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Seasonal cover deciles - Landsat, JRSRP algorithm, Australia Coverage
Last modified by Bec Trevithick on 2018/07/23 15:05
Seasonal cover deciles - Landsat, JRSRP algorithm, Australia Coverage
Contents
Link to the data
Data licence and Access rights
| Item | Detail |
|---|---|
| Rights | Copyright 2010-2020. JRSRP. Rights owned by the Joint Remote Sensing Research Program (JRSRP). |
| Licence | Creative Common Attribution (CC-BY) 4.0 |
| Access | While every care is taken to ensure the accuracy of this information, the Rights owned by the Joint Remote Sensing Research Program (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. |
Alternate title
Seasonal fractional vegetation cover deciles for Australia derived from USGS Landsat images
Abstract or Summary
Two fractional cover decile products, green cover and total cover, are currently produced. These products compare, at the per-pixel level, the level of cover for the specific season of interest against the long term cover for that same season. For each pixel all cover values over the entire time-series of seasonal images are classified into deciles. The cover value for the pixel in the season of interest is then classified according to the decile in which it falls. This is an excellent way of identifying areas of low or high cover, relative to what is normal at that location, at that time of year.
Spatial and Temporal extents
| Item | Detail |
|---|---|
| Spatial resolution (metres) | 30 |
| Spatial coverage (degrees) | north:-6; south:-45; west:108 east:160 |
| Temporal resolution | Seasonally - At least one image per standard calender season. |
| Temporal coverage | 1986 ongoing |
| Sensor & platform | Landsat 5&7&8 |
| Item | Detail |
|---|---|
| Spatial representation type | grid |
| Spatial reference system | Australian Albers. EPSG:3577 |
Point of contact
| Item | Detail |
|---|---|
| Name | Rebecca Trevithick |
| Organisation | QLD Department of Science, Information Technology and Innovation |
| Position | Senior Scientist (Remote Sensing) |
| rebecca.trevithick@dsiti.qld.gov.au | |
| Role | pointOfContact |
| Address | Remote Sensing Centre, DSITI, EcoSciences Precinct |
| Telephone | +61 7 3170 5679 |
| URL | http://www.qld.gov.au/dsiti/ |
Credit
Landsat 5 TM and Landsat 7 ETM+ images were acquired from United States Geologic Survey.
Filenaming convention
Filenames for the seasonal fractional cover deciles product conforms to the AusCover standard naming convention. The standard form of this convention is:
<satellite category code><instrument code><product code>_<where>_<when>_<processing stage code>_<additional dataset specific tags>
Details for the unique codes used for this dataset can be found in the following table.
| Data Naming Element | Possible Code(s) | Descriptor |
|---|---|---|
| Standard Elements | ||
| satellite category | lz | Landsat - all possible |
| instrument | tm | thematic |
| product | re | reflective |
| where | qld, nsw, vic, tas, sa, nt, wa | state |
| when | myyyymmyyyymm | season start date (1st day of month) and season end date (last day of month) |
| processing stage | dis dit | total cover green cover |
| data projection | a2 | Australian Albers Equal Area |
Keywords
| Thesauri | Keyword |
|---|---|
| GCMD | EARTH SCIENCE > BIOSPHERE > VEGETATION > VEGETATION COVER |
| CF | vegetation_area_fraction |
| FoR | Environmental Sciences > Ecological Applications = 0501 |
There are three main thesauri that AusCover recommends:
- Global Change Master Directory (http://gcmd.nasa.gov)
- Climate and Forecast (CF) convention standard names (http://cfconventions.org/standard-names.html).
- Fields of Research codes (http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E?
opendocument).
Data quality
Horizontal Positional Accuracy
All the data described here has been generated from the analysis of Landsat TM, ETM+ and OLI data, which has a spatial resolution of approximately 30 m. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 m at these control points. However, it is possible that errors up to ±50 m occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) based on the National Aeronautics and Space Administration (NASA), Shuttle Radar Topography Mission (SRTM). It is not recommended that these data sets be used at scales more detailed than 1:100,000.
Vertical Positional Accuracy
All the data described here has been generated from the analysis of Landsat TM, ETM+ and OLI data, which has a spatial resolution of approximately 30 m.
The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 m at these control points. However, it is possible that errors up to ±50 m occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) based on the National Aeronautics and Space Administration (NASA), Shuttle Radar Topography Mission (SRTM). It is not recommended that these data sets be used at scales more detailed than 1:100,000.
Validation status
No validation is required for these products.
Related products
Landsat Seasonal Fractional Cover
References
| Item | Detail or link |
|---|---|
| Publication | 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. Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference, Brisbane, Australia, September 2002. |
| Publication | 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. Vol. Section 3. Remote Sensing of Biophysical and Biochemical Characteristics in Savannas How different remote sensing technologies contribute to measurement and understanding of savannas. Taylor and Francis, Remote sensing of tree-grass systems: The Eastern Australian Woodlands. |
| Publication | Danaher, T. and Collett, L (2006). Development, Optimisation and Multi-temporal Application of a Simple Landsat Based Water Index. Proceedings of the 13th Australasian Remote Sensing and Photogrammetry Conference, Canberra, Australia, November 2006. |
| Publication | 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. |
| Publication | Flood, N. (2013) Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-dimensional Median). Remote Sens. 2013, 5(12), 6481-6500; doi:10.3390/rs5126481 |
| Publication | 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 |
| Publication | Muir, J., Schmidt, M., Tindall, D., Trevithick, R., Scarth, P., Stewart, J., 2011. Guidelines for Field measurement of fractional ground cover: a technical handbook supporting the Australian collaborative land use and management program. Tech. rep., Queensland Department of Environment and Resource Management for the Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra. |
| Publication | Robertson, K. (1989) Spatial transformation for rapid scan-line surface shadowing, IEEE Compter Graphics and Applications. |
| Publication | 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), 13-17 September, Alice Springs, Australia. Alice Springs, NT. |
| Publication | Zhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118 (2012) 83-94. Extra test performed to mask saturated cloud. Cloud shadow mask, from Fmask Landsat TM cloud algorithm. Zhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118 (2012) 83-94. |
Algorithm summary
The two decile products (total and green) are derived from the historical timeseries of seasonal fractional cover images. The reader is directed to the metadata for that product for details on the algorithm used in their creation (Seasonal Fractional Cover Metadata).
The seasonal fractional cover timeseries for the period Autumn (Mar-May) 1988 to Summer (Dec-Feb) 2012/2013 is used as a baseline, and every season (including those outside the baseline period) is ranked against every available seasonal value within the baseline period, for the corresponding season, on a per-pixel basis. Rankings are expressed as deciles, so a cover value which falls within the bottom 10% of values in the baseline period is given as decile 1, and a cover value which falls in the top 10% is given as decile 10. Two versions of this are created – one which ranks total cover (green + non-green), and one which ranks only the green fraction.
For example, a total cover value for Spring 2013 is ranked against the total cover in all the Spring images in the baseline period. A green cover value in Spring 2013 is ranked against the green cover fractions in all the Spring images in the baseline period.
Because the seasonal composites are used, the data for the target season, and for all corresponding seasons in the baseline period, are representative of the season, and robust against outliers, and the baseline period is sampled evenly across the whole period, so there is no bias towards the years with more imagery available.
Product version history
| Version label | Detail |
|---|---|
| 1.0 | Initial release |
Metadata history
| Date | Detail |
|---|---|
| 2013-11-25 | Metadata creation date |
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