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National ASTER Map green vegetation content

Note: This metadata describes the dataset in accordance with the ANZLIC (Australia New Zealand Land Information Council) Core Metadata Guidelines Version 2.

Dataset citation

ANZLIC unique identifier: ANZCW0703016280

Title: National ASTER Map green vegetation content


Custodian: Geoscience Australia

Jurisdiction: Australia



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.

ANZLIC search words:


Spatial domain:

locality map

Geographic extent name: AUSTRALIA EXCLUDING EXTERNAL TERRITORIES - AUS - Australia - Australia

Geographic extent polygon: 111.5 -9, 155 -9, 155 -44.5, 111.5 -44.5, 111.5 -9,

Note: The format for each Geographic extent name is: Name - Identifier - Category - Jurisdiction (as appropriate) See GEN Register

Geographic bounding box:
North bounding latitude: -9 °
South bounding latitude: -44.5 °
East bounding longitude: 155 °
West bounding longitude: 111.5 °

Data currency

Beginning date: Not Known

Ending date: Not Known

Dataset status

Progress: Complete

Maintenance and update frequency: Not Known


Stored data format:
DIGITAL - xml eXtended Markup Language GDA94
Available format type:
DIGITAL - xml eXtended Markup Language GDA94

Access constraints:

All the derived geoscience products are copyright owned by CSIRO, Geoscience Australia and the Geological Surveys of Western Australia, South Australia, Northern Territory and Queensland who financially sponsored this project.

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Data quality


For detailed product notes and history please see associated "NATIONAL ASTER MAP PRODUCT NOTES" More accurate mapping of land surface composition at a continental-scale for improved resource exploration is becoming possible through a new generation of remote sensing technologies. These include the multi-spectral Japanese ASTER sensor onboard the US TERRA satellite which was launched in December 1999 and has now collected an image archive that effectively covers the Earth's land surface three times over. 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. The project demonstrated that geochemical information about alteration chemistry associated with footprints of mineral systems can be acquired by analysing spectral ground response, particularly in short-wave infra-red. Key materials that can be identified include clays and magnesium/iron/ aluminium oxyhydroxides, as well as information on mineral composition, abundance and physicochemistry (including crystallinity) for minerals such as kaolinite, which can be used as a surrogate for identifying transported versus in situ regolith material. High resolution mineral maps, from instruments such as HyMap, and Hyperion allow the recognition of various types of hydrothermal alteration, and can map and distinguish between distinct geochemical and mineralogical alteration halos and fluid pathways. The techniques and applications applied in the Mount Isa program were extended into a similar study for the eastern Gawler and Curnamona Cratons in South Australia, and now into the National ASTER mosaic and maps of Australia, using Hyperion satellite data as a means to calibrate the lower resolution ASTER data The following is a summary of the ASTER image processing procedure: Details will be provided in related publications currently in preparation. 1. Acquisition of the required ASTER L1B radiance@sensor data with SWIR cross-talk correction applied ( Note that ASTER L2 "surface radiance" or "surface reflectance" can also be used; 2. SWIR Cross-talk correction (ERSDAC GDS software); 3. Geometric correction; 4. Converting the three 15 m VNIR bands to 30 m pixel resolution; 5. Generating a single nine band VNIR-SWIR image file (L1B) for each ASTER scene; 6. Solar irradiance correction; 7. Masking clouds and green vegetation; 8. Generation of ERMapper headers; 9. Calculation of statistics for masked-image overlaps and global scene response; 10. Scene ordering (best scenes up front in the mosaic); 11. Application of gains and offsets to cross-calibrate all images to a global response; 12. Reduction to "surface" reflectance using independent validation data (e.g. satellite Hyperion data). This requires selecting overlapping "regions of interest" (ROI) and calculating statistics to generate regression coefficients (gains and offsets). Alternatively, if independent EO data are not available then an estimate of the additive component (Equations 1 and 2) can be measured using a "dark-pixel" approach. The "dark pixel" can be estimated using: (1) deep water (very effective for SWIR bands away from sun glint angle); or (2) extrapolation to the dark-point using at least different materials illuminated under a range of different topographic conditions; 13. Application of the correction data (offset +/- gain for each band per scene/mosaic); 14. Geoscience information extraction: Application of "normalisation" scripts (see Tables 1 and 2 for product details);

Positional accuracy:


Attribute accuracy:

Not Supplied

Logical Consistency:

Not Supplied


Not Supplied

Contact information

Contact organisation: Commonwealth of Australia (Geoscience Australia) (GA)
Contact position: Manager Client Services
Mail address: Cnr Jerrabomberra Ave and Hindmarsh Dr
Mail address: GPO Box 378
Locality: Canberra
State: ACT
Country: Australia
Postcode: 2601
Telephone: 02 6249 9966
Facsimile: 02 6249 9960
Electronic mail address:

Metadata information

Metadata date: 2013-03-08

Additional metadata

Metadata reference XHTML:

Metadata reference XML:

15. QC of normalised products using methods such as: o Images are "flat" with both sides of topographic relief showing the same colour information. That is, the surface composition is not dependent on topographic shading; o Appearance of spatially-apparent "random" pixel behaviour in areas of deep shade or water (in SWIR); o No correlation between normalised products and non-normalised spectral bands; and o Relationships to published geology and associated ASTER products; This project has been financially supported through CSIRO, GA, GSWA, NTGS, DMITRE and its PACE exploration incentive program, WA Department of Commerce Centres of Excellence Funding for C3DMM as well as CSIRO MDU and the NCRIS Auscope Grid projects. ASTER data were secured through ERSDAC, NASA-JPL and the ASTER Science Team. In particular, Mike Abrams from NAS-JPL was instrumental in securing the ASTER data access. ASTER data were delivered to CSIRO through the USGS and Geoscience Australia. Mike Caccetta (CSIRO Earth Science and Resource Engineering - CESRE) was responsible for overseeing successful completion of all parts of the ASTER data processing. Image processing support, especially validation and QC, was provided by Matilda Thomas (GA), Alan Mauger (DMITRE), Joanne Chia (CMIS) and Tom Cudahy (CSIRO). Simon Collings (CSIRO Mathematics and Information Sciences - CMIS) conducted the statistical cross-calibration of the ASTER mosaic using in-house CMIS software. ASTER pre-processing support including masking for green vegetation, cloud and water was provided by Cindy Ong, Andrew Rodger, Ian Lau, Carsten Laukamp (all from CESRE) and Joanne Chia (CMIS). Web-access support was provided by Derrick Wong (Curtin University), Ryan Fraser (CESRE) and Peter Warren (CESRE). Geoscience product development was assisted by Carsten Laukamp, Maarten Haest, Cindy Ong and Tom Cudahy (all from CESRE-C3DMM). Hyperion data were provided by NASA/USGS via Alex Held (CSIRO Marine and Atmospheric Research). Airborne HyMap data were sourced from CSIRO Earth Observation Centre and CESRE-related archives.

Authors:Thomas, M.