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Product pages » Seasonal surface reflectance - Landsat, JRSRP algorithm, Australia coverage

Seasonal surface reflectance - Landsat, JRSRP algorithm, Australia coverage

Last modified by Bec Trevithick on 2017/10/11 16:18

Seasonal surface reflectance - Landsat, JRSRP algorithm, Australia coverage

surface_reflectance.png

Link to the data

DescriptorData linkLayer name
Dataset digital object identifier (DOI)Not Available Yet 
GeoNetwork recordNot Available Yet

QLD Tiff mosaicsftp://qld.auscover.org.au/landsat/surface_reflectance
surface_reflectance
Sub-setting tool (experimental 'clip and ship')http://vegcover.com/chopper/Seasonal Surface Reflectance

Data licence and Access rights

ItemDetail
RightsCopyright 2010-2020. JRSRP. 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 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

The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Landsat TM/ETM+ imagery. The imagery has been composited over a season to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. This creates a regular time series of reflectance values which captures the variability at seasonal time scales. The benefits are a regular time series with minimal missing data or contamination from various sources of noise as well as data reduction. Each season has exactly one value (per band) for each pixel (or is null, i.e., missing), and the value for that season is assumed to be the representative of the whole season. The algorithm is based on the medoid (in reflectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values.

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 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 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,nsw,vic,tas,nt,sa,wastate
when myyyymmyyyymm season start date (1st day of month) and season end date (last day of month) 
processing stagedbiseasonal surface reflectance
data projectiona2Australian Albers Equal Area


Related products

References

ItemDetail or link
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

Robertson, P. Spatial transformations for rapid scan-line surface shadowing. IEEE Comput. Graph.Appl. 1989, 9, 30–38.

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

A brief overview of the algorithm is presented here. Please see the published paper (Flood, 2013) on this product for full details. 

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 and topographic effects, using the methods detailed by Flood et al. (2013). 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, and the methods described by Robertson.

The seasonal composites were calculated using the medoid in the 6-dimensional space of reflectance values from the six Landsat reflective bands. The medoid is a “measure of centre” of a multi-variate set of points, similar in nature to the median of a univariate dataset. The medoid has similar desirable properties to the univariate median. In a general cluster of points, in n-dimensional space, the medoid will lie roughly in the centre of the cluster, making it a good choice as representative of that set of points. Most importantly, it is robust against the presence of outliers in the set, until at least half of the points are to be considered as outliers, after which it breaks down.

In estimating a value which is representative of the season, we choose to add a restriction on the number of input points. If a given pixel has less than three observations available for the season, after masking, we define the result as missing, on the principle that we do not have enough data to know how representative our choice might be. Neither the medoid nor the geometric median are robust against a single outlier in the case of less than three observations.

Product version history

Version labelDetail
1.0Initial release

Metadata history

DateDetail
2014-06-20Metadata creation date
Tags:
Created by Bec Trevithick on 2014/06/19 12:57

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