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Data

Seasonal Ground Cover Summary Statistics - Landsat, JRSRP Algorithm Version 3.0, Queensland Coverage 

Ver: 4.0
Status of Data: completed
Update Frequency: notPlanned
Security Classification: unclassified
Dataset Published: 2022-12-07
Dataset Last Modified: 2026-07-07
Metadata viewed 532 times
Dataset accessed 163 times
Metadata Created: 2015-09-18
Metadata Last Modified: 2026-07-07
How to cite this collection:
Joint Remote Sensing Research Program & Department of the Environment, T. (2022). Seasonal Ground Cover Summary Statistics - Landsat, JRSRP Algorithm Version 3.0, Queensland Coverage. Version 4.0. Terrestrial Ecosystem Research Network. Dataset. https://portal.tern.org.au/metadata/b9c2e3d3-0dc4-4599-a1dd-6affb0ad3f74 
Data can be accessed from the following links:
HTTPCloud Optimised GeoTIFFs - Seasonal Ground Cover Statistics - Queensland
WMSSeasonal Ground Cover Statistics
HTTPLandscape Data Visualiser - Seasonal Ground Cover Statistics - Landsat, JRSRP Algorithm Version 3.0, Queensland Coverage
HTTPPoint-of-truth metadata URL
HTTPro-crate-metadata.json
The Seasonal Ground Cover Summary Statistics datasets provide long-term statistical summaries derived from the seasonal ground cover v3 data, calculated separately for each fraction. Two distinct product types are available, differentiated by their seasonal aggregation and statistical content.
  1. Seasonal Statistics per Fraction (Product Code: dpi)
    For each season and ground cover fraction, a separate raster image is generated for the full time series of available imagery. Each image includes the following statistical layers: include:
    band 1 – 5th percentile minimum;
    band 2 – mean value for pixel over full time series;
    band 3 – median value for pixel over full time series;
    band 4 – 95th percentile maximum;
    band 5 – Standard deviation - the temporal standard deviation of the full time-series;
    band 6 – Count - the number of observations statistics for that pixel are based on.
  2. All-Seasons Percentile Summary (Product Code: dph)
    This product summarises the 5th and 95th percentiles across all seasons for each ground cover fraction. It is delivered as a 2-band image, capturing the overall long-term minimum and maximum percentiles across the full time series (currently 1990-2020).
Version 4 update: Dataset filenames have been revised to now include fraction and season tags, replacing multiple stage codes. Related products are grouped under a single code for improved clarity and usability. Additionally, band values are now expressed as percentages (0–100) to match the parent seasonal ground cover dataset, rather than using the previous percent + 100 scaling.
 
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 (https://www.jrsrp.org.au/) using data sourced from US Geological Survey.
 
Purpose
These products display the variation of the seasonal ground cover fractions over long term temporal periods (e.g. 1990-2020).
These products are based upon the JRSRP Fractional Cover 3.0 algorithm. 
Lineage
Landsat surface reflectance data > multiple single-date fractional cover datasets > seasonal composite of fractional cover > seasonal persistent green > seasonal composite of ground cover > seasonal ground cover summary statistics 
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.Flood, 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. 

The long-term statistical summary products are derived from the seasonal ground cover product https://portal.tern.org.au/seasonal-ground-cover-australia-coverage/22022, also produced by the QLD DETSI remote sensing sciences team. The statistical products are derived from the full time series of imagery available for each season, defined by standard calendar seasons. For each pixel, the following statistics are calculated: 5th percentile, mean, median, 95th percentile, standard deviation and count.
 

2. 

Seasonal Statistics - 6 Band images:
band 1 - 5th percentile minimum - robust minimum value for that pixel over the length of the full time series for that season only (percentage)
band 2 - mean value for pixel over full time series for that season only (percentage) 
band 3 - median value for pixel over full time series for that season only (percentage)
band 4 - 95th percentile maximum - robust maximum value for that pixel over the length of the full time series for that season only (percentage)
band 5 - Standard deviation - the temporal standard deviation of the full time-series for that season only 
band 6 - Count - the number of observations statistics for that pixel are based on for that season only. 
The no data value for all bands is 255.
 

3. 

Min/Max Statistics - 2 Band images:
A two band image is produced for the Min/Max for full time series:
band 1 - 5th percentile minimum - robust minimum value for that pixel over the length of the full time series (percentage)
band 2 - 95th percentile maximum - robust maximum value for that pixel over the length of the full time series (percentage)
 

Spatial Description
Queensland 
Temporal Coverage
From 1990-01-01 to 2020-12-31 
Spatial Resolution

Distance of 30 Metres

Vertical Extent

Data not provided.

Data Quality Assessment Scope
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. 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
The USGS aims to provide image-to-image registration with an accuracy of 12m. Refer to the L8 Data Users Handbook for more detail. 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%.
 
ANZSRC - FOR
Climate change impacts and adaptation
Environmental management
Data Stream
Satellite Remote Sensing
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
Platforms
LANDSAT-7
LANDSAT-8
LANDSAT-9
Project
TERN Landscapes
Temporal Resolution
Seasonal
Topic
environment
imageryBaseMapsEarthCover
Author
Joint Remote Sensing Research Program
Department of the Environment, Tourism, Science and Innovation, Queensland Government
Contact Point
Data Enquiries, Earth Observation and Social Sciences (EOSS)
Publisher
Terrestrial Ecosystem Research Network
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. 
By Parent record
Seasonal Ground Cover - Landsat, JRSRP Algorithm Version 3.0, Australia Coverage
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia.
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While every care is taken to ensure the accuracy of this information, the Joint Remote Sensing Research Project (JRSRP) 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.
 
It is not recommended that these data sets be used at scales more detailed than 1:100,000.
 
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