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Seasonal Fractional Cover - Landsat, JRSRP Algorithm, Australia Coverage 

Ver: 1.0
Status of Data: superseded
Update Frequency: notPlanned
Security Classification: unclassified
Record Last Modified: 2025-12-02
Viewed 33797 times
Accessed 1699 times
Dataset Created: 2013-11-25
Dataset Published: 2021-09-13
Data can be accessed from the following links:
HTTPPoint-of-truth metadata URLHTTPSeasonal Fractional Cover via HTTPHTTPseasonal_fractional_cover_band_descriptions_and_fil_4ygcbjt.txtHTTPro-crate-metadata.jsonHTTPVegmachine Timeseries Viewer
How to cite this collection:
Joint Remote Sensing Research Program (2021). Seasonal Fractional Cover - Landsat, JRSRP Algorithm, Australia Coverage. Version 1.0. Terrestrial Ecosystem Research Network. Dataset. https://portal.tern.org.au/metadata/f0c32576-9ad7-4c9c-9aa9-22787867e28b 
This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/TERN/0997cb3c-e2e2-45be-ac82-f5e13d24331c. The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover across a season. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. The green and non-green fractions may include a mix of woody and non-woody vegetation. 
Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging. This dataset was produced by the Joint Remote Sensing Research Program using data sourced from US Geological Survey. 
Purpose
This product captures variability in fractional cover at seasonal (i.e. three-monthly) time scales, forming a consistent time series from 1987 - present. It is useful for investigating inter-annual changes in vegetation cover and analysing regional comparisons. For applications that focus on non-woody vegetation, the ground cover product, derived from fractional cover, may be more suitable. For applications investigating rapid change during a season, monthly composite or single-date (available on request) fractional cover products may be more appropriate. Note: A new fractional cover algorithm will be implemented during 2021, based on additional field validation and a new machine learning approach. 
Lineage
Summary of processing: Landsat surface reflectance data > multiple single-date fractional cover datasets > medoid calculation for seasonal composite of fractional cover
Further details are provided in the Methods section. 
Method DocumentationData not provided.
Procedure Steps

1. 

Image Pre-Processing: All input Landsat TM/ETM+/OLI imagery was downloaded from the USGS EarthExplorer website as level L1T imagery. Images which the EarthExplorer site rated as having greater than 80% cloud cover were not downloaded. The imagery has been corrected for atmospheric effects, and bi-directional reflectance and topographic effects, using the methods detailed by Flood et al (2013). The result is surface reflectance standardised to a fixed viewing and illumination geometry. Cloud, cloud shadow and snow have been masked out using the Fmask automatic cloud mask algorithm. Topographic shadowing has been masked using the Shuttle Radar Topographic Mission DEM at 30 m resolution. Water has been masked out using the methods outlines in Danaher & Collett (2006). 

2. 

Fractional Cover Model: The bare soil, green vegetation and non-green vegetation endmembers are calculated using models linked to an intensive field sampling program whereby more than 1500 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 is applied to the image archive using the derived endmembers and has an overall model Root Mean Squared Error (RMSE) of 11.6%. 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 - Model fitting error. 

3. 

Seasonal Compositing: The method of compositing used selection of representative pixels through the determination of the medoid (multi-dimensional equivalent of the median) of three months (a season) of fractional cover imagery. The medoid is the point which minimises the total distance between the selected point and all other points. Thus the selected point is “in the middle” of the set of points. The value selected is a specific data point and not an averaged or blended value. It is robust against extreme values, inherently avoiding the selection of outliers, such as occurs when cloud or cloud shadow goes undetected. At least three pixels from the time-series of imagery for the season must be available. Unfortunately, due to the high level of cloud cover in some areas, often three cloud free pixels are not available, resulting in data gaps in the seasonal fractional cover image. For further details on this method see Flood (2013). 

Australia
Temporal Coverage
From 1987-12-01 to on going 
Spatial Resolution

Data not provided.

Vertical Extent

Data not provided.

Data Quality Assessment Scope
1) The input imagery was processed to level L1T by the USGS. Geodetic accuracy of the product depends on the image quality and the accuracy, number, and distribution of the ground control points. 2) The fractional cover model was compared to samples drawn from 1500 field reference sites. 
Data Quality Report
Data not provided. 
Data Quality Assessment Outcome
1) The USGS aims to provide image-to-image registration with an accuracy of 12m. Refer to the L8 Data Users Handbook for more detail. 2) The fractional cover model achieved an overall model Root Mean Squared Error (RMSE) of 11.6% against field reference sites. 
ANZSRC - FOR
Climate change impacts and adaptation
Environmental management
GCMD Sciences
BIOSPHERE - VEGETATION COVER
LAND SURFACE - LAND USE/LAND COVER
LAND SURFACE - SOILS
Horizontal Resolution
30 meters - < 100 meters
Instruments
ETM+
OLI
TM
Parameters
bare soil fraction
non-photosynthetic vegetation fraction
photosynthetic vegetation fraction
vegetation area fraction
Platforms
LANDSAT-5
LANDSAT-7
LANDSAT-8
Temporal Resolution
Weekly - < Monthly
Topic
environment
imageryBaseMapsEarthCover
Author
Joint Remote Sensing Research Program
Contact Point
Data Enquiries, Earth Observation and Social Sciences (EOSS)
Publisher
Terrestrial Ecosystem Research Network
Rights Holder
Department of the Environment, Tourism, Science and Innovation, Queensland Government
de 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.
Zhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118. doi:10.1016/j.rse.2011.10.028
Armston, 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.
Robertson, P (1989). Spatial Transformations for Rapid Scan-Line Surface Shadowing. IEEE Computer Graphics and Applications, vol. 9. doi: 10.1109/38.19049
Danaher, 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.
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
Flood, N. (2013) Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-dimensional Median). Remote Sens. 2013, 5(12), 6481-6500
Scarth, 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)
Muir, J. et al (2011), Field measurement of fractional ground cover: supporting ground cover monitoring for Australia. ABARES. Canberra
By Child records
Seasonal Fractional Cover Summary Statistics - Landsat, JRSRP Algorithm Version 3.0, Queensland Coverage
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