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Product pages » Australian woody vegetation cover

Australian woody vegetation cover

Last modified by Tony Gill on 2019/05/06 08:18

Landsat 2000-2010 woody vegetation cover

Link to the data

DescriptorData linkLayer name
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Data licence and Access rights

RightsCopyright 2010-2015. JRSRP. Rights owned by the Joint Remote Sensing Research Project (JRSRP).
LicenceCreative Common Attribution (CC-BY) 3.0
AccessWhile every care is taken to ensure the accuracy of this information, the Department of Science, Information Technology, Innovation and the Arts (DSITIA) 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.


We realised that there was no easily accessed map of woody-vegetation cover of Australia, produced consistently across the continent, for land managers and ecologists to use at a local-scale. In recent years, researchers and governments have opened access to their field, airborne and satellite image data, making the task of creating such a map possible. We built on these efforts to create a map of woody-vegetation cover of Australia for the decade from 2000 to 2010.


What maps are available?

Three maps are available:

  1. foliage projective cover
  2. forest extent, attributed with the foliage projective cover
  3. accuracy of the extent maps, which also acts as masks of forest and other wooded lands.

Each pixel in map 1 estimates the fraction of the ground covered by green foliage.

Each pixel in map 2 shows two pieces of information. The first is a classification of whether the vegetation is forest or not. The pixels classified as forest are attributed with the second piece of information: the foliage projective cover.

Each pixel in map 3 is a class that provides information on the classification accuracies of the woody extent.

The meanings of pixel values in each map are given below.

On the THREDDS and ftp servers these products can be identified using the 3-character stage codes in the filenames:

  1. foliage projective cover: dma
  2. forest cover: dm7
  3. accuracy of the extent maps: dmb.

How accurate are the maps?

The overall classification accuracy of the woody vegetation extent is 81.9%. The user's and producer's accuracy for the woody class were 85.6% and 90.6%, respectively.

The user's and producer's accuracies for areas mapped as forest were high at 92.2% and 95.9% respectively. The user's and producer's accuracies for other wooded lands is 75.7% and 61.3%, respectively.

See further information on the pixel values below.

Validation of woody foliage projective cover with field-measurements gave a coefficient of determination, R2 of 0.918 and a RMSE of 0.70.

Key specifications

Spatial resolution30 m
Spatial coverageAustralian Continent
Temporal resolutionSingle image. Best estimate of persistent green cover based on annual dry season Landsat imagery from 2000 to 2010
Sensor & platformLandsat 5 TM and Landsat 7 ETM+
CustodianJoint Remote Sensing Reserach Program
Coordinate systemAustralian Albers: EPSG 3577

What do the pixel values mean?

foliage projective cover - dma

  • 0 - null pixels
  • 100-200 - scaled foliage projective cover. To convert to units use: cover_fraction = pixel*0.01 - 1. 

forest cover - dm7

  • 0 - null pixels
  • 100 - not forest
  • 110-200 - scaled foliage projective cover. To convert to units use: cover_fraction = pixel*0.01 - 1.

Accuracy classes for persistent-green extent - dmb

  • 0  - null pixels
  • 1 - other wooded lands. That is, classified as woody with a foliage projective cover < 0.1
  • 2 - not woody and a foliage projective cover < 0.1
  • 3 - forest. That is, classified as woody with a foliage projective cover >= 0.1
  • 4 - not woody and fpc >= 0.1.

The user's and producers accuracies, respectively, for each class are:

  • 1 - 72.9% and 79.8% [40.4% and 100% after these pixels were reclassified to not persistent-green because their cover fractions were less than 0.1]
  • 2 - 65.4% and 56.3%
  • 3 - 92.2% and 95.5%
  • 4 - 75.7% and 61.3%

Future development

None planned


Contact Tony Gill for further information.



The foliage projective cover product is derived from an inter-annual time series of the green layer of the Landsat fractional cover product. The Landsat fractional cover product provides an estimates of the vertically-projected fraction of green vegetation, not-green vegetation and bare ground for each pixel.

Landsat 5 TM and Landsat 7 ETM+ images were obtained for 374 world wide reference system 2 (wrs2) scenes covering Australia. One dry-season image per year was acquired between 2000 and 2010 for each scene except those where cloud or wet conditions precluded image acquistion for a year.

The imagery were processed to BRDF and topographically adjusted reflectance; fractional cover estimates produced; and masks for cloud, cloud shadow, water, topographic shadow, incidence and exitance angle greater than 80 degrees, and snow created.

A robust regression of the form Y~b0 + b1*X, where Y is the green fraction and X is time, was fit to the masked time-series of green vegetation fractions. The following statistics were derived from the regression modelling for each pixel:

  1. fitted fraction from the model at 30 June 2005.

  2. number of observations in the time series

  3. minimum green fraction in the time series once outliers are removed, where an outlier is defined as a point whose residual (observed-fitted) is greater than MAD/0.6745 where MAD is the median absolute deviation of observations from the fitted line.

  4. a measure of the standard error of the robust regression fit calculated as sqrt( chisqd/(N-2) ) where N is the number of observations in the time series and chisqd is the weighted sum of squares of residuals.

  5. a measure of the normalised standard error of the robust regression fit calculated as standard error divided by the minimum.

  6. The slope of the regression line in units of percent green fraction per day

  7. The standard deviation of negative residuals, i.e. those observations below the fitted line.

A random forest classifier, using the minimum fraction and standard error was trained on 6597 field or image interpreted observations of woody vegetation presence or absence.

The woody foliage projective cover was calculated using P = F - (A*V*tanh(B-F)). F is the robust-regression fitted fraction on 30 June 2005. V is the standard deviation of the negative residuals. A and B were parameters that were optimised and were A=7.93 and B=0.66.


Gill, T., Johansen, K., Phinn, S., Trevithick, R., Scarth, P., Armston, J. 2017. A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series, International Journal of Remote Sensing, 38(3), pp 679-705. doi: 10.1080/01431161.2016.1266112

Guerschman, JP., P. Scarth, T.R. McVicar, T.J. Malthus, J.B. Stewart, J.E. Rickards, and L.J. Renzullo. 2015. Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data.  Remote Sensing of Environment 161: 12-26. doi:10.1016/j.rse.2015.01.021.

N. Draper and H. Smith, Applied Regression Analysis, 3rd Ed., New York, John Wiley & Sons, Inc., 1998

Johansen, K., Gill, T., Trevithick, R., Armston, J., Scarth, P., Flood, N. and Phinn, S. Landsat based Persistent Green-Vegetation Fraction for Australia. 16th Australasian remote sensing and photogrammery conference. Melbourne, Australia, 2012.

Flood, N., Danaher, T., Gill, T., Gillingham, S. 2013. An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia.  Remote Sensing 5 (1):83-109. doi: 10.3390/rs5010083.

Zhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118 (2012) 83-94. Danaher, T., Collett, L. Development, optimisation, and multi-temporal application of a simple Landsat based water index. 13th Australasian remote sensing and photogrammetry conference, Canberra, 2006.

Robertson, K. Spatial transformation for rapid scan-line surface shadowing, IEEE Compter Graphics and Applications, 1989.

Created by Kasper Johansen on 2012/03/21 10:58

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