43.8291965530654 154.080299801132 -10.2371048142508 low_tide_composite image/png application/json Time Date 2000-01-01 2000-01-01 WholeWorld_WebMercator EPSG:3111 DEA Burnt Area Characteristic Layers (Deprecated, Sentinel 2 Near Real-Time) DEA Burnt Area Characteristic Layers (Deprecated, Sentinel 2 Near Real-Time, Provisional) PRODUCT DEPRECATED ON 30/08/2023, NO RESULTS ARE GENERATED AFTER THIS DATE. Bushfires pose a serious and increasing threat to Australia. The detection and mapping of burns have many applications to support communities and ecosystems impacted by fire. However, the identification of bushfire burn using Earth Observation is often manual, can come with a significant time delay, and at a relatively small scale. Digital Earth Australia (DEA) offer a provisional and preliminary change detection data product, for all of Australia, which uses same day satellite data and cloud-based infrastructure to automatically and rapidly identify areas that show burn characteristics. Commonly, burnt area analysis is run on data from manually selected, cloud-free, pre-fire and post-fire satellite scenes. Automating the rapid identification of pre- and post-fire scenes is problematic due to cloud cover, differing fire duration, and the difficulty in automatically identifying a suitable pre-fire scene. Therefore, Analysing the change between a pre-fire reference baseline and the latest near real-time satellite data enables rapid, automatic, screening of areas that have changed to show characteristics of undergoing a burn. This Near Real-Time (NRT) change detection product is based on: - a pre-fire reference baseline (baseline-metric) dataset; DEA Sentinel 2 Barest Earth (Roberts, et al. 2019). The Barest Earth data shows the spectral data for an area at its least vegetated state based on the Sentinel 2 data archive. The Barest Earth product is produced by a novel high-dimensional statistical technique that extracts a noise-reduced, cloud-free, and robust estimate of the spectral response of the barest state. - the latest (NRT-metric) daily Sentinel-2 (A and B combined) Near Real-Time provisional satellite data. The NRT service provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections. The following metrics were calculated to identify burnt area characteristics: - Bare Soil Index (BSI) - Normalized Difference Vegetation Index (NDVI) - Normalized Burn Ratio (NBR) Change (delta) in each metric was calculated by differencing the baseline metric and the post event (NRT) metric. Each delta layer contains values between -1 and +1, with positive values being more characterisitc of a burn. These layers have been produced as input data into a burnt area vectorisor tool (link will be provided when in production). This data is preliminary and provisional in nature and is still undergoing further development. These metrics should be used as a preliminary screening tool, and not an accurate identification of fire extent. These metrics should be used in combination with each other and can be used with other datasets to strengthen the agreement that the area has indeed been burnt. No decisions on life or property should be made based on this data. **Bare Soil** Bare Soil Index (BSI) identifies soil or bare-land characteristics by combining blue, red, near infrared (NIR), and short wave infrared (SWIR) spectral bands. SWIR and red spectral bands can be used to identify basic soil mineralogy while blue and NIR spectral bands can help to detect vegetation. BSI (Rikimaru et al. 2002) is calculated as: BSI = ((SWIR2 + RED) - (NIR + BLUE)) / ((SWIR2 + RED) + (NIR + BLUE)). Delta BSI (dBSI) shows the change in soil characteristics as the difference between a baseline BSI and a post event BSI (NRT-BSI). The change (delta) in BSI is calculated as: dBSI = (baseline-BSI - NRT-BSI) * -1. Delta BSI is multiplied by negative 1 in order to reverse the scaling so that it can be presented the same way as the other delta indexes, which all have positive values for areas that shows characteristics of being burnt. **Vegetation** Normalized Difference Vegetation Index (NDVI) is used to detect green vegetation characteristics by identifying the difference between red/visible and near infrared (NIR) spectral bands (Huete and Jackson 1987). NDVI is calculated as: NDVI = (NIR - RED)/(NIR + RED). Delta NDVI (dNDVI) shows the change in vegetation characteristics as the difference between a baseline-NDVI and a post event NDVI (NRT-NDVI). The change (delta) in NDVI is calculated as: dNDVI = baseline-NDVI - NRT-NDVI. **Burnt area** Normalized Burn Ratio (NBR) identifies areas that have the characteristics of being burnt. NBR looks at the relationship between near infrared (NIR) and short wave infrared (SWIR) spectral signatures. High SWIR reflectance values with low NIR reflectance values are indicative of an area that has been burnt by fire(s), while the opposite is seen in healthy vegetation. NBR is calculated as: NBR = (NIR – SWIR2) / (NIR + SWIR2). Delta NBR shows the change in burn characteristics as the difference between a baseline-NBR and a post event NBR (NRT-NBR). The change (delta) in NBR is calculated as: dNBR = baseline-NBR - NRT-NBR. Normally dNBR analysis is run on data from a clear pre-fire and a post-fire satellite scene. However, in order to automatically and rapidly identify areas that are characteristic of burns for all of Australia, and on a near real-time basis, the Sentinel 2 Barest Earth layer has been used as a conservative pre-fire reference image. dNBR is styled in two layers, as a colour ramp between -1 and +1, and also as a thresholded class layer. Burn classes and thresholds values are a simplified version of those proposed by USGS, as "Unburnt", "Low to Medium" and "High to Severe". Colour coding was established by UN-SPIDER. For service status information, see https://status.dea.ga.gov.au 111.966746816605 -44.3414673034495 155.213824039639 -9.93000738897011 ga_s2_ba_provisional_3 image/png application/json Time Date 2021-07-01 2023-02-01 2023-02-02 2023-02-03 2023-02-04 2023-02-05 2023-02-06 2023-02-07 2023-02-08 2023-02-09 2023-02-10 2023-02-11 2023-02-12 2023-02-13 2023-02-14 2023-02-15 2023-02-16 2023-02-17 2023-02-18 2023-02-19 2023-02-20 2023-02-21 2023-02-22 2023-02-23 2023-02-24 2023-02-25 2023-02-26 2023-02-27 2023-02-28 2023-03-01 2023-03-02 2023-03-03 2023-03-04 2023-03-05 2023-03-06 2023-03-07 2023-03-08 2023-03-09 2023-03-10 2023-03-11 2023-03-12 2023-03-13 2023-03-14 2023-03-15 2023-03-16 2023-03-17 2023-03-18 2023-03-19 2023-03-20 2023-03-21 2023-03-22 2023-03-23 2023-03-24 2023-03-25 2023-03-26 2023-03-27 2023-03-28 2023-03-29 2023-03-30 2023-03-31 2023-04-01 2023-04-02 2023-04-03 2023-04-04 2023-04-05 2023-04-06 2023-04-07 2023-04-08 2023-04-09 2023-04-10 2023-04-11 2023-04-12 2023-04-13 2023-04-14 2023-04-15 2023-04-16 2023-04-17 2023-04-18 2023-04-19 2023-04-20 2023-04-21 2023-04-22 2023-04-23 2023-04-24 2023-04-25 2023-04-26 2023-04-27 2023-04-28 2023-04-29 2023-04-30 2023-05-01 2023-05-02 2023-05-03 2023-05-04 2023-05-05 2023-05-06 2023-05-07 2023-05-08 2023-05-09 2023-05-10 2023-05-11 2023-05-12 2023-05-13 2023-05-14 2023-05-15 2023-05-16 2023-05-17 2023-05-18 2023-05-19 2023-05-20 2023-05-21 2023-05-22 2023-05-23 2023-05-24 2023-05-25 2023-05-26 2023-05-27 2023-05-28 2023-05-29 2023-05-30 2023-05-31 2023-06-01 2023-06-02 2023-06-03 2023-06-04 2023-06-05 2023-06-06 2023-06-07 2023-06-08 2023-06-09 2023-06-10 2023-06-11 2023-06-12 2023-06-13 2023-06-14 2023-06-15 2023-06-16 2023-06-17 2023-06-18 2023-06-19 2023-06-20 2023-06-21 2023-06-22 2023-06-23 2023-06-24 2023-06-25 2023-06-26 2023-06-27 2023-06-28 2023-06-29 2023-06-30 2023-07-01 2023-07-02 2023-07-03 2023-07-04 2023-07-05 2023-07-06 2023-07-07 2023-07-08 2023-07-09 2023-07-10 2023-07-11 2023-07-12 2023-07-13 2023-07-14 2023-07-15 2023-07-16 2023-07-17 2023-07-18 2023-07-19 2023-07-20 2023-07-21 2023-07-22 2023-07-23 2023-07-24 2023-07-25 2023-07-26 2023-07-27 2023-07-28 2023-07-29 2023-07-30 2023-07-31 2023-08-01 2023-08-02 2023-08-03 2023-08-04 2023-08-05 2023-08-06 2023-08-07 2023-08-08 2023-08-09 2023-08-10 2023-08-11 2023-08-12 2023-08-13 2023-08-14 2023-08-15 2023-08-16 2023-08-17 2023-08-18 2023-08-19 2023-08-20 2023-08-21 2023-08-22 2023-08-23 2023-08-24 2023-08-25 2023-08-26 2023-08-27 2023-08-28 2023-08-29 2023-08-29 WholeWorld_WebMercator EPSG:3111 DEA FMC Sentinel-2 (A, B & C) DEA FMC Sentinel-2 (A, B & C) This product calculates the Fuel Moisture Content (FMC) of vegetation which is the percentage of water mass relative to dry mass in living vegetation. Values range from 0–300%, representing, by weight percent, the amount of water in leaves compared to dry plant material. A value of 0% would indicate that there is no water content. A value of 100% indicates there is an equal weight of water and dry plant material. A value of 300% would mean there is approximately three times as much water as dry plant material. Values in the range of 0–150% are particularly relevant to fire behaviour analysis, as this range is strongly associated with changes in vegetation flammability and the likelihood of ignition. This product presents pixel-level FMC values for each corresponding Sentinel-2 (A, B and C) scene since July 2015. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-fuel-moisture-content/ For service status information, see https://status.dea.ga.gov.au 71.9335801182649 -56.033068214974 169.065352168893 -8.12502254507701 ga_s2_fmc_layer image/png application/json Time Date 2015-07-12 2015-07-25 2015-07-31 2015-08-01 2015-08-04 2015-08-05 2015-08-10 2015-08-11 2015-08-12 2015-08-14 2015-08-15 2015-08-18 2015-08-20 2015-08-21 2015-08-22 2015-08-23 2015-08-25 2015-08-26 2015-08-27 2015-08-28 2015-08-29 2015-08-30 2015-08-31 2015-09-02 2015-09-03 2015-09-04 2015-09-05 2015-09-08 2015-09-09 2015-09-11 2015-09-12 2015-09-13 2015-09-14 2015-09-15 2015-09-19 2015-09-22 2015-09-23 2015-09-29 2015-09-30 2015-10-01 2015-10-02 2015-10-03 2015-10-04 2015-10-05 2015-10-06 2015-10-07 2015-10-08 2015-10-20 2015-10-21 2015-10-22 2015-10-23 2015-10-24 2015-10-28 2015-11-12 2015-11-13 2015-11-14 2015-11-15 2015-11-17 2015-11-18 2015-11-20 2015-11-21 2015-11-22 2015-11-23 2015-11-24 2015-11-25 2015-11-26 2015-11-27 2015-11-28 2015-11-29 2015-11-30 2015-12-01 2015-12-03 2015-12-04 2015-12-05 2015-12-06 2015-12-07 2015-12-08 2015-12-09 2015-12-11 2015-12-16 2015-12-17 2015-12-18 2015-12-19 2015-12-21 2015-12-22 2015-12-23 2015-12-24 2015-12-25 2015-12-26 2015-12-27 2015-12-28 2015-12-29 2015-12-30 2015-12-31 2016-01-01 2016-01-02 2016-01-03 2016-01-04 2016-01-05 2016-01-06 2016-01-07 2016-01-08 2016-01-09 2016-01-10 2016-01-11 2016-01-12 2016-01-13 2016-01-14 2016-01-15 2016-01-16 2016-01-17 2016-01-18 2016-01-19 2016-01-20 2016-01-21 2016-01-22 2016-01-23 2016-01-24 2016-01-25 2016-01-26 2016-01-29 2016-01-30 2016-01-31 2016-02-01 2016-02-02 2016-02-03 2016-02-04 2016-02-05 2016-02-06 2016-02-07 2016-02-08 2016-02-09 2016-02-10 2016-02-11 2016-02-12 2016-02-13 2016-02-14 2016-02-15 2016-02-17 2016-02-18 2016-02-19 2016-02-20 2016-03-02 2016-03-03 2016-03-04 2016-03-05 2016-03-06 2016-03-07 2016-03-08 2016-03-09 2016-03-10 2016-03-11 2016-03-14 2016-03-15 2016-03-16 2016-03-18 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WholeWorld_WebMercator EPSG:3111 DEA FMC Sentinel-2 Most Recent Observation DEA FMC Sentinel-2 (A, B & C) Most Recent Observation This product calculates the Fuel Moisture Content (FMC) of vegetation which is the percentage of water mass relative to dry mass in living vegetation. Values range from 0–300%, representing, by weight percent, the amount of water in leaves compared to dry plant material. A value of 0% would indicate that there is no water content. A value of 100% indicates there is an equal weight of water and dry plant material. A value of 300% would mean there is approximately three times as much water as dry plant material. Values in the range of 0–150% are particularly relevant to fire behaviour analysis, as this range is strongly associated with changes in vegetation flammability and the likelihood of ignition. This product is a mosaic of the most recent available data from Sentinel-2 satellites (A, B and C) captured over the Australian continent. You can click on the map to view date of observation information for each pixel. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-fuel-moisture-content/ For service status information, see https://status.dea.ga.gov.au 71.9335801182649 -56.033068214974 169.065352168893 -8.12502254507701 ga_s2m_fmc_mosaic image/png application/json WholeWorld_WebMercator EPSG:3111 ALOS Displacement Cumulative displacement time series derived from combination of ascending and descending line-of-sight InSAR data from six ALOS data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The time series spans the period 2008-02-11 to 2010-10-22, interpolated to 12-day sampling. The reference (i.e. zero displacement) for the time series is 2008-04-23. 150.330509919584 -34.5250413940277 151.258021405841 -33.772472435988 alos_displacement image/png application/json Time Date 2008-02-11 2008-02-23 2008-03-06 2008-03-18 2008-03-30 2008-04-11 2008-04-23 2008-05-05 2008-05-17 2008-05-29 2008-06-10 2008-06-22 2008-07-04 2008-07-16 2008-07-28 2008-08-09 2008-08-21 2008-09-02 2008-09-14 2008-09-26 2008-10-08 2008-10-20 2008-11-01 2008-11-13 2008-11-25 2008-12-07 2008-12-19 2008-12-31 2009-01-12 2009-01-24 2009-02-05 2009-02-17 2009-03-01 2009-03-13 2009-03-25 2009-04-06 2009-04-18 2009-04-30 2009-05-12 2009-05-24 2009-06-05 2009-06-17 2009-06-29 2009-07-11 2009-07-23 2009-08-04 2009-08-16 2009-08-28 2009-09-09 2009-09-21 2009-10-03 2009-10-15 2009-10-27 2009-11-08 2009-11-20 2009-12-02 2009-12-14 2009-12-26 2010-01-07 2010-01-19 2010-01-31 2010-02-12 2010-02-24 2010-03-08 2010-03-20 2010-04-01 2010-04-13 2010-04-25 2010-05-07 2010-05-19 2010-05-31 2010-06-12 2010-06-24 2010-07-06 2010-07-18 2010-07-30 2010-08-11 2010-08-23 2010-09-04 2010-09-16 2010-09-28 2010-10-10 2010-10-22 2010-10-22 WholeWorld_WebMercator EPSG:3111 ALOS Velocity Velocity (linear displacement rate) of ground movement derived from combination of ascending and descending line-of-sight InSAR data from six ALOS data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The velocity is calculated for the period 2006-05-16 to 2011-01-07. 150.331038253243 -34.5250413940277 151.258021405841 -33.772472435988 alos_velocity image/png application/json Time Date 2009-06-15 2009-06-15 WholeWorld_WebMercator EPSG:3111 ENVISAT Displacement Cumulative displacement time series derived from combination of ascending and descending line-of-sight InSAR data from seven Envisat data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The time series spans the period 2006-06-26 to 2010-08-28, interpolated to 12-day sampling. The reference (i.e. zero displacement) for the time series is 2007-04-10. 150.416357298779 -34.5283535864513 151.184355816078 -33.5035077252927 envisat_displacement image/png application/json Time Date 2006-06-26 2006-07-08 2006-07-20 2006-08-01 2006-08-13 2006-08-25 2006-09-06 2006-09-18 2006-09-30 2006-10-12 2006-10-24 2006-11-05 2006-11-17 2006-11-29 2006-12-11 2006-12-23 2007-01-04 2007-01-16 2007-01-28 2007-02-09 2007-02-21 2007-03-05 2007-03-17 2007-03-29 2007-04-10 2007-04-22 2007-05-04 2007-05-16 2007-05-28 2007-06-09 2007-06-21 2007-07-03 2007-07-15 2007-07-27 2007-08-08 2007-08-20 2007-09-01 2007-09-13 2007-09-25 2007-10-07 2007-10-19 2007-10-31 2007-11-12 2007-11-24 2007-12-06 2007-12-18 2007-12-30 2008-01-11 2008-01-23 2008-02-04 2008-02-16 2008-02-28 2008-03-11 2008-03-23 2008-04-04 2008-04-16 2008-04-28 2008-05-10 2008-05-22 2008-06-03 2008-06-15 2008-06-27 2008-07-09 2008-07-21 2008-08-02 2008-08-14 2008-08-26 2008-09-07 2008-09-19 2008-10-01 2008-10-13 2008-10-25 2008-11-06 2008-11-18 2008-11-30 2008-12-12 2008-12-24 2009-01-05 2009-01-17 2009-01-29 2009-02-10 2009-02-22 2009-03-06 2009-03-18 2009-03-30 2009-04-11 2009-04-23 2009-05-05 2009-05-17 2009-05-29 2009-06-10 2009-06-22 2009-07-04 2009-07-16 2009-07-28 2009-08-09 2009-08-21 2009-09-02 2009-09-14 2009-09-26 2009-10-08 2009-10-20 2009-11-01 2009-11-13 2009-11-25 2009-12-07 2009-12-19 2009-12-31 2010-01-12 2010-01-24 2010-02-05 2010-02-17 2010-03-01 2010-03-13 2010-03-25 2010-04-06 2010-04-18 2010-04-30 2010-05-12 2010-05-24 2010-06-05 2010-06-17 2010-06-29 2010-07-11 2010-07-23 2010-08-04 2010-08-16 2010-08-28 2010-08-28 WholeWorld_WebMercator EPSG:3111 ENVISAT Velocity Velocity (linear displacement rate) of ground movement derived from combination of ascending and descending line-of-sight InSAR data from seven Envisat data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The velocity is calculated for the period 2006-06-02 to 2010-09-25. 150.416357298779 -34.5283535864513 151.184355816078 -33.5035077252927 envisat_velocity image/png application/json Time Date 2008-06-15 2008-06-15 WholeWorld_WebMercator EPSG:3111 RADARSAT2 Displacement Cumulative displacement time series derived from combination of ascending and descending line-of-sight InSAR data from two Radarsat-2 data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The time series spans the period 2015-07-15 to 2019-05-31, interpolated to 12-day sampling. The reference (i.e. zero displacement) for the time series is 2015-07-15. 150.540420399293 -34.3792688432228 151.151613477574 -33.8798432478718 radarsat2_displacement image/png application/json Time Date 2015-07-15 2015-07-27 2015-08-08 2015-08-20 2015-09-01 2015-09-13 2015-09-25 2015-10-07 2015-10-19 2015-10-31 2015-11-12 2015-11-24 2015-12-06 2015-12-18 2015-12-30 2016-01-11 2016-01-23 2016-02-04 2016-02-16 2016-02-28 2016-03-11 2016-03-23 2016-04-04 2016-04-16 2016-04-28 2016-05-10 2016-05-22 2016-06-03 2016-06-15 2016-06-27 2016-07-09 2016-07-21 2016-08-02 2016-08-14 2016-08-26 2016-09-07 2016-09-19 2016-10-01 2016-10-13 2016-10-25 2016-11-06 2016-11-18 2016-11-30 2016-12-12 2016-12-24 2017-01-05 2017-01-17 2017-01-29 2017-02-10 2017-02-22 2017-03-06 2017-03-18 2017-03-30 2017-04-11 2017-04-23 2017-05-05 2017-05-17 2017-05-29 2017-06-10 2017-06-22 2017-07-04 2017-07-16 2017-07-28 2017-08-09 2017-08-21 2017-09-02 2017-09-14 2017-09-26 2017-10-08 2017-10-20 2017-11-01 2017-11-13 2017-11-25 2017-12-07 2017-12-19 2017-12-31 2018-01-12 2018-01-24 2018-02-05 2018-02-17 2018-03-01 2018-03-13 2018-03-25 2018-04-06 2018-04-18 2018-04-30 2018-05-12 2018-05-24 2018-06-05 2018-06-17 2018-06-29 2018-07-11 2018-07-23 2018-08-04 2018-08-16 2018-08-28 2018-09-09 2018-09-21 2018-10-03 2018-10-15 2018-10-27 2018-11-08 2018-11-20 2018-12-02 2018-12-14 2018-12-26 2019-01-07 2019-01-19 2019-01-31 2019-02-12 2019-02-24 2019-03-08 2019-03-20 2019-04-01 2019-04-13 2019-04-25 2019-05-07 2019-05-19 2019-05-31 2019-05-31 WholeWorld_WebMercator EPSG:3111 RADARSAT2 Velocity Velocity (linear displacement rate) of ground movement derived from combination of ascending and descending line-of-sight InSAR data from two Radarsat-2 data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The velocity is calculated for the period 2015-07-15 to 2019-05-31. 150.540420399293 -34.3792688432228 151.151613477574 -33.8798432478718 radarsat2_velocity image/png application/json Time Date 2017-06-15 2017-06-15 WholeWorld_WebMercator EPSG:3111 False Colour Mosaic The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 False colour RGB composite - Red: B3 - Green: B2 - Blue: B1 (red = green vegetation) Use this image to help understand non-geological differences within and between ASTER scenes caused by green vegetation (red), fire scars, thin and thick cloud and cloud shadows. Use band 2 only for a gray-scale background to the content, composition and index colour products. For 'False Colour Mosaic' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74348 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -44.013698912363 153.640054299875 -10.2856586 aster_false_colour image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 Regolith Ratios The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 3 band RGB composite - Red: B3/B2 - Green: B3/B7 - Blue: B4/B7 (white = green vegetation) Use this image to help interpret: (1) the amount of green vegetation cover (appears as white); (2) basic spectral separation (colour) between different regolith and geological units and regions/provinces; and (3) evidence for unmasked cloud (appears as green). For 'Regolith Ratios' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74349 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -44.013698912363 153.640054299875 -10.2856586 aster_regolith_ratios image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 AlOH Group Composition The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B5/B7 - Blue is well ordered kaolinite, Al-rich muscovite/illite, paragonite, pyrophyllite - Red is Al-poor (Si-rich) muscovite (phengite) Useful for mapping: (1) exposed saprolite/saprock is often white mica or Al-smectite (warmer colours) whereas transported materials are often kaolin-rich (cooler colours); (2) clays developed over carbonates, especially Al-smectite (montmorillonite, beidellite) will produce middle to warmers colours; (3) stratigraphic mapping based on different clay-types; and (4) lithology-overprinting hydrothermal alteration, e.g. Si-rich and K-rich phengitic mica (warmer colours). Combine with Ferrous iron in MgOH and FeOH content products to look for evidence of overlapping/juxtaposed potassic metasomatism in ferromagnesian parents rocks (e.g. Archaean greenstone associated Au mineralisation) +/- associated distal propyllitic alteration (e.g. chlorite, amphibole) For 'AlOH Group Composition' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74356 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_aloh_group_composition image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 AlOH Group Content The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: (B5+B7)/B6 - Blue is low abundance - Red is high abundance (potentially includes: phengite, muscovite, paragonite, lepidolite, illite, brammalite, montmorillonite, beidellite, kaolinite, dickite) Useful for mapping: (1) exposed saprolite/saprock; (2) clay-rich stratigraphic horizons; (3) lithology-overprinting hydrothermal phyllic (e.g. white mica) alteration; and (4) clay-rich diluents in ore systems (e.g. clay in iron ore). Also combine with AlOH composition to help map: (1) exposed in situ parent material persisting through “cover” which can be expressed as: (a) more abundant AlOH content + (b) long-wavelength (warmer colour) AlOH composition (e.g. muscovite/phengite) For 'AlOH Group Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74355 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_aloh_group_content image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 FeOH Group Content The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: (B6+B8)/B7 - Blue is low content, - Red is high content (potentially includes: chlorite, epidote, jarosite, nontronite, gibbsite, gypsum, opal-chalcedony Useful for mapping: (1) jarosite (acid conditions) – in combination with ferric oxide content (high); (2) gypsum/gibbsite – in combination with ferric oxide content (low); (3) magnesite - in combination with ferric oxide content (low) and MgOH content (moderate-high); (4) chlorite (e.g. propyllitic alteration) – in combination with Ferrous in MgOH (high); and (5) epidote (calc-silicate alteration) – in combination with Ferrous in MgOH (low). For 'FeOH Group Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74358 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_feoh_group_content image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 Ferric Oxide Composition The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B2/B1 - Blue-cyan is goethite rich, - Green is hematite-goethite, - Red-yellow is hematite-rich Useful For: (1) Mapping transported materials (including palaeochannels) characterised by hematite (relative to geothite). Combine with AlOH composition to find co-located areas of hematite and poorly ordered kaolin to map transported materials; and (2) hematite-rish areas in drier conditions (eg above the water table) whereas goethite-rich in wetter conditions (eg at/below the water or areas recently exposed). May also be climate driven. For 'Ferric Oxide Composition' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74352 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_ferric_oxide_composition image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 Ferric Oxide Content The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B4/B3 - Blue is low abundance, - Red is high abundance Useful for: (1) Exposed iron ore (hematite-goethite). Use in combination with the “Opaques index” to help separate/map dark: (a) surface lags (e.g. maghemite gravels) which can be misidentified in visible and false colour imagery; and (b) magnetite in BIF and/or bedded iron ore; and (2) Acid conditions: combine with FeOH Group content to help map jarosite which will have high values in both products. Mapping hematite versus goethite mapping is NOT easily achieved as ASTER’s spectral bands were not designed to capture diagnostic iron oxide spectral behaviour. However, some information on visible colour relating in part to differences in hematite and/or goethite content can be obtained using a ratio of B2/B1 especially when this is masked using a B4/B3 to locate those pixels with sufficient iro oxide content. For 'Ferric Oxide Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74351 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_ferric_oxide_content image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 Ferrous Iron Content in MgOH/Carbonate The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B5/B4 - Blue is low ferrous iron content in carbonate and MgOH minerals like talc and tremolite. - Red is high ferrous iron content in carbonate and MgOH minerals like chlorite and actinolite. Useful for mapping: (1) un-oxidised “parent rocks” – i.e. mapping exposed parent rock materials (warm colours) in transported cover; (2) talc/tremolite (Mg-rich – cool colours) versus actinolite (Fe-rich – warm colours); (3) ferrous-bearing carbonates (warm colours) potentially associated with metasomatic “alteration”; (4) calcite/dolomite which are ferrous iron-poor (cool colours); and (5) epidote, which is ferrous iron poor (cool colours) – in combination with FeOH content product (high). For 'Ferrous Iron Content in MgOH/Carbonate' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74361 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_ferrous_iron_content_in_mgoh image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 Ferrous Iron Index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B5/B4 - Blue is low abundance, - Red is high abundance This product can help map exposed “fresh” (un-oxidised) rocks (warm colours) especially mafic and ultramafic lithologies rich in ferrous silicates (e.g. actinolite, chlorite) and/or ferrous carbonates (e.g. ferroan dolomite, ankerite, siderite). Applying an MgOH Group content mask to this product helps to isolate ferrous bearing non-OH bearing minerals like pyroxenes (e.g. jadeite) from OH-bearing or carbonate-bearing ferrous minerals like actinolite or ankerite, respectively. Also combine with the FeOH Group content product to find evidence for ferrous-bearing chlorite (e.g. chamosite). For 'Ferrous Iron Index' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74353 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_ferrous_iron_index image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 Green Vegetation Content The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B3/B2 - Blue is low content, - Red is high content Use this image to help interpret the amount of “obscuring/complicating” green vegetation cover. For 'Green Vegetation Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74350 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_green_vegetation image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 Gypsum Index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band Ratio: (B10+B12)/B11 - Blue is low gypsum content, - Red is high gypsum content Useful for mapping: (1) evaporative environments (e.g. salt lakes) and associated arid aeolian systems (e.g. dunes); (2) acid waters (e.g. from oxidising sulphides) invading carbonate rich materials including around mine environments; and (3) hydrothermal (e.g. volcanic) systems. For service status information, see https://status.dea.ga.gov.au 112.917024138111 -43.7806027057097 153.640358457438 -10.28257228 aster_gypsum_index image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 Kaolin Group Index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band Ratio: B6/B5 - Blue is low content, - Red is high content (potentially includes: pyrophyllite, alunite, well-ordered kaolinite) Useful for mapping: (1) different clay-type stratigraphic horizons; (2) lithology-overprinting hydrothermal alteration, e.g. high sulphidation, “advanced argillic” alteration comprising pyrophyllite, alunite, kaolinite/dickite; and (3) well-ordered kaolinite (warmer colours) versus poorly-ordered kaolinite (cooler colours) which can be used for mapping in situ versus transported materials, respectively. For 'Kaolin Group Index' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74357 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_kaolin_group_index image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 MgOH Group Composition The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B7/B8 - Blue-cyan is magnesite-dolomite, amphibole, chlorite - Red is calcite, epidote, amphibole Useful for mapping: (1) exposed parent material persisting through "cover"; (2) "dolomitization" alteration in carbonates - combine with Ferrous iron in MgOH product to help separate dolomite versus ankerite; (3) lithology-cutting hydrothermal (e.g. propyllitic) alteration - combine with FeOH content product and ferrous iron in Mg-OH to isolate chlorite from actinolite versus talc versus epidote; and (4) layering within mafic/ultramafic intrusives For 'MgOH Group Composition' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74360 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_mgoh_group_composition image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 MgOH Group Content The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: (B6+B9)/(B7+B8) - Blue is low content, - Red is high content (potentially includes: calcite, dolomite, magnesite, chlorite, epidote, amphibole, talc, serpentine) Useful for mapping: (1) “hydrated” ferromagnesian rocks rich in OH-bearing tri-octahedral silicates like actinolite, serpentine, chlorite and talc; (2) carbonate-rich rocks, including shelf (palaeo-reef) and valley carbonates(calcretes, dolocretes and magnecretes); and (3) lithology-overprinting hydrothermal alteration, e.g. “propyllitic alteration” comprising chlorite, amphibole and carbonate. The nature (composition) of the silicate or carbonate mineral can be further assessed using the MgOH composition product. For 'MgOH Group Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74359 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_mgoh_group_content image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 Opaque Index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B1/B4 - Blue is low abundance, - Red is high abundance (potentially includes carbon black (e.g. ash), magnetite, Mn oxides, and sulphides in unoxidised environments) Useful for mapping: (1) magnetite-bearing rocks (e.g. BIF); (2) maghemite gravels; (3) manganese oxides; (4) graphitic shales. Note: (1) and (4) above can be evidence for “reduced” rocks when interpreting REDOX gradients. Combine with AlOH group Content (high values) and Composition (high values) products, to find evidence for any invading “oxidised” hydrothermal fluids which may have interacted with reduced rocks evident in the Opaques index product. For 'Opaque Index' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74354 For service status information, see https://status.dea.ga.gov.au 112.917275536606 -43.7806433511675 153.640054299875 -10.2856586 aster_opaque_index image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 TIR Silica index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B13/B10 - Blue is low silica content, - Red is high silica content (potentially includes Si-rich minerals, such as quartz, feldspars, Al-clays) Geoscience Applications: Broadly equates to the silica content though the intensity (depth) of this reststrahlen feature is also affected by particle size <250 micron. Useful product for mapping: (1) colluvial/alluvial materials; (2) silica-rich (quartz) sediments (e.g. quartzites); (3) silification and silcretes; and (4) quartz veins. Use in combination with quartz index, which is often correlated with the Silica index. For 'TIR Silica index' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74362 For service status information, see https://status.dea.ga.gov.au 112.917024138111 -43.7806027057097 153.640358457438 -10.28257228 aster_silica_index image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 TIR Quartz Index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B11/(B10+B12) - Blue is low quartz content, - Red is high quartz content Geoscience Applications: Use in combination with Silica index to more accurately map “crystalline” quartz rather than poorly ordered silica (e.g. opal), feldspars and compacted clays. For 'TIR Quartz Index' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74363 For service status information, see https://status.dea.ga.gov.au 112.917024138111 -43.7806027057097 153.640358457438 -10.28257228 aster_quartz_index image/png application/json Time Date 2004-03-02 2004-03-02 WholeWorld_WebMercator EPSG:3111 Multi-Scale Topographic Position Multi-Scale Topographic Position A Multi-scale topographic position image of Australia has been generated by combining a topographic position index and topographic ruggedness. Topographic Position Index (TPI) measures the topographic slope position of landforms. Ruggedness informs on the roughness of the surface and is calculated as the standard deviation of elevations. Both these terrain attributes are therefore scale dependent and will vary according to the size of the analysis window. Based on an algorithm developed by Lindsay et al. (2015) we have generated multi-scale topographic position model over the Australian continent using 3 second resolution (~90m) DEM derived from the Shuttle Radar Topography Mission (SRTM). The algorithm calculates topographic position scaled by the corresponding ruggedness across three spatial scales (window sizes) of 0.2-8.1 Km; 8.2-65.2 Km and 65.6-147.6 Km. The derived ternary image captures variations in topographic position across these spatial scales (blue local, green intermediate and red regional) and gives a rich representation of nested landform features that have broad application in understanding geomorphological and hydrological processes and in mapping regolith and soils over the Australian continent. Lindsay, J, B., Cockburn, J.M.H. and Russell, H.A.J. 2015. An integral image approach to performing multi-scale topographic position analysis, Geomorphology 245, 51–61. For service status information, see https://status.dea.ga.gov.au 112.9995833 -44.0004167 153.9995833 -10.0004167 multi_scale_topographic_position image/png application/json Time Date 2018-01-01 2018-01-01 WholeWorld_WebMercator EPSG:3111 Weathering Intensity Weathering Intensity Weathering intensity or the degree of weathering is an important characteristic of the earth’s surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. For service status information, see https://status.dea.ga.gov.au 112.9995833 -44.0004167 153.9995833 -10.0004167 weathering_intensity image/png application/json Time Date 2018-01-01 2018-01-01 WholeWorld_WebMercator EPSG:3111 WholeWorld_WebMercator urn:ogc:def:crs:EPSG::3857 urn:ogc:def:wkss:OGC:1.0:GoogleMapsCompatible 0 559082264.0287178 -20037508.3427892 20037508.3427892 256 256 1 1 1 279541132.0143589 -20037508.3427892 20037508.3427892 256 256 2 2 2 139770566.0071794 -20037508.3427892 20037508.3427892 256 256 4 4 3 69885283.00358972 -20037508.3427892 20037508.3427892 256 256 8 8 4 34942641.50179486 -20037508.3427892 20037508.3427892 256 256 16 16 5 17471320.75089743 -20037508.3427892 20037508.3427892 256 256 32 32 6 8735660.375448715 -20037508.3427892 20037508.3427892 256 256 64 64 7 4367830.187724357 -20037508.3427892 20037508.3427892 256 256 128 128 8 2183915.093862179 -20037508.3427892 20037508.3427892 256 256 256 256 9 1091957.546931089 -20037508.3427892 20037508.3427892 256 256 512 512 10 545978.7734655447 -20037508.3427892 20037508.3427892 256 256 1024 1024 11 272989.3867327723 -20037508.3427892 20037508.3427892 256 256 2048 2048 12 136494.6933663862 -20037508.3427892 20037508.3427892 256 256 4096 4096 13 68247.34668319309 -20037508.3427892 20037508.3427892 256 256 8192 8192 14 34123.67334159654 -20037508.3427892 20037508.3427892 256 256 16384 16384 EPSG:3111 urn:ogc:def:crs:EPSG::3111 0 7559538.928601667 1786000.0 3081000.0 512 512 2 1 1 3779769.4643008336 1786000.0 3081000.0 512 512 4 2 2 1889884.7321504168 1786000.0 3081000.0 512 512 8 4 3 944942.3660752084 1786000.0 3081000.0 512 512 16 8 4 472471.1830376042 1786000.0 3081000.0 512 512 32 16 5 236235.5915188021 1786000.0 3081000.0 512 512 64 32 6 94494.23660752083 1786000.0 3081000.0 512 512 128 64 7 47247.11830376041 1786000.0 3081000.0 512 512 256 128 8 23623.559151880207 1786000.0 3081000.0 512 512 512 256 9 9449.423660752083 1786000.0 3081000.0 512 512 1024 512 10 4724.711830376042 1786000.0 3081000.0 512 512 2048 1024 11 2362.355915188021 1786000.0 3081000.0 512 512 4096 2048 12 1181.1779575940104 1786000.0 3081000.0 512 512 8192 4096 13 755.9538928601667 1786000.0 3081000.0 512 512 16384 8192 Digital Earth Australia - OGC Web Services Digital Earth Australia OGC Web Services digital_earth_australia_ogc_web_services Baseline satellite data baseline_satellite_data DEA Surface Reflectance (Landsat and Sentinel-2) Geoscience Australia Landsat and Sentinel-2 Analysis Ready Data Collection 3 This product takes Sentinel-2 and Landsat imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. Where Sentinel-2 and Landsat data are both available, the higher resolution Sentinel-2 data is used. This product combines: * Sentinel-2A https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2a-msi/ * Sentinel-2B https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2b-msi/ * Sentinel-2C https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2c-msi/ * Landsat-5 https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-5-tm/ * Landsat-7 https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-7-etm/ * Landsat-8 https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-8-oli-tirs/ * Landsat-9 https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-9-oli-tirs/ For service status information, see https://status.dea.ga.gov.au s2_ls_combined s2_ls_combined DEA Surface Reflectance (Landsat) Geoscience Australia Landsat Analysis Ready Data Collection 3 This product takes Landsat imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. This product combines: Landsat 9 imagery https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-9-oli-tirs/, Landsat 8 imagery https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-8-oli-tirs/, Landsat 7 imagery https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-7-etm/ and Landsat 5 Imagery https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-5-tm/ For service status information, see https://status.dea.ga.gov.au ga_ls_ard_3 ga_ls_ard_3 DEA Surface Reflectance (Sentinel-2) Geoscience Australia Sentinel-2 Analysis Ready Data Collection 3 This product takes Sentinel-2 imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2A, Sentinel-2B and Sentinel-2C. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections. The resolution is a 10/20/60 m grid based on the ESA Level 1C archive. Full Sentinel-2A product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2a-msi/ Full Sentinel-2B product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2b-msi/ Full Sentinel-2C product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2c-msi/ For service status information, see https://status.dea.ga.gov.au ga_s2m_ard_3 ga_s2m_ard_3 DEA Surface Reflectance Calendar Year (Landsat) dea_surface_reflectance_calendar_year_landsat DEA GeoMAD (Landsat 8 & 9 OLI-TIRS) Geoscience Australia Landsat 8 and 9 Geometric Median and Median Absolute Deviation Collection 3 This product provides statistical tools to exploit the time series of Earth Observation data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year. The geomedian part of the product provides an "average" cloud-free image over the given year. The geomedian image is calculated with a multi-dimensional median, using all the spectral measurements from the satellite imagery at the same time in order to maintain the relationships among the measurements. The median absolute deviation part of the product uses three measures of variance, each of which provides a 'second order' high dimensional statistical composite for the given year. The three variance measures show much an area varies from the "average" in terms of 'distance' based on factors such as brightness and spectra: Euclidean distance (EMAD) Cosine (spectral) distance (SMAD) Bray Curtis dissimilarity (BCMAD) Together, they provide information on variance in the landscape over the given year and are useful for change detection applications. v4.0.0 Changelog: Cloud masks have been cleaned using a 3-pixel morphological opening on clouds and a 6-pixel dilation on cloud and shadows. The south-west origin point of the DEA Summary Product Grid has been shifted 18 tiles west and 15 tiles south in this version. A single combined product for Landsat 8 and 9 is provided. A standalone Landsat 8 product will no longer be provided from calendar year 2022 and onwards. Band measurement names were updated for consistency with DEA's ARD products by adding the 'nbart' prefix. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls8cls9c_gm_cyear_3 ga_ls8cls9c_gm_cyear_3 DEA GeoMAD (Landsat 7 ETM+) Geoscience Australia Landsat 7 Geometric Median and Median Absolute Deviation Collection 3 This product provides statistical tools to exploit the time series of Earth Observation data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year. The geomedian part of the product provides an "average" cloud-free image over the given year. The geomedian image is calculated with a multi-dimensional median, using all the spectral measurements from the satellite imagery at the same time in order to maintain the relationships among the measurements. The median absolute deviation part of the product uses three measures of variance, each of which provides a 'second order' high dimensional statistical composite for the given year. The three variance measures show much an area varies from the "average" in terms of 'distance' based on factors such as brightness and spectra: Euclidean distance (EMAD) Cosine (spectral) distance (SMAD) Bray Curtis dissimilarity (BCMAD) Together, they provide information on variance in the landscape over the given year and are useful for change detection applications. v4.0.0 Changelog: Cloud masks have been cleaned using a 3-pixel morphological opening on clouds and a 6-pixel dilation on cloud and shadows. The south-west origin point of the DEA Summary Product Grid has been shifted 18 tiles west and 15 tiles south in this version. A single combined product for Landsat 8 and 9 is provided. A standalone Landsat 8 product will no longer be provided from calendar year 2022 and onwards. Band measurement names were updated for consistency with DEA's ARD products by adding the 'nbart' prefix. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls7e_gm_cyear_3 ga_ls7e_gm_cyear_3 DEA GeoMAD (Landsat 5 TM) Geoscience Australia Landsat 5 Geometric Median and Median Absolute Deviation Collection 3 This product provides statistical tools to exploit the time series of Earth Observation data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year. The geomedian part of the product provides an "average" cloud-free image over the given year. The geomedian image is calculated with a multi-dimensional median, using all the spectral measurements from the satellite imagery at the same time in order to maintain the relationships among the measurements. The median absolute deviation part of the product uses three measures of variance, each of which provides a 'second order' high dimensional statistical composite for the given year. The three variance measures show much an area varies from the "average" in terms of 'distance' based on factors such as brightness and spectra: Euclidean distance (EMAD) Cosine (spectral) distance (SMAD) Bray Curtis dissimilarity (BCMAD) Together, they provide information on variance in the landscape over the given year and are useful for change detection applications. v4.0.0 Changelog: Cloud masks have been cleaned using a 3-pixel morphological opening on clouds and a 6-pixel dilation on cloud and shadows. The south-west origin point of the DEA Summary Product Grid has been shifted 18 tiles west and 15 tiles south in this version. A single combined product for Landsat 8 and 9 is provided. A standalone Landsat 8 product will no longer be provided from calendar year 2022 and onwards. Band measurement names were updated for consistency with DEA's ARD products by adding the 'nbart' prefix. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls5t_gm_cyear_3 ga_ls5t_gm_cyear_3 DEA Surface Reflectance (Landsat) Collection 3 represents a consistent processing and upgrade to the Geoscience Australia's Landsat baseline and derivative products. For service status information, see https://status.dea.ga.gov.au dea_surface_reflectance_landsat DEA Surface Reflectance (Landsat 9 OLI-TIRS) Geoscience Australia Landsat 9 OLI-TIRS Analysis Ready Data Collection 3 This product takes Landsat 9 imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Operational Land Imager (OLI) and Thermal Infra-Red Scanner (TIRS) sensors aboard Landsat 9. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. It contains three sub-products that provide corrections or attribution information: DEA Surface Reflectance NBAR (Landsat 9 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbar-landsat-9-oli-tirs/ DEA Surface Reflectance NBART (Landsat 9 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-landsat-9-oli-tirs/ DEA Surface Reflectance OA (Landsat 9 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-landsat-9-oli-tirs/ The resolution is a 30 m grid based on the USGS Landsat Collection 2 archive. https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-9-oli-tirs/ For service status information, see https://status.dea.ga.gov.au ga_ls9c_ard_3 ga_ls9c_ard_3 DEA Surface Reflectance (Landsat 8 OLI-TIRS) Geoscience Australia Landsat 8 OLI-TIRS Analysis Ready Data Collection 3 This product takes Landsat 8 imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Operational Land Imager (OLI) and Thermal Infra-Red Scanner (TIRS) sensors aboard Landsat 8. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. It contains three sub-products that provide corrections or attribution information: DEA Surface Reflectance NBAR (Landsat 8 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbar-landsat-8-oli-tirs/ DEA Surface Reflectance NBART (Landsat 8 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-landsat-8-oli-tirs/ DEA Surface Reflectance OA (Landsat 8 OLI-TIRS) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-landsat-8-oli-tirs/ The resolution is a 30 m grid based on the USGS Landsat Collection 1 and 2 archive. https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-8-oli-tirs/ For service status information, see https://status.dea.ga.gov.au ga_ls8c_ard_3 ga_ls8c_ard_3 DEA Surface Reflectance (Landsat 7 ETM+) Geoscience Australia Landsat 7 ETM+ Analysis Ready Data Collection 3 This product takes Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. It contains three sub-products that provide corrections or attribution information: DEA Surface Reflectance NBAR (Landsat 7 ETM+) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbar-landsat-7-etm/ DEA Surface Reflectance NBART (Landsat 7 ETM+) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-landsat-7-etm/ DEA Surface Reflectance OA (Landsat 7 ETM+) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-landsat-7-etm/ The resolution is a 30 m grid based on the USGS Landsat Collection 1 and 2 archive. https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-7-etm/ For service status information, see https://status.dea.ga.gov.au ga_ls7e_ard_3 ga_ls7e_ard_3 DEA Surface Reflectance (Landsat 5 TM) Geoscience Australia Landsat 5 TM Analysis Ready Data Collection 3 This product takes Landsat 5 Thematic Mapper (TM) imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. It contains three sub-products that provide corrections or attribution information: DEA Surface Reflectance NBAR (Landsat 5 TM) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbar-landsat-5-tm/ DEA Surface Reflectance NBART (Landsat 5 TM) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-landsat-5-tm/ DEA Surface Reflectance OA (Landsat 5 TM) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-landsat-5-tm/ The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive. https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-5-tm/ For service status information, see https://status.dea.ga.gov.au ga_ls5t_ard_3 ga_ls5t_ard_3 DEA Surface Reflectance (Sentinel-2) dea_surface_reflectance_sentinel_2 DEA Surface Reflectance (Sentinel-2A MSI) Geoscience Australia Sentinel-2A MSI Analysis Ready Data Collection 3 This product takes Sentinel-2A imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2A. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections. It contains two sub-products that provide corrections or attribution information: * DEA Surface Reflectance NBART (Sentinel-2A MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-sentinel-2a-msi/ * DEA Surface Reflectance OA (Sentinel-2A MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-sentinel-2a-msi/ The resolution is a 10/20/60 m grid based on the ESA Level 1C archive. Full product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2a-msi/ For service status information, see https://status.dea.ga.gov.au ga_s2am_ard_3 ga_s2am_ard_3 DEA Surface Reflectance (Sentinel-2B MSI) Geoscience Australia Sentinel-2B MSI Analysis Ready Data Collection 3 This product takes Sentinel-2B imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2B. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections. It contains two sub-products that provide corrections or attribution information: * DEA Surface Reflectance NBART (Sentinel-2B MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-sentinel-2b-msi/ * DEA Surface Reflectance OA (Sentinel-2B MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-sentinel-2b-msi/ The resolution is a 10/20/60 m grid based on the ESA Level 1C archive. Full product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2b-msi/ For service status information, see https://status.dea.ga.gov.au ga_s2bm_ard_3 ga_s2bm_ard_3 DEA Surface Reflectance (Sentinel-2C MSI) Geoscience Australia Sentinel-2C MSI Analysis Ready Data Collection 3 This product takes Sentinel-2C imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2C. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections. It contains two sub-products that provide corrections or attribution information: * DEA Surface Reflectance NBART (Sentinel-2C MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-sentinel-2c-msi/ * DEA Surface Reflectance OA (Sentinel-2C MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-sentinel-2c-msi/ The resolution is a 10/20/60 m grid based on the ESA Level 1C archive. Full product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2c-msi/ For service status information, see https://status.dea.ga.gov.au ga_s2cm_ard_3 ga_s2cm_ard_3 Land and Vegetation land_and_vegetation DEA Land Cover dea_land_cover DEA Land Cover Calendar Year (Landsat) DEA Land Cover Calendar Year (Landsat) Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps the understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time allows the observation of the state of land cover at specific times and therefore the way that land cover changes. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_landcover ga_ls_landcover DEA Land Cover Environmental Descriptors DEA Land Cover Environmental Descriptors Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps the understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time allows the observation of the state of land cover at specific times and therefore the way that land cover changes. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_landcover_descriptors ga_ls_landcover_descriptors DEA Land Cover Collection 3 dea_land_cover_collection_3 DEA Land Cover Collection 3 Calendar Year (Landsat) DEA Land Cover Collection 3 Calendar Year (Landsat) Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps the understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time allows the observation of the state of land cover at specific times and therefore the way that land cover changes. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_landcover_c3 ga_ls_landcover_c3 DEA Land Cover Environmental Descriptors DEA Land Cover Environmental Descriptors Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps the understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time allows the observation of the state of land cover at specific times and therefore the way that land cover changes. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_landcover_c3_descriptors ga_ls_landcover_c3_descriptors DEA Fractional Cover Geoscience Australia Landsat Fractional Cover Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 30m x 30m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program, for more information please see https://knowledge.dea.ga.gov.au/data/product/dea-fractional-cover-landsat/ Fractional Cover products use Water Observations from Space (WOfS) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 10 clear observations over the year. For service status information, see https://status.dea.ga.gov.au dea_fractional_cover DEA Fractional Cover (Landsat) Geoscience Australia Landsat Fractional Cover Collection 3 Fractional Cover (FC), developed by the Joint Remote Sensing Research Program, is a measurement that splits the landscape into three parts, or fractions: green (leaves, grass, and growing crops) brown (branches, dry grass or hay, and dead leaf litter) bare ground (soil or rock) DEA uses Fractional Cover to characterise every 30 m square of Australia for any point in time from 1987 to today. https://knowledge.dea.ga.gov.au/data/product/dea-fractional-cover-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_fc_3 ga_ls_fc_3 DEA Fractional Cover Percentiles Calendar Year (Landsat) Fractional Cover 30m Percentiles 3.0.0 (Landsat, Annual) Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 30m x 30m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program. This contains the percentage of green vegetation, non-green vegetation and bare soil per pixel at the 10th, 50th (median) and 90th percentiles respectively for observations acquired in each full calendar year (1st of January - 31st December) from 1987 to the most recent full calendar year. Fractional Cover products use Water Observations (WO) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 3 clear observations over the year. https://knowledge.dea.ga.gov.au/data/product/dea-fractional-cover-percentiles-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_fc_pc_cyear_3 ga_ls_fc_pc_cyear_3 DEA Mangroves dea_mangroves DEA Mangroves (Landsat) Mangrove Canopy Cover 30m (Landsat, Collection 3) Mangrove canopy cover version, 30 metre. Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. The mangrove canopy cover product provides valuable information about the extent and canopy density of mangroves for each full year between 1987 and the present for the entire Australian coastline. The canopy cover classes are: 20-50% (pale green), 50-80% (mid green), 80-100% (dark green). The product consists of a sequence (one per year) of 30 meter resolution maps that are generated by analysing the Landsat fractional cover (https://doi.org/10.6084/m9.figshare.94250.v1) developed by the Joint Remote Sensing Research Program and the Global Mangrove Watch layers (https://doi.org/10.1071/MF13177) developed by the Japanese Aerospace Exploration Agency. https://knowledge.dea.ga.gov.au/data/product/dea-mangrove-canopy-cover-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_mangrove_cover_cyear_3 ga_ls_mangrove_cover_cyear_3 Barest Earth barest_earth GA Barest Earth (Sentinel-2) GA Barest Earth (Sentinel-2) Abstract The Sentinel-2 Bare Earth thematic product provides the first national scale mosaic of the Australian continent to support improved mapping of soil and geology. The bare earth algorithm using all available Sentinel-2 A and Sentinel-2 B observations up to September 2020 preferentially weights bare pixels through time to significantly reduce the effect of seasonal vegetation in the imagery. The result are image pixels that are more likely to reflect the mineralogy and/or geochemistry of soil and bedrock. The algorithm uses a high-dimensional weighted geometric median approach that maintains the spectral relationships across all Sentinel-2 bands. A similar bare earth algorithm has been applied to Geoscience Australia’s deeper Landsat time series archive (please search for "Landsat barest Earth". Both bare earth products have spectral bands in the visible near infrared and shortwave infrared region of the electromagnetic spectrum. However, the main visible and near-infrared Sentinel-2 bands have a spatial resolution of 10 meters compared to 30m for the Landsat TM equivalents. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. Not all the sentinel-2 bands have been processed - we have excluded atmospheric bands including 1, 9 and 10. The remaining bands have been re-number 1-10 and these bands correlate to the original bands in brackets below: 1 = blue (2) , 2 = green (3) , 3 = red (4), 4 = vegetation red edge (5), 5 = vegetation red edge (6), 6= vegetation red edge (7), 7 = NIR(8), 8 = Narrow NIR (8a), 9 = SWIR1 (11) and 10 = SWIR2(12). All 10 bands have been resampled to 10 meters to facilitate band integration and use in machine learning. Pixel quality and the degree of barest recorded on the ground will largely depend on the number of ‘clean’ cloud free bare earth observation. We have noticed some image artefacts near the vicinity of Lake Eyre where highly reflective surface materials are not being well separated by the cloud filter – these artefacts will be address in a future improved version of the bare earth model. Lineage Statement Large-scale image composites are increasingly important for a variety of applications such as land cover mapping, soil and bedrock mapping, change detection, and the generation of high-quality data to parameterise and validate bio-physical and geophysical models. A number of compositing methodologies are being used in remote sensing in general, however challenges such as maintaining the spectral relationship between bands, mitigating against boundary artifacts due to mosaicking scenes from different epochs, and ensuring spatial regularity across the mosaic image still exist. The creation of good composite images is a particularly important technology since the opening of the Landsat archive by the United States Geological Survey. The greater availability of satellite imagery has resulted in demand to provide large regional mosaics that are representative of conditions over specific time periods while also being free of clouds and other unwanted image noise. One approach is the stitching together of a number of clear images. Another is the creation of mosaics where pixels from different epochs are combined based on some algorithm from a time series of observations. This ‘pixel composite’ approach to mosaic generation provides a more consistent result compared with stitching clear images due to the improved color balance created by the combining of one-by-one pixel representative images. Another strength of pixel-based composites is their ability to be automated for application to very large data collections and time series such as national satellite data archives. The Bare Earth pixel composite mosaic (BE-PCM) provides an approach that leverages high-dimensional statistical theory to deliver a spectrally consistent, artefact-free pixel composite product that is representative of the barest (i.e., least vegetation) state at each pixel over the specific time period. The BE-PCM is derived from Sentinel-2 A and Sentinel-2 B observations from 2014 to September 2020 corrected to measurements of NBAR surface reflectance (e.g., SR-N_25_2.0.0 or SR-NT_25_2.0.0). The data are masked for cloud, shadows and other image artefacts using the pixel quality product (PQ_25_2.0.0) to help provide as clear a set of observations as possible from which to calculate the BE-PCM. The BE-PCM methodology and algorithm is given in Roberts, Wilford, Ghattas (2018). The technology builds on the earlier work of Roberts et al. (2017) where a method for producing cloud-free pixel composite mosaics using ‘geometric medians’ was proposed. Note: The constituent pixels in the BE-PCM pixel composite mosaics are synthetic, meaning that the pixels have not been physically observed by the satellite. Rather they are the computed high-dimensional median of a time series of pixels which gives a robust estimate of the median state of the Earth at its barest (i.e., least vegetation). References Roberts, D., Wilford, J., Ghattas, O. (2018). Revealing the Australian Continent at its Barest. Submitted and under review. Roberts, D., Mueller, N., Mcintyre, A. (2017). High-dimensional pixel composites from earth observation time series. IEEE Transactions on Geoscience and Remote Sensing 55 (11), 6254-6264 For service status information, see https://status.dea.ga.gov.au s2_barest_earth s2_barest_earth GA Barest Earth (Landsat 8 OLI/TIRS) GA Barest Earth (Landsat 8 OLI/TIRS) A `weighted geometric median’ approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 OLI observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted. Mosaics are available for the following years: Landsat 8: 2013 to 2017; For service status information, see https://status.dea.ga.gov.au ls8_barest_earth_mosaic ls8_barest_earth_mosaic GA Barest Earth (Landsat) GA Barest Earth (Landsat) An estimate of the spectra of the barest state (i.e., least vegetation) observed from imagery of the Australian continent collected by the Landsat 5, 7, and 8 satellites over a period of more than 30 years (1983 - 2018). The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. This product complements the Landsat-8 Barest Earth which is based on the same algorithm but just uses Landsat8 satellite imagery from 2013-2108. Landsat-8's OLI sensor provides improved signal-to-noise radiometric (SNR) performance quantised over a 12-bit dynamic range compared to the 8-bit dynamic range of Landsat-5 and Landsat-7 data. However the Landsat 30+ Barest Earth has a greater capacity to find the barest ground due to the greater temporal depth. Reference: Roberts, D., Wilford, J., Ghattas, O. (2019). Exposed Soil and Mineral Map of the Australian Continent Revealing the Land at its Barest. Nature Communications. Mosaics are available for the following years: Landsat 5 / Landsat 7 / Landsat 8 - 1983 to 2018; For service status information, see https://status.dea.ga.gov.au landsat_barest_earth landsat_barest_earth Inland water inland_water DEA Wetness Percentiles (Landsat) dea_wetness_percentiles_landsat DEA Wetness Percentiles (Landsat) Tasseled Cap Wetness Percentiles 25m 2.0.0 The Tasseled Cap Wetness Percentiles provide a multi-decadal summary of landscape wetness that can be used to identify wetlands and groundwater ecosystems. They provide statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap wetness index from 1987 to 2017. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. Geoscience Australia Landsat Collection 2 Tasseled Cap Wetness Percentiles 1986-2018, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Areas that are partially covered in water, or where water is mixed with vegetation when viewed from above, provide habitat for a wide range of aquatic organisms. The ability to map partial inundation is also crucial to understand patterns of human water use. We need to be able to identify potential wetlands and groundwater dependent ecosystems on the Australian continent so that they can be monitored and managed. The Tasseled Cap Wetness Percentiles provide a multi-decadal summary of landscape wetness that can be used to identify wetlands and groundwater ecosystems. They provide statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap wetness index from 1987 to 2017. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. This product provides valuable discrimination for characterising: - vegetated wetlands, - salt flats, - salt lakes, - coastal land cover classes The Tasseled Cap wetness transform translates the six spectral bands of Landsat into a single wetness index. The wetness index can be used to identify areas in the landscape that are potentially wetlands or groundwater dependent ecosystems. The Tasseled Cap Wetness Percentiles capture how the wetness index behaves over time. The percentiles are well suited to characterising wetlands, salt flats/salt lakes and coastal ecosystems. However, care should be applied when analysing the wetness index, as soil colour and fire scars can cause misleading results. In areas of high relief caused by cliffs or steep terrain, terrain shadows can cause false positives (a falsely high wetness index). The 10th, 50th and 90th percentiles of the Tasseled Cap wetness index are intended to capture the extreme (10th and 90th percentile) values and long-term average (50th percentile) values of the wetness index. Percentiles are used in preference to minimum, maximum and mean, as the min/max/mean statistical measures are more sensitive to undetected cloud/cloud shadow, and can be misleading for non-normally distributed data. The Tasseled Cap Wetness Percentiles are intended to complement the Water Observations from Space (WOfS) algorithm. WOfS is designed to discriminate open water, but the Tasseled Cap wetness index identifies areas of water and areas where water and vegetation are mixed together; i.e. mangroves and palustrine wetlands. If you are interested in terrestrial vegetation (where water in the pixel is not a factor), use the Fractional Cover product, which provides a better biophysical characterisation of green vegetation fraction, dry vegetation fraction and bare soil vegetation fraction. In terms of limitations, caution should be used, especially with the Tasseled Cap wetness index results in areas where residual terrain shadow, or dark soils can cause high 'wetness' index values. One of the limitations of using the Tasseled Cap wetness index is that it will identify all 'wet' things, including potential wetlands, groundwater dependent ecosystems, irrigated crops/pasture, man-made water storages and sewerage treatment, and does not discriminate between these. As such it should be used in conjunction with other contextual data to ensure that features identified using the Tasseled Cap Wetness Percentiles are features of interest rather than false positives. We used the Tasseled Cap transforms described in Crist et al. (1985). Crist, E. P. (1985). A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17(3), 301–306. https://doi.org/10.1016/0034-4257(85)90102-6 For service status information, see https://status.dea.ga.gov.au ga_ls_tcw_percentiles_2 ga_ls_tcw_percentiles_2 DEA Tasseled Cap Indices Percentiles (Landsat) dea_tasseled_cap_indices_percentiles_landsat DEA Tasseled Cap Indices Percentiles Calendar Year (Landsat) Tasseled Cap Indices Percentiles 30m 1.0.0 The Tasseled Cap Indices Percentiles provide an annual summary of landscape wetness, greenness and brightness indices that can be used to identify wetlands and groundwater ecosystems. They provide annual statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap indices. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. Geoscience Australia Landsat Collection 3 Tasseled Cap Indices Percentiles, 30 metre, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Areas that are partially covered in water, or where water is mixed with vegetation when viewed from above, provide habitat for a wide range of aquatic organisms. The ability to map partial inundation is also crucial to understand patterns of human water use. We need to be able to identify potential wetlands and groundwater dependent ecosystems on the Australian continent so that they can be monitored and managed. The Tasseled Cap Wetness Percentiles provide a multi-decadal summary of landscape wetness that can be used to identify wetlands and groundwater ecosystems. They provide statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap wetness index from 1987 to 2017. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. This product provides valuable discrimination for characterising: - vegetated wetlands, - salt flats, - salt lakes, - coastal land cover classes The Tasseled Cap indices transform translates the six spectral bands of Landsat into a single wetness index. The wetness index can be used to identify areas in the landscape that are potentially wetlands or groundwater dependent ecosystems. The Tasseled Cap Indices Percentiles capture how the wetness, greeness and brightness index behaves over time. The percentiles are well suited to characterising wetlands, salt flats/salt lakes and coastal ecosystems. However, care should be applied when analysing the wetness index, as soil colour and fire scars can cause misleading results. In areas of high relief caused by cliffs or steep terrain, terrain shadows can cause false positives (a falsely high wetness index). The 10th, 50th and 90th percentiles of the Tasseled Cap Indices are intended to capture the extreme (10th and 90th percentile) values and long-term average (50th percentile) values of the indices. Percentiles are used in preference to minimum, maximum and mean, as the min/max/mean statistical measures are more sensitive to undetected cloud/cloud shadow, and can be misleading for non-normally distributed data. The Tasseled Cap Indices Percentiles are intended to complement the Water Observations (WO) algorithm. WO is designed to discriminate open water, but the Tasseled Cap wetness index identifies areas of water and areas where water and vegetation are mixed together; i.e. mangroves and palustrine wetlands. If you are interested in terrestrial vegetation (where water in the pixel is not a factor), use the Fractional Cover product, which provides a better biophysical characterisation of green vegetation fraction, dry vegetation fraction and bare soil vegetation fraction. In terms of limitations, caution should be used, especially with the Tasseled Cap Indices results in areas where residual terrain shadow, or dark soils can cause high 'wetness' index values. One of the limitations of using the Tasseled Cap wetness index is that it will identify all 'wet' things, including potential wetlands, groundwater dependent ecosystems, irrigated crops/pasture, man-made water storages and sewerage treatment, and does not discriminate between these. As such it should be used in conjunction with other contextual data to ensure that features identified using the Tasseled Cap Wetness Percentiles are features of interest rather than false positives. We used the Tasseled Cap transforms described in Crist et al. (1985). Crist, E. P. (1985). A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17(3), 301–306. https://doi.org/10.1016/0034-4257(85)90102-6 For service status information, see https://status.dea.ga.gov.au ga_ls_tc_pc_cyear_3 ga_ls_tc_pc_cyear_3 DEA Water Observations Digital Earth Australia (DEA) Water Observations from Space (WOfS) dea_water_observations DEA Water Observations (Landsat) **Geoscience Australia Water Observations (Landsat, Collection 3, 30 m, Individual Observations, 2.0.0).** Water Observations are the principal Digital Earth Australia (DEA) Water product (previously known as Water Observations from Space (WOfS)). This product shows where surface water was observed within each individual Landsat (5, 7 and 8) satellite image on each particular day since mid 1986. These daily data layers are termed Water Observations (WOs). WOs show the extent of water in a corresponding Landsat scene, along with the degree to which the scene was obscured by clouds, shadows or where sensor problems cause parts of a scene to not be observable. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_wo_3 ga_ls_wo_3 DEA Water Observations Provisional (Sentinel-2 NRT) **Prototype Geoscience Australia Sentinel 2 Near Real Time Water Observations Provisional Collection 3** This product is the implementation of the DEA Water Observations (DEA WO) product (previously known as Water Observations from Space, or WOfS) on the Geoscience Australia Sentinel-2 Near Real Time surface reflectance product. This is a rapid, provisional, product. It has not been validated and is of unknown accuracy. The Provisional Digital Earth Australia Water Observations (Sentinel-2) product shows where surface water was observed by the Sentinel 2A and Sentinel 2B satellites on any particular day over the most recent 3 months. The surface water observations are derived from Geoscience Australia Sentinel-2 Near Real Time surface reflectance imagery for all of Australia. The provisional, Near Real Time product is available for a rolling window of the most recent three months of data, and is produced within 24 hours of the satellite passing over an area. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-sentinel-2-nrt/ ga_s2_wo_provisional_3 ga_s2_wo_provisional_3 DEA Water Observations Multi Year (Landsat) **Geoscience Australia Water Observations, Multi Year Frequency Statistics, 1986 to near present (Landsat, Collection 3, 30 m, WO-STATS, Frequency, 2.0.0).** The DEA Multi Year Water Observation Statistic is a statistical summary that combines all years (1986 to near present) of the DEA Water Observations product and helps the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations that were observed over the specifed time period in the landscape. **What this product offers** Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected in observations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_wo_fq_myear_3 ga_ls_wo_fq_myear_3 DEA Water Observations Calendar Year (Landsat) **Geoscience Australia Water Observations, Annual Frequency Statistics, Calendar Year (Landsat, Collection 3, 30 m, WO-STATS-ANNUAL, 2.0.0).** The DEA Annual Water Observation Statistic is a set of calendar year statistical summaries of the DEA Water Observations product that combines satellite observations, that occur within each calendar year from 1986 to present, into summary products that help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; and the percentage of wet observations that were observed over the specified time period in the landscape. **What this product offers** Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected in observations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_wo_fq_cyear_3 ga_ls_wo_fq_cyear_3 DEA Water Observations April to October (Landsat) **Geoscience Australia Water Observations, Seasonal Frequency Statistics, April to October (Landsat, Collection 3, 30 m, WO-STATS-APR-OCT, 2.0.0).** The DEA Seasonal Water Observation (April to October) Statistic is a set of seasonal statistical summaries of the DEA Water Observations product. The product combines satellite observations, that occur during April to October within each year, into summary products that help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; and the percentage of wet observations that were observed over the specified time period in the landscape. **What this product offers** Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected in observations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_wo_fq_apr_oct_3 ga_ls_wo_fq_apr_oct_3 DEA Water Observations November to March (Landsat) **Geoscience Australia Water Observations, Seasonal Frequency Statistics, November to March (Landsat, Collection 3, 30 m, WO-STATS-NOV-MAR, 2.0.0).** The DEA Seasonal Water Observation (November to March) Statistic is a set of seasonal statistical summaries of the DEA Water Observations product. The product combines satellite observations, that occur during November to March, into summary products that help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; and the percentage of wet observations that were observed over the specified time period in the landscape. **What this product offers** Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected in observations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/ For service status information, see https://status.dea.ga.gov.au ga_ls_wo_fq_nov_mar_3 ga_ls_wo_fq_nov_mar_3 DEA Multi-Year Water Observation Frequency Filtered Statistics (Landsat, DEPRECATED) Water Observations from Space Filtered Statistics 25m 2.1.5 (Landsat, Filtered) Water Observations from Space (WOfS) Filtered Statistics helps provide the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out. WOfS Filtered Statistics consists of a Confidence layer that compares the WOfS Statistics water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the WOfS Statistics water summary where Confidence is low. This layer is Filtered Water Summary: A simplified version of the Water Summary, showing the frequency of water observations where the Confidence is above a cutoff level. No clear observations of water causes an area to appear transparent, few clear observations of water correlate with red and yellow colours, deep blue and purple correspond to an area being wet through 90%-100% of clear observations. The Filtered Water Summary layer is a noise-reduced view of surface water across Australia. Even though confidence filtering is applied to the Filtered Water Summary, some cloud and shadow, and sensor noise does persist. For more information please see: https://data.dea.ga.gov.au/?prefix=WOfS/filtered_summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au wofs_filtered_summary wofs_filtered_summary DEA Multi-Year Clear Observation Statistics (Landsat, DEPRECATED) Water Observations from Space Statistics 25m 2.1.5 (Landsat, Clear) Water Observations from Space (WOfS) Statistics is a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products which help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This layer contains Clear Count: how many times an area could be clearly seen (ie. not affected by clouds, shadows or other satellite observation problems). No clear observations causes an area to appear transparent, 1-300 total clear observations of water correlate with red and yellow colours, 400 clear observations correlates with light green, 800 clear observations and above correlates with dark green. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics Filtered Summary product, which provide a noise-reduced view of the water summary. For more information please see: https://data.dea.ga.gov.au/WOfS/summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au wofs_summary_clear wofs_summary_clear DEA Multi-Year Wet Observation Statistics (Landsat, DEPRECATED) Water Observations from Space Statistics 25m 2.1.5 (Landsat, Wet) Water Observations from Space (WOfS) Statistics is a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products which help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This layer contains Wet Count: how many times water was detected in observations that were clear. No clear observations of water causes an area to appear transparent, 1-50 total clear observations of water correlate with red and yellow colours, 100 clear observations of water correlate with green, 200 clear observations of water correlates with light blue, 300 clear observations of water correlates to deep blue and 400 and over observations of clear water correlate to purple. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics Filtered Summary product, which provide a noise-reduced view of the water summary. For more information please see: https://data.dea.ga.gov.au/WOfS/summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au wofs_summary_wet wofs_summary_wet DEA Multi-Year Water Observation Frequency Statistics (Landsat, DEPRECATED) Water Observations from Space Statistics 25m 2.1.5 (Landsat, Frequency) Water Observations from Space (WOfS) Statistics is a set of statistical summaries of the WOfS product which combines WOfS observations into summary products that help the understanding of surface water across Australia. WOfS Statistics is calculated from the full depth time series (1986 – 2018). The water detected for each location is summed through time and then compared to the number of clear observations of that location. The result is a percentage value of the number of times water was observed at the location. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time (water summary). This layer contains the Water Summary: the percentage of clear observations which were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations of water causes an area to appear transparent, few clear observations of water correlate with red and yellow colours, deep blue and purple correspond to an area being wet through 90%-100% of clear observations. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics Filtered Summary product, which provide a noise-reduced view of the water summary. For more information please see: https://data.dea.ga.gov.au/WOfS/summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au Water Observations from Space Statistics Water Observations from Space Statistics DEA Multi-Year Water Observation Confidence Statistics (Landsat, DEPRECATED) Water Observations from Space Filtered Statistics 25m 2.1.5 (Landsat, Confidence) Water Observations from Space (WOfS) Filtered Statistics helps provide the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out. WOfS Filtered Statistics consists of a Confidence layer that compares the WOfS Statistics water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the WOfS Statistics water summary where Confidence is low. This layer is Confidence: the degree of agreement between water shown in the Water Summary and other national datasets. Areas where there is less than 1% confidence appears black, areas with confidence for between 1% 10% confidence are styled between black and red, areas with 25% confidence are styled yellow, areas with 75% confidence and above correspond to green. The Confidence layer provides understanding of whether the water shown in the Water Summary agrees with where water should exist in the landscape, such as due to sloping land or whether water has been detected in a location by other means. For more information please see: https://data.dea.ga.gov.au/WOfS/filtered_summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au wofs_filtered_summary_confidence wofs_filtered_summary_confidence Sea, ocean and coast sea_ocean_and_coast DEA Intertidal dea_intertidal DEA Intertidal (Sentinel-2, Landsat) Geoscience Australia Sentinel-2 Landsat Intertidal Calendar Year Collection 3 The DEA Intertidal product suite maps the changing extent, elevation and topography of Australia's exposed intertidal zone, the complex zone that defines the interface between land and sea. Incorporating both Sentinel-2 and Landsat data, the product suite provides an annual 10 m resolution elevation product for the intertidal zone, enabling users to better monitor and understand some of the most dynamic regions of Australia’s coastlines. Utilising an improved tidal modelling capability, the product suite includes a continental scale mapping of intertidal exposure over time, enabling scientists and managers to integrate the data into ecological and migratory species applications and modelling. Product documentation: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/ Service status information: https://status.dea.ga.gov.au ga_s2ls_intertidal_cyear_3 ga_s2ls_intertidal_cyear_3 DEA Tidal Composites dea_tidal_composites DEA Tidal Composites (Sentinel-2) Geoscience Australia Sentinel-2 Tidal Composites Calendar Year Collection 3 Digital Earth Australia (DEA) Tidal Composites are cloud-free imagery mosaics of Australia's coasts, estuaries and reefs at low and high tide. Calculated using a geometric median of Sentinel-2 imagery from the highest and lowest 15% of observed tides, DEA Tidal Composites deliver an annually updated snapshot of high and low tide Australian coastal environments. Product documentation: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/ Service status information: https://status.dea.ga.gov.au ga_s2_tidal_composites_cyear_3 ga_s2_tidal_composites_cyear_3 DEA Coastal Ecosystems dea_coastal_ecosystems DEA Coastal Ecosystems (Sentinel-2) Geoscience Australia Sentinel-2 Coastal Ecosystems Calendar Year Collection 3 The Digital Earth Australia Coastal Ecosystems product suite includes a categorical classification of four coastal ecosystems, supported by probability layers for individual ecosystems and selected quality assurance layers to support product interpretation. Product documentation: https://knowledge.dea.ga.gov.au/data/product/dea-coastal-ecosystems/ Service status information: https://status.dea.ga.gov.au ga_s2_coastalecosystems_cyear_3_v1 ga_s2_coastalecosystems_cyear_3_v1 Other other Intertidal Extents Model (ITEM) Intertidal Extents Model 25m 2.0.0 (Extents) This product is deprecated; users are advised to transition to using DEA Intertidal instead: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/ For service status information, see https://status.dea.ga.gov.au ITEM_V2.0.0 ITEM_V2.0.0 Intertidal Extents Model (ITEM) confidence Intertidal Extents Model 25m 2.0.0 (Confidence) This product is deprecated; users are advised to transition to using DEA Intertidal instead: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/ For service status information, see https://status.dea.ga.gov.au ITEM_V2.0.0_Conf ITEM_V2.0.0_Conf National Intertidal Digital Elevation Model (NIDEM) National Intertidal Digital Elevation Model 25m 1.0.0 This product is deprecated; users are advised to transition to using DEA Intertidal instead: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/ For service status information, see https://status.dea.ga.gov.au NIDEM NIDEM High and Low Tide Composites (HLTC) High and Low Tide Composites (HLTC) This product is deprecated; users are advised to transition to using DEA Tidal Composites instead: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/ For service status information, see https://status.dea.ga.gov.au high_and_low_tide_composites_hltc High Tide Composite (HLTC) High Tide and Low Tide Composites 2.0.0 (Landsat, High Tide) This product is deprecated; users are advised to transition to using DEA Tidal Composites instead: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/ For service status information, see https://status.dea.ga.gov.au high_tide_composite high_tide_composite Low Tide Composite (HLTC) High Tide and Low Tide Composites 2.0.0 (Landsat, Low Tide) This product is deprecated; users are advised to transition to using DEA Tidal Composites instead: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/ For service status information, see https://status.dea.ga.gov.au low_tide_composite low_tide_composite Hazards hazards DEA Burnt Area Characteristic Layers (Deprecated, Sentinel 2 Near Real-Time) dea_burnt_area_characteristic_layers_deprecated_sentinel_2_near_real_time DEA Burnt Area Characteristic Layers (Deprecated, Sentinel 2 Near Real-Time) DEA Burnt Area Characteristic Layers (Deprecated, Sentinel 2 Near Real-Time, Provisional) PRODUCT DEPRECATED ON 30/08/2023, NO RESULTS ARE GENERATED AFTER THIS DATE. Bushfires pose a serious and increasing threat to Australia. The detection and mapping of burns have many applications to support communities and ecosystems impacted by fire. However, the identification of bushfire burn using Earth Observation is often manual, can come with a significant time delay, and at a relatively small scale. Digital Earth Australia (DEA) offer a provisional and preliminary change detection data product, for all of Australia, which uses same day satellite data and cloud-based infrastructure to automatically and rapidly identify areas that show burn characteristics. Commonly, burnt area analysis is run on data from manually selected, cloud-free, pre-fire and post-fire satellite scenes. Automating the rapid identification of pre- and post-fire scenes is problematic due to cloud cover, differing fire duration, and the difficulty in automatically identifying a suitable pre-fire scene. Therefore, Analysing the change between a pre-fire reference baseline and the latest near real-time satellite data enables rapid, automatic, screening of areas that have changed to show characteristics of undergoing a burn. This Near Real-Time (NRT) change detection product is based on: - a pre-fire reference baseline (baseline-metric) dataset; DEA Sentinel 2 Barest Earth (Roberts, et al. 2019). The Barest Earth data shows the spectral data for an area at its least vegetated state based on the Sentinel 2 data archive. The Barest Earth product is produced by a novel high-dimensional statistical technique that extracts a noise-reduced, cloud-free, and robust estimate of the spectral response of the barest state. - the latest (NRT-metric) daily Sentinel-2 (A and B combined) Near Real-Time provisional satellite data. The NRT service provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections. The following metrics were calculated to identify burnt area characteristics: - Bare Soil Index (BSI) - Normalized Difference Vegetation Index (NDVI) - Normalized Burn Ratio (NBR) Change (delta) in each metric was calculated by differencing the baseline metric and the post event (NRT) metric. Each delta layer contains values between -1 and +1, with positive values being more characterisitc of a burn. These layers have been produced as input data into a burnt area vectorisor tool (link will be provided when in production). This data is preliminary and provisional in nature and is still undergoing further development. These metrics should be used as a preliminary screening tool, and not an accurate identification of fire extent. These metrics should be used in combination with each other and can be used with other datasets to strengthen the agreement that the area has indeed been burnt. No decisions on life or property should be made based on this data. **Bare Soil** Bare Soil Index (BSI) identifies soil or bare-land characteristics by combining blue, red, near infrared (NIR), and short wave infrared (SWIR) spectral bands. SWIR and red spectral bands can be used to identify basic soil mineralogy while blue and NIR spectral bands can help to detect vegetation. BSI (Rikimaru et al. 2002) is calculated as: BSI = ((SWIR2 + RED) - (NIR + BLUE)) / ((SWIR2 + RED) + (NIR + BLUE)). Delta BSI (dBSI) shows the change in soil characteristics as the difference between a baseline BSI and a post event BSI (NRT-BSI). The change (delta) in BSI is calculated as: dBSI = (baseline-BSI - NRT-BSI) * -1. Delta BSI is multiplied by negative 1 in order to reverse the scaling so that it can be presented the same way as the other delta indexes, which all have positive values for areas that shows characteristics of being burnt. **Vegetation** Normalized Difference Vegetation Index (NDVI) is used to detect green vegetation characteristics by identifying the difference between red/visible and near infrared (NIR) spectral bands (Huete and Jackson 1987). NDVI is calculated as: NDVI = (NIR - RED)/(NIR + RED). Delta NDVI (dNDVI) shows the change in vegetation characteristics as the difference between a baseline-NDVI and a post event NDVI (NRT-NDVI). The change (delta) in NDVI is calculated as: dNDVI = baseline-NDVI - NRT-NDVI. **Burnt area** Normalized Burn Ratio (NBR) identifies areas that have the characteristics of being burnt. NBR looks at the relationship between near infrared (NIR) and short wave infrared (SWIR) spectral signatures. High SWIR reflectance values with low NIR reflectance values are indicative of an area that has been burnt by fire(s), while the opposite is seen in healthy vegetation. NBR is calculated as: NBR = (NIR – SWIR2) / (NIR + SWIR2). Delta NBR shows the change in burn characteristics as the difference between a baseline-NBR and a post event NBR (NRT-NBR). The change (delta) in NBR is calculated as: dNBR = baseline-NBR - NRT-NBR. Normally dNBR analysis is run on data from a clear pre-fire and a post-fire satellite scene. However, in order to automatically and rapidly identify areas that are characteristic of burns for all of Australia, and on a near real-time basis, the Sentinel 2 Barest Earth layer has been used as a conservative pre-fire reference image. dNBR is styled in two layers, as a colour ramp between -1 and +1, and also as a thresholded class layer. Burn classes and thresholds values are a simplified version of those proposed by USGS, as "Unburnt", "Low to Medium" and "High to Severe". Colour coding was established by UN-SPIDER. For service status information, see https://status.dea.ga.gov.au ga_s2_ba_provisional_3 ga_s2_ba_provisional_3 DEA Fuel Moisture Content Digital Earth Australia (DEA) Fuel Moisture Content (FMC) dea_fuel_moisture_content DEA FMC Sentinel-2 (A, B & C) DEA FMC Sentinel-2 (A, B & C) This product calculates the Fuel Moisture Content (FMC) of vegetation which is the percentage of water mass relative to dry mass in living vegetation. Values range from 0–300%, representing, by weight percent, the amount of water in leaves compared to dry plant material. A value of 0% would indicate that there is no water content. A value of 100% indicates there is an equal weight of water and dry plant material. A value of 300% would mean there is approximately three times as much water as dry plant material. Values in the range of 0–150% are particularly relevant to fire behaviour analysis, as this range is strongly associated with changes in vegetation flammability and the likelihood of ignition. This product presents pixel-level FMC values for each corresponding Sentinel-2 (A, B and C) scene since July 2015. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-fuel-moisture-content/ For service status information, see https://status.dea.ga.gov.au ga_s2_fmc_layer ga_s2_fmc_layer DEA FMC Sentinel-2 Most Recent Observation DEA FMC Sentinel-2 (A, B & C) Most Recent Observation This product calculates the Fuel Moisture Content (FMC) of vegetation which is the percentage of water mass relative to dry mass in living vegetation. Values range from 0–300%, representing, by weight percent, the amount of water in leaves compared to dry plant material. A value of 0% would indicate that there is no water content. A value of 100% indicates there is an equal weight of water and dry plant material. A value of 300% would mean there is approximately three times as much water as dry plant material. Values in the range of 0–150% are particularly relevant to fire behaviour analysis, as this range is strongly associated with changes in vegetation flammability and the likelihood of ignition. This product is a mosaic of the most recent available data from Sentinel-2 satellites (A, B and C) captured over the Australian continent. You can click on the map to view date of observation information for each pixel. For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-fuel-moisture-content/ For service status information, see https://status.dea.ga.gov.au ga_s2m_fmc_mosaic ga_s2m_fmc_mosaic Other Other nonDEA services other Camden Environmental Monitoring Project InSAR These InSAR-derived datasets were produced by Geoscience Australia under the Camden Environmental Monitoring Project. Products are given for three separately processed satellite radar datasets: ALOS, Envisat and Radarsat-2. Products are derived in up-down and east-west direction from combination of different viewing geometries of the same satellite sensor. The slanted InSAR line-of-sight viewing geometry is insensitive to the north-south direction. Negative signals indicate either downward (in up-down products) or westward (in east-west products) surface movements. Uncertainties of each product result from error propagation of initial line-of-sight data uncertainties during the data combination step. The InSAR processing method used to create these products only uses high-quality pixels with very little signal noise. The resulting products are sparse in some areas (particularly highly vegetated areas) but have a high accuracy as demonstrated by validation with GPS data described in the GA Record. Different InSAR processing methods could be used to retrieve a denser coverage of displacement and velocity observations, but with reduced accuracy. ALOS products generally have denser spatial coverage than Envisat and Radarsat-2 products. This is because ALOS uses a longer radar wavelength (~24 cm) than Envisat and Radarsat-2 (~6 cm) which enables the radar to better penetrate vegetation. A full description of the project and methods used to derive these InSAR products is given in the associated GA Record. camden_environmental_monitoring_project_insar ALOS Displacement Cumulative displacement time series derived from combination of ascending and descending line-of-sight InSAR data from six ALOS data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The time series spans the period 2008-02-11 to 2010-10-22, interpolated to 12-day sampling. The reference (i.e. zero displacement) for the time series is 2008-04-23. alos_displacement alos_displacement ALOS Velocity Velocity (linear displacement rate) of ground movement derived from combination of ascending and descending line-of-sight InSAR data from six ALOS data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The velocity is calculated for the period 2006-05-16 to 2011-01-07. alos_velocity alos_velocity ENVISAT Displacement Cumulative displacement time series derived from combination of ascending and descending line-of-sight InSAR data from seven Envisat data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The time series spans the period 2006-06-26 to 2010-08-28, interpolated to 12-day sampling. The reference (i.e. zero displacement) for the time series is 2007-04-10. envisat_displacement envisat_displacement ENVISAT Velocity Velocity (linear displacement rate) of ground movement derived from combination of ascending and descending line-of-sight InSAR data from seven Envisat data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The velocity is calculated for the period 2006-06-02 to 2010-09-25. envisat_velocity envisat_velocity RADARSAT2 Displacement Cumulative displacement time series derived from combination of ascending and descending line-of-sight InSAR data from two Radarsat-2 data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The time series spans the period 2015-07-15 to 2019-05-31, interpolated to 12-day sampling. The reference (i.e. zero displacement) for the time series is 2015-07-15. radarsat2_displacement radarsat2_displacement RADARSAT2 Velocity Velocity (linear displacement rate) of ground movement derived from combination of ascending and descending line-of-sight InSAR data from two Radarsat-2 data stacks in the Sydney Basin area. Original data points were interpolated to 50 m pixel spacing. The velocity is calculated for the period 2015-07-15 to 2019-05-31. radarsat2_velocity radarsat2_velocity ASTER Geoscience Map of Australia The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. The individual geoscience products are a combination of bands and band ratios to highlight different mineral groups and parameters including: - False Colour Mosaic - CSIRO Landsat TM - Regolith Ratios - AlOH Group Composition - AlOH Group Content - FeOH Group Content - Ferric Oxide Composition - Ferric Oxide Content - Ferrous Iron Content in MgOH/Carbonate - Ferrous Iron Index - Green Vegetation Content - Gypsum Index - Kaolin Group Index - MgOH Group Composition - MgOH Group Content - Opaque Index - TIR Silica index - TIR Quartz Index - Surface mineral group distribution (relative abundance and composition) For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 aster_geoscience_map_of_australia False Colour Mosaic The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 False colour RGB composite - Red: B3 - Green: B2 - Blue: B1 (red = green vegetation) Use this image to help understand non-geological differences within and between ASTER scenes caused by green vegetation (red), fire scars, thin and thick cloud and cloud shadows. Use band 2 only for a gray-scale background to the content, composition and index colour products. For 'False Colour Mosaic' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74348 For service status information, see https://status.dea.ga.gov.au aster_false_colour aster_false_colour Regolith Ratios The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 3 band RGB composite - Red: B3/B2 - Green: B3/B7 - Blue: B4/B7 (white = green vegetation) Use this image to help interpret: (1) the amount of green vegetation cover (appears as white); (2) basic spectral separation (colour) between different regolith and geological units and regions/provinces; and (3) evidence for unmasked cloud (appears as green). For 'Regolith Ratios' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74349 For service status information, see https://status.dea.ga.gov.au aster_regolith_ratios aster_regolith_ratios AlOH Group Composition The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B5/B7 - Blue is well ordered kaolinite, Al-rich muscovite/illite, paragonite, pyrophyllite - Red is Al-poor (Si-rich) muscovite (phengite) Useful for mapping: (1) exposed saprolite/saprock is often white mica or Al-smectite (warmer colours) whereas transported materials are often kaolin-rich (cooler colours); (2) clays developed over carbonates, especially Al-smectite (montmorillonite, beidellite) will produce middle to warmers colours; (3) stratigraphic mapping based on different clay-types; and (4) lithology-overprinting hydrothermal alteration, e.g. Si-rich and K-rich phengitic mica (warmer colours). Combine with Ferrous iron in MgOH and FeOH content products to look for evidence of overlapping/juxtaposed potassic metasomatism in ferromagnesian parents rocks (e.g. Archaean greenstone associated Au mineralisation) +/- associated distal propyllitic alteration (e.g. chlorite, amphibole) For 'AlOH Group Composition' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74356 For service status information, see https://status.dea.ga.gov.au aster_aloh_group_composition aster_aloh_group_composition AlOH Group Content The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: (B5+B7)/B6 - Blue is low abundance - Red is high abundance (potentially includes: phengite, muscovite, paragonite, lepidolite, illite, brammalite, montmorillonite, beidellite, kaolinite, dickite) Useful for mapping: (1) exposed saprolite/saprock; (2) clay-rich stratigraphic horizons; (3) lithology-overprinting hydrothermal phyllic (e.g. white mica) alteration; and (4) clay-rich diluents in ore systems (e.g. clay in iron ore). Also combine with AlOH composition to help map: (1) exposed in situ parent material persisting through “cover” which can be expressed as: (a) more abundant AlOH content + (b) long-wavelength (warmer colour) AlOH composition (e.g. muscovite/phengite) For 'AlOH Group Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74355 For service status information, see https://status.dea.ga.gov.au aster_aloh_group_content aster_aloh_group_content FeOH Group Content The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: (B6+B8)/B7 - Blue is low content, - Red is high content (potentially includes: chlorite, epidote, jarosite, nontronite, gibbsite, gypsum, opal-chalcedony Useful for mapping: (1) jarosite (acid conditions) – in combination with ferric oxide content (high); (2) gypsum/gibbsite – in combination with ferric oxide content (low); (3) magnesite - in combination with ferric oxide content (low) and MgOH content (moderate-high); (4) chlorite (e.g. propyllitic alteration) – in combination with Ferrous in MgOH (high); and (5) epidote (calc-silicate alteration) – in combination with Ferrous in MgOH (low). For 'FeOH Group Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74358 For service status information, see https://status.dea.ga.gov.au aster_feoh_group_content aster_feoh_group_content Ferric Oxide Composition The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B2/B1 - Blue-cyan is goethite rich, - Green is hematite-goethite, - Red-yellow is hematite-rich Useful For: (1) Mapping transported materials (including palaeochannels) characterised by hematite (relative to geothite). Combine with AlOH composition to find co-located areas of hematite and poorly ordered kaolin to map transported materials; and (2) hematite-rish areas in drier conditions (eg above the water table) whereas goethite-rich in wetter conditions (eg at/below the water or areas recently exposed). May also be climate driven. For 'Ferric Oxide Composition' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74352 For service status information, see https://status.dea.ga.gov.au aster_ferric_oxide_composition aster_ferric_oxide_composition Ferric Oxide Content The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B4/B3 - Blue is low abundance, - Red is high abundance Useful for: (1) Exposed iron ore (hematite-goethite). Use in combination with the “Opaques index” to help separate/map dark: (a) surface lags (e.g. maghemite gravels) which can be misidentified in visible and false colour imagery; and (b) magnetite in BIF and/or bedded iron ore; and (2) Acid conditions: combine with FeOH Group content to help map jarosite which will have high values in both products. Mapping hematite versus goethite mapping is NOT easily achieved as ASTER’s spectral bands were not designed to capture diagnostic iron oxide spectral behaviour. However, some information on visible colour relating in part to differences in hematite and/or goethite content can be obtained using a ratio of B2/B1 especially when this is masked using a B4/B3 to locate those pixels with sufficient iro oxide content. For 'Ferric Oxide Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74351 For service status information, see https://status.dea.ga.gov.au aster_ferric_oxide_content aster_ferric_oxide_content Ferrous Iron Content in MgOH/Carbonate The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B5/B4 - Blue is low ferrous iron content in carbonate and MgOH minerals like talc and tremolite. - Red is high ferrous iron content in carbonate and MgOH minerals like chlorite and actinolite. Useful for mapping: (1) un-oxidised “parent rocks” – i.e. mapping exposed parent rock materials (warm colours) in transported cover; (2) talc/tremolite (Mg-rich – cool colours) versus actinolite (Fe-rich – warm colours); (3) ferrous-bearing carbonates (warm colours) potentially associated with metasomatic “alteration”; (4) calcite/dolomite which are ferrous iron-poor (cool colours); and (5) epidote, which is ferrous iron poor (cool colours) – in combination with FeOH content product (high). For 'Ferrous Iron Content in MgOH/Carbonate' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74361 For service status information, see https://status.dea.ga.gov.au aster_ferrous_iron_content_in_mgoh aster_ferrous_iron_content_in_mgoh Ferrous Iron Index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B5/B4 - Blue is low abundance, - Red is high abundance This product can help map exposed “fresh” (un-oxidised) rocks (warm colours) especially mafic and ultramafic lithologies rich in ferrous silicates (e.g. actinolite, chlorite) and/or ferrous carbonates (e.g. ferroan dolomite, ankerite, siderite). Applying an MgOH Group content mask to this product helps to isolate ferrous bearing non-OH bearing minerals like pyroxenes (e.g. jadeite) from OH-bearing or carbonate-bearing ferrous minerals like actinolite or ankerite, respectively. Also combine with the FeOH Group content product to find evidence for ferrous-bearing chlorite (e.g. chamosite). For 'Ferrous Iron Index' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74353 For service status information, see https://status.dea.ga.gov.au aster_ferrous_iron_index aster_ferrous_iron_index Green Vegetation Content The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B3/B2 - Blue is low content, - Red is high content Use this image to help interpret the amount of “obscuring/complicating” green vegetation cover. For 'Green Vegetation Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74350 For service status information, see https://status.dea.ga.gov.au aster_green_vegetation aster_green_vegetation Gypsum Index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band Ratio: (B10+B12)/B11 - Blue is low gypsum content, - Red is high gypsum content Useful for mapping: (1) evaporative environments (e.g. salt lakes) and associated arid aeolian systems (e.g. dunes); (2) acid waters (e.g. from oxidising sulphides) invading carbonate rich materials including around mine environments; and (3) hydrothermal (e.g. volcanic) systems. For service status information, see https://status.dea.ga.gov.au aster_gypsum_index aster_gypsum_index Kaolin Group Index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band Ratio: B6/B5 - Blue is low content, - Red is high content (potentially includes: pyrophyllite, alunite, well-ordered kaolinite) Useful for mapping: (1) different clay-type stratigraphic horizons; (2) lithology-overprinting hydrothermal alteration, e.g. high sulphidation, “advanced argillic” alteration comprising pyrophyllite, alunite, kaolinite/dickite; and (3) well-ordered kaolinite (warmer colours) versus poorly-ordered kaolinite (cooler colours) which can be used for mapping in situ versus transported materials, respectively. For 'Kaolin Group Index' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74357 For service status information, see https://status.dea.ga.gov.au aster_kaolin_group_index aster_kaolin_group_index MgOH Group Composition The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B7/B8 - Blue-cyan is magnesite-dolomite, amphibole, chlorite - Red is calcite, epidote, amphibole Useful for mapping: (1) exposed parent material persisting through "cover"; (2) "dolomitization" alteration in carbonates - combine with Ferrous iron in MgOH product to help separate dolomite versus ankerite; (3) lithology-cutting hydrothermal (e.g. propyllitic) alteration - combine with FeOH content product and ferrous iron in Mg-OH to isolate chlorite from actinolite versus talc versus epidote; and (4) layering within mafic/ultramafic intrusives For 'MgOH Group Composition' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74360 For service status information, see https://status.dea.ga.gov.au aster_mgoh_group_composition aster_mgoh_group_composition MgOH Group Content The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: (B6+B9)/(B7+B8) - Blue is low content, - Red is high content (potentially includes: calcite, dolomite, magnesite, chlorite, epidote, amphibole, talc, serpentine) Useful for mapping: (1) “hydrated” ferromagnesian rocks rich in OH-bearing tri-octahedral silicates like actinolite, serpentine, chlorite and talc; (2) carbonate-rich rocks, including shelf (palaeo-reef) and valley carbonates(calcretes, dolocretes and magnecretes); and (3) lithology-overprinting hydrothermal alteration, e.g. “propyllitic alteration” comprising chlorite, amphibole and carbonate. The nature (composition) of the silicate or carbonate mineral can be further assessed using the MgOH composition product. For 'MgOH Group Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74359 For service status information, see https://status.dea.ga.gov.au aster_mgoh_group_content aster_mgoh_group_content Opaque Index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B1/B4 - Blue is low abundance, - Red is high abundance (potentially includes carbon black (e.g. ash), magnetite, Mn oxides, and sulphides in unoxidised environments) Useful for mapping: (1) magnetite-bearing rocks (e.g. BIF); (2) maghemite gravels; (3) manganese oxides; (4) graphitic shales. Note: (1) and (4) above can be evidence for “reduced” rocks when interpreting REDOX gradients. Combine with AlOH group Content (high values) and Composition (high values) products, to find evidence for any invading “oxidised” hydrothermal fluids which may have interacted with reduced rocks evident in the Opaques index product. For 'Opaque Index' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74354 For service status information, see https://status.dea.ga.gov.au aster_opaque_index aster_opaque_index TIR Silica index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B13/B10 - Blue is low silica content, - Red is high silica content (potentially includes Si-rich minerals, such as quartz, feldspars, Al-clays) Geoscience Applications: Broadly equates to the silica content though the intensity (depth) of this reststrahlen feature is also affected by particle size <250 micron. Useful product for mapping: (1) colluvial/alluvial materials; (2) silica-rich (quartz) sediments (e.g. quartzites); (3) silification and silcretes; and (4) quartz veins. Use in combination with quartz index, which is often correlated with the Silica index. For 'TIR Silica index' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74362 For service status information, see https://status.dea.ga.gov.au aster_silica_index aster_silica_index TIR Quartz Index The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B11/(B10+B12) - Blue is low quartz content, - Red is high quartz content Geoscience Applications: Use in combination with Silica index to more accurately map “crystalline” quartz rather than poorly ordered silica (e.g. opal), feldspars and compacted clays. For 'TIR Quartz Index' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74363 For service status information, see https://status.dea.ga.gov.au aster_quartz_index aster_quartz_index Multi-Scale Topographic Position multi_scale_topographic_position Multi-Scale Topographic Position Multi-Scale Topographic Position A Multi-scale topographic position image of Australia has been generated by combining a topographic position index and topographic ruggedness. Topographic Position Index (TPI) measures the topographic slope position of landforms. Ruggedness informs on the roughness of the surface and is calculated as the standard deviation of elevations. Both these terrain attributes are therefore scale dependent and will vary according to the size of the analysis window. Based on an algorithm developed by Lindsay et al. (2015) we have generated multi-scale topographic position model over the Australian continent using 3 second resolution (~90m) DEM derived from the Shuttle Radar Topography Mission (SRTM). The algorithm calculates topographic position scaled by the corresponding ruggedness across three spatial scales (window sizes) of 0.2-8.1 Km; 8.2-65.2 Km and 65.6-147.6 Km. The derived ternary image captures variations in topographic position across these spatial scales (blue local, green intermediate and red regional) and gives a rich representation of nested landform features that have broad application in understanding geomorphological and hydrological processes and in mapping regolith and soils over the Australian continent. Lindsay, J, B., Cockburn, J.M.H. and Russell, H.A.J. 2015. An integral image approach to performing multi-scale topographic position analysis, Geomorphology 245, 51–61. For service status information, see https://status.dea.ga.gov.au multi_scale_topographic_position multi_scale_topographic_position Weathering Intensity weathering_intensity Weathering Intensity Weathering Intensity Weathering intensity or the degree of weathering is an important characteristic of the earth’s surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. For service status information, see https://status.dea.ga.gov.au weathering_intensity weathering_intensity