Fractional cover - Landsat, Joint Remote Sensing Research Program algorithm, Australia coverage

Fractional cover - Landsat, Joint Remote Sensing Research Program algorithm, Australia coverage

Link to the data

Data licence and Access rights

Alternate title

Fractional ground cover for Australia derived from USGS Landsat images

Abstract or Summary

Landcover 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. Ground cover is defined as the vegetation (living and dead), biological crusts and stones that are in contact with the soil surface (Muir et al, 2011). Ground cover information is important for soil erosion and nutrient flux estimates into the stream network. Ground cover levels may vary due to anthropogenic management of grazing enterprises and agricultural land management practices, or natural changes in seasonal rainfall. The fractions can change rapidly following rainfall or due to grazing so a mosaic of single data imagery will not be seamless

Spatial and Temporal extents

Point of contact

Credit

Joint Remote Sensing Research Program.
Landsat 5 TM and Landsat 7 ETM+ images were acquired from United States Geologic Survey.

Keywords

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

Quantitative Attribute Accuracy Assessment
Attribute Accuracy Value: fractional Ground Cover RMSE is 11.8%.

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.

Validation status

The overall error of the product is 11.8%, while the error margins vary for the three different layers: green RMSE: 11.0%, non-green RMSE: 17.4% and bare RMSE: 12.5%

Related products

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

References

Algorithm summary

Image Pre-Processing

Landsat TM and ETM+ L1T images were acquired from the USGS on an annual basis in the mid to late dry season to cover all of Australia. These values are scaled radiance values, after calibration. Georegistration is also by USGS. For further information, see the USGS website http://glovis.usgs.gov/, from where this data was downloaded. These dry season dates were selected to enhance spectral contrast between evergreen tree and shrub canopies and the predominantly senescent ground cover. All images are corrected to minimise the confounding effects of geometric distortion, radiometric variability and illumination geometry using the procedure described in Danaher et al (2010). Angle adjustment uses a Walthall-like equation, with coefficients tuned from all Landsat overlaps available at the time. Topographic correction uses the simple adjustment described in Dymond et al. (2001) A simple physical model of vegetation reflectance for standardising optical satellite imagery. Rem. Sens. Env. Image edges are trimmed to remove ragged ends of scanlines, where data is missing for some bands and not others. Areas of cloud, associated shadow, topographic shadow, cast shadow and water contamination are masked, with the mask type encoded as a fourth band. The bare soil, green vegetation and non-green vegetation endmenbers are calculated using models linked to an intensive field sampling program whereby more than 600 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 using the derived endmembers has an overall model Root Mean Squared Error (RMSE) of 11.8%. 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 - Mask Layer encoding cloud, cloud shadow, water and areas with topographic shadow. A value of 1 indicates good data. Value of 0 indicates no-data. Value of 2 indicates unmixing error was excessive, Value of 3 indicates water was detected in the pixel. Value of 4 indicates the pixel had cast shadow. Value of 5 indicates the pixel incidence or exidence angle exceeded 80 degrees. Value of 6 indicates a cloud shadow was detected. Value of 7 indicates a cloud was detected.

Satellite and sun azimuth and zenith angles are calculated per pixel directly from the orbital geometry. Satellite orbit deduced from the orientation of the data region of the image (which makes some assumptions about the clipping of the data)

Incidence, exitance and relative azimuth angles are the satellite and sun angles, but transformed so that they are relative to the plane of the surface terrain. The incidence angle is the angle between the sun and the normal to the surface. The exitance angle is the angle between the satellite and the normal to the surface. The relative azimuth is the angle lying in the plane of the surface, between the projections into that plane of the lines to the sun and satellite

Landsat based water index. The index was developed using Canonical Variates Analysis (CVA) of visually identified water and non-water signatures in radiometrically calibrated Queensland wide Landsat imagery. The index is a linear combination of bands, Log transformations of bands and interactive band terms. See Danaher, T. and Collett, L. 2006. Development, optimisation and multi-temporal application of a simple Landsat-based water index. Proc. of 13th Australasian Remote Sensing and Photogrammetry conference, Nov., 2006, Canberra, Aust. The optimal threshold for water masking: water index less than or equal to 68

Topographic shadow mask. This mask was created by  a ray casting technique described by Robertson, K.   Spatial transformation for rapid scan-line  surface shadowing, IEEE Compter Graphics and Applications, 1989.  However, it was assumed that the light source was at infinity - i.e. all light  is parallel and therefore the adjustment for perspective was  not required. The solar zenith angle was adjusted by DELTASZ degrees before  computing. A positive value indicates the sun is placed lower.  The output from the ray casting is an estimate of the  height in metres that each pixel is above the shadow line

Cloud 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. Extra test performed to mask saturated cloud. /n 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.

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 – Mask Layer.

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