Brief description
This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/23880. The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover across a season. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. The green and non-green fractions may include a mix of woody and non-woody vegetation.Lineage
Summary of processing: Landsat surface reflectance data > multiple single-date fractional cover datasets > medoid calculation for seasonal composite of fractional coverFurther details are provided in the Methods section.
Data Creation
Image Pre-Processing:
All input Landsat TM/ETM+/OLI 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. The imagery has been corrected for atmospheric effects, and bi-directional reflectance and topographic effects, using the methods detailed by Flood et al (2013). The result is surface reflectance standardised to a fixed viewing and illumination geometry. 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 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 selection of representative pixels through the determination of the medoid (multi-dimensional equivalent of the median) of three months (a season) of fractional cover imagery. 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. The value selected is a specific data point and not an averaged or blended value. It is robust against extreme values, inherently avoiding the selection of outliers, such as occurs when cloud or cloud shadow goes undetected. 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 seasonal fractional cover image. For further details on this method see Flood (2013).
Notes
CreditWe at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
This dataset was produced by the Joint Remote Sensing Research Program using data sourced from US Geological Survey.
This product captures variability in fractional cover at seasonal (i.e. three-monthly) time scales, forming a consistent time series from 1987 - present. It is useful for investigating inter-annual changes in vegetation cover and analysing regional comparisons. For applications that focus on non-woody vegetation, the ground cover product, derived from fractional cover, may be more suitable. For applications investigating rapid change during a season, monthly composite or single-date (available on request) fractional cover products may be more appropriate. Note: A new fractional cover algorithm will be implemented during 2021, based on additional field validation and a new machine learning approach.
Data Quality Assessment Scope
local :
dataset
1) The input imagery was processed to level L1T by the USGS. Geodetic accuracy of the product depends on the image quality and the accuracy, number, and distribution of the ground control points.
2) The fractional cover model was compared to samples drawn from 1500 field reference sites.
Data Quality Assessment Result
local :
Quality Result
1) The USGS aims to provide image-to-image registration with an accuracy of 12m. Refer to the L8 Data Users Handbook for more detail.
2) The fractional cover model achieved an overall model Root Mean Squared Error (RMSE) of 11.6% against field reference sites.
Created: 2013-11-25
Issued: 2021-09-13
Modified: 2024-09-24
Data time period: 1987-12-01
text: Australia
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Point-of-truth metadata URL- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/f0c32576-9ad7-4c9c-9aa9-22787867e28b
- global : f0c32576-9ad7-4c9c-9aa9-22787867e28b
