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Product pages » Seasonal ground cover - Landsat, JRSRP, Australia Coverage

Seasonal ground cover - Landsat, JRSRP, Australia Coverage

Last modified by Bec Trevithick on 2017/10/10 15:36

Seasonal ground cover - Landsat, JRSRP algorithm, Australia Coverage

aus-ground_cover.png

Link to the data

DescriptorData linkLayer name
Persistent URLhttp://www.auscover.org.au/purl/landsat-seasonal-ground-cover 
GeoNetwork recordNot Available Yet

Sub-setting tool (experimental 'clip and ship')http://vegcover.com/chopper/ Seasonal Ground Cover 
QLD Tiff mosaics - FTP accesshost: ftp://qld.auscover.org.au/landsat/seasonal_fractional_cover/ground_cover/
username: anonymous 
ground_cover
Geoserver examplehttp://qld.auscover.org.au/geoserver/aus/wms?service=WMS&version=1.1.0&request=GetMap&layers=aus:ground_cover&styles=&bbox=-1910505.0,-4910195.0,2299785.0,-855425.0&width=768&height=739&srs=EPSG:3577&format=application/openlayers&time=2013-06aus:ground_cover
Forage report (QLD only)https://www.longpaddock.qld.gov.au/forage/groundcover.phpregional comparison report 
Timeseries Toolhttp://vegmachine.netground cover 

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.

Alternate title

Seasonal fractional ground cover for Australia derived from USGS Landsat images.

Abstract or Summary

The seasonal fractional ground cover product is derived directly from the seasonal fractional cover product, also produced by DSITI's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each calendar season. However, the seasonal fractional cover product 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.

Currently, this is an experimental product which has not been fully validated. 

Spatial and Temporal extents

ItemDetail
Spatial resolution (metres)30
Spatial coverage (degrees)north:-6; south:-45; west:108 east:160  
Temporal resolutionSeasonally - At least one image per standard calendar season.
Temporal coverage1990 ongoing
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://www.qld.gov.au/dsiti/

Credit

Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI 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 categorylzLandsat - all possible
instrumenttmthematic
productrereflective
whereqld, nsw, tas, sa, vic, nt, wa state
when myyyymmyyyymm season start date (1st day of month) and season end date (last day of month) 
processing stagedixground cover
data projectiona2Australian Albers Equal Area

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.

 

Validation status

This product is presently a demonstration product only (v.0.0) and has had minimal validation undertaken.

Related products

Landsat Seasonal Fractional Cover

SLATS Star Transects

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

When viewed from above, as by the satellite, mid- and over-storey vegetation obscures the ground layer fractions (bare ground, green ground cover and dry ground cover).  The seasonal fractional cover product, from which this ground cover product is derived, does not distinguish between these vegetation layers. As such, a high estimate in the green fraction may be due to green vegetation in any of the vegetation layers.

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-story foliage. This estimate of cover is an estimate of the combined persistent dry and persistent green layers. That is, all vegetation in the mid- and upper- stories. The mid and upper storage foliage is effectively removed from the estimates of cover and the ground cover estimate is therefore based only 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.

The algorithm to create the ground cover layer relies on two satellite products (seasonal fractional cover and seasonal persistent green), as well as an estimate of persistent dry derived from field data.   

A simplified description of the algorithm follows:

1. Base imagery required is seasonal fractional cover and seasonal persistent green products.

2. Estimate of persistent dry is derived from field data relationship

3. The ‘gap-fraction’ is calculated (total pixel area - pixel area composed of persistent dry - pixel area composed of persistent green)

4. Adjust all three fractions for each pixel using persistent dry and persistent green estimates

 

 

1.       Base Imagery

All seasonal ground cover images are derived directly from the equivalent date seasonal fractional cover image. See the seasonal fractional cover product metadata for details. Persistent green images can only be created retrospectively, so when the most current ground cover images are created using the most recent persistent green, whatever this may be. These images are tagged ‘_vinterim’ to indicate they will eventually be replaced, when the exact date persistent green becomes available. It is expected that the interim images will be almost identical in nature to the final products when they become available.

Fractional cover

Land cover fractions representing the proportions of green, non-green and bare cover retrieved by inverting multiple linear regression estimates and using synthetic endmembers in a constrained non-negative least squares unmixing model. 

The opening of the Landsat archive has provided an opportunity to composite imagery into representative seasonal images. 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.

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.  The underlying premise of the persistent green product is the separation of the fractional cover product green fraction into variable and trend components.  The trend component is the green fraction which is always present regardless of season and is therefore assumed to be associated with perennial vegetation and therefore ‘persistent’. 

 

2.       Persistent Dry

The adjustments made rely both on satellite estimates of persistent green and persistent dry, as the calculation of total gap depends on this.  While we currently have satellite estimates of persistent green vegetation, we do not have satellite estimates of persistent dry. We can, however, derive a relationship between persistent dry and persistent green from the field data. This relationship can then be used to estimate persistent dry from the satellite persistent green product.

 

3.       Canopy Gap Fraction

The visible mid FPC is the proportion of total mid-level green measurements multiplied by the proportion of the site you can see because it is not excluded by canopy leaves, branches or dead vegetation (canopy gap). The canopy gap is calculated from the field data as the total number of points observed in the over-storey divided by the total number of observations taken at the site. The mid FPC is the total mid storey green observations divided by the total number of observations at the site.

 

Data storage
 A 3 band (byte) image is produced:

o    band 1 – bare ground fraction (in percent) + 100

o    band 2 - green vegetation fraction (in percent) +100

o    band 3 – non-green vegetation fraction (in percent) + 100

Product version history

Version labelDetail
0.0Demonstration release

Metadata history

DateDetail
2014-05-01Metadata creation date
Tags:
Created by Bec Trevithick on 2014/04/11 14:16

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