WMS
Digital Earth Australia - OGC Web Services
Digital Earth Australia OGC Web Services
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
Digital Earth Australia
Geoscience Australia
postal
GPO Box 378
Canberra
ACT
2609
Australia
+61 2 6249 9111
earth.observation@ga.gov.au
none
none
1
512
512
text/xml
image/png
application/json
text/html
XML
Baseline satellite data
EPSG:3857
EPSG:4326
EPSG:3577
EPSG:4238
EPSG:3111
EPSG:32756
WIKID:102171
s2_ls_combined
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/
* 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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
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ga_ls_ard_3
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.696007613984
156.154528040633
-45.6734535062289
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ga_s2m_ard_3
DEA Surface Reflectance (Sentinel-2)
Geoscience Australia Sentinel-2 Analysis Ready Data Collection 3
This product takes Sentinel-2A and 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-2A and 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.
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/
For service status information, see https://status.dea.ga.gov.au
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
111.959005160882
155.219424436049
-44.3414673034495
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DEA Surface Reflectance Calendar Year (Landsat)
ga_ls8c_nbart_gm_cyear_3
DEA GeoMAD (Landsat 8 OLI-TIRS)
DEA Geometric Median and Median Absolute Deviation (Landsat 8 OLI-TIRS)
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.
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.413246718272
157.044900204052
-46.2302085135865
-8.10857383542487
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
ga_ls7e_nbart_gm_cyear_3
DEA GeoMAD (Landsat 7 ETM+)
DEA Geometric Median and Median Absolute Deviation (Landsat 7 ETM+)
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.
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.413246718272
156.432609321534
-45.1432282004529
-8.21783704144064
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
ga_ls5t_nbart_gm_cyear_3
DEA GeoMAD (Landsat 5 TM)
DEA Geometric Median and Median Absolute Deviation (Landsat 5 TM)
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.
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.413246718272
156.432609321534
-45.0401572488294
-7.21314878610402
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
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
ga_ls9c_ard_3
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.719942581569
156.134023088158
-44.247533161884
-9.07621726239926
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ga_ls8c_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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.718297795307
156.154528040633
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ga_ls7e_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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.696007613984
155.711647298981
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ga_ls5t_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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.757249124468
155.662004153478
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DEA Surface Reflectance (Sentinel-2)
ga_s2am_ard_3
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
111.959005160882
155.21942356906
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ga_s2bm_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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
111.959102848976
155.219424436049
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Land and Vegetation
EPSG:3857
EPSG:4326
EPSG:3577
EPSG:4238
EPSG:3111
EPSG:32756
WIKID:102171
DEA Land Cover
ga_ls_landcover
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.731828633068
154.267421772154
-44.2342184871416
-9.93509678400507
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
ga_ls_landcover_descriptors
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.731828633068
154.267421772154
-44.2342184871416
-9.93509678400507
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
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
ga_ls_fc_3
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.696007613984
156.154528040633
-45.6734535062289
-8.13764292647926
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ga_ls_fc_pc_cyear_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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.343354889631
154.378185360282
-44.2467683625153
-8.51120628432549
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
DEA Mangroves
ga_ls_mangrove_cover_cyear_3
DEA Mangroves (Landsat)
Mangrove Canopy Cover 30m 3.0.0 (Landsat, Collection 3)
Mangrove canopy cover version 3.0.0, 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 year between 1987 and 2021 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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.492257439061
154.264053741666
-39.1292216144938
-9.5698963139854
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
Barest Earth
s2_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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.324372771065
154.70510751296
-43.9381826788341
-8.82186564540388
ls8_barest_earth_mosaic
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
111.492400120054
155.066563941708
-44.3357065215098
-8.83515695199939
landsat_barest_earth
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
111.033686003887
155.790571411147
-44.4285210281062
-8.49453875182811
Inland water
EPSG:3857
EPSG:4326
EPSG:3577
EPSG:4238
EPSG:3111
EPSG:32756
WIKID:102171
DEA Wetness Percentiles (Landsat)
ga_ls_tcw_percentiles_2
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.501524524947
154.340852639902
-44.315077785668
-9.07349125191758
DEA Tasseled Cap Indices Percentiles (Landsat)
ga_ls_tc_pc_cyear_3
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.343354889631
154.378185360282
-44.2467683625153
-8.51120628432549
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
DEA Water Observations
Digital Earth Australia (DEA) Water Observations from Space (WOfS)
ga_ls_wo_3
DEA Water Observations (Landsat)
**Geoscience Australia Water Observations (Landsat, Collection 3, 30 m, Individual Observations, 3.1.6).**
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.696007613984
156.154528040633
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ga_s2_wo_provisional_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-provisional-sentinel-2-nrt/
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
111.959005160882
155.219024052459
-44.3414673034495
-9.93000738897011
2021-07-01,2023-03-14,2023-03-15,2023-03-16,2023-03-17,2023-03-18,2023-03-19,2023-03-20,2023-03-21,2023-03-22,2023-03-23,2023-03-24,2023-03-25,2023-03-26,2023-03-27,2023-03-28,2023-03-29,2023-03-30,2023-03-31,2023-04-01,2023-04-02,2023-04-03,2023-04-04,2023-04-05,2023-04-06,2023-04-07,2023-04-08,2023-04-09,2023-04-10,2023-04-11,2023-04-12,2023-04-13,2023-04-14,2023-04-15,2023-04-16,2023-04-17,2023-04-18,2023-04-19,2023-04-20,2023-04-21,2023-04-22,2023-04-23,2023-04-24,2023-04-25,2023-04-26,2023-04-27,2023-04-28,2023-04-29,2023-04-30,2023-05-01,2023-05-02,2023-05-03,2023-05-04,2023-05-05,2023-05-06,2023-05-07,2023-05-08,2023-05-09,2023-05-10,2023-05-11,2023-05-12,2023-05-13,2023-05-14,2023-05-15,2023-05-16,2023-05-17,2023-05-18,2023-05-19,2023-05-20,2023-05-21,2023-05-22,2023-05-23,2023-05-24,2023-05-25,2023-05-26,2023-05-27,2023-05-28,2023-05-29,2023-05-30,2023-05-31,2023-06-01,2023-06-02,2023-06-03,2023-06-04,2023-06-05,2023-06-06,2023-06-07,2023-06-08,2023-06-09,2023-06-10,2023-06-11,2023-06-12,2023-06-13,2023-06-14,2023-06-15,2023-06-16,2023-06-17,2023-06-18,2023-06-19,2023-06-20,2023-06-21,2023-06-22,2023-06-23,2023-06-24,2023-06-25,2023-06-26,2023-06-27,2023-06-28,2023-06-29,2023-06-30,2023-07-01,2023-07-02,2023-07-03,2023-07-04,2023-07-05,2023-07-06,2023-07-07,2023-07-08,2023-07-09,2023-07-10,2023-07-11,2023-07-12,2023-07-13,2023-07-14,2023-07-15,2023-07-16,2023-07-17,2023-07-18,2023-07-19,2023-07-20,2023-07-21,2023-07-22,2023-07-23,2023-07-24,2023-07-25,2023-07-26,2023-07-27,2023-07-28,2023-07-29,2023-07-30,2023-07-31,2023-08-01,2023-08-02,2023-08-03,2023-08-04,2023-08-05,2023-08-06,2023-08-07,2023-08-08,2023-08-09,2023-08-10,2023-08-11,2023-08-12,2023-08-13,2023-08-14,2023-08-15,2023-08-16,2023-08-17,2023-08-18,2023-08-19,2023-08-20,2023-08-21,2023-08-22,2023-08-23,2023-08-24,2023-08-25,2023-08-26,2023-08-27,2023-08-28,2023-08-29,2023-08-30,2023-08-31,2023-09-01,2023-09-02,2023-09-03,2023-09-04,2023-09-05,2023-09-06,2023-09-07,2023-09-08,2023-09-09,2023-09-10,2023-09-11,2023-09-12,2023-09-13,2023-09-14,2023-09-15,2023-09-16,2023-09-17,2023-09-18,2023-09-19,2023-09-20,2023-09-21,2023-09-22,2023-09-23,2023-09-24,2023-09-25,2023-09-26,2023-09-27,2023-09-28,2023-09-29,2023-09-30,2023-10-01,2023-10-02,2023-10-03,2023-10-04,2023-10-05,2023-10-06,2023-10-07,2023-10-08,2023-10-09,2023-10-10,2023-10-11,2023-10-12,2023-10-13,2023-10-14,2023-10-15,2023-10-16,2023-10-17,2023-10-18,2023-10-19,2023-10-20,2023-10-21,2023-10-22,2023-10-23,2023-10-24,2023-10-25,2023-10-26,2023-10-27,2023-10-28,2023-10-29,2023-10-30,2023-10-31,2023-11-01,2023-11-02,2023-11-03,2023-11-04,2023-11-05,2023-11-06,2023-11-07,2023-11-08,2023-11-09,2023-11-10,2023-11-11,2023-11-12,2023-11-13,2023-11-14,2023-11-15,2023-11-16,2023-11-17,2023-11-18,2023-11-19,2023-11-20,2023-11-21,2023-11-22,2023-11-23,2023-11-24,2023-11-25,2023-11-26,2023-11-27,2023-11-28,2023-11-29,2023-11-30,2023-12-01,2023-12-02,2023-12-03,2023-12-04,2023-12-05,2023-12-06,2023-12-07,2023-12-08,2023-12-09,2023-12-10,2023-12-11,2023-12-12,2023-12-13,2023-12-14,2023-12-15,2023-12-16,2023-12-17,2023-12-18,2023-12-19,2023-12-20,2023-12-21,2023-12-22,2023-12-23,2023-12-24,2023-12-25,2023-12-26,2023-12-27,2023-12-28,2023-12-29,2023-12-30,2023-12-31,2024-01-01,2024-01-02,2024-01-03,2024-01-04,2024-01-05,2024-01-06,2024-01-07,2024-01-08,2024-01-09,2024-01-10,2024-01-11,2024-01-12,2024-01-13,2024-01-14,2024-01-15,2024-01-16,2024-01-17,2024-01-18,2024-01-19,2024-01-20,2024-01-21,2024-01-22,2024-01-23,2024-01-24,2024-01-25,2024-01-26,2024-01-27,2024-01-28,2024-01-29,2024-01-30,2024-01-31,2024-02-01,2024-02-02,2024-02-03,2024-02-04,2024-02-05,2024-02-06,2024-02-07,2024-02-08,2024-02-09,2024-02-10,2024-02-11,2024-02-12,2024-02-13,2024-02-14,2024-02-15,2024-02-16,2024-02-17,2024-02-18,2024-02-19,2024-02-20,2024-02-21,2024-02-22,2024-02-23,2024-02-24,2024-02-25,2024-02-26,2024-02-27,2024-02-28,2024-02-29,2024-03-01,2024-03-02,2024-03-03,2024-03-04,2024-03-05,2024-03-06,2024-03-07,2024-03-08,2024-03-09,2024-03-10,2024-03-11,2024-03-12,2024-03-13,2024-03-14,2024-03-15,2024-03-16,2024-03-17,2024-03-18,2024-03-19,2024-03-20,2024-03-21,2024-03-22,2024-03-23,2024-03-24,2024-03-25,2024-03-26,2024-03-27,2024-03-28,2024-03-29
ga_ls_wo_fq_myear_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, 3.1.6).**
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
111.533088723661
155.073704691127
-44.3403132062908
-8.51120628432549
2004-07-02,2004-12-31
ga_ls_wo_fq_cyear_3
DEA Water Observations Calendar Year (Landsat)
**Geoscience Australia Water Observations, Annual Frequency Statistics, Calendar Year (Landsat, Collection 3, 30 m, WO-STATS-ANNUAL, 3.1.6).**
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.413246718272
157.044900204052
-46.1419438144744
-8.10857383542487
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
ga_ls_wo_fq_apr_oct_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, 3.1.6).**
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.413246718272
157.044900204052
-46.1419438144744
-8.10857383542487
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
ga_ls_wo_fq_nov_mar_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, 3.1.6).**
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
110.413246718272
157.044900204052
-46.1419438144744
-8.21783704144064
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
wofs_filtered_summary
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
111.859562290899
155.169368098324
-44.9351287319957
-9.00692244744483
wofs_summary_clear
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.99986111
153.999861110328
-44.0008652894921
-10.0001388899999
wofs_summary_wet
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.99986111
153.999861110328
-44.0008652894921
-10.0001388899999
Water Observations from Space Statistics
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.99986111
153.999861110328
-44.0008652894921
-10.0001388899999
wofs_filtered_summary_confidence
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
111.859562290899
155.169368098324
-44.9351287319957
-9.00692244744483
Sea, ocean and coast
EPSG:3857
EPSG:4326
EPSG:3577
EPSG:4238
EPSG:3111
EPSG:32756
WIKID:102171
DEA Intertidal (ITEM, NIDEM)
ITEM_V2.0.0
DEA Intertidal Extents (Landsat)
Intertidal Extents Model 25m 2.0.0 (Extents)
The Intertidal Extents Model (ITEM v2.0) product analyses GA’s historic archive of satellite imagery to derive a model of the spatial extents of the intertidal zone throughout the tidal cycle. The model can assist in understanding the relative elevation profile of the intertidal zone,
delineating exposed areas at differing tidal heights and stages.
The product differs from previous methods used to map the intertidal zone which have been predominately focused on analysing a small number of individual satellite images per location (e.g Ryu et al., 2002; Murray et al., 2012).
By utilising a full 30 year time series of observations and a global tidal model (Egbert and Erofeeva, 2002), the methodology enables us to overcome the requirement for clear, high quality observations acquired concurrent to the time of high and low tide.
*Accuracy and limitations*
Due the sun-synchronous nature of the various Landsat sensor observations; it is unlikely that the full physical extents of the tidal range in any cell will be observed. Hence, terminology has been adopted for the product to reflect the highest modelled tide observed in a given cell (HOT) and the lowest modelled tide observed (LOT) (see Sagar et al. 2017). These measures are relative to Mean Sea Level, and have no consistent relationship to Lowest (LAT) and Highest Astronomical Tide (HAT).
The inclusion of the lowest (LMT) and highest (HMT) modelled tide values for each tidal polygon indicates the highest and lowest tides modelled for that location across the full time series by the OTPS model. The relative difference between the LOT and LMT (and HOT and HMT) heights gives an indication of the extent of the tidal range represented in the Relative Extents Model.
As in ITEM v1.0, v2.0 contains some false positive land detection in open ocean regions. These are a function of the lack of data at the extremes of the observed tidal range, and features like glint and undetected cloud in these data poor regions/intervals. Methods to isolate and remove these features are in development for future versions. Issues in the DEA archive and data noise in the Esperance, WA region off Cape Le Grande and Cape Arid (Polygons 236,201,301) has resulted in significant artefacts in the model, and use of the model in this area is not recommended.
The Confidence layer is designed to assess the reliability of the Relative Extent Model. Within each tidal range percentile interval, the pixel-based standard deviation of the NDWI values for all observations in the interval subset is calculated. The average standard deviation across all tidal range intervals is then calculated and retained as a quality indicator in this product layer.
The Confidence Layer reflects the pixel based consistency of the NDWI values within each subset of observations, based on the tidal range. Higher standard deviation values indicate water classification changes not based on the tidal cycle, and hence lower confidence in the extent model.
Possible drivers of these changes include:
Inadequacies of the tidal model, due perhaps to complex coastal bathymetry or estuarine structures not captured in the model. These effects have been reduced in ITEM v2.0 compared to previous versions, through the use of an improved tidal modelling frameworkChange in the structure and exposure of water/non-water features NOT driven by tidal variation.
For example, movement of sand banks in estuaries, construction of man-made features (ports etc.).Terrestrial/Inland water features not influenced by the tidal cycle.
File naming:
THE RELATIVE EXTENTS MODEL v2.0
ITEM_REL_<TIDAL POLYGON NUMBER>_<LONGITUDE>_<LATITUDE>
TIDAL POLYGON NUMBER relates to the id of the tidal polygon referenced by the file
LONGITUDE is the longitude of the centroid of the tidal polygon
LATITUDE is the latitude of the centroid of the tidal polygon
THE CONFIDENCE LAYER v2.0
ITEM_STD_<TIDAL POLYGON NUMBER>_<LONGITUDE>_<LATITUDE>
TIDAL POLYGON NUMBER relates to the id of the tidal polygon referenced by the file
LONGITUDE is the longitude of the centroid of the tidal polygon
LATITUDE is the latitude of the centroid of the tidal polygon
*Overview*
The Intertidal Extents Model product is a national scale gridded dataset characterising the spatial extents of the exposed intertidal zone, at intervals of the observed tidal range (Sagar et al. 2017).The current version (2.0) utilises all Landsat observations (5, 7, and 8) for Australian coastal regions (excluding off-shore Territories) between 1986 and 2016 (inclusive).
ITEM v2.0 has implemented an improved tidal modelling framework (see Sagar et al. 2018) over that utilised in ITEM v1.0. The expanded Landsat archive within the Digital Earth Australia (DEA) has also enabled the model extent to be increased to cover a number of offshore reefs, including the full Great Barrier Reef and southern sections of the Torres Strait Islands.
The DEA archive and new tidal modelling framework has improved the coverage and quality of the ITEM v2.0 relative extents model, particularly in regions where AGDC cell boundaries in ITEM v1.0 produced discontinuities or the imposed v1.0 cell structure resulted in poor quality tidal modelling (see Sagar et al. 2017).
https://knowledge.dea.ga.gov.au/data/product/dea-intertidal-extents-landsat/
For service status information, see https://status.dea.ga.gov.au
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.459622677896
153.670408736335
-43.7203758299765
-10.3096529183452
ITEM_V2.0.0_Conf
DEA Intertidal Extents confidence
Intertidal Extents Model 25m 2.0.0 (Confidence)
The Intertidal Extents Model (ITEM v2.0) product analyses GA’s historic archive of satellite imagery to derive a model of the spatial extents of the intertidal zone throughout the tidal cycle. The model can assist in understanding the relative elevation profile of the intertidal zone,
delineating exposed areas at differing tidal heights and stages.
The product differs from previous methods used to map the intertidal zone which have been predominately focused on analysing a small number of individual satellite images per location (e.g Ryu et al., 2002; Murray et al., 2012).
By utilising a full 30 year time series of observations and a global tidal model (Egbert and Erofeeva, 2002), the methodology enables us to overcome the requirement for clear, high quality observations acquired concurrent to the time of high and low tide.
*Accuracy and limitations*
Due the sun-synchronous nature of the various Landsat sensor observations; it is unlikely that the full physical extents of the tidal range in any cell will be observed. Hence, terminology has been adopted for the product to reflect the highest modelled tide observed in a given cell (HOT) and the lowest modelled tide observed (LOT) (see Sagar et al. 2017). These measures are relative to Mean Sea Level, and have no consistent relationship to Lowest (LAT) and Highest Astronomical Tide (HAT).
The inclusion of the lowest (LMT) and highest (HMT) modelled tide values for each tidal polygon indicates the highest and lowest tides modelled for that location across the full time series by the OTPS model. The relative difference between the LOT and LMT (and HOT and HMT) heights gives an indication of the extent of the tidal range represented in the Relative Extents Model.
As in ITEM v1.0, v2.0 contains some false positive land detection in open ocean regions. These are a function of the lack of data at the extremes of the observed tidal range, and features like glint and undetected cloud in these data poor regions/intervals. Methods to isolate and remove these features are in development for future versions. Issues in the DEA archive and data noise in the Esperance, WA region off Cape Le Grande and Cape Arid (Polygons 236,201,301) has resulted in significant artefacts in the model, and use of the model in this area is not recommended.
The Confidence layer is designed to assess the reliability of the Relative Extent Model. Within each tidal range percentile interval, the pixel-based standard deviation of the NDWI values for all observations in the interval subset is calculated. The average standard deviation across all tidal range intervals is then calculated and retained as a quality indicator in this product layer.
The Confidence Layer reflects the pixel based consistency of the NDWI values within each subset of observations, based on the tidal range. Higher standard deviation values indicate water classification changes not based on the tidal cycle, and hence lower confidence in the extent model.
Possible drivers of these changes include:
Inadequacies of the tidal model, due perhaps to complex coastal bathymetry or estuarine structures not captured in the model. These effects have been reduced in ITEM v2.0 compared to previous versions, through the use of an improved tidal modelling frameworkChange in the structure and exposure of water/non-water features NOT driven by tidal variation.
For example, movement of sand banks in estuaries, construction of man-made features (ports etc.).Terrestrial/Inland water features not influenced by the tidal cycle.
File naming:
THE RELATIVE EXTENTS MODEL v2.0
ITEM_REL_<TIDAL POLYGON NUMBER>_<LONGITUDE>_<LATITUDE>
TIDAL POLYGON NUMBER relates to the id of the tidal polygon referenced by the file
LONGITUDE is the longitude of the centroid of the tidal polygon
LATITUDE is the latitude of the centroid of the tidal polygon
THE CONFIDENCE LAYER v2.0
ITEM_STD_<TIDAL POLYGON NUMBER>_<LONGITUDE>_<LATITUDE>
TIDAL POLYGON NUMBER relates to the id of the tidal polygon referenced by the file
LONGITUDE is the longitude of the centroid of the tidal polygon
LATITUDE is the latitude of the centroid of the tidal polygon
*Overview*
The Intertidal Extents Model product is a national scale gridded dataset characterising the spatial extents of the exposed intertidal zone, at intervals of the observed tidal range (Sagar et al. 2017).The current version (2.0) utilises all Landsat observations (5, 7, and 8) for Australian coastal regions (excluding off-shore Territories) between 1986 and 2016 (inclusive).
ITEM v2.0 has implemented an improved tidal modelling framework (see Sagar et al. 2018) over that utilised in ITEM v1.0. The expanded Landsat archive within the Digital Earth Australia (DEA) has also enabled the model extent to be increased to cover a number of offshore reefs, including the full Great Barrier Reef and southern sections of the Torres Strait Islands.
The DEA archive and new tidal modelling framework has improved the coverage and quality of the ITEM v2.0 relative extents model, particularly in regions where AGDC cell boundaries in ITEM v1.0 produced discontinuities or the imposed v1.0 cell structure resulted in poor quality tidal modelling (see Sagar et al. 2017).
https://knowledge.dea.ga.gov.au/data/product/dea-intertidal-extents-landsat/
For service status information, see https://status.dea.ga.gov.au
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.459622677896
153.670408736335
-43.7203758299765
-10.3096529183452
NIDEM
DEA Intertidal Elevation (Landsat)
National Intertidal Digital Elevation Model 25m 1.0.0
The National Intertidal Digital Elevation Model (NIDEM; Bishop-Taylor et al. 2019) is a continental-scale elevation dataset for Australia's exposed intertidal zone. NIDEM provides the first three-dimensional representation of Australia's intertidal sandy beaches and shores, tidal flats and rocky shores and reefs at 25 m spatial resolution, addressing a key gap between the availability of sub-tidal bathymetry and terrestrial elevation data. NIDEM was generated by combining global tidal modelling with a 30-year time series archive of spatially and spectrally calibrated Landsat satellite data managed within the Digital Earth Australia (DEA) platform. NIDEM complements existing intertidal extent products, and provides data to support a new suite of use cases that require a more detailed understanding of the three-dimensional topography of the intertidal zone, such as hydrodynamic modelling, coastal risk management and ecological habitat mapping.
*Overview*
Intertidal environments support important ecological habitats (e.g. sandy beaches and shores, tidal flats and rocky shores and reefs), and provide many valuable benefits such as storm surge protection, carbon storage and natural resources for recreational and commercial use. However, intertidal zones are faced with increasing threats from coastal erosion, land reclamation (e.g. port construction), and sea level rise. Accurate elevation data describing the height and shape of the coastline is needed to help predict when and where these threats will have the greatest impact. However, this data is expensive and challenging to map across the entire intertidal zone of a continent the size of Australia.
The rise and fall of the ocean can be used to describe the three-dimensional shape of the coastline by mapping the land-sea boundary (or 'waterline') across a range of known tides (e.g. low tide, high tide). Assuming that these waterlines represent lines of constant height relative to mean sea level (MSL), elevations can be modelled for the area of coastline located between the lowest and highest observed tide. To model the elevation of Australia's entire intertidal zone, 30 years of satellite images of the coastline (between 1986 and 2016 inclusive) were obtained from the archive of spatially and spectrally calibrated Landsat observations managed within the Digital Earth Australia (DEA) platform. Using the improved tidal modelling framework of the Intertidal Extents Model v2.0 (ITEM 2.0; Sagar et al. 2017, 2018), each satellite observation in the 30 year time series could be accurately associated with a modelled tide height using the global TPX08 ocean tidal model. These satellite observations were converted into a water index (NDWI), composited into discrete ten percent intervals of the observed tide range (e.g. the lowest 10% of observed tides etc), and used to extract waterlines using a spatially consistent and automated waterline extraction procedure. Triangulated irregular network (TIN) interpolation was then used to derive elevations relative to modelled mean sea level for each 25 x 25 m Landsat pixel across approximately 15,387 sq. km of intertidal terrain along Australia's entire coastline.
NIDEM differs from previous methods used to model the elevation of the intertidal zone which have predominately focused on extracting waterlines from a limited selection of satellite images using manual digitisation and visual interpretation (e.g. Chen and Rau 1998; Zhao et al. 2008; Liu et al. 2013; Chen et al. 2016). This manual process introduces subjectivity, is impractical to apply at a continental-scale, and has inherent restrictions based on the availability of high quality image data at appropriate tidal stages. By developing an automated approach to generating satellite-derived elevation data based on a 30 year time series of observations managed within the Digital Earth Australia (DEA) platform, it was possible to produce the first continental-scale three-dimensional model of the intertidal zone.
*Accuracy*
To assess the accuracy of NIDEM, we compared modelled elevations against three independent elevation and bathymetry validation datasets: the DEM of Australia derived from LiDAR 5 Metre Grid (Geoscience Australia, 2015), elevation data collected from Real Time Kinematic (RTK) GPS surveys (Danaher & Collett, 2006; HydroSurvey Australia, 2009), and 1.0 m resolution multibeam bathymetry surveys (Solihuddin et al., 2016). We assessed overall accuracy across three distinct intertidal environments: sandy beaches and shores, tidal flats, and rocky shores and reefs:
- Sandy beaches and shores, 5 sites: Pearson's correlation = 0.92, Spearman's correlation = 0.93, RMSE +/- 0.41 m
- Tidal flats, 9 sites: Pearson's correlation = 0.78, Spearman's correlation = 0.81, RMSE +/- 0.39 m
- Rocky shores and reefs, 7 sites: Pearson's correlation = 0.46, Spearman's correlation = 0.79, RMSE +/- 2.98 m
*Limitations*
NIDEM covers the exposed intertidal zone which includes sandy beaches and shores, tidal flats and rocky shores and reefs. The model excludes intertidal vegetation communities such as mangroves.
Areas with comparatively steep coastlines and small tidal ranges are poorly captured in the 25 m spatial resolution input Landsat data and resulting NIDEM model. This includes much of the south eastern and southern Australian coast (e.g. New South Wales, Victoria, Tasmania).
Poor validation results for rocky shore and reef sites within the southern Kimberly region highlighted limitations in the NIDEM model that occur when the global OTPS TPX08 Atlas Tidal Model was unable to predict complex and asynchronous local tidal patterns. This is likely to also reduce model accuracy in complex estuaries and coastal wetlands where river flow or vegetative resistance causes hydrodynamic attenuation in tidal flow.
The complex temporal behaviour of tides mean that a sun synchronous sensor like Landsat does not observe the full range of the tidal cycle at all locations. This causes spatial bias in the proportion of the tidal range observed in different regions, which can prevent NIDEM from providing elevation data for areas of the intertidal zone exposed or inundated at the extremes of the tidal range. Accordingly, NIDEM provides elevation data for the portion of the tidal range observed by Landsat, rather than the full tidal range.
While image compositing and masking methods have been applied to remove the majority of noise and non-tidal artefacts from NIDEM, issues remain in several locations. It is recommended that the data be used with caution in the following areas:
- The Recherche Archipelago in southern Western Australia
- Port Phillip Bay in Victoria
- The eastern coast of Tasmania and King Island
- Saunders Reef and surrounds in the northern Coral Sea
*Data access and additional information*
- Journal article: Bishop-Taylor et al. 2019 (https://doi.org/10.1016/j.ecss.2019.03.006)
- Data available on THREDDS: http://dapds00.nci.org.au/thredds/catalogs/fk4/nidem_1_0.html
- eCat catalogue listing including data access: http://pid.geoscience.gov.au/dataset/ga/123678
- listing for extended metadata: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal-elevation-landsat/
For service status information, see https://status.dea.ga.gov.au
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.223058990767
154.080299801132
-43.8291965530654
-10.2371048142508
DEA High and Low Tide Imagery (HLTC)
DEA High and Low Tide Imagery (HLTC)
The High and Low Tide Composites product is composed of two surface reflectance composite mosaics
of Landsat TM and ETM+ (Landsat 5 and Landsat 7 respectively) and OLI (Landsat 8)
surface reflectance data (Li et al., 2012). These products have been produced using
Digital Earth Australia (DEA). The two mosaics allow cloud free and noise reduced visualisation
of the shallow water and inter-tidal coastal regions of Australia, as observed at
high and low tide respectively.The composites are generated utilising the geomedian approach of
Roberts et al (2017) to ensure a valid surface reflectance spectra suitable for uses such as
habitat mapping. The time range used for composite generation in each polygon of the mosaic is
tailored to ensure dynamic coastal features are captured whilst still allowing a clean and cloud
free composite to be generated. The concepts of the Observed Tidal Range (OTR),
and Highest and Lowest Observed Tide (HOT, LOT) are discussed and described fully in Sagar et al.
(2017) and the product description for the ITEM v 1.0 product (Geoscience Australia, 2016).
https://knowledge.dea.ga.gov.au/data/product/dea-high-and-low-tide-imagery-landsat/
For service status information, see https://status.dea.ga.gov.au
high_tide_composite
DEA High Tide Imagery (Landsat)
High Tide and Low Tide Composites 2.0.0 (Landsat, High Tide)
The High and Low Tide Composites product is composed of two surface reflectance composite mosaics of Landsat TM and ETM+ (Landsat 5 and Landsat 7 respectively) and OLI (Landsat 8) surface reflectance data (Li et al., 2012). These products have been produced using Digital Earth Australia (DEA).
The two mosaics allow cloud free and noise reduced visualisation of the shallow water and inter-tidal coastal regions of Australia, as observed at high and low tide respectively (Sagar et al. 2018).
The composites are generated utilising the geomedian approach of Roberts et al (2017) to ensure a valid surface reflectance spectra suitable for uses such as habitat mapping.
The time range used for composite generation in each polygon of the mosaic is tailored to ensure dynamic coastal features are captured whilst still allowing a clean and cloud free composite to be generated. The concepts of the Observed Tidal Range (OTR), and Highest and Lowest Observed Tide (HOT, LOT) are discussed and described fully in Sagar et al. (2017) and the product description for the ITEM v 1.0 product (Geoscience Australia, 2016).
*Overview*
Inter-tidal zones are difficult regions to characterise due to the dynamic nature of the tide. They are highly changeable environments, subject to forcings from the land, sea and atmosphere and yet they form critical habitats for a wide range of organisms from birds to fish and sea grass.
By harnessing the long archive of satellite imagery over Australia's coastal zones in the DEA and pairing the images with regional tidal modelling, the archive can be sorted by tide height rather than date, enabling the inter-tidal zone to be viewed at any stage of the tide regime.
The High Low Tide Composites (HLTC_25) product is composed of two mosaics, distinguished by tide height, representing a composite image of the synthetic geomedian surface reflectance from Landsats 5 TM, Landsat 7 ETM+ and Landsat 8 OLI NBAR data (Li et al., 2012; Roberts et al., 2017). Oregon State Tidal Prediction (OTPS) software (Egbert and Erofeeva, 2002, 2010) was used to generate tide heights, relative to mean sea level, for the Australian continental coastline, split into 306 distinct tidal regions.
These time and date stamped tidal values were then attributed to all coastal tile observations for their time of acquisition, creating a range of observed tide heights for the Australian coastline. The two mosaics in HLTC_25 are composited from the highest and lowest 20 % of observed tide in the ensemble and are termed HOT and LOT respectively.
A geomedian composite for each Landsat band is calculated from the tiles in each ensemble subset to produce the respective HOT and LOT composites. Note that Landsat 7 ETM+ observations are excluded after May 2003 due to a large number of data artifacts.
The time range used for composite generation in each of the 306 polygons of the mosaics are tailored to ensure dynamic coastal features are captured whilst still allowing a clean and cloud free composite to be generated.
The maximum epoch for which the products are calculated is between 1995-2017, although this varies due to data resolution and observation quality. The product also includes a count of clear observations per pixel for both mosaics and attribute summaries per polygon that include the date range, the highest and lowest modeled astronomical tide as well as the highest and lowest observed tide for that time range, the total observation count and the maximum count of observations for any one pixel in the polygon, the polygon ID number (from 1 to 306), the polygon centroid in longitude and latitude and the count of tide stages attributed to every observation used in that polygon of the mosaic. For the count of tidal stage observations, e = ebbing tide, f = flowing tide, ph = peak high tide and pl = peak low tide.
The tide stages were calculated bycomparison to the modeled tide data for 15 minutes either side of the observation to determine the ebb, flow or peak movement of the tide.
Observations are filtered to remove poor quality observations including cloud, cloud shadow and band saturation (of any band).
https://knowledge.dea.ga.gov.au/data/product/dea-high-and-low-tide-imagery-landsat/
For service status information, see https://status.dea.ga.gov.au
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.223058990767
154.080299801132
-43.8291965530654
-10.2371048142508
low_tide_composite
DEA Low Tide Imagery (Landsat)
High Tide and Low Tide Composites 2.0.0 (Landsat, Low Tide)
The High and Low Tide Composites product is composed of two surface reflectance composite mosaics of Landsat TM and ETM+ (Landsat 5 and Landsat 7 respectively) and OLI (Landsat 8) surface reflectance data (Li et al., 2012). These products have been produced using Digital Earth Australia (DEA).
The two mosaics allow cloud free and noise reduced visualisation of the shallow water and inter-tidal coastal regions of Australia, as observed at high and low tide respectively (Sagar et al. 2018).
The composites are generated utilising the geomedian approach of Roberts et al (2017) to ensure a valid surface reflectance spectra suitable for uses such as habitat mapping.
The time range used for composite generation in each polygon of the mosaic is tailored to ensure dynamic coastal features are captured whilst still allowing a clean and cloud free composite to be generated. The concepts of the Observed Tidal Range (OTR), and Highest and Lowest Observed Tide (HOT, LOT) are discussed and described fully in Sagar et al. (2017) and the product description for the ITEM v 1.0 product (Geoscience Australia, 2016).
*Overview*
Inter-tidal zones are difficult regions to characterise due to the dynamic nature of the tide. They are highly changeable environments, subject to forcings from the land, sea and atmosphere and yet they form critical habitats for a wide range of organisms from birds to fish and sea grass.
By harnessing the long archive of satellite imagery over Australia's coastal zones in the DEA and pairing the images with regional tidal modelling, the archive can be sorted by tide height rather than date, enabling the inter-tidal zone to be viewed at any stage of the tide regime.
The High Low Tide Composites (HLTC_25) product is composed of two mosaics, distinguished by tide height, representing a composite image of the synthetic geomedian surface reflectance from Landsats 5 TM, Landsat 7 ETM+ and Landsat 8 OLI NBAR data (Li et al., 2012; Roberts et al., 2017). Oregon State Tidal Prediction (OTPS) software (Egbert and Erofeeva, 2002, 2010) was used to generate tide heights, relative to mean sea level, for the Australian continental coastline, split into 306 distinct tidal regions.
These time and date stamped tidal values were then attributed to all coastal tile observations for their time of acquisition, creating a range of observed tide heights for the Australian coastline. The two mosaics in HLTC_25 are composited from the highest and lowest 20 % of observed tide in the ensemble and are termed HOT and LOT respectively.
A geomedian composite for each Landsat band is calculated from the tiles in each ensemble subset to produce the respective HOT and LOT composites. Note that Landsat 7 ETM+ observations are excluded after May 2003 due to a large number of data artifacts.
The time range used for composite generation in each of the 306 polygons of the mosaics are tailored to ensure dynamic coastal features are captured whilst still allowing a clean and cloud free composite to be generated.
The maximum epoch for which the products are calculated is between 1995-2017, although this varies due to data resolution and observation quality. The product also includes a count of clear observations per pixel for both mosaics and attribute summaries per polygon that include the date range, the highest and lowest modeled astronomical tide as well as the highest and lowest observed tide for that time range, the total observation count and the maximum count of observations for any one pixel in the polygon, the polygon ID number (from 1 to 306), the polygon centroid in longitude and latitude and the count of tide stages attributed to every observation used in that polygon of the mosaic. For the count of tidal stage observations, e = ebbing tide, f = flowing tide, ph = peak high tide and pl = peak low tide.
The tide stages were calculated bycomparison to the modeled tide data for 15 minutes either side of the observation to determine the ebb, flow or peak movement of the tide.
Observations are filtered to remove poor quality observations including cloud, cloud shadow and band saturation (of any band).
https://knowledge.dea.ga.gov.au/data/product/dea-high-and-low-tide-imagery-landsat/
For service status information, see https://status.dea.ga.gov.au
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.223058990767
154.080299801132
-43.8291965530654
-10.2371048142508
Hazards
EPSG:3857
EPSG:4326
EPSG:3577
EPSG:4238
EPSG:3111
EPSG:32756
WIKID:102171
DEA Burnt Area Characteristic Layers (Deprecated, Sentinel 2 Near Real-Time)
ga_s2_ba_provisional_3
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
111.966746816605
155.213824039639
-44.3414673034495
-9.93000738897011
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
Other
Other nonDEA services
EPSG:3857
EPSG:4326
EPSG:3577
EPSG:4238
EPSG:3111
EPSG:32756
WIKID:102171
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.
alos_displacement
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.
HLTC
fractional-cover
burn characteristics
sentinel-1
radarsat-2
bare-earth
mangrove
landsat
time-series
envisat
WOfS
insar
alos-palsar
australia
NIDEM
geomedian
150.330509919584
151.258021405841
-34.5250413940276
-33.772472435988
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
Digital Earth Australia
image/png
alos_velocity
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.
HLTC
fractional-cover
burn characteristics
sentinel-1
radarsat-2
bare-earth
mangrove
landsat
time-series
envisat
WOfS
insar
alos-palsar
australia
NIDEM
geomedian
150.331038253243
151.258021405841
-34.5250413940276
-33.772472435988
Digital Earth Australia
image/png
envisat_displacement
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.
HLTC
fractional-cover
burn characteristics
sentinel-1
radarsat-2
bare-earth
mangrove
landsat
time-series
envisat
WOfS
insar
alos-palsar
australia
NIDEM
geomedian
150.416357298779
151.184355816078
-34.5283535864513
-33.5035077252927
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
Digital Earth Australia
image/png
envisat_velocity
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.
HLTC
fractional-cover
burn characteristics
sentinel-1
radarsat-2
bare-earth
mangrove
landsat
time-series
envisat
WOfS
insar
alos-palsar
australia
NIDEM
geomedian
150.416357298779
151.184355816078
-34.5283535864513
-33.5035077252927
Digital Earth Australia
image/png
radarsat2_displacement
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.
HLTC
fractional-cover
burn characteristics
sentinel-1
radarsat-2
bare-earth
mangrove
landsat
time-series
envisat
WOfS
insar
alos-palsar
australia
NIDEM
geomedian
150.540420399293
151.151613477574
-34.3792688432228
-33.8798432478719
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
Digital Earth Australia
image/png
radarsat2_velocity
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.
HLTC
fractional-cover
burn characteristics
sentinel-1
radarsat-2
bare-earth
mangrove
landsat
time-series
envisat
WOfS
insar
alos-palsar
australia
NIDEM
geomedian
150.540420399293
151.151613477574
-34.3792688432228
-33.8798432478719
Digital Earth Australia
image/png
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_false_colour
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-44.013698912363
-10.2856586
aster_regolith_ratios
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-44.013698912363
-10.2856586
aster_aloh_group_composition
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_aloh_group_content
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_feoh_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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_ferric_oxide_composition
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_ferric_oxide_content
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_ferrous_iron_content_in_mgoh
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_ferrous_iron_index
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_green_vegetation
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_gypsum_index
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917024138111
153.640358457438
-43.7806027057097
-10.28257228
aster_kaolin_group_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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_mgoh_group_composition
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_mgoh_group_content
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_opaque_index
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917275536606
153.640054299875
-43.7806433511675
-10.2856586
aster_silica_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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917024138111
153.640358457438
-43.7806027057097
-10.28257228
aster_quartz_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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.917024138111
153.640358457438
-43.7806027057097
-10.28257228
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.9995833
153.9995833
-44.0004167
-10.0004167
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
bare-earth
mangrove
landsat
time-series
HLTC
fractional-cover
burn characteristics
WOfS
australia
NIDEM
geomedian
112.9995833
153.9995833
-44.0004167
-10.0004167