Joint Remote Sensing Research Program & Department of the Environment, T. (2022). Seasonal Fractional Cover - Landsat, JRSRP Algorithm Version 3.0, Australia Coverage. Version 3.0. Terrestrial Ecosystem Research Network. Dataset. https://portal.tern.org.au/metadata/0997cb3c-e2e2-45be-ac82-f5e13d24331c
Seasonal Fractional Cover - Landsat, JRSRP Algorithm Version 3.0, Australia Coverage
Ver: 3.0
Status of Data: onGoing
Update Frequency: quarterly
Security Classification: unclassified
Record Last Modified: 2025-12-02
Viewed 1917921 times
Accessed 1092 times
Dataset Created: 2022-03-28
Dataset Published: 2022-05-03
Data can be accessed from the following links:
HTTPPoint-of-truth metadata URLHTTPCloud Optimised GeoTIFFs - Seasonal Fractional CoverWMSaus:fractional_cover_v3HTTPLandscape Data Visualiser - Seasonal fractional cover - Landsat, JRSRP algorithm Version 3.0, Australia coverageHTTPDifferences Between Fractional Cover Version 2 and Version 3HTTPro-crate-metadata.jsonHTTPVegmachine Timeseries ViewerHTTPGitLab Code for JRSRP Fractional Cover v3
How to cite this collection:
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 that predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season across Australia from 1987 to the present. The green and non-green fractions may include a mix of woody and non-woody vegetation. A 3 band (byte) image is produced: band 1 – bare ground fraction (in percent), band 2 - green vegetation fraction (in percent), band 3 – non-green vegetation fraction (in percent). The no data value is 255.
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. This product is based upon the JRSRP Fractional Cover 3.0 algorithm.
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.
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 DocumentationZhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94.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.Flood, N. (2013). Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median). Remote Sensing, 5(12), 6481–6500.Danaher, T., & Collett, L. (2006, November). Development, optimisation and multi-temporal application of a simple Landsat based water index. In Proceeding of the 13th Australasian Remote Sensing and Photogrammetry Conference, Canberra, ACT, Australia (Vol. 2024).Muir, J., Schmidt, M., Tindall, D., Trevithick, R., Scarth, P., & Stewart, J. B. (2011). Field measurement of fractional ground cover.
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).
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 (Version 3.0):
A multilayer perceptron (MLP) model is used to estimate percentage cover in three fractions – bare ground, photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) from surface reflectance, for every image captured within the season. The MLP model was trained with Tensorflow using Landsat TM, ETM+ and OLI surface reflectance and a collection of approximately 4000 field observations of overstorey and ground cover. The field observations covered a wide variety of vegetation, soil and climate types across Australia, and were collected between 1997 and 2018 following the procedure outlined in Muir et al. (2011). The model was assessed to predict the vegetation cover fractions with MAE/wMAPE/RMSE of:
bare - 6.9%/34.9%/14.5%
PV - 4.6%/37.9%/10.6%
NPV - 9.8%/25.2%/16.9%.
A multilayer perceptron (MLP) model is used to estimate percentage cover in three fractions – bare ground, photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) from surface reflectance, for every image captured within the season. The MLP model was trained with Tensorflow using Landsat TM, ETM+ and OLI surface reflectance and a collection of approximately 4000 field observations of overstorey and ground cover. The field observations covered a wide variety of vegetation, soil and climate types across Australia, and were collected between 1997 and 2018 following the procedure outlined in Muir et al. (2011). The model was assessed to predict the vegetation cover fractions with MAE/wMAPE/RMSE of:
bare - 6.9%/34.9%/14.5%
PV - 4.6%/37.9%/10.6%
NPV - 9.8%/25.2%/16.9%.
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).
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
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 approximately 4000 field reference sites.
2) The fractional cover model was compared to samples drawn from approximately 4000 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 12 m. Refer to the L8 Data Users Handbook for more detail.https://www.usgs.gov/landsat-missions/landsat-8-data-users-handbook
2) The fractional cover model predicts the vegetation cover fractions with MAE/wMAPE/RMSE of:
bare - 6.9%/34.9%/14.5%
PV - 4.6%/37.9%/10.6%
NPV - 9.8%/25.2%/16.9%.
2) The fractional cover model predicts the vegetation cover fractions with MAE/wMAPE/RMSE of:
bare - 6.9%/34.9%/14.5%
PV - 4.6%/37.9%/10.6%
NPV - 9.8%/25.2%/16.9%.
Horizontal Resolution
Parameters
Temporal Resolution
Supplemental Information
Data are available as cloud optimised GeoTIFF (COG) files. COG files are easier and more efficient for users to access data corresponding to particular areas of interest without the need to download the data first.
Resource Specific Usage
Data not provided.
Environment Description
Data not provided.
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia.
Contact Us
TERN services are provided on an “as-is” and “as available” basis. Users use any TERN services at their discretion and risk.
They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.
Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.
Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting
It is not recommended that these data sets be used at scales more detailed than 1:100,000.