Seasonal fractional cover - Landsat, JRSRP algorithm Version 3.0, Australia coverage
<p>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. </p> <p>A 3 band (byte) image is produced:</p> <p>band 1 – bare ground fraction (in percent),</p> <p>band 2 - green vegetation fraction (in percent),</p> <p>band 3 – non-green vegetation fraction (in percent).</p> <p>The no data value is 255.</p>
Simple
Identification info
- Date (Creation)
- 2022-03-28
- Date (Publication)
- 2022-05-03
- Date (Revision)
- 2024-10-23
- Edition
- 3.0
Publisher
Author
Joint Remote Sensing Research Program
Chancellors Place, St Lucia, Queensland, 4072, Australia
St Lucia
Queensland
4072
Australia
Rights holder
Joint Remote Sensing Research Program
Chancellors Place, St Lucia, Queensland, 4072, Australia
St Lucia
Queensland
4072
Australia
Author
Department of Environment, Science and Innovation, Queensland Government
41 Boggo Road, Dutton Park, Queensland, 4102, Australia
Dutton Park
Queensland
4102
Australia
- Website
- https://www.tern.org.au/
- Purpose
- <p>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.</p> <p>This product is based upon the JRSRP Fractional Cover 3.0 algorithm.</p>
- 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.
- Credit
- <p></p><p>This dataset was produced by the Joint Remote Sensing Research Program using data sourced from US Geological Survey.</p>
- Status
- On going
Point of contact
Department of Environment and Science (2017-2023), Queensland Government - van den Berg, Deanna (Principal Scientist (Remote Sensing))
41 Boggo Road, Dutton Park, Queensland, 4102, Australia
41 Boggo Road
Dutton Park
Queensland
4102
Australia
Spatial resolution
- Spatial resolution
- 30
- Topic category
-
- Environment
- Imagery base maps earth cover
Extent
- Description
- Australia
N
S
E
W
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Temporal extent
- Time period
- 1987-12-01
- Maintenance and update frequency
- Quarterly
- GCMD Science Keywords
- ANZSRC Fields of Research
- TERN Platform Vocabulary
- TERN Instrument Vocabulary
- TERN Parameter Vocabulary
- QUDT Units of Measure
- GCMD Horizontal Resolution Ranges
- GCMD Temporal Resolution Ranges
Resource constraints
- Use limitation
- The Creative Commons Attribution 4.0 International (CC BY 4.0) license allows others to copy, distribute, display, and create derivative works provided that they credit the original source and any other nominated parties. Details are provided at https://creativecommons.org/licenses/by/4.0/
- File name
- 88x31.png
- File description
- CCBy Logo from creativecommons.org
- File type
- png
- Title
- Creative Commons Attribution 4.0 International Licence
- Alternate title
- CC-BY
- Edition
- 4.0
- Access constraints
- License
- Use constraints
- Other restrictions
- Other constraints
- 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. <br />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. <br /><br />Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting
- Other constraints
- It is not recommended that these data sets be used at scales more detailed than 1:100,000.
Resource constraints
- Classification
- Unclassified
- Supplemental Information
- <p>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.</p>
Distribution Information
Distributor
Distributor
- Distribution format
-
- NetCDF
- OnLine resource
- Differences Between Fractional Cover Version 2 and Version 3
Distribution Information
Distributor
Distributor
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia
Indooroopilly
Queensland
4068
Australia
- Distribution format
-
- OnLine resource
- Cloud Optimised GeoTIFFs - Seasonal Fractional Cover
- OnLine resource
- GitLab Code for JRSRP Fractional Cover v3
- OnLine resource
-
Seasonal Fractional Cover v3
aus:fractional_cover_v3
- OnLine resource
- Landscape Data Visualiser - Seasonal fractional cover - Landsat, JRSRP algorithm Version 3.0, Australia coverage
- OnLine resource
- ro-crate-metadata.json
Data quality info
- Hierarchy level
- Dataset
- Other
- 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.<br> 2) The fractional cover model was compared to samples drawn from approximately 4000 field reference sites.
Report
Result
- Statement
- 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<br> 2) The fractional cover model predicts the vegetation cover fractions with MAE/wMAPE/RMSE of:<br> bare - 6.9%/34.9%/14.5%<br> PV - 4.6%/37.9%/10.6%<br> NPV - 9.8%/25.2%/16.9%.<br>
Resource lineage
- Statement
- <p>Summary of processing:<br>Landsat surface reflectance data > multiple single-date fractional cover datasets > medoid calculation for seasonal composite of fractional cover <br />Further details are provided in the Methods section.</p>
- Hierarchy level
- Dataset
- Title
- 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.
- Website
-
https://doi.org/10.3390/rs5010083
Method documentation
- Title
- Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94.
- Website
-
https://doi.org/10.1016/J.RSE.2011.10.028
Method documentation
- Title
- 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).
- Website
-
https://trove.nla.gov.au/work/33486869
Method documentation
- Title
- Muir, J., Schmidt, M., Tindall, D., Trevithick, R., Scarth, P., & Stewart, J. B. (2011). Field measurement of fractional ground cover.
- Website
-
https://www.researchgate.net/publication/236022381_Field_measurement_of_fractional_ground_cover
Method documentation
- Title
- Flood, N. (2013). Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median). Remote Sensing, 5(12), 6481–6500.
- Website
-
https://doi.org/10.3390/rs5126481
Method documentation
Process step
- Description
- <p>Image Pre-Processing:<br> 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).</p>
Process step
- Description
- <p>Fractional Cover Model (Version 3.0):<br> 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:<br> bare - 6.9%/34.9%/14.5% <br> PV - 4.6%/37.9%/10.6% <br> NPV - 9.8%/25.2%/16.9%.</p>
Process step
- Description
- <p>Seasonal Compositing:<br> 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).</p>
Reference System Information
- Reference system identifier
- EPSG/EPSG:3577
- Reference system type
- Geodetic Geographic 2D
Metadata
- Metadata identifier
-
urn:uuid/0997cb3c-e2e2-45be-ac82-f5e13d24331c
- Title
- TERN GeoNetwork UUID
- Language
- English
- Character encoding
- UTF8
Point of contact
Type of resource
- Resource scope
- Dataset
- Metadata linkage
-
https://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/0997cb3c-e2e2-45be-ac82-f5e13d24331c
Point-of-truth metadata URL
- Date info (Creation)
- 2022-03-28T00:00:00
- Date info (Revision)
- 2024-10-23T00:00:00
Metadata standard
- Title
- ISO 19115-1:2014/AMD 1:2018 Geographic information - Metadata - Fundamentals
- Edition
- 1
Metadata standard
- Title
- ISO/TS 19115-3:2016
- Edition
- 1.0
Metadata standard
- Title
- ISO/TS 19157-2:2016
- Edition
- 1.0
- Title
- Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016
- Date (published)
- 2021
- Edition
- 1.0
Identifier
Overviews
Spatial extent
N
S
E
W
Provided by
Associated resources
Not available