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Product pages » Foliage Projective Cover - Landsat, DSITI algorithm, QLD coverage

Foliage Projective Cover - Landsat, DSITI algorithm, QLD coverage

Last modified by Bec Trevithick on 2017/01/19 15:44


Link to the data

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QLD tiff mosaics - FTP access

Data licence and Access rights

RightsCopyright 2010-2020. DSITI. Rights owned by the Department of Science, Information Technology and Innovation (DSITI).
LicenceCreative Common Attribution (CC-BY) 4.0
AccessWhile every care is taken to ensure the accuracy of this information, the Department of Science, Information Technology and Innovation (DSITI) 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.

Abstract or Summary

Foliage Projective Cover (FPC) is the percentage of ground area occupied by the vertical projection of foliage. The Remote Sensing Centre FPC mapping is based on regression models applied to dry season (May to October) Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI imagery for the period 1988-2014.  An annual woody spectral index image is created for each year using a multiple regression model trained from field data collected mostly over the period 1996-1999.  A robust regression of the time series of the annual woody spectral index is then performed.  The estimated foliage projective cover is the prediction at the date of the selected dry season image for 2014. Where this deviates significantly from the woody spectral index for that date, further tests are undertaken before this estimate is accepted. In some cases, the final estimate is the woody spectral index value rather than the robust regression prediction. The product is further masked to remove areas classified as non-woody. Corrections have been applied to remove errors due to topographic effects, cloud, cloud shadow, water, cropping, and regrowth following clearing. Some errors may remain. The product was generated from WRS-2 path/row scenes obtained from the United States Geological Survey (USGS).

Spatial and Temporal extents

Spatial resolution (metres)30 (25 for datasets prior to 2013)
Spatial coverage (degrees)north:-10; south:-29; west:138 east:155  
Temporal resolutionannually
Temporal coverage1986 - 2014
Sensor & platformLandsat 5&7&8
Spatial representation typegrid
Spatial reference systemAustralian Albers. EPSG:3577

Point of contact

NameFiona Watson
OrganisationQLD Department of Science, Information Technology and Innovation
PositionSenior Scientist (Remote Sensing)
AddressRemote Sensing Centre, DSITI, EcoSciences Precinct
Telephone(07) 37105670


  • Joint Remote Sensing Research Program.
  • Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI images were acquired from United States Geologic Survey.


FoREnvironmental Sciences > Ecological Applications = 0501

There are three main thesauri that AusCover recommends:

  1. Global Change Master Directory (
  2. Climate and Forecast (CF) convention standard names (
  3. Fields of Research codes (

Data quality

Horizontal Positional Accuracy

All the data described here has been generated from the analysis of Landsat TM, ETM+ and OLI data, which has a spatial resolution of approximately 30 m. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 m at these control points. However, it is possible that errors up to ±50 m occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) based on 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 TM, ETM+ and OLI data acquired as ortho-rectified L1T images from USGS. Imagery has a spatial resolution of 30m. Analyses by the USGS suggest that the locational error is below a single pixel (Storey et al, 2014).

Completeness (omission)

Areas of deep shadow on the south-eastern sides of land of high relief were unclassified. Some offshore islands including Torres Strait islands have not been included.


Attribute accuracy (non quantitative)

Comparison of overstorey FPC estimates with independent field estimates of perennial FPC acquired for a range of vegetation types over Queensland, show very good agreement (r2=0.84). Further independent quantitative validation of the final FPC product showed good agreement with field sites (r2 0.78) and lidar data (r2 0.93) estimates of FPC.

naming convention

Filenames for the seasonal fractional cover deciles product conforms to the AusCover standard naming convention. The standard form of this convention is:

<satellite category code><instrument code><product code>_<where>_<when>_<processing stage code>_<additional dataset specific tags>

Details for the unique codes used for this dataset can be found in the following table.

 Data Naming Element Possible
Standard Elements  
satellite categorylzLandsat - all possible
whereqld queensland
when eyyyy e <start year> <end year>
processing stagechh
Woody mask also incorporating the regrowth corrections
Woody mask also incorporating the regrowth corrections
Woody FPC
FPC, with non-woody extent set
data projectiona2Australian Albers Equal Area

Related products

Seasonal Persistent Green

SLATS Star Transects


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.
PublicationArmston, J.D., Denham, R.J., Danaher, T.J., Scarth, P.F. and Moffiet, T., 2008, Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ im;agery for Queensland, Australia. Journal of Applied Remote Sensing, 3: 033540-28.
PublicationDanaher, T., Scarth, P., Armston, J., Collet, L., Kitchen, J., and 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.
PublicationDanaher, T. and Collett, L (2006). Development, Optimisation and Multi-temporal Application of a Simple Landsat Based Water Index. Proceedings of the 13th Australasian Remote Sensing and Photogrammetry Conference, Canberra, Australia, November 2006.
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.

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

Publication Kitchen, J., Armston, J., Clark, A., Danaher, T., and Scarth, P., 2010, Operational use of annual Landsat-5 TM and Landsat-7 ETM+ image time-series for mapping wooded extent and foliage projective cover in north-eastern Australia. Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference, Alice Springs, Australia, 13 -17 September 2010.
PublicationRobertson, K. (1989)  Spatial transformation for rapid scan-line surface shadowing, IEEE Compter Graphics and Applications.
Publication  Storey J, Choate M, and Lee K, (2014) Landsat 8 Operational Land Imagery On-Orbit Geometric Calibration and Performance. Remote Sensing, 6(11), pp. 11127-11152.
PublicationZhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118 (2012) 83-94. Extra test performed to mask saturated cloud.  Cloud shadow mask, from Fmask Landsat TM cloud algorithm. 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.

Algorithm summary

FPC is the percentage of ground area occupied by the vertical projection of foliage. Image value =  FPC + 100 (i.e. FPC of 5% = image value of 105). The pixel values in the QLD_FPC_X data set represent the predicted FPC values. Range is 100-200 which is equivalent to 0-100% FPC. Values erroneously predicted above 100% or below 0% have been classed as 200 and 100 respectively. Zero values indicate null data.  A number of datasets have been used to mask out certain features from the FPC dataset:

  • Non-woody vegetation has been masked using the 2014 Woody Vegetation Extent dataset.
  • Cropping and plantation areas have been masked using the 1999 Queensland Land Use Mapping Program (QLUMP) dataset (
  • A Landsat-derived water index was used to mask water bodies from each individual FPC image (Danaher and Collett 2006). An additional mask is applied for areas with persistent inundation based on multi-temporal analysis of the water index.
  • A topographic correction based on the 1 second SRTM derived Digital Surface Model, Version 1.0, was applied to misclassified steep east-facing slopes. The SRTM data consisted of a DEM and slope layer as processed by Geosciences Australia.

Landsat Foliage Projective Cover was previously incorporated with the wooded extent product (see for example Wooded extent and foliage projective cover - Queensland 2013). There has been no change in methodology.

Product version history

Version labelDetail
1.0Initial release

Metadata history

2017-01-19Metadata creation date
Created by Bec Trevithick on 2017/01/19 12:55

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