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Product pages » Seasonal persistent green - Landsat, JRSRP algorithm, Australia coverage

Seasonal persistent green - Landsat, JRSRP algorithm, Australia coverage

Last modified by Bec Trevithick on 2017/10/10 16:41

aus-persistent_green.png

Link to the data

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

Sub-setting tool (experimental 'clip and ship')http://vegcover.com/chopper/Seasonal Persistent Green 
TIFF mosaics - FTP accessftp://qld.auscover.org.au/landsat/seasonal_fractional_cover/persistent_green/persistent_green
Geoserver Examplehttp://qld.auscover.org.au/geoserver/aus/wms?service=WMS&version=1.1.0&request=GetMap&layers=aus:persistent_greenpersistent_green
Timeseries Analysishttp://vegmachine.net Persistent Green

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 persistent green vegetation cover for Queensland derived from USGS Landsat images

Abstract or Summary

An estimate of persistent green cover per season.This is intended to estimate the portion of vegetation that does not completely senesce within a year, which primarily consists of woody vegetation (trees and shrubs), although there are exceptions where non-woody cover remains green all year round. Derived by fitting a multi-iteration minimum weighted smoothing spline through the green fraction of the seasonal fractional cover (dim) time series.

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

  • 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 mapper
productrereflective
whereqld, nsw, vic, tas, sa, nt, tasstate
when myyyymmyyyymm season start date (1st day of month) and season end date (last day of month) 
processing stagedjaseasonal persistent green cover
data projectiona2Australian Albers Equal Area


Related products

Landsat Fractional Cover

Persistent Green-Vegetation Fraction and Wooded Mask - Landsat, Australia coverage

SLATS Star Transects

References

ItemDetail or link
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.
PublicationFlood, 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

Flood, N. (2014) Continuity of Reflectance Data between Landsat-7 ETM+ and Landsat-8 OLI for Both Top-of-Atmosphere and Surface Reflectance: A study in the Australian Landscape. Remote Sens. 2014, 6, 83-109. doi:10.3390/rs6097952

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.
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.
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

Image Pre-Processing

All input Landsat TM/ETM+ 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, on the assumption that they would contribute little, and would add extra noise in the form of undetected cloud, shadow, and mis-registration (which is a greater risk in very cloudy images). The imagery has been corrected for atmospheric effects, and bi-directional reflectance and topographic effects, using the methods detailed by Flood et al (2013).This is a preprocessing scheme for minimising atmospheric, topographic and bi-directional reflectance effects on Landsat-5 TM, Landsat-7 ETM+ and SPOT-5 HRG imagery. The approach involves atmospheric correction to compute surface-leaving radiance, and bi-directional reflectance modelling to remove the effects of topography and angular variation in reflectance. The bi-directional reflectance model has been parameterised for eastern Australia, but the general approach is more widely applicable. The result is surface reflectance standardised to a fixed viewing and illumination geometry. The method can be applied to the entire record for these instruments, without intervention. The resulting imagery is expressed as surface reflectance. 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).

Fractional Cover Model

The bare soil, green vegetation and non-green vegetation endmembers for the combination of Landsat-5 TM and Landsat-7 are calculated using models linked to an intensive field sampling program whereby more than 1500 sites covering a wide variety of vegetation, soil and climate types were sampled to measure overstorey and ground cover following the procedure outlined in Muir et al (2011). A constrained linear spectral unmixing is applied to the image archive using the derived endmembers and has an overall model Root Mean Squared Error (RMSE) of 11.6%. Values are reported as percentages of cover plus 100. The fractions stored in the 4 image layers are:  Band1 - bare (bare ground, rock, disturbed), Band2 - green vegetation, Band3 - non green vegetation (litter, dead leaf and branches), Band4 - Model fitting error.

Seasonal Compositing

The method of compositing used in the creation of the seasonal fractional cover product is the selection of representative pixels through the determination of the medoid of three months (a season) of fractional cover imagery.The medoid is a multi-dimensional equivalent of the median. Within the three-dimensional space of the cover fractions, 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, in terms of the cover fractions. Like the median, the value selected is a specific data point and not an averaged or blended value made up of different image layers.The pixel selected is therefore a true representative pixel. In addition, because it selects the centrally located point in the multi-dimensional space, it is robust against extreme values, inherently avoiding the selection of outliers, such as occurs when cloud or cloud shadow goes undetected. For further details on this method see Flood (2013). The nature of the medoid means that for each pixel in the representative image created 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 resulting seasonal fractional cover image.

Persistent Green Fractional Cover

Smoothing splines are fitted in multiple iterations per pixel through the full time series of seasonal fractional cover (green fraction only). At each iteration, zero weight is given to observations that lie above the spline, and observation below the line are weighted proportion to the size of the residual. Observations greater than 3 standard deviations from the residual mean are given zero weight, and those between 2 and 3 standard deviations are given less weight,  this avoids contamination by outliers. Persistent green fractional cover for each season is estimated from the final spline iteration at each seasonal time step. Values reported are as for fractional cover, ie. percentages of cover plus 100.

Areas with frequent seasonal fractional cover data gaps due to cloud may produce unreliable estimates of persistent green cover.

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

  • band 1 – persistent green vegetation cover (in percent) + 100

Product version history

Version labelDetail
1.0Initial release

Metadata history

DateDetail
2013-11-25Metadata creation date
2014-02-19Metadata revision - algorithm updated and references added
2015-03-13Added a persistent URL
2017-10-10Updated image to reflect greater coverage
Tags:
Created by Bec Trevithick on 2015/04/22 10:45

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