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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.

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   * Sentinel-2B https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2b-msi/
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11-26,2025-11-27,2025-11-28,2025-11-29,2025-11-30,2025-12-01,2025-12-02,2025-12-03,2025-12-04,2025-12-05,2025-12-06,2025-12-07,2025-12-08,2025-12-09,2025-12-10,2025-12-11,2025-12-12,2025-12-13,2025-12-14,2025-12-15,2025-12-16,2025-12-17,2025-12-18,2025-12-19,2025-12-20,2025-12-21,2025-12-22,2025-12-23,2025-12-24,2025-12-25,2025-12-26,2025-12-27,2025-12-28,2025-12-29,2025-12-30,2025-12-31,2026-01-01,2026-01-02,2026-01-03,2026-01-04,2026-01-05,2026-01-06,2026-01-07,2026-01-08,2026-01-09,2026-01-10,2026-01-11,2026-01-12,2026-01-13,2026-01-14,2026-01-15,2026-01-16,2026-01-17,2026-01-18,2026-01-19,2026-01-20,2026-01-21,2026-01-22,2026-01-23,2026-01-24,2026-01-25,2026-01-26,2026-01-27,2026-01-28,2026-01-29,2026-01-30,2026-01-31,2026-02-01,2026-02-02,2026-02-03,2026-02-04,2026-02-05,2026-02-06,2026-02-07,2026-02-08,2026-02-09,2026-02-10,2026-02-11,2026-02-12,2026-02-13,2026-02-14,2026-02-15,2026-02-16,2026-02-17,2026-02-18,2026-02-19,2026-02-20,2026-02-21,2026-02-22,2026-02-23,2026-02-24,2026-02-25,2026-02-26,2026-02-27,2026-02-28,2026-03-01,2026-03-02,2026-03-03,2026-03-04,2026-03-05</Dimension>
        

        
        

        
        
        
        
            <Style>
                <Name>true_colour</Name>
                <Title>True Colour</Title>
                <Abstract>True-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>false_colour</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>Normalised Difference Vegetation Index - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/s2_ls_combined/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>Normalised Difference Water Index - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/s2_ls_combined/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>Modified Normalised Difference Water Index - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/s2_ls_combined/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>nbr</Name>
                <Title>Normalised Burn Ratio - NIR, SWIR</Title>
                <Abstract>Normalised Burn Ratio - a derived index that that uses the differences in the way health green vegetation and burned vegetation reflect light to find burned area</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/s2_ls_combined/nbr/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue</Title>
                <Abstract>Visible blue band (approx 480nm)</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green</Title>
                <Abstract>Visible green band (approx 550nm)</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red</Title>
                <Abstract>Visible red band (approx 650nm)</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near infrared</Title>
                <Abstract>Broad near-infrared band (approx 850nm)</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir1</Name>
                <Title>Shortwave infrared band 1</Title>
                <Abstract>Shortwave near-infrared band 1 (approx 1600nm)</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir2</Name>
                <Title>Shortwave infrared band 2</Title>
                <Abstract>Shortwave near-infrared band 2 (approx 2200nm)</Abstract>
                
            </Style>
        
            <Style>
                <Name>fmask</Name>
                <Title>Fmask Classification</Title>
                <Abstract>Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-8 and Sentinel 2 images.</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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    <Layer queryable="1">
        <Name>ga_ls_ard_3</Name>
        <Title>DEA Surface Reflectance (Landsat)</Title>
        <Abstract>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</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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11-26,2025-11-27,2025-11-28,2025-11-29,2025-11-30,2025-12-01,2025-12-02,2025-12-03,2025-12-04,2025-12-05,2025-12-06,2025-12-07,2025-12-08,2025-12-09,2025-12-10,2025-12-11,2025-12-12,2025-12-13,2025-12-14,2025-12-15,2025-12-16,2025-12-17,2025-12-18,2025-12-19,2025-12-20,2025-12-21,2025-12-22,2025-12-23,2025-12-24,2025-12-25,2025-12-26,2025-12-27,2025-12-28,2025-12-29,2025-12-30,2025-12-31,2026-01-01,2026-01-02,2026-01-03,2026-01-04,2026-01-05,2026-01-06,2026-01-07,2026-01-08,2026-01-09,2026-01-10,2026-01-11,2026-01-12,2026-01-13,2026-01-14,2026-01-15,2026-01-16,2026-01-17,2026-01-18,2026-01-19,2026-01-20,2026-01-21,2026-01-22,2026-01-23,2026-01-24,2026-01-25,2026-01-26,2026-01-27,2026-01-28,2026-01-29,2026-01-30,2026-01-31,2026-02-01,2026-02-02,2026-02-03,2026-02-04,2026-02-05,2026-02-06,2026-02-07,2026-02-08,2026-02-09,2026-02-10,2026-02-11,2026-02-12,2026-02-13,2026-02-14,2026-02-15,2026-02-16,2026-02-17,2026-02-18,2026-02-19,2026-02-20,2026-02-21,2026-02-22,2026-02-23,2026-02-24,2026-02-25,2026-02-26,2026-02-27,2026-02-28,2026-03-01,2026-03-02,2026-03-03,2026-03-04,2026-03-05</Dimension>
        

        
        

        
        
        
        
            <Style>
                <Name>true_colour</Name>
                <Title>True Colour</Title>
                <Abstract>True-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>false_colour</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>Normalised Difference Vegetation Index - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls_ard_3/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>Normalised Difference Water Index - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls_ard_3/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>Modified Normalised Difference Water Index - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls_ard_3/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>nbr</Name>
                <Title>Normalised Burn Ratio - NIR, SWIR</Title>
                <Abstract>Normalised Burn Ratio - a derived index that that uses the differences in the way health green vegetation and burned vegetation reflect light to find burned area</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls_ard_3/nbr/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
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This product takes Sentinel-2 imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change.

The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2A, Sentinel-2B and Sentinel-2C.

This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections.

The resolution is a 10/20/60 m grid based on the ESA Level 1C archive.

Full Sentinel-2A product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2a-msi/
Full Sentinel-2B product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2b-msi/
Full Sentinel-2C product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2c-msi/

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       

        
        

        
        
        
        
            <Style>
                <Name>simple_rgb</Name>
                <Title>Simple RGB</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>Normalised Difference Vegetation Index - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2m_ard_3/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>Normalised Difference Water Index - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2m_ard_3/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>Modified Normalised Difference Water Index - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2m_ard_3/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndci</Name>
                <Title>Normalised Difference Chlorophyll Index - Red Edge, Red</Title>
                <Abstract>Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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            </Style>
        
            <Style>
                <Name>nbr</Name>
                <Title>Normalised Burn Ratio - NIR, SWIR</Title>
                <Abstract>Normalised Burn Ratio - a derived index that that uses the differences in the way health green vegetation and burned vegetation reflect light to find burned area</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>aerosol</Name>
                <Title>Narrow Blue - 440</Title>
                <Abstract>Coastal Aerosol or Narrow Blue band, approximately 435nm to 450nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 490</Title>
                <Abstract>Blue band, approximately 453nm to 511nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, approximately 534nm to 588nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 670</Title>
                <Abstract>Red band, roughly 637nm to 672nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_1</Name>
                <Title>Vegetation Red Edge - 710</Title>
                <Abstract>Near infra-red band, centred on 710nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_2</Name>
                <Title>Vegetation Red Edge - 740</Title>
                <Abstract>Near infra-red band, centred on 740nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_3</Name>
                <Title>Vegetation Red Edge - 780</Title>
                <Abstract>Near infra-red band, centred on 780nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, roughly 853nm to 876nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>narrow_nir</Name>
                <Title>Narrow Near Infrared - 870</Title>
                <Abstract>Near infra-red band, centred on 865nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir1</Name>
                <Title>Shortwave Infrared (SWIR) - 1610</Title>
                <Abstract>Short wave infra-red band 1, roughly 1575nm to 1647nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir2</Name>
                <Title>Shortwave Infrared (SWIR) - 2190</Title>
                <Abstract>Short wave infra-red band 2, roughly 2117nm to 2285nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>fmask</Name>
                <Title>Fmask Classification</Title>
                <Abstract>Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-8 and Sentinel 2 images.</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
                <Name>s2cloudless_mask</Name>
                <Title>S2 Cloudless Mask Classification</Title>
                <Abstract>S2 Cloudless Mask is used for automated cloud masking Sentinel 2 images.</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
                <Name>s2cloudless_prob</Name>
                <Title>S2 Cloudless Mask Probability</Title>
                <Abstract>S2 Cloudless Probabilities given for s2cloudless_mask classification</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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    </Layer>
    
    
        
            
        
    <Layer>
        <Title>DEA Surface Reflectance Calendar Year (Landsat)</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>ga_ls8cls9c_gm_cyear_3</Name>
        <Title>DEA GeoMAD (Landsat 8 &amp; 9 OLI-TIRS)</Title>
        <Abstract>Geoscience Australia Landsat 8 and 9 Geometric Median and Median Absolute Deviation Collection 3

This product provides statistical tools to exploit the time series of Earth Observation data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year.

The geomedian part of the product provides an &#34;average&#34; 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 &#39;second order&#39; high dimensional statistical composite for the given year. The three variance measures show much an area varies from the &#34;average&#34; in terms of &#39;distance&#39; based on factors such as brightness and spectra:

Euclidean distance (EMAD)
Cosine (spectral) distance (SMAD)
Bray Curtis dissimilarity (BCMAD)
Together, they provide information on variance in the landscape over the given year and are useful for change detection applications.

v4.0.0 Changelog:
Cloud masks have been cleaned using a 3-pixel morphological opening on clouds and a 6-pixel dilation on cloud and shadows.
The south-west origin point of the DEA Summary Product Grid has been shifted 18 tiles west and 15 tiles south in this version.
A single combined product for Landsat 8 and 9 is provided. A standalone Landsat 8 product will no longer be provided from calendar year 2022 and onwards.
Band measurement names were updated for consistency with DEA&#39;s ARD products by adding the &#39;nbart&#39; prefix.


For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/

For service status information, see https://status.dea.ga.gov.au</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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            <Style>
                <Name>simple_rgb</Name>
                <Title>Simple RGB</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir1</Name>
                <Title>Shortwave Infrared (SWIR) - 1650</Title>
                <Abstract>Short wave infra-red band 1, centered on 1650nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir2</Name>
                <Title>Shortwave Infrared (SWIR) - 2220</Title>
                <Abstract>Short wave infra-red band 2, centered on 2220nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>arcsec_sdev</Name>
                <Title>Spectral MAD (SMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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            </Style>
        
            <Style>
                <Name>log_edev</Name>
                <Title>Euclidean MAD (EMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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            </Style>
        
            <Style>
                <Name>log_bcdev</Name>
                <Title>Bray-Curtis MAD (BCMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls8cls9c_gm_cyear_3/log_bcdev/legend.png"/>
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            </Style>
        
            <Style>
                <Name>tmad_rgb_std</Name>
                <Title>MADs - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>tmad_rgb_sens</Name>
                <Title>MADs (desert) - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for arid land and desert</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>count</Name>
                <Title>Clear observation count</Title>
                <Abstract>Count of observations included in geomedian/MAD calculations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ga_ls7e_gm_cyear_3</Name>
        <Title>DEA GeoMAD (Landsat 7 ETM+)</Title>
        <Abstract>Geoscience Australia Landsat 7 Geometric Median and Median Absolute Deviation Collection 3

This product provides statistical tools to exploit the time series of Earth Observation data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year.

The geomedian part of the product provides an &#34;average&#34; 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 &#39;second order&#39; high dimensional statistical composite for the given year. The three variance measures show much an area varies from the &#34;average&#34; in terms of &#39;distance&#39; based on factors such as brightness and spectra:

Euclidean distance (EMAD)
Cosine (spectral) distance (SMAD)
Bray Curtis dissimilarity (BCMAD)
Together, they provide information on variance in the landscape over the given year and are useful for change detection applications.

v4.0.0 Changelog:
Cloud masks have been cleaned using a 3-pixel morphological opening on clouds and a 6-pixel dilation on cloud and shadows.
The south-west origin point of the DEA Summary Product Grid has been shifted 18 tiles west and 15 tiles south in this version.
A single combined product for Landsat 8 and 9 is provided. A standalone Landsat 8 product will no longer be provided from calendar year 2022 and onwards.
Band measurement names were updated for consistency with DEA&#39;s ARD products by adding the &#39;nbart&#39; prefix.

For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/

For service status information, see https://status.dea.ga.gov.au</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>simple_rgb</Name>
                <Title>Simple RGB</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
                <Name>blue</Name>
                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
            </Style>
        
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                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
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                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
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                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
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                <Name>swir1</Name>
                <Title>Shortwave Infrared (SWIR) - 1650</Title>
                <Abstract>Short wave infra-red band 1, centered on 1650nm</Abstract>
                
            </Style>
        
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                <Name>swir2</Name>
                <Title>Shortwave Infrared (SWIR) - 2220</Title>
                <Abstract>Short wave infra-red band 2, centered on 2220nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>arcsec_sdev</Name>
                <Title>Spectral MAD (SMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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            </Style>
        
            <Style>
                <Name>log_edev</Name>
                <Title>Euclidean MAD (EMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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                <Name>log_bcdev</Name>
                <Title>Bray-Curtis MAD (BCMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                        <Format>image/png</Format>
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            <Style>
                <Name>tmad_rgb_std</Name>
                <Title>MADs - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                        <Format>image/png</Format>
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            </Style>
        
            <Style>
                <Name>tmad_rgb_sens</Name>
                <Title>MADs (desert) - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for arid land and desert</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>count</Name>
                <Title>Clear observation count</Title>
                <Abstract>Count of observations included in geomedian/MAD calculations</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ga_ls5t_gm_cyear_3</Name>
        <Title>DEA GeoMAD (Landsat 5 TM)</Title>
        <Abstract>Geoscience Australia Landsat 5 Geometric Median and Median Absolute Deviation Collection 3

This product provides statistical tools to exploit the time series of Earth Observation data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year.

The geomedian part of the product provides an &#34;average&#34; 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 &#39;second order&#39; high dimensional statistical composite for the given year. The three variance measures show much an area varies from the &#34;average&#34; in terms of &#39;distance&#39; based on factors such as brightness and spectra:

Euclidean distance (EMAD)
Cosine (spectral) distance (SMAD)
Bray Curtis dissimilarity (BCMAD)
Together, they provide information on variance in the landscape over the given year and are useful for change detection applications.

v4.0.0 Changelog:
Cloud masks have been cleaned using a 3-pixel morphological opening on clouds and a 6-pixel dilation on cloud and shadows.
The south-west origin point of the DEA Summary Product Grid has been shifted 18 tiles west and 15 tiles south in this version.
A single combined product for Landsat 8 and 9 is provided. A standalone Landsat 8 product will no longer be provided from calendar year 2022 and onwards.
Band measurement names were updated for consistency with DEA&#39;s ARD products by adding the &#39;nbart&#39; prefix.

For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/

For service status information, see https://status.dea.ga.gov.au</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>simple_rgb</Name>
                <Title>Simple RGB</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>NDVI - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>NDWI - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
                <Name>mndwi</Name>
                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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            <Style>
                <Name>blue</Name>
                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir1</Name>
                <Title>Shortwave Infrared (SWIR) - 1650</Title>
                <Abstract>Short wave infra-red band 1, centered on 1650nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir2</Name>
                <Title>Shortwave Infrared (SWIR) - 2220</Title>
                <Abstract>Short wave infra-red band 2, centered on 2220nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>arcsec_sdev</Name>
                <Title>Spectral MAD (SMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                <Name>log_edev</Name>
                <Title>Euclidean MAD (EMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                <Name>log_bcdev</Name>
                <Title>Bray-Curtis MAD (BCMAD)</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
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                <Name>tmad_rgb_std</Name>
                <Title>MADs - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for cropland and forest</Abstract>
                
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                        <Format>image/png</Format>
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                <Name>tmad_rgb_sens</Name>
                <Title>MADs (desert) - SMAD, EMAD, BCMAD</Title>
                <Abstract>Good for arid land and desert</Abstract>
                
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                        <Format>image/png</Format>
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                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>count</Name>
                <Title>Clear observation count</Title>
                <Abstract>Count of observations included in geomedian/MAD calculations</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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    </Layer>
    
    
        
    </Layer>
    
    
        
            
        
    <Layer>
        <Title>DEA Surface Reflectance (Landsat)</Title>

        <Abstract>
            Collection 3 represents a consistent processing and upgrade to the Geoscience Australia&#39;s Landsat baseline and derivative products.

For service status information, see https://status.dea.ga.gov.au
        </Abstract>

        
        
            
        
    
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        <Name>ga_ls9c_ard_3</Name>
        <Title>DEA Surface Reflectance (Landsat 9 OLI-TIRS)</Title>
        <Abstract>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</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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       

        
        

        
        
        
        
            <Style>
                <Name>true_colour</Name>
                <Title>True Colour</Title>
                <Abstract>True-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>false_colour</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>Normalised Difference Vegetation Index - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls9c_ard_3/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>Normalised Difference Water Index - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
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                <Title>Modified Normalised Difference Water Index - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
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            <Style>
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                <Title>Normalised Burn Ratio - NIR, SWIR</Title>
                <Abstract>Normalised Burn Ratio - a derived index that that uses the differences in the way health green vegetation and burned vegetation reflect light to find burned area</Abstract>
                
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                        <Format>image/png</Format>
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                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
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                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
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            <Style>
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                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
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                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
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                <Title>Shortwave Infrared (SWIR) - 1650</Title>
                <Abstract>Short wave infra-red band 1, centered on 1650nm</Abstract>
                
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                <Title>Shortwave Infrared (SWIR) - 2220</Title>
                <Abstract>Short wave infra-red band 2, centered on 2220nm</Abstract>
                
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            <Style>
                <Name>fmask</Name>
                <Title>Fmask Classification</Title>
                <Abstract>Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-8 and Sentinel 2 images.</Abstract>
                
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        <Abstract>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</Abstract>
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            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
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       

        
        

        
        
        
        
            <Style>
                <Name>true_colour</Name>
                <Title>True Colour</Title>
                <Abstract>True-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>false_colour</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>Normalised Difference Vegetation Index - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls8c_ard_3/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>Normalised Difference Water Index - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls8c_ard_3/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>Modified Normalised Difference Water Index - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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            <Style>
                <Name>nbr</Name>
                <Title>Normalised Burn Ratio - NIR, SWIR</Title>
                <Abstract>Normalised Burn Ratio - a derived index that that uses the differences in the way health green vegetation and burned vegetation reflect light to find burned area</Abstract>
                
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                        <Format>image/png</Format>
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            <Style>
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                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
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            <Style>
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                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
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            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir1</Name>
                <Title>Shortwave Infrared (SWIR) - 1650</Title>
                <Abstract>Short wave infra-red band 1, centered on 1650nm</Abstract>
                
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            <Style>
                <Name>swir2</Name>
                <Title>Shortwave Infrared (SWIR) - 2220</Title>
                <Abstract>Short wave infra-red band 2, centered on 2220nm</Abstract>
                
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            <Style>
                <Name>fmask</Name>
                <Title>Fmask Classification</Title>
                <Abstract>Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-8 and Sentinel 2 images.</Abstract>
                
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                        <Format>image/png</Format>
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                <Name>panchromatic</Name>
                <Title>Panchromatic - 590</Title>
                <Abstract>Panchromatic, centered on 590nm</Abstract>
                
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                <Name>aerosol</Name>
                <Title>Narrow Blue - 440</Title>
                <Abstract>Coastal Aerosol or Narrow Blue band, approximately 435nm to 450nm</Abstract>
                
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        <Title>DEA Surface Reflectance (Landsat 7 ETM+)</Title>
        <Abstract>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</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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,2022-03-26,2022-03-27,2022-03-28,2022-03-29,2022-03-30,2022-03-31,2022-04-01,2022-04-02,2022-04-03,2022-04-04,2022-04-05,2022-04-06</Dimension>
        

        
        

        
        
        
        
            <Style>
                <Name>true_colour</Name>
                <Title>True Colour</Title>
                <Abstract>True-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>false_colour</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>Normalised Difference Vegetation Index - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls7e_ard_3/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>Normalised Difference Water Index - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls7e_ard_3/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>Modified Normalised Difference Water Index - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls7e_ard_3/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>nbr</Name>
                <Title>Normalised Burn Ratio - NIR, SWIR</Title>
                <Abstract>Normalised Burn Ratio - a derived index that that uses the differences in the way health green vegetation and burned vegetation reflect light to find burned area</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls7e_ard_3/nbr/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir1</Name>
                <Title>Shortwave Infrared (SWIR) - 1650</Title>
                <Abstract>Short wave infra-red band 1, centered on 1650nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir2</Name>
                <Title>Shortwave Infrared (SWIR) - 2220</Title>
                <Abstract>Short wave infra-red band 2, centered on 2220nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>fmask</Name>
                <Title>Fmask Classification</Title>
                <Abstract>Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-8 and Sentinel 2 images.</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls7e_ard_3/fmask/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>panchromatic</Name>
                <Title>Panchromatic - 710</Title>
                <Abstract>Panchromatic, centered on 710nm</Abstract>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ga_ls5t_ard_3</Name>
        <Title>DEA Surface Reflectance (Landsat 5 TM)</Title>
        <Abstract>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</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
        <EX_GeographicBoundingBox>
            <westBoundLongitude>110.68734388386</westBoundLongitude>
            <eastBoundLongitude>156.365649522865</eastBoundLongitude>
            <southBoundLatitude>-44.2624681575318</southBoundLatitude>
            <northBoundLatitude>-6.78664175448088</northBoundLatitude>
        </EX_GeographicBoundingBox>
        
            <BoundingBox CRS="EPSG:3857"
                 
                    
                         minx="12321658.758411258" maxx="17406544.48244481"
                    
                    
                         miny="-5506149.984943489" maxy="-757258.3311971319"
                    
                
            />
        
            <BoundingBox CRS="EPSG:4326"
                 
                     
                         minx="-44.2624681575318" maxx="-6.78664175448088"
                     
                     
                         miny="110.68734388386" maxy="156.365649522865"
                     
                 
            />
        
            <BoundingBox CRS="EPSG:3577"
                 
                    
                         minx="-2153255.2758118785" maxx="2436270.292794993"
                    
                    
                         miny="-4928351.6396267" maxy="-1033180.3363544056"
                    
                
            />
        
            <BoundingBox CRS="EPSG:4238"
                 
                    
                         minx="110.68708752265707" maxx="141.00971094350967"
                    
                    
                         miny="-10.980022673942587" maxy="-6.78666168406529"
                    
                
            />
        
            <BoundingBox CRS="EPSG:3111"
                 
                    
                         minx="2126335.9455075907" maxx="2966077.313809796"
                    
                    
                         miny="2244252.055553195" maxy="2835169.745635876"
                    
                
            />
        
            <BoundingBox CRS="EPSG:32756"
                 
                    
                         minx="168350.36628452106" maxx="831649.6337154789"
                    
                    
                         miny="5094597.184634996" maxy="9249837.042818638"
                    
                
            />
        
            <BoundingBox CRS="WIKID:102171"
                 
                    
                         minx="2126335.9455075907" maxx="2966077.313809796"
                    
                    
                         miny="2244252.055553195" maxy="2835169.745635876"
                    
                
            />
        
        
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       

        
        

        
        
        
        
            <Style>
                <Name>true_colour</Name>
                <Title>True Colour</Title>
                <Abstract>True-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>false_colour</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>Normalised Difference Vegetation Index - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls5t_ard_3/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>Normalised Difference Water Index - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls5t_ard_3/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>Modified Normalised Difference Water Index - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls5t_ard_3/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>nbr</Name>
                <Title>Normalised Burn Ratio - NIR, SWIR</Title>
                <Abstract>Normalised Burn Ratio - a derived index that that uses the differences in the way health green vegetation and burned vegetation reflect light to find burned area</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls5t_ard_3/nbr/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 480</Title>
                <Abstract>Blue band, centered on 480nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir1</Name>
                <Title>Shortwave Infrared (SWIR) - 1650</Title>
                <Abstract>Short wave infra-red band 1, centered on 1650nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir2</Name>
                <Title>Shortwave Infrared (SWIR) - 2220</Title>
                <Abstract>Short wave infra-red band 2, centered on 2220nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>fmask</Name>
                <Title>Fmask Classification</Title>
                <Abstract>Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-8 and Sentinel 2 images.</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls5t_ard_3/fmask/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
    </Layer>
    
    
        
            
        
    <Layer>
        <Title>DEA Surface Reflectance (Sentinel-2)</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>ga_s2am_ard_3</Name>
        <Title>DEA Surface Reflectance (Sentinel-2A MSI)</Title>
        <Abstract>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</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
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,2024-07-20,2024-07-21,2024-07-22,2024-07-23,2024-07-24,2024-07-25,2024-07-26,2024-07-27,2024-07-28,2024-07-29,2024-07-30,2024-07-31,2024-08-01,2024-08-02,2024-08-03,2024-08-04,2024-08-05,2024-08-06,2024-08-07,2024-08-08,2024-08-09,2024-08-10,2024-08-11,2024-08-12,2024-08-13,2024-08-14,2024-08-15,2024-08-16,2024-08-17,2024-08-18,2024-08-19,2024-08-20,2024-08-21,2024-08-22,2024-08-23,2024-08-24,2024-08-25,2024-08-26,2024-08-27,2024-08-28,2024-08-29,2024-08-30,2024-08-31,2024-09-01,2024-09-02,2024-09-03,2024-09-04,2024-09-05,2024-09-06,2024-09-07,2024-09-08,2024-09-09,2024-09-10,2024-09-11,2024-09-12,2024-09-13,2024-09-14,2024-09-15,2024-09-16,2024-09-17,2024-09-18,2024-09-19,2024-09-20,2024-09-21,2024-09-22,2024-09-23,2024-09-24,2024-09-25,2024-09-26,2024-09-27,2024-09-28,2024-09-29,2024-09-30,2024-10-01,2024-10-02,2024-10-03,2024-10-04,2024-10-05,2024-10-06,2024-10-07,2024-10-08,2024-10-09,2024-10-10,2024-10-11,2024-10-12,2024-10-13,2024-10-14,2024-10-15,2024-10-16,2024-10-17,2024-10-18,2024-10-19,2024-10-20,2024-10-21,2024-10-22,2024-10-23,2024-10-24,2024-10-25,2024-10-26,2024-10-27,2024-10-28,2024-10-29,2024-10-30,2024-10-31,2024-11-01,2024-11-02,2024-11-03,2024-11-04,2024-11-05,2024-11-06,2024-11-07,2024-11-08,2024-11-09,2024-11-10,2024-11-11,2024-11-12,2024-11-13,2024-11-14,2024-11-15,2024-11-16,2024-11-17,2024-11-18,2024-11-19,2024-11-20,2024-11-21,2024-11-22,2024-11-23,2024-11-24,2024-11-25,2024-11-26,2024-11-27,2024-11-28,2024-11-29,2024-11-30,2024-12-01,2024-12-02,2024-12-03,2024-12-04,2024-12-05,2024-12-06,2024-12-07,2024-12-08,2024-12-09,2024-12-10,2024-12-11,2024-12-12,2024-12-13,2024-12-14,2024-12-15,2024-12-16,2024-12-17,2024-12-18,2024-12-19,2024-12-20,2024-12-21,2024-12-22,2024-12-23,2024-12-24,2024-12-25,2024-12-26,2024-12-27,2024-12-28,2024-12-29,2024-12-30,2024-12-31,2025-01-01,2025-01-02,2025-01-03,2025-01-04,2025-01-05,2025-01-06,2025-01-07,2025-01-09,2025-01-10,2025-01-11,2025-01-12,2025-01-13,2025-01-14,2025-01-15,2025-01-16,2025-01-17,2025-01-18,2025-01-19,2025-01-20,2025-01-21,2025-03-14,2025-03-15,2025-03-16,2025-03-17,2025-03-18,2025-03-19,2025-03-21,2025-03-22,2025-03-23,2025-03-24,2025-03-25,2025-03-26,2025-03-29,2025-03-30,2025-03-31,2025-04-01,2025-04-02,2025-04-03,2025-04-04,2025-04-05,2025-04-06,2025-04-07,2025-04-08,2025-04-10,2025-04-11,2025-04-12,2025-04-13,2025-04-14,2025-04-15,2025-04-17,2025-04-18,2025-04-19,2025-04-20,2025-04-21,2025-04-22,2025-04-23,2025-04-24,2025-04-25,2025-04-26,2025-04-27,2025-04-28,2025-04-30,2025-05-01,2025-05-02,2025-05-03,2025-05-04,2025-05-05,2025-05-08,2025-05-09,2025-05-10,2025-05-11,2025-05-12,2025-05-13,2025-05-14,2025-05-15,2025-05-16,2025-05-17,2025-05-18,2025-05-20,2025-05-21,2025-05-22,2025-05-23,2025-05-24,2025-05-25,2025-05-27,2025-05-28,2025-05-29,2025-05-30,2025-05-31,2025-06-01,2025-06-02,2025-06-03,2025-06-04,2025-06-05,2025-06-06,2025-06-07,2025-06-09,2025-06-10,2025-06-11,2025-06-12,2025-06-13,2025-06-14,2025-06-16,2025-06-17,2025-06-18,2025-06-19,2025-06-20,2025-06-21,2025-06-22,2025-06-23,2025-06-24,2025-06-25,2025-06-26,2025-06-27,2025-06-29,2025-06-30,2025-07-01,2025-07-02,2025-07-03,2025-07-04,2025-07-06,2025-07-07,2025-07-08,2025-07-09,2025-07-10,2025-07-11,2025-07-12,2025-07-13,2025-07-14,2025-07-15,2025-07-16,2025-07-17,2025-07-19,2025-07-20,2025-07-21,2025-07-22,2025-07-23,2025-07-24,2025-07-26,2025-07-27,2025-07-28,2025-07-29,2025-07-30,2025-07-31,2025-08-01,2025-08-02,2025-08-03,2025-08-04,2025-08-05,2025-08-06,2025-08-08,2025-08-09,2025-08-10,2025-08-11,2025-08-12,2025-08-13,2025-08-15,2025-08-16,2025-08-17,2025-08-18,2025-08-19,2025-08-20,2025-08-21,2025-08-22,2025-08-23,2025-08-24,2025-08-25,2025-08-26,2025-08-28,2025-08-29,2025-08-30,2025-08-31,2025-09-01,2025-09-02,2025-09-04,2025-09-05,2025-09-06,2025-09-07,2025-09-08,2025-09-09,2025-09-10,2025-09-11,2025-09-12,2025-09-13,2025-09-14,2025-09-15,2025-09-17,2025-09-18,2025-09-19,2025-09-20,2025-09-21,2025-09-22,2025-09-24,2025-09-25,2025-09-26,2025-09-27,2025-09-28,2025-09-29,2025-10-01,2025-10-02,2025-10-03,2025-10-04,2025-10-05,2025-10-06,2025-10-07,2025-10-08,2025-10-09,2025-10-10,2025-10-11,2025-10-12,2025-10-14,2025-10-15,2025-10-17,2025-10-18,2025-10-19,2025-10-20,2025-10-21,2025-10-22,2025-10-23,2025-10-24,2025-10-25,2025-10-26,2025-10-27,2025-10-28,2025-10-29,2025-10-30,2025-10-31,2025-11-01,2025-11-03,2025-11-04,2025-11-06,2025-11-07,2025-11-08,2025-11-09,2025-11-10,2025-11-11,2025-11-13,2025-11-14,2025-11-15,2025-11-16,2025-11-17,2025-11-18,2025-11-19,2025-11-20,2025-11-21,2025-11-23,2025-11-24,2025-11-26,2025-11-27,2025-11-28,2025-11-29,2025-11-30,2025-12-01,2025-12-02,2025-12-03,2025-12-04,2025-12-05,2025-12-06,2025-12-07,2025-12-08,2025-12-09,2025-12-10,2025-12-11,2025-12-13,2025-12-16,2025-12-17,2025-12-18,2025-12-19,2025-12-20,2025-12-21,2025-12-22,2025-12-23,2025-12-24,2025-12-25,2025-12-26,2025-12-27,2025-12-28,2025-12-30,2025-12-31,2026-01-03,2026-01-05,2026-01-06,2026-01-07,2026-01-08,2026-01-09,2026-01-10,2026-01-11,2026-01-12,2026-02-02,2026-02-03,2026-02-04,2026-02-05,2026-02-06,2026-02-07,2026-02-09,2026-02-10,2026-02-11,2026-02-12,2026-02-13,2026-02-14,2026-02-15,2026-02-16,2026-02-17,2026-02-19,2026-02-20,2026-02-21,2026-02-22,2026-02-23,2026-02-24,2026-02-25,2026-02-26</Dimension>
        

        
        

        
        
        
        
            <Style>
                <Name>simple_rgb</Name>
                <Title>Simple RGB</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>Normalised Difference Vegetation Index - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2am_ard_3/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>Normalised Difference Water Index - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2am_ard_3/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>Modified Normalised Difference Water Index - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2am_ard_3/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndci</Name>
                <Title>Normalised Difference Chlorophyll Index - Red Edge, Red</Title>
                <Abstract>Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2am_ard_3/ndci/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>nbr</Name>
                <Title>Normalised Burn Ratio - NIR, SWIR</Title>
                <Abstract>Normalised Burn Ratio - a derived index that that uses the differences in the way health green vegetation and burned vegetation reflect light to find burned area</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2am_ard_3/nbr/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>aerosol</Name>
                <Title>Narrow Blue - 440</Title>
                <Abstract>Coastal Aerosol or Narrow Blue band, approximately 435nm to 450nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 490</Title>
                <Abstract>Blue band, approximately 453nm to 511nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, approximately 534nm to 588nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 670</Title>
                <Abstract>Red band, roughly 637nm to 672nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_1</Name>
                <Title>Vegetation Red Edge - 710</Title>
                <Abstract>Near infra-red band, centred on 710nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_2</Name>
                <Title>Vegetation Red Edge - 740</Title>
                <Abstract>Near infra-red band, centred on 740nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_3</Name>
                <Title>Vegetation Red Edge - 780</Title>
                <Abstract>Near infra-red band, centred on 780nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, roughly 853nm to 876nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>narrow_nir</Name>
                <Title>Narrow Near Infrared - 870</Title>
                <Abstract>Near infra-red band, centred on 865nm</Abstract>
                
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                <Title>Fmask Classification</Title>
                <Abstract>Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-8 and Sentinel 2 images.</Abstract>
                
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                <Abstract>S2 Cloudless Mask is used for automated cloud masking Sentinel 2 images.</Abstract>
                
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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</Abstract>
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            <Keyword>HLTC</Keyword>
            
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-12-28,2024-12-29,2024-12-30,2024-12-31,2025-01-01,2025-01-02,2025-01-03,2025-01-04,2025-01-05,2025-01-06,2025-01-07,2025-01-08,2025-01-09,2025-01-10,2025-01-11,2025-01-12,2025-01-13,2025-01-14,2025-01-15,2025-01-16,2025-01-17,2025-01-18,2025-01-19,2025-01-20,2025-01-21,2025-01-22,2025-01-23,2025-01-24,2025-01-25,2025-01-26,2025-01-27,2025-01-28,2025-01-29,2025-01-30,2025-01-31,2025-02-01,2025-02-02,2025-02-03,2025-02-04,2025-02-05,2025-02-06,2025-02-07,2025-02-08,2025-02-09,2025-02-10,2025-02-11,2025-02-12,2025-02-13,2025-02-14,2025-02-15,2025-02-16,2025-02-17,2025-02-18,2025-02-19,2025-02-20,2025-02-21,2025-02-22,2025-02-23,2025-02-24,2025-02-25,2025-02-26,2025-02-27,2025-02-28,2025-03-01,2025-03-02,2025-03-03,2025-03-04,2025-03-05,2025-03-06,2025-03-07,2025-03-08,2025-03-09,2025-03-10,2025-03-11,2025-03-12,2025-03-13,2025-03-14,2025-03-15,2025-03-16,2025-03-17,2025-03-18,2025-03-19,2025-03-20,2025-03-21,2025-03-22,2025-03-23,2025-03-24,2025-03-25,2025-03-26,2025-03-27,2025-03-28,2025-03-29,2025-03-30,2025-03-31,2025-04-01,2025-04-02,2025-04-04,2025-04-05,2025-04-06,2025-04-07,2025-04-08,2025-04-09,2025-04-10,2025-04-11,2025-04-12,2025-04-13,2025-04-14,2025-04-15,2025-04-16,2025-04-17,2025-04-18,2025-04-19,2025-04-20,2025-04-21,2025-04-22,2025-04-23,2025-04-24,2025-04-25,2025-04-26,2025-04-27,2025-04-28,2025-04-29,2025-04-30,2025-05-01,2025-05-02,2025-05-03,2025-05-04,2025-05-05,2025-05-06,2025-05-07,2025-05-08,2025-05-09,2025-05-10,2025-05-11,2025-05-12,2025-05-13,2025-05-14,2025-05-15,2025-05-16,2025-05-17,2025-05-18,2025-05-19,2025-05-20,2025-05-21,2025-05-22,2025-05-23,2025-05-24,2025-05-25,2025-05-26,2025-05-27,2025-05-28,2025-05-29,2025-05-30,2025-05-31,2025-06-01,2025-06-02,2025-06-03,2025-06-04,2025-06-05,2025-06-06,2025-06-07,2025-06-08,2025-06-09,2025-06-10,2025-06-11,2025-06-12,2025-06-13,2025-06-14,2025-06-15,2025-06-16,2025-06-17,2025-06-18,2025-06-19,2025-06-20,2025-06-21,2025-06-22,2025-06-23,2025-06-24,2025-06-25,2025-06-26,2025-06-27,2025-06-28,2025-06-29,2025-06-30,2025-07-01,2025-07-02,2025-07-03,2025-07-04,2025-07-05,2025-07-06,2025-07-07,2025-07-08,2025-07-09,2025-07-10,2025-07-11,2025-07-12,2025-07-13,2025-07-14,2025-07-15,2025-07-16,2025-07-17,2025-07-18,2025-07-19,2025-07-20,2025-07-21,2025-07-22,2025-07-23,2025-07-24,2025-07-25,2025-07-26,2025-07-27,2025-07-28,2025-07-29,2025-07-30,2025-07-31,2025-08-01,2025-08-02,2025-08-03,2025-08-04,2025-08-05,2025-08-06,2025-08-07,2025-08-08,2025-08-09,2025-08-10,2025-08-11,2025-08-12,2025-08-13,2025-08-14,2025-08-15,2025-08-16,2025-08-17,2025-08-18,2025-08-19,2025-08-20,2025-08-21,2025-08-22,2025-08-23,2025-08-24,2025-08-25,2025-08-26,2025-08-27,2025-08-28,2025-08-29,2025-08-30,2025-08-31,2025-09-01,2025-09-02,2025-09-03,2025-09-04,2025-09-05,2025-09-06,2025-09-07,2025-09-08,2025-09-09,2025-09-10,2025-09-11,2025-09-12,2025-09-13,2025-09-14,2025-09-15,2025-09-16,2025-09-17,2025-09-18,2025-09-19,2025-09-20,2025-09-21,2025-09-22,2025-09-23,2025-09-24,2025-09-25,2025-09-26,2025-09-27,2025-09-28,2025-09-29,2025-09-30,2025-10-01,2025-10-02,2025-10-03,2025-10-04,2025-10-05,2025-10-06,2025-10-07,2025-10-08,2025-10-09,2025-10-10,2025-10-11,2025-10-12,2025-10-13,2025-10-14,2025-10-15,2025-10-16,2025-10-17,2025-10-18,2025-10-19,2025-10-20,2025-10-21,2025-10-22,2025-10-23,2025-10-24,2025-10-25,2025-10-26,2025-10-27,2025-10-28,2025-10-29,2025-10-30,2025-10-31,2025-11-01,2025-11-02,2025-11-03,2025-11-04,2025-11-05,2025-11-06,2025-11-07,2025-11-08,2025-11-09,2025-11-10,2025-11-11,2025-11-12,2025-11-13,2025-11-14,2025-11-15,2025-11-16,2025-11-17,2025-11-18,2025-11-19,2025-11-20,2025-11-21,2025-11-22,2025-11-23,2025-11-24,2025-11-25,2025-11-26,2025-11-27,2025-11-28,2025-11-29,2025-11-30,2025-12-01,2025-12-02,2025-12-03,2025-12-04,2025-12-05,2025-12-06,2025-12-07,2025-12-08,2025-12-09,2025-12-10,2025-12-11,2025-12-12,2025-12-13,2025-12-14,2025-12-15,2025-12-16,2025-12-17,2025-12-18,2025-12-19,2025-12-20,2025-12-21,2025-12-22,2025-12-23,2025-12-24,2025-12-25,2025-12-26,2025-12-27,2025-12-28,2025-12-29,2025-12-30,2025-12-31,2026-01-01,2026-01-02,2026-01-03,2026-01-04,2026-01-05,2026-01-06,2026-01-07,2026-01-08,2026-01-09,2026-01-10,2026-01-11,2026-01-12,2026-01-13,2026-01-14,2026-01-15,2026-01-16,2026-01-17,2026-01-18,2026-01-19,2026-01-20,2026-01-21,2026-01-22,2026-01-23,2026-01-24,2026-01-25,2026-01-26,2026-01-27,2026-01-28,2026-01-30,2026-01-31,2026-02-01,2026-02-02,2026-02-03,2026-02-04,2026-02-05,2026-02-06,2026-02-07,2026-02-08,2026-02-09,2026-02-10,2026-02-11,2026-02-12,2026-02-13,2026-02-14,2026-02-15,2026-02-16,2026-02-17,2026-02-18,2026-02-19,2026-02-20,2026-02-21,2026-02-22,2026-02-23,2026-02-24,2026-02-25,2026-02-26,2026-02-27,2026-02-28,2026-03-01,2026-03-02,2026-03-03,2026-03-04,2026-03-05</Dimension>
        

        
        

        
        
        
        
            <Style>
                <Name>simple_rgb</Name>
                <Title>Simple RGB</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
        
            <Style>
                <Name>infrared_green</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>ndvi</Name>
                <Title>Normalised Difference Vegetation Index - Red, NIR</Title>
                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2bm_ard_3/ndvi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndwi</Name>
                <Title>Normalised Difference Water Index - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2bm_ard_3/ndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>mndwi</Name>
                <Title>Modified Normalised Difference Water Index - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2bm_ard_3/mndwi/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>ndci</Name>
                <Title>Normalised Difference Chlorophyll Index - Red Edge, Red</Title>
                <Abstract>Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2bm_ard_3/ndci/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>nbr</Name>
                <Title>Normalised Burn Ratio - NIR, SWIR</Title>
                <Abstract>Normalised Burn Ratio - a derived index that that uses the differences in the way health green vegetation and burned vegetation reflect light to find burned area</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2bm_ard_3/nbr/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>aerosol</Name>
                <Title>Narrow Blue - 440</Title>
                <Abstract>Coastal Aerosol or Narrow Blue band, approximately 435nm to 450nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>blue</Name>
                <Title>Blue - 490</Title>
                <Abstract>Blue band, approximately 453nm to 511nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>green</Name>
                <Title>Green - 560</Title>
                <Abstract>Green band, approximately 534nm to 588nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red</Name>
                <Title>Red - 670</Title>
                <Abstract>Red band, roughly 637nm to 672nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_1</Name>
                <Title>Vegetation Red Edge - 710</Title>
                <Abstract>Near infra-red band, centred on 710nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_2</Name>
                <Title>Vegetation Red Edge - 740</Title>
                <Abstract>Near infra-red band, centred on 740nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>red_edge_3</Name>
                <Title>Vegetation Red Edge - 780</Title>
                <Abstract>Near infra-red band, centred on 780nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>nir</Name>
                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, roughly 853nm to 876nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>narrow_nir</Name>
                <Title>Narrow Near Infrared - 870</Title>
                <Abstract>Near infra-red band, centred on 865nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir1</Name>
                <Title>Shortwave Infrared (SWIR) - 1610</Title>
                <Abstract>Short wave infra-red band 1, roughly 1575nm to 1647nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>swir2</Name>
                <Title>Shortwave Infrared (SWIR) - 2190</Title>
                <Abstract>Short wave infra-red band 2, roughly 2117nm to 2285nm</Abstract>
                
            </Style>
        
            <Style>
                <Name>fmask</Name>
                <Title>Fmask Classification</Title>
                <Abstract>Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-8 and Sentinel 2 images.</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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                <Abstract>S2 Cloudless Mask is used for automated cloud masking Sentinel 2 images.</Abstract>
                
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        <Title>DEA Surface Reflectance (Sentinel-2C MSI)</Title>
        <Abstract>Geoscience Australia Sentinel-2C MSI Analysis Ready Data Collection 3

This product takes Sentinel-2C imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change.

The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2C.

This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections.

It contains two sub-products that provide corrections or attribution information:

   * DEA Surface Reflectance NBART (Sentinel-2C MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-sentinel-2c-msi/
   * DEA Surface Reflectance OA (Sentinel-2C MSI) https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-sentinel-2c-msi/

The resolution is a 10/20/60 m grid based on the ESA Level 1C archive.

Full product description: https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2c-msi/

For service status information, see https://status.dea.ga.gov.au</Abstract>
        <KeywordList>
            
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            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
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                <Title>Simple RGB</Title>
                <Abstract>Simple true-colour image, using the red, green and blue bands</Abstract>
                
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                <Name>infrared_green</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
                <Abstract>False Colour image with SWIR1-&gt;Red, NIR-&gt;Green, and Green-&gt;Blue</Abstract>
                
            </Style>
        
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                <Abstract>Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation</Abstract>
                
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                        <Format>image/png</Format>
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                <Title>Normalised Difference Water Index - Green, NIR</Title>
                <Abstract>Normalised Difference Water Index - a derived index that correlates well with the existence of water (McFeeters 1996)</Abstract>
                
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                        <Format>image/png</Format>
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                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
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                <Title>Normalised Difference Chlorophyll Index - Red Edge, Red</Title>
                <Abstract>Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll</Abstract>
                
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                <Abstract>Coastal Aerosol or Narrow Blue band, approximately 435nm to 450nm</Abstract>
                
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                <Title>Blue - 490</Title>
                <Abstract>Blue band, approximately 453nm to 511nm</Abstract>
                
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                <Title>Green - 560</Title>
                <Abstract>Green band, approximately 534nm to 588nm</Abstract>
                
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                <Abstract>Near infra-red band, roughly 853nm to 876nm</Abstract>
                
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                <Abstract>Near infra-red band, centred on 865nm</Abstract>
                
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                <Title>Shortwave Infrared (SWIR) - 1610</Title>
                <Abstract>Short wave infra-red band 1, roughly 1575nm to 1647nm</Abstract>
                
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                <Title>Shortwave Infrared (SWIR) - 2190</Title>
                <Abstract>Short wave infra-red band 2, roughly 2117nm to 2285nm</Abstract>
                
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                <Name>fmask</Name>
                <Title>Fmask Classification</Title>
                <Abstract>Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-8 and Sentinel 2 images.</Abstract>
                
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                <Title>S2 Cloudless Mask Classification</Title>
                <Abstract>S2 Cloudless Mask is used for automated cloud masking Sentinel 2 images.</Abstract>
                
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                <Title>S2 Cloudless Mask Probability</Title>
                <Abstract>S2 Cloudless Probabilities given for s2cloudless_mask classification</Abstract>
                
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        <Title>Land and Vegetation</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
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        <Title>DEA Land Cover</Title>

        <Abstract>
            
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        <Title>DEA Land Cover Calendar Year (Landsat)</Title>
        <Abstract>DEA Land Cover Calendar Year (Landsat)

Land cover is the observed physical cover on the Earth&#39;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</Abstract>
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            <Keyword>australia</Keyword>
            
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            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
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Land cover is the observed physical cover on the Earth&#39;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</Abstract>
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        <Title>DEA Land Cover Collection 3 Calendar Year (Landsat)</Title>
        <Abstract>DEA Land Cover Collection 3 Calendar Year (Landsat)

Land cover is the observed physical cover on the Earth&#39;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</Abstract>
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            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
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        <Abstract>DEA Land Cover Environmental Descriptors

Land cover is the observed physical cover on the Earth&#39;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</Abstract>
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            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
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        <Abstract>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</Abstract>
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            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
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                         miny="-10.980022673942587" maxy="-7.630176405363705"
                    
                
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            <BoundingBox CRS="EPSG:3111"
                 
                    
                         minx="2126335.9455075907" maxx="2966077.313809796"
                    
                    
                         miny="2244252.055553195" maxy="2835169.745635876"
                    
                
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                         minx="2126335.9455075907" maxx="2966077.313809796"
                    
                    
                         miny="2244252.055553195" maxy="2835169.745635876"
                    
                
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       

        
        

        
        
        
        
            <Style>
                <Name>fc_rgb</Name>
                <Title>Three-band Fractional Cover</Title>
                <Abstract>Fractional cover medians - red is bare soil, green is green vegetation and blue is non-green vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls_fc_3/fc_rgb/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>bare_ground_c3</Name>
                <Title>Bare Ground</Title>
                <Abstract>Bare Soil</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls_fc_3/bare_ground_c3/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>green_veg_c3</Name>
                <Title>Green Vegetation</Title>
                <Abstract>Green Vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls_fc_3/green_veg_c3/legend.png"/>
                    </LegendURL>
                
            </Style>
        
            <Style>
                <Name>non_green_veg_c3</Name>
                <Title>Non-Green vegetation</Title>
                <Abstract>Non Green Vegetation</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_ls_fc_3/non_green_veg_c3/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ga_ls_fc_pc_cyear_3</Name>
        <Title>DEA Fractional Cover Percentiles Calendar Year (Landsat)</Title>
        <Abstract>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</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
        <EX_GeographicBoundingBox>
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        <Title>DEA Mangroves</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    
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        <Name>ga_ls_mangrove_cover_cyear_3</Name>
        <Title>DEA Mangroves (Landsat)</Title>
        <Abstract>Mangrove Canopy Cover 30m (Landsat, Collection 3)

Mangrove canopy cover version, 30 metre. Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region.
The mangrove canopy cover product provides valuable information about the extent and canopy density of mangroves for each full year between 1987 and the present for the entire Australian coastline.
The canopy cover classes are:
20-50% (pale green), 50-80% (mid green), 80-100% (dark green).
The product consists of  a sequence (one per year) of 30 meter resolution maps that are generated by analysing the Landsat fractional cover (https://doi.org/10.6084/m9.figshare.94250.v1) developed by the Joint Remote Sensing Research Program and the Global Mangrove Watch layers (https://doi.org/10.1071/MF13177) developed by the Japanese Aerospace Exploration Agency.

https://knowledge.dea.ga.gov.au/data/product/dea-mangrove-canopy-cover-landsat/

For service status information, see https://status.dea.ga.gov.au</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
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        <Title>Barest Earth</Title>

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    <Layer queryable="1">
        <Name>s2_barest_earth</Name>
        <Title>GA Barest Earth (Sentinel-2)</Title>
        <Abstract>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 &#34;Landsat barest Earth&#34;.  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</Abstract>
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            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
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                <Name>infrared_green</Name>
                <Title>False colour - Green, SWIR, NIR</Title>
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                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
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                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
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                <Title>Vegetation Red Edge 1 - 705</Title>
                <Abstract>Vegetation Red Edge, centered on 705nm</Abstract>
                
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                <Title>Vegetation Red Edge 3 - 783</Title>
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        <Title>GA Barest Earth (Landsat 8 OLI/TIRS)</Title>
        <Abstract>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</Abstract>
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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&#39;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</Abstract>
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            <Keyword>bare-earth</Keyword>
            
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        <Abstract>
            
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        <Title>DEA Wetness Percentiles (Landsat)</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    
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        <Title>DEA Wetness Percentiles (Landsat)</Title>
        <Abstract>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 &#39;wetness&#39; index values.
One of the limitations of using the Tasseled Cap wetness index is that it will identify all &#39;wet&#39; 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</Abstract>
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            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
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        <Abstract>
            
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        <Abstract>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 &#39;wetness&#39; index values.
One of the limitations of using the Tasseled Cap wetness index is that it will identify all &#39;wet&#39; 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</Abstract>
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            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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            Digital Earth Australia (DEA) Water Observations from Space (WOfS)
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        <Abstract>**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</Abstract>
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       

        
        

        
        
        
        
            <Style>
                <Name>observations</Name>
                <Title>Water Observations</Title>
                <Abstract>Observations</Abstract>
                
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                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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            <Style>
                <Name>wet</Name>
                <Title>Wet Only</Title>
                <Abstract>Wet Only</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
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        <Name>ga_s2_wo_provisional_3</Name>
        <Title>DEA Water Observations Provisional (Sentinel-2 NRT)</Title>
        <Abstract>**Prototype Geoscience Australia Sentinel 2 Near Real Time Water Observations Provisional Collection 3**


This product is the implementation of the DEA Water Observations (DEA WO) product (previously known as Water Observations from Space, or WOfS) on the Geoscience Australia Sentinel-2 Near Real Time surface reflectance product. This is a rapid, provisional, product. It has not been validated and is of unknown accuracy.

The Provisional Digital Earth Australia Water Observations (Sentinel-2) product shows where surface water was observed by the Sentinel 2A and Sentinel 2B satellites on any particular day over the most recent 3 months. The surface water observations are derived from Geoscience Australia Sentinel-2 Near Real Time surface reflectance imagery for all of Australia. The provisional, Near Real Time product is available for a rolling window of the most recent three months of data, and is produced within 24 hours of the satellite passing over an area.

For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-sentinel-2-nrt/</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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       

        
        

        
        
        
        
            <Style>
                <Name>observations</Name>
                <Title>Water Observations</Title>
                <Abstract>Observations</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2_wo_provisional_3/observations/legend.png"/>
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            </Style>
        
            <Style>
                <Name>wet</Name>
                <Title>Wet Only</Title>
                <Abstract>Wet Only</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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        <Abstract>**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</Abstract>
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            <Keyword>landsat</Keyword>
            
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            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
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        <Name>ga_ls_wo_fq_cyear_3</Name>
        <Title>DEA Water Observations Calendar Year (Landsat)</Title>
        <Abstract>**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
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            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
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        <Abstract>**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
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            <Keyword>burn characteristics</Keyword>
            
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            <Keyword>time-series</Keyword>
            
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        <Abstract>**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</Abstract>
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            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
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            <Style>
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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</Abstract>
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            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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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
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            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
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        <Abstract>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</Abstract>
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        <Abstract>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</Abstract>
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                <Title>Water Summary (Blue)</Title>
                <Abstract>WOfS summary showing the frequency of Wetness</Abstract>
                
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        <Name>wofs_filtered_summary_confidence</Name>
        <Title>DEA Multi-Year Water Observation Confidence Statistics (Landsat, DEPRECATED)</Title>
        <Abstract>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
</Abstract>
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            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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                <Title>Confidence</Title>
                <Abstract>WOfS Confidence</Abstract>
                
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    <Layer>
        <Title>Sea, ocean and coast</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    <Layer>
        <Title>DEA Intertidal</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    
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        <Title>DEA Intertidal (Sentinel-2, Landsat)</Title>
        <Abstract>Geoscience Australia Sentinel-2 Landsat Intertidal Calendar Year Collection 3

The DEA Intertidal product suite maps the changing extent, elevation and topography of Australia&#39;s exposed intertidal zone, the complex zone that defines the interface between land and sea.

Incorporating both Sentinel-2 and Landsat data, the product suite provides an annual 10 m resolution elevation product for the intertidal zone, enabling users to better monitor and understand some of the most dynamic regions of Australia’s coastlines. Utilising an improved tidal modelling capability, the product suite includes a continental scale mapping of intertidal exposure over time, enabling scientists and managers to integrate the data into ecological and migratory species applications and modelling.

Product documentation: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/

Service status information: https://status.dea.ga.gov.au</Abstract>
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            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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                <Title>Elevation</Title>
                <Abstract>Intertidal elevation in metres above Mean Sea Level</Abstract>
                
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                <Title>Elevation uncertainty</Title>
                <Abstract>Intertidal elevation uncertainty</Abstract>
                
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                <Title>Exposure</Title>
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                <Title>Extents</Title>
                <Abstract>Intertidal Extents classification</Abstract>
                
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                <Title>Clear observation count</Title>
                <Abstract>Count of satellite observations included in DEA Intertidal</Abstract>
                
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    <Layer>
        <Title>DEA Tidal Composites</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>ga_s2_tidal_composites_cyear_3</Name>
        <Title>DEA Tidal Composites (Sentinel-2)</Title>
        <Abstract>Geoscience Australia Sentinel-2 Tidal Composites Calendar Year Collection 3

Digital Earth Australia (DEA) Tidal Composites are cloud-free imagery mosaics of Australia&#39;s coasts, estuaries and reefs at low and high tide. Calculated using a geometric median of Sentinel-2 imagery from the highest and lowest 15% of observed tides, DEA Tidal Composites deliver an annually updated snapshot of high and low tide Australian coastal environments.

Product documentation: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/

Service status information: https://status.dea.ga.gov.au</Abstract>
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            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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                <Title>True colour – Low tide</Title>
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                <Name>low_false</Name>
                <Title>False colour – Low tide</Title>
                <Abstract>Low tide false colour image, using the SWIR2, Green and Blue bands</Abstract>
                
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                <Name>high_false</Name>
                <Title>False colour – High tide</Title>
                <Abstract>High tide false colour image, using the SWIR2, Green and Blue bands</Abstract>
                
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                <Name>low_mndwi</Name>
                <Title>MNDWI – Low tide</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
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                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
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    <Layer>
        <Title>DEA Coastal Ecosystems</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    
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        <Name>ga_s2_coastalecosystems_cyear_3_v1</Name>
        <Title>DEA Coastal Ecosystems (Sentinel-2)</Title>
        <Abstract>Geoscience Australia Sentinel-2 Coastal Ecosystems Calendar Year Collection 3

The Digital Earth Australia Coastal Ecosystems product suite includes a categorical classification of four coastal ecosystems, supported by probability layers for individual ecosystems and selected quality assurance layers to support product interpretation.

Product documentation: https://knowledge.dea.ga.gov.au/data/product/dea-coastal-ecosystems/

Service status information: https://status.dea.ga.gov.au</Abstract>
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            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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                <Name>coastalecosystems_classification</Name>
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                <Abstract>Coastal Ecosystems classification</Abstract>
                
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This product is deprecated; users are advised to transition to using DEA Intertidal instead: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/

For service status information, see https://status.dea.ga.gov.au</Abstract>
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This product is deprecated; users are advised to transition to using DEA Intertidal instead: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/

For service status information, see https://status.dea.ga.gov.au</Abstract>
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This product is deprecated; users are advised to transition to using DEA Intertidal instead: https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/

For service status information, see https://status.dea.ga.gov.au</Abstract>
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    <Layer>
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This product is deprecated; users are advised to transition to using DEA Tidal Composites instead: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/

For service status information, see https://status.dea.ga.gov.au
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This product is deprecated; users are advised to transition to using DEA Tidal Composites instead: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/

For service status information, see https://status.dea.ga.gov.au</Abstract>
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This product is deprecated; users are advised to transition to using DEA Tidal Composites instead: https://knowledge.dea.ga.gov.au/data/product/dea-tidal-composites/

For service status information, see https://status.dea.ga.gov.au</Abstract>
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                <Title>MNDWI - Green, SWIR</Title>
                <Abstract>Modified Normalised Difference Water Index - a derived index that correlates well with the existence of water (Xu 2006)</Abstract>
                
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                <Title>Blue - 490</Title>
                <Abstract>Blue band, centered on 490nm</Abstract>
                
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                <Title>Green - 560</Title>
                <Abstract>Green band, centered on 560nm</Abstract>
                
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                <Title>Red - 660</Title>
                <Abstract>Red band, centered on 660nm</Abstract>
                
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                <Title>Near Infrared (NIR) - 840</Title>
                <Abstract>Near infra-red band, centered on 840nm</Abstract>
                
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                <Title>Shortwave Infrared (SWIR) - 1650</Title>
                <Abstract>Short wave infra-red band 1, centered on 1650nm</Abstract>
                
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            <Style>
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                <Title>Shortwave Infrared (SWIR) - 2220</Title>
                <Abstract>Short wave infra-red band 2, centered on 2220nm</Abstract>
                
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    <Layer>
        <Title>Hazards</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    <Layer>
        <Title>DEA Burnt Area Characteristic Layers (Deprecated, Sentinel 2 Near Real-Time)</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>ga_s2_ba_provisional_3</Name>
        <Title>DEA Burnt Area Characteristic Layers (Deprecated, Sentinel 2 Near Real-Time)</Title>
        <Abstract>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 &#34;Unburnt&#34;, &#34;Low to Medium&#34; and &#34;High to Severe&#34;. Colour coding was established by UN-SPIDER.

For service status information, see https://status.dea.ga.gov.au</Abstract>
        <KeywordList>
            
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            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ga_s2_dnbr_provisional_3</Name>
                <Title>Delta Normalized Burn Ratio (dNBR)</Title>
                <Abstract>Delta Normalized Burn Ratio (dNBR) based on Sentinel 2 Near Real-Time (Provisional) Surface Reflectance and Sentinel 2 Barest Earth Data.

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 (Wagtendonk et al. 2004).

NBR is calculated as: NBR = (NIR – SWIR2) / (NIR + SWIR2).
Delta NBR shows the change in burn characteristics as the difference between a baseline (baseline-NBR) and a post event (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 rapidly identify areas that are characteristic of burns for all of Australia, and on a near real-time basis, this dNBR product is based on two input datasets:
    1) The baseline NBR is calculated on DEA Sentinel 2 Barest Earth (Roberts, et al. 2019).
    2) The post event NBR (NRT-NBR) is calculated on the latest daily Sentinel-2 (A and B combined) Near Real-Time provisional data. The Near Real-Time capability provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections.

The dNBR data is a general screening tool to identify areas that are likely to have undergone burn. dNBR is a basic metric that can be used to identify areas that show characteristics of burn, but there is no guarantee that values are precise. For example, Landcover change, such as deforestation or changes to water bodies, can often be identified as false positives.

The delta NBR layer contains values between -1 and +1, with positive values being more indicative that an area has undergone a burn. This metric should be used in combination with the other datasets to strengthen the agreement that the area has indeed been burnt.

For service status information, see https://status.dea.ga.gov.au</Abstract>
                
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            <Style>
                <Name>ga_s2_dnbrclasses_provisional_3</Name>
                <Title>Delta Normalized Burn Ratio (dNBR) Classes</Title>
                <Abstract>Delta Normalized Burn Ratio (DNBR) based on Sentinel 2 Near Real-Time (Provisional) Surface Reflectance and Sentinel 2 Barest Earth Data.

Burn classes and thresholds are a simplified version of those proposed by USGS, as &#34;Unburnt&#34;, &#34;Low to Medium&#34; and &#34;High to Severe&#34;. Colour coding was established by UN-SPIDER.

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 (Wagtendonk et al. 2004).

NBR is calculated as: NBR = (NIR – SWIR2) / (NIR + SWIR2).
Delta NBR (dNBR) shows the change in burn characteristics as the difference between a baseline (baseline-NBR) and post-event (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 rapidly identify areas that are characteristic of burns for all of Australia, and on a near real-time basis, this dNBR product is based on two input datasets:
    1) The baseline NBR is calculated on DEA Sentinel 2 Barest Earth (Roberts, et al. 2019).
    2) The post event NBR (NRT-NBR) is calculated on the latest daily Sentinel-2 (A and B combined) Near Real-Time provisional data. The Near Real-Time capability provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections.

This dNBR data is a general screening tool to identify areas that are likely to have undergone burn. dNBR is a basic metric that can be used to identify areas that show characteristics of burn, but there is no guarantee that values are precise. For example, Landcover change, such as deforestation or changes to water bodies, can often be identified as false positives.

The delta NBR layer contains values between -1 and +1, with positive values being more indicative that an area has undergone a burn. This metric should be used in combination with the other datasets to strengthen the agreement that the area has indeed been burnt.

For service status information, see https://status.dea.ga.gov.au</Abstract>
                
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            <Style>
                <Name>ga_s2_dbsi_provisional_3</Name>
                <Title>Delta Bare Soil Index (dBSI)</Title>
                <Abstract>Delta Bare Soil Index (dBSI) based on Sentinel 2 Near Real-Time (Provisional) Surface Reflectance and Sentinel 2 Barest Earth Data.

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 a change in soil characteristics as the difference between a baseline (baseline-BSI) and post event (NRT-BSI). The change (delta) in BSI is calculated as: dBSI = (baseline-BSI - NRT-BSI) * -1.
dBSI is multiplied by negative 1 in order to reverse the scale so that dBSI values are presented the same way as the other delta indexes, which both have positive values for areas that show burnt characteristics.

This dBSI product is based on two input datasets:
    1) The baseline BSI is calculated on DEA Sentinel 2 Barest Earth (Roberts, et al. 2019).
    2) The post event (NRT-BSI) is calculated on the latest daily Sentinel-2 (A and B combined) Near Real-Time provisional data. The Near Real-Time capability provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections.
The dBSI layer contains values between -1 and +1, with a positive value being more indicative that an area has increased exposure of bare soil, and therefore the characteristics a burn.
This metric should be used in combination with the other datasets to strengthen the agreement that the area has indeed been burnt.

For service status information, see https://status.dea.ga.gov.au</Abstract>
                
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            <Style>
                <Name>ga_s2_dndvi_provisional_3</Name>
                <Title>Delta Normalized Difference Vegetation Index (dNDVI)</Title>
                <Abstract>Delta Normalized Difference Vegetation Index (dNDVI) based on Sentinel 2 Near Real-Time (Provisional) Surface Reflectance and Sentinel 2 Barest Earth Data.

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 reference baseline (baseline-NDVI) and post event (NRT-NDVI). The change (delta) in NDVI is calculated as: dNDVI = baseline-NDVI - NRT-NDVI.

This dNDVI product is based on two input datasets:
    1) The baseline NDVI is calculated on DEA Sentinel 2 Barest Earth (Roberts, et al. 2019).
    2) The post event NDVI is calculated on the latest daily Sentinel-2 (A and B combined) Near Real-Time provisional data. The Near Real-Time capability provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections.
The dNDVI layer contains values between -1 and +1, with positive values being more indicative that the presence of green vegetation in an area has decreased, and therefore showing the characteristics of a burn.
This metric should be used in combination with the other datasets to strengthen the agreement that the area has indeed been burnt.

For service status information, see https://status.dea.ga.gov.au</Abstract>
                
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    </Layer>
    
    
        
    </Layer>
    
    
        
            
        
    <Layer>
        <Title>DEA Fuel Moisture Content</Title>

        <Abstract>
            Digital Earth Australia (DEA) Fuel Moisture Content (FMC)
        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>ga_s2_fmc_layer</Name>
        <Title>DEA FMC Sentinel-2 (A, B &amp; C)</Title>
        <Abstract>DEA FMC Sentinel-2 (A, B &amp; C)

This product calculates the Fuel Moisture Content (FMC) of vegetation which is the percentage of water mass relative to dry mass in living vegetation.

Values range from 0–300%, representing, by weight percent, the amount of water in leaves compared to dry plant material. A value of 0% would indicate that there is no water content. A value of 100% indicates there is an equal weight of water and dry plant material. A value of 300% would mean there is approximately three times as much water as dry plant material. Values in the range of 0–150% are particularly relevant to fire behaviour analysis, as this range is strongly associated with changes in vegetation flammability and the likelihood of ignition.

This product presents pixel-level FMC values for each corresponding Sentinel-2 (A, B and C) scene since July 2015.

For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-fuel-moisture-content/

For service status information, see https://status.dea.ga.gov.au</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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       

        
        

        
        
        
        
            <Style>
                <Name>FMC_daily</Name>
                <Title>DEA FMC Sentinel-2 (A, B &amp; C)</Title>
                <Abstract>Fuel Moisture Content observations grouped by day of collection.</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/ga_s2_fmc_layer/FMC_daily/legend.png"/>
                    </LegendURL>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>ga_s2m_fmc_mosaic</Name>
        <Title>DEA FMC Sentinel-2 Most Recent Observation</Title>
        <Abstract>DEA FMC Sentinel-2 (A, B &amp; C) Most Recent Observation

This product calculates the Fuel Moisture Content (FMC) of vegetation which is the percentage of water mass relative to dry mass in living vegetation.

Values range from 0–300%, representing, by weight percent, the amount of water in leaves compared to dry plant material. A value of 0% would indicate that there is no water content. A value of 100% indicates there is an equal weight of water and dry plant material. A value of 300% would mean there is approximately three times as much water as dry plant material. Values in the range of 0–150% are particularly relevant to fire behaviour analysis, as this range is strongly associated with changes in vegetation flammability and the likelihood of ignition.

This product is a mosaic of the most recent available data from Sentinel-2 satellites (A, B and C) captured over the Australian continent. You can click on the map to view date of observation information for each pixel.

For more information, see https://knowledge.dea.ga.gov.au/data/product/dea-fuel-moisture-content/

For service status information, see https://status.dea.ga.gov.au</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
        <EX_GeographicBoundingBox>
            <westBoundLongitude>71.9335801182649</westBoundLongitude>
            <eastBoundLongitude>169.065352168893</eastBoundLongitude>
            <southBoundLatitude>-56.033068214974</southBoundLatitude>
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        <Title>Camden Environmental Monitoring Project InSAR</Title>

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            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.
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             The time series spans the period 2008-02-11 to 2010-10-22, interpolated to 12-day sampling.
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            <Keyword>envisat</Keyword>
            
            <Keyword>australia</Keyword>
            
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            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.
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            <Keyword>fractional-cover</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>alos-palsar</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>radarsat-2</Keyword>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>sentinel-1</Keyword>
            
            <Keyword>envisat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>insar</Keyword>
            
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    <Layer queryable="1">
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        <Title>RADARSAT2 Displacement</Title>
        <Abstract>
            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.
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        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>alos-palsar</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>radarsat-2</Keyword>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>sentinel-1</Keyword>
            
            <Keyword>envisat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>insar</Keyword>
            
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            <Attribution>
                
                    <Title>Digital Earth Australia</Title>
                
                
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                <Title>InSAR Displacement Up-Down </Title>
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                        <Format>image/png</Format>
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            <Style>
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                <Title>InSAR Displacement East-West </Title>
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                    <LegendURL>
                        <Format>image/png</Format>
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            <Style>
                <Name>insar_disp_ud_std</Name>
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            <Style>
                <Name>insar_disp_ew_std</Name>
                <Title>InSAR Displacement Uncertainty East-West </Title>
                <Abstract>Uncertainty in mm</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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    <Layer queryable="1">
        <Name>radarsat2_velocity</Name>
        <Title>RADARSAT2 Velocity</Title>
        <Abstract>
        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.
        </Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>alos-palsar</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>radarsat-2</Keyword>
            
            <Keyword>WOfS</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>sentinel-1</Keyword>
            
            <Keyword>envisat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>insar</Keyword>
            
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            <Attribution>
                
                    <Title>Digital Earth Australia</Title>
                
                
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            <Style>
                <Name>insar_velocity_ud</Name>
                <Title>InSAR Velocity Up-Down </Title>
                <Abstract>Average InSAR Velocity in mm/year</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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                <Name>insar_velocity_ew</Name>
                <Title>InSAR Velocity East-West </Title>
                <Abstract>Average InSAR Velocity in mm/year</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            </Style>
        
            <Style>
                <Name>insar_vel_ud_std</Name>
                <Title>InSAR Velocity Uncertainty Up-Down </Title>
                <Abstract>Uncertainty in mm</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
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            <Style>
                <Name>insar_vel_ew_std</Name>
                <Title>InSAR Velocity Uncertainty East-West </Title>
                <Abstract>Uncertainty in mm</Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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    <Layer>
        <Title>ASTER Geoscience Map of Australia</Title>

        <Abstract>
            
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

        </Abstract>

        
        
            
        
    
    <Layer queryable="1">
        <Name>aster_false_colour</Name>
        <Title>False Colour Mosaic</Title>
        <Abstract>
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 &#39;False Colour Mosaic&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>false_colour</Name>
                <Title>False Colour</Title>
                <Abstract>Simple false-colour image using ASTER Bands 3 as red, 2 as green and 1 as blue</Abstract>
                
            </Style>
        
            <Style>
                <Name>gray</Name>
                <Title>B2 Grayscale</Title>
                <Abstract>Simple grayscale image using ASTER Band 2</Abstract>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>aster_regolith_ratios</Name>
        <Title>Regolith Ratios</Title>
        <Abstract>
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 &#39;Regolith Ratios&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>simple_rgb</Name>
                <Title>Simple RGB</Title>
                <Abstract>Simple  true-colour image, using the red, green and blue bands</Abstract>
                
            </Style>
         
    </Layer>
    
    
        
            
        
    
    <Layer queryable="1">
        <Name>aster_aloh_group_composition</Name>
        <Title>AlOH Group Composition</Title>
        <Abstract>
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 &#39;AlOH Group Composition&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ramp</Name>
                <Title>B5/B7 </Title>
                <Abstract></Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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    <Layer queryable="1">
        <Name>aster_aloh_group_content</Name>
        <Title>AlOH Group Content</Title>
        <Abstract>
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 &#39;AlOH Group Content&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ramp</Name>
                <Title>(B5+B7)/B6 </Title>
                <Abstract></Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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    <Layer queryable="1">
        <Name>aster_feoh_group_content</Name>
        <Title>FeOH Group Content</Title>
        <Abstract>
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 &#39;FeOH Group Content&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ramp</Name>
                <Title>(B6+B8)/B7 </Title>
                <Abstract></Abstract>
                
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    <Layer queryable="1">
        <Name>aster_ferric_oxide_composition</Name>
        <Title>Ferric Oxide Composition</Title>
        <Abstract>
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 &#39;Ferric Oxide Composition&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ramp</Name>
                <Title>B2/B1 </Title>
                <Abstract></Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink"
                                        xlink:type="simple"
                                        xlink:href="https://ows.dea.ga.gov.au/legend/aster_ferric_oxide_composition/ramp/legend.png"/>
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    <Layer queryable="1">
        <Name>aster_ferric_oxide_content</Name>
        <Title>Ferric Oxide Content</Title>
        <Abstract>
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 &#39;Ferric Oxide Content&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
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                <Title>B4/B3 </Title>
                <Abstract></Abstract>
                
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                        <Format>image/png</Format>
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        <Name>aster_ferrous_iron_content_in_mgoh</Name>
        <Title>Ferrous Iron Content in MgOH/Carbonate</Title>
        <Abstract>
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 &#39;Ferrous Iron Content in MgOH/Carbonate&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ramp</Name>
                <Title>B5/B4 </Title>
                <Abstract></Abstract>
                
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        <Name>aster_ferrous_iron_index</Name>
        <Title>Ferrous Iron Index</Title>
        <Abstract>
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 &#39;Ferrous Iron Index&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ramp</Name>
                <Title>B5/B4 </Title>
                <Abstract></Abstract>
                
                    <LegendURL>
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    <Layer queryable="1">
        <Name>aster_green_vegetation</Name>
        <Title>Green Vegetation Content</Title>
        <Abstract>
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 &#39;Green Vegetation Content&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ramp</Name>
                <Title>B3/B2 </Title>
                <Abstract></Abstract>
                
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    <Layer queryable="1">
        <Name>aster_gypsum_index</Name>
        <Title>Gypsum Index</Title>
        <Abstract>
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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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            <Style>
                <Name>ramp</Name>
                <Title>(B10+B12)/B11 </Title>
                <Abstract></Abstract>
                
                    <LegendURL>
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    <Layer queryable="1">
        <Name>aster_kaolin_group_index</Name>
        <Title>Kaolin Group Index</Title>
        <Abstract>
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 &#39;Kaolin Group Index&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
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                <Title>B6/B5 </Title>
                <Abstract></Abstract>
                
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    <Layer queryable="1">
        <Name>aster_mgoh_group_composition</Name>
        <Title>MgOH Group Composition</Title>
        <Abstract>
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 &#34;cover&#34;;

(2) &#34;dolomitization&#34; 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 &#39;MgOH Group Composition&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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                <Title>B7/B8 </Title>
                <Abstract></Abstract>
                
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        <Name>aster_mgoh_group_content</Name>
        <Title>MgOH Group Content</Title>
        <Abstract>
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 &#39;MgOH Group Content&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ramp</Name>
                <Title>(B6+B9/(B7+B8) </Title>
                <Abstract></Abstract>
                
                    <LegendURL>
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    <Layer queryable="1">
        <Name>aster_opaque_index</Name>
        <Title>Opaque Index</Title>
        <Abstract>
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 &#39;Opaque Index&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ramp</Name>
                <Title>B1/B4 </Title>
                <Abstract></Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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    <Layer queryable="1">
        <Name>aster_silica_index</Name>
        <Title>TIR Silica index</Title>
        <Abstract>
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 &amp;lt;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 &#39;TIR Silica index&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
        </KeywordList>
        
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            <Style>
                <Name>ramp</Name>
                <Title>B13/B10 </Title>
                <Abstract></Abstract>
                
                    <LegendURL>
                        <Format>image/png</Format>
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    <Layer queryable="1">
        <Name>aster_quartz_index</Name>
        <Title>TIR Quartz Index</Title>
        <Abstract>
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 &#39;TIR Quartz Index&#39; 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
</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
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        <Name>multi_scale_topographic_position</Name>
        <Title>Multi-Scale Topographic Position</Title>
        <Abstract>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</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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    <Layer>
        <Title>Weathering Intensity</Title>

        <Abstract>
            
        </Abstract>

        
        
            
        
    
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        <Title>Weathering Intensity</Title>
        <Abstract>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</Abstract>
        <KeywordList>
            
            <Keyword>fractional-cover</Keyword>
            
            <Keyword>geomedian</Keyword>
            
            <Keyword>landsat</Keyword>
            
            <Keyword>australia</Keyword>
            
            <Keyword>mangrove</Keyword>
            
            <Keyword>burn characteristics</Keyword>
            
            <Keyword>HLTC</Keyword>
            
            <Keyword>time-series</Keyword>
            
            <Keyword>NIDEM</Keyword>
            
            <Keyword>bare-earth</Keyword>
            
            <Keyword>WOfS</Keyword>
            
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            <Style>
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                <Title>Weathering Intensity</Title>
                <Abstract>Weather Intensity Index (0-6)</Abstract>
                
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