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Product pages » Linear seasonal persistent green trend - Landsat, QLD DSITIA algorithm, QLD Coverage

Linear seasonal persistent green trend - Landsat, QLD DSITIA algorithm, QLD Coverage

Last modified by Matt Paget on 2016/06/17 14:21

Linear seasonal persistent green trend - Landsat, QLD DSITIA algorithm, QLD Coverage

seasonal.jpg

Figure 1: Caption

Link to the data

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

Tiff mosaicsNot Available Yetseasonal_cover
Geoserver exampleNot Available YetseasonalCoverLatest

Data licence and Access rights

ItemDetail
RightsCopyright 2010-2015. JRSRP. Rights owned by the Joint Remote Sensing Research Project (JRSRP).
LicenceCreative Common Attribution (CC-BY) 3.0
AccessWhile every care is taken to ensure the accuracy of this information, the Department of Science, Information Technology, Innovation and the Arts (DSITIA) 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

Linear trend of seasonal fractional vegetation cover for Queensland derived from USGS Landsat images

Abstract or Summary

Seasonal persistent green cover is derived from seasonal cover using a weighted smooth spline fitting routine. This weights a smooth line to the minimum values of the seasonal green cover. This smooth minimum is designed to represent the slower changing green component, ideally consisting of perennial vegetation including over-storey, mid-storey and persistent ground cover. The seasonal persistent green is then summarised using simple linear regression, and the slope of the fitted line is captured in this product. The original units are percentage points per year. Values are later truncated and scaled.

Spatial and Temporal extents

ItemDetail
Spatial resolution (metres)30
Spatial coverage (degrees)north:-10; south:-29; west:138 east:155  
Temporal resolutionSeasonally - At least one image per standard calender season.
Temporal coverage1986 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, Innovation and the Arts
PositionScientist (Remote Sensing)
Emailrebecca.trevithick@science.dsitia.qld.gov.au
RolepointOfContact
AddressRemote Sensing Centre, DSITIA, EcoSciences Precinct
Telephone+61 7 3170 5679
URLhttp://derm.qld.gov.au

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 Thematic Mapper (TM) data, which has a spatial resolution of 30 m. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 metres at these control points. However, it is possible that errors up to ±50 meters occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) 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 Thematic Mapper (TM) data, which has a spatial resolution of 30 m. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 metres at these control points. However, it is possible that errors up to ±50 meters occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) 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 product conforms to the AusCover standard naming convention. The standard form of this convention is:

<satellite category code><instrument code><product code>_<where>_<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
processing stagedimseasonal fractional from surface reflectance inputs
data projectiona2Australian Albers Equal Area

Related products

Landsat Fractional Cover

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

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.
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.

Data storage
A 4 band (byte) image is produced:

  • band 1 – bare ground fraction (in percent) + 100
  • band 2 - green vegetation fraction (in percent) +100
  • band 3 – non-green vegetation fraction (in percent) + 100
  • band 4 – Error Layer representing the RMSE between the predicted pixel value and the actual pixel value on a nominal scale of 100 (no error) to 200 (very large error).

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
Tags:
Created by Robert Denham on 2014/03/24 13:27

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