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Product pages » Seasonal ground cover statistics - Landsat, JRSRP algorithm, QLD coverage

Seasonal ground cover statistics - Landsat, JRSRP algorithm, QLD coverage

Last modified by Bec Trevithick on 2017/10/11 14:03

Seasonal ground cover statistics - Landsat, JRSRP algorithm, QLD coverage

stats_metadata_image.jpg

Link to the data

DescriptorData linkLayer name
Persistent URLNot Available Yet 
GeoNetwork recordNot Available Yet

QLD tiff mosaics - FTP accessftp://qld.auscover.org.au/landsat/seasonal_fractional_cover/groundcover_timeseries_statistics/

Data licence and Access rights

ItemDetail
RightsCopyright 2010-2020. JRSRP. Rights owned by the Joint Remote Sensing Research Project (JRSRP).
LicenceCreative Common Attribution (CC-BY) 4.0
AccessWhile 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.

Abstract or Summary

Temporal statistic products derived from the seasonal ground cover product for each fraction. Statistics include: 5th percentile minimum, mean, median, 95th percentile maximum, standard deviation and observation count. There is one raster image for each season and each fraction (bare, green and non-green). Additionally min/max (5th and 95th percentile) products are made for each fraction using all seasonal ground cover images available.

Spatial and Temporal extents

ItemDetail
Spatial resolution (metres)30
Spatial coverage (degrees)north:-10; south:-29; west:138 east:155  
Temporal resolution5 yearly
Temporal coverage1986 - 2014
Sensor & platformLandsat 5&7&8
ItemDetail
Spatial representation typegrid
Spatial reference systemAustralian Albers. EPSG:3577

Point of contact

ItemDetail
NameRebecca Trevithick
OrganisationQLD Department of Science, Information Technology and Innovation
PositionSenior Scientist (Remote Sensing)
Emailrebecca.trevithick@dsiti.qld.gov.au
RolepointOfContact
AddressRemote Sensing Centre, DSITI, EcoSciences Precinct
Telephone+61 7 3170 5679
URLhttp://qld.gov.au/dsiti/

Credit

  • Joint Remote Sensing Research Program.
  • Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI images were acquired from United States Geologic Survey.

Keywords

ThesauriKeyword
GCMDEARTH SCIENCE > BIOSPHERE > VEGETATION > VEGETATION COVER
CFvegetation_area_fraction
FoREnvironmental Sciences > Ecological Applications = 0501

There are three main thesauri that AusCover recommends:

  1. Global Change Master Directory (http://gcmd.nasa.gov)
  2. Climate and Forecast (CF) convention standard names (http://cfconventions.org/standard-names.html).
  3. 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.

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 categorylzLandsat - all possible
instrumenttmthematic
productrereflective
whereqld queensland
when eyyyy e <start year> <end year>
processing stagedjl
djm
djn

djo
djp
djq
djr
bare min/max all seasons
green min/max all seasons
dry min/max all seasons

summer bare ground stats

autumn bare ground stats
winter bare ground stats
spring bare ground stats
 indexibare
igreen
idry
bare ground statistics
green ground cover statistics
dry ground cover statistics
data projectiona2Australian Albers Equal Area


Related products

Seasonal Ground Cover

References

ItemDetail or link
PublicationArmston, 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.
PublicationDanaher, 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.
PublicationDanaher, 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.
Publicationde 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

PublicationMuir, 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.
PublicationRobertson, K. (1989)  Spatial transformation for rapid scan-line surface shadowing, IEEE Compter Graphics and Applications.
PublicationScarth, 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.
PublicationTrevithick, 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.
PublicationZhu, 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

Algorithm

The long-term statistical summary products are derived from the seasonal ground cover product, also produced by the DSITI remote sensing centre. 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. 

Data storage

A 6 band (byte) image is produced for the statistics for each season:

  • band 1 – 5th percentile minimum - robust minimum value for that pixel over the length of the full time series for that season only (percentage + 100)
  • band 2  -  mean value for pixel over full time series for that season only (percentage + 100)
  • band 3 – median value for pixel over full time series for that season only  (percentage + 100)
  • band 4 – 95th percentile maximum - robust maximum value for that pixel over the length of the full time series for that season only  (percentage + 100)
  • 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 first 4 bands are recorded with a scaling factor of 100 for data management purposes (to ensure there are no negative values in the image). To interpret the values correctly it is necessary to subtract 100 from the values in these bands.

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 + 100)
  • band 2  - 95th percentile maximum - robust maximum value for that pixel over the length of the full time series (percentage + 100)

Product version history

Version labelDetail
1.0Initial release

Metadata history

DateDetail
2015-09-18Metadata creation date
Tags:
Created by Bec Trevithick on 2015/09/15 13:47

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