Department of the Environment, T. & Joint Remote Sensing Research Program (2022). Seasonal Ground Cover - Landsat, JRSRP Algorithm Version 3.0, Australia Coverage. Version 3.0. Terrestrial Ecosystem Research Network. Dataset. https://portal.tern.org.au/metadata/fe9d86e1-54e8-4866-a61c-0422aee8c699
Seasonal Ground 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 278214 times
Accessed 516 times
Dataset Created: 2021-03-28
Dataset Published: 2022-05-03
Data can be accessed from the following links:
HTTPPoint-of-truth metadata URLHTTPCloud Optimised GeoTIFFs - Seasonal Ground Cover AustraliaWMSground_cover_v3HTTPLandscape Data Visualiser - Seasonal Ground Cover - Landsat, JRSRP Algorithm Version 3.0, Australia CoverageHTTPro-crate-metadata.jsonHTTPVegmachine Timeseries ViewerHTTPForage Report (QLD only)
How to cite this collection:
The seasonal fractional ground cover product is a spatially explicit raster product that shows the proportion of bare ground, green and non-green ground cover at medium resolution (30 m per-pixel) for each 3-month calendar season for Australia from 1989 - present. It is derived directly from the seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre.
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. The seasonal fractional cover product predicts vegetation cover, but does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% 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. The seasonal fractional cover product predicts vegetation cover, but does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% 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 ground cover at seasonal (i.e. three-monthly) time scales, forming a consistent time series from 1989 - present. The most recent two years are interim products, as there is a two year lag in finalising the persistent green product. The seasonal ground cover product is useful for investigating inter-annual changes in ground cover and analysing regional comparisons. For applications that focus on all vegetation, the fractional cover product 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, which benefited from additional field validation and a new machine learning approach. Version 2 will no longer be available from 2023.
Lineage
Summary of processing: Landsat surface reflectance data > multiple single-date fractional cover datasets > seasonal composite of fractional cover > seasonal persistent green > seasonal ground cover
Method DocumentationFlood, 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. https://doi.org/10.3390/rs5010083Flood, N. (2013). Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median). Remote Sensing, 5(12), 6481–6500. https://doi.org/10.3390/rs5126481Trevithick, R., Scarth, P., Tindall, D., Denham, R. and Flood, N. (2014). Cover under trees: RP64G Synthesis Report. Department of Science, Information Technology, Innovation and the Arts. Brisbane.Muir, J., Schmidt, M., Tindall, D., Trevithick, R., Scarth, P., & Stewart, J. B. (2011). Field measurement of fractional ground cover: A technical handbook supporting ground cover monitoring for Australia.
Procedure Steps
1.
To produce the ground cover product, the fractional cover product is adjusted using an estimate of the proportion of the pixel obscured by mid and over-storey foliage. We obtain this estimate of mid and over-storey vegetation by combining the persistent dry and persistent green layers. The persistent dry is an estimate derived from field data relationships, while the persistent green is calculated based on time-series analysis. By effectively estimating and then removing the mid and over-storey foliage from the pixel, we can estimate ground cover for that pixel. This estimate is based on the proportion of ground that was visible by the satellite. We assume that fractional cover fractions in each vegetation layer are independent, that is that the presence of mid and over-storey vegetation does not influence the distribution of the ground cover fractions.
2.
Fractional Cover:
Land cover fractions representing the proportions of green, non-green and bare cover are estimated for each Landsat image within the season, using the JRSRP Fractional Cover 3.0 multi layer perceptron model.
The individual date fractional cover images are then composited into representative seasonal images, using the method described in Flood, 2013. The benefits of compositing in this manner are the creation of a regular time-series capturing seasonal variability, and the minimisation of missing data and contamination present in single date imagery (Flood, 2013). Further details of the product can be found on the seasonal fractional cover product page.
Land cover fractions representing the proportions of green, non-green and bare cover are estimated for each Landsat image within the season, using the JRSRP Fractional Cover 3.0 multi layer perceptron model.
The individual date fractional cover images are then composited into representative seasonal images, using the method described in Flood, 2013. The benefits of compositing in this manner are the creation of a regular time-series capturing seasonal variability, and the minimisation of missing data and contamination present in single date imagery (Flood, 2013). Further details of the product can be found on the seasonal fractional cover product page.
3.
Persistent Green:
The persistent green model estimates persistent green by investigating the long term green fraction of the fractional cover product and determining the minimum green that is present regardless of seasonality. This component is assumed to be associated with perennial vegetation and therefore ‘persistent’.
Persistent green images can only be created retrospectively, so ground cover images within the last two years are created using the most recent persistent green. These images are tagged ‘_vinterim’ to indicate they will eventually be replaced, when the persistent green later for the corresponding season becomes available. It is expected that the interim images will be almost identical in nature to the final products when they become available.
The persistent green model estimates persistent green by investigating the long term green fraction of the fractional cover product and determining the minimum green that is present regardless of seasonality. This component is assumed to be associated with perennial vegetation and therefore ‘persistent’.
Persistent green images can only be created retrospectively, so ground cover images within the last two years are created using the most recent persistent green. These images are tagged ‘_vinterim’ to indicate they will eventually be replaced, when the persistent green later for the corresponding season becomes available. It is expected that the interim images will be almost identical in nature to the final products when they become available.
4.
Data Storage:
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).
Australia
Temporal Coverage
From 1989-12-01 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 12m. Refer to the L8 Data Users Handbook for more detail.
2) The fractional cover model predicts the vegetation cover fractions with MAE/wMAPE/RMSE of:
bare - 6.9%/34.9%/14.5%
photosynthetic vegetation (PV) - 4.6%/37.9%/10.6%
non-photosynthetic vegetation (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%
photosynthetic vegetation (PV) - 4.6%/37.9%/10.6%
non-photosynthetic vegetation (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.
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