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Product pages » Fractional cover - Landsat, Joint Remote Sensing Research Program algorithm, Queensland coverage

Fractional cover - Landsat, Joint Remote Sensing Research Program algorithm, Queensland coverage

Last modified by Matt Paget on 2012/12/12 13:40

Fractional cover - Landsat, Joint Remote Sensing Research Program algorithm, National coverage

landsatFractionalCover.2.png

Link to the data

DescriptorData linkLayer name
Dataset digital object identifier (DOI)
 
GeoNetwork record

Tiles NetCDFhttp://tern-auscover.science.uq.edu.au/thredds/catalog/auscover/fractionalcover/catalog.html
Geoserver example

Data licence and Access rights

ItemDetail
Rights
LicenceCreative Common Attribution (CC-BY) 3.0
AccessWhile every care is taken to ensure the accuracy of this information, the Department of Environment and Resource Management makes no representations or warranties about its accuracy, reliability, completeness or suitability for any particular purpose and disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which might be incurred as a result of the information being inaccurate or incomplete in any way and for any reason.

Alternate title

Landsat fractional ground cover for Queensland

Abstract or Summary

Landcover fractions representing the proportions of green, non-green and bare cover retrieved by inverting multiple linear regression estimates and using synthetic endmembers in a constrained non-negative least squares unmixing model.

The Queensland fractional Ground Cover data report on fractions of bare, green and non-green vegetation in areas with a Foliage Projective Cover (FPC) of less than 15%.

A linear spectral unmixing has been applied to Landsat 5 TM and Landsat 7 ETM+ imagery for state of Queensland. Values are reported as percentages of ground cover plus 100. The fractions stored in the 5 image layers are:  layer 1 - bare (bare ground, rock, disturbed), Layer 2 - green vegetation, Layer 3 - non green vegetation (litter, dead leaf and branches), Layer 4 – residual error estimate of the unmixing model per pixel, Layer 5 - sum of the fractions bare, green, non-green (as a second measure of model error); all layers have a value of 100 added. The overall model Root Mean Squared Error (RMSE) is 11.8%.

Spatial and Temporal extents

ItemDetail
Spatial resolution (metres)30
Spatial coverage (degrees)north:-9.905677 N; south:-29.345854 N; west:137.372070 E east:154.418569 E
Temporal resolution
Temporal coveragestart:1986-09-21, ongoing
Sensor & platformLandsat 5&7
ItemDetail
Spatial representation typegrid
Spatial reference systemWGS 84

Point of contact

ItemDetail
NamePeter Scarth
OrganisationDepartment of Environment and Resource Management
PositionPrincipal Scientist (Remote Sensing)
EmailPeter.Scarth@derm.qld.gov.au
Roleauthor
Address
Telephone+61 7 3170 5678
URLhttp://derm.qld.gov.au
ItemDetail
NameMichael Schmidt
OrganisationDepartment of Environment and Resource Management
PositionSenior Scientist (Remote Sensing)
EmailMichael.Schmidt@derm.qld.gov.au
Roleauthor

Credit

Joint Remote Sensing Research Program.
Landsat 5 TM and Landsat 7 ETM+ images were acquired from United States Geologic Survey and partly the Australian National Earth Observation Group in Geoscience Australia.

Keywords

ThesauriKeyword
GCMDEARTH SCIENCE > BIOSPHERE > VEGETATION > VEGETATION COVER
CFvegetation_area_fraction
FoREnvironmental Sciences > Ecological Applications = 0501

There are three main thesauri that AusCover recommends:

  1. Global Change Master Directory (http://gcmd.nasa.gov)
  2. Climate and Forecast (CF) convention standard names (http://cf-pcmdi.llnl.gov/documents/cf-standard-names).
  3. Fields of Research codes (http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E?
    opendocument).

Data quality

Quantitative Attribute Accuracy Assessment
Attribute Accuracy Value: fractional Ground Cover RMSE is 11.8%.

Horizontal Positional Accuracy
All the data described here has been generated from the analysis of Landsat Thematic Mapper (TM) data, which has a spatial resolution of 30 m. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 metres at these control points. However, it is possible that errors up to ±50 meters occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) the National Aeronautics and Space Administration (NASA), Shuttle Radar Topography Mission (SRTM). It is not recommended that these data sets be used at scales more detailed than 1:100,000.

Vertical Positional Accuracy
All the data described here has been generated from the analysis of Landsat Thematic Mapper (TM) data, which has a spatial resolution of 30 m. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 metres at these control points. However, it is possible that errors up to ±50 meters occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) the National Aeronautics and Space Administration (NASA), Shuttle Radar Topography Mission (SRTM). It is not recommended that these data sets be used at scales more detailed than 1:100,000.

Validation status

Landsat TM and ETM+ L1T images were acquired from the USGS on an annual basis in the mid to late dry season to cover all of Australia. These values are scaled radiance values, after calibration. Georegistration is also by USGS. For further information, see the USGS website http://glovis.usgs.gov/, from where this data was downloaded. These dry season dates were selected to enhance spectral contrast between evergreen tree and shrub canopies and the predominantly senescent ground cover. All images are corrected to minimise the confounding effects of geometric distortion, radiometric variability and illumination geometry using the procedure described in Danaher et al (2010). Angle adjustment uses a Walthall-like equation, with coefficients tuned from all Landsat overlaps available at the time. Topographic correction uses the simple adjustment described in Dymond et al. (2001) A simple physical model of vegetation reflectance for standardising optical satellite imagery. Rem. Sens. Env. Image edges are trimmed to remove ragged ends of scanlines, where data is missing for some bands and not others. Areas of cloud, associated shadow, topographic shadow, cast shadow and water contamination are masked, with the mask type encoded as a fourth band. The bare soil, green vegetation and non-green vegetation endmenbers are calculated using models linked to an intensive field sampling program whereby more than 600 sites covering a wide variety of vegetation, soil and climate types were sampled to measure overstorey and ground cover following the procedure outlined in Muir et al (2011). A constrained linear spectral unmixing using the derived endmembers has an overall model Root Mean Squared Error (RMSE) of 11.8%. Values are reported as percentages of cover plus 100. The fractions stored in the 4 image layers are:  Band1 - bare (bare ground, rock, disturbed), Band2 - green vegetation, Band3 - non green vegetation (litter, dead leaf and branches), Band4 - Mask type code

Ground cover is defined as the vegetation (living and dead), biological crusts and stones that are in contact with the soil surface (Muir et al, 2011). Ground cover information is important for soil erosion and nutrient flux estimates into the stream network. Ground cover levels may vary due to anthropogenic management of grazing enterprises and agricultural land management practices, or natural changes in seasonal rainfall. 

The Department of Environment and Resource Management (DERM) has developed a fractional Ground Cover product based on applying a linear spectral unmixing approach (Scarth et al, 2010) to field data consisting of ground cover point intercepts (Muir et al, 2011). The model can be applied across large areas with different soil types that have a variety of reflective characteristics. Reference spectra for fractions of bare ground, green vegetation and non-green vegetation have been extracted from more than 800 field data across Queensland and coinciding Satellite observations; see Scarth et al (2010) for more details.

The overall error of the product is 11.8%, while the error margins vary for the three different layers: green RMSE: 11.0%, non-green RMSE: 17.4% and bare RMSE: 12.5%

Each image was masked for cloud, cloud shadow, water and areas with greater than 15% foliage projective cover (FPC) using the 2009 FPC time-series product (Danaher et al, 2010.

Related products

Foliage Projective Cover for Queensland.

References

ItemDetail or link
PublicationArmston, J. D., Danaher, T.J., Goulevitch, B. M., and Byrne, M. I., 2002. Geometric correction of Landsat MSS, TM, and ETM+ imagery for mapping of woody vegetation cover and change detection in Queensland. Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference, Brisbane, Australia, September 2002.
PublicationDanaher, T., Scarth, P., Armston, J., Collet, L., Kitchen, J., Gillingham, S., 2010. Ecosystem Function in Savannas: Measurement and Modelling at Landscape to Global Scales. Vol. Section 3. Remote Sensing of Biophysical and Biochemical Characteristics in Savannas How different remote sensing technologies contribute to measurement and understanding of savannas. Taylor and Francis, Remote sensing of tree-grass systems: The Eastern Australian Woodlands.
PublicationMuir, J., Schmidt, M., Tindall, D., Trevithick, R., Scarth, P., Stewart, J., 2011. Guidelines for Field measurement of fractional ground cover: a technical handbook supporting the Australian collaborative land use and management program. Tech. rep., Queensland Department of Environment and Resource Management for the Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra.
PublicationScarth, P., Röder, A., Schmidt, M., 2010b. Tracking grazing pressure and climate interaction - the role of Landsat fractional cover in time series analysis. In: Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference (ARSPC), 13-17 September, Alice Springs, Australia. Alice Springs, NT.
Publicationde Vries, C., Danaher, T., Denham, R., Scarth, P. & Phinn, S. 2007, "An operational radiometric calibration procedure for the Landsat sensors based on pseudo-invariant target sites", Remote Sensing of Environment, vol. 107, no. 3, pp. 414-429.
Validation report
Online info

Algorithm summary

Image Pre-Processing
All images were corrected to a standardised geometric baseline of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images with a Root Mean Squared Error (RMSE) of less than 0.5 pixels (Armston et al. 2002). A radiometric correction procedure, developed by the Remote Sensing Centre of the Queensland Department of Environment and Resource Management (DERM), was applied to all images. The procedure uses the post-launch gains and offsets supplied by NASA to convert the digital numbers to top-of atmosphere reflectance based on a bi-directional reflectance distribution function (BRDF) (de Vries et al. 2007).

Data storage
A 5 band (byte) image is produced:

  • band 1 – bare ground fraction (in percent) + 100
  • band 2 - green vegetation fraction (in percent) +100
  • band 3 – non-green vegetation fraction (in percent) + 100
  • band 4 – RMSE + 100
  • band 5 – Sum of bare, green and non-green fraction (in percent) + 100.

The Queensland fractional Ground Cover product is produced for each Landsat Tm/ETM+ image in the archive. The fGC was scaled to fit into the unsigned 8-bit data range for ease of display, so that the output value is equal to the 100 plus. Some precision in terms of decimal places is also lost in this transformation, which is below the product’s error margins.

Product version history

Version labelDetail
1.0Initial release  FPC (available from Qld Govt data)
2.0Persistent Green

Other items [optional]

Metadata history

DateDetail
yyyy-mm-ddMetadata creation date
yyyy-mm-ddUpdated some section
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Created by Matt Paget on 2012/11/15 09:19

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