Foliage Projective Cover (FPC) is the percentage of ground area occupied by the vertical projection of foliage. The Remote Sensing Centre FPC mapping is based on regression models applied to dry season (May to October) Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI imagery for the period 1988-2014. An annual woody spectral index image is created for each year using a multiple regression model trained from field data collected mostly over the period 1996-1999. A robust regression of the time series of the annual woody spectral index is then performed. The estimated foliage projective cover is the prediction at the date of the selected dry season image for 2014. Where this deviates significantly from the woody spectral index for that date, further tests are undertaken before this estimate is accepted. In some cases, the final estimate is the woody spectral index value rather than the robust regression prediction. The product is further masked to remove areas classified as non-woody.

Three maps are available: 1) foliage projective cover, 2) forest extent, attributed with the foliage projective cover and 3) accuracy of the extent maps, which also acts as masks of forest and other wooded lands. Each pixel in map 1 estimates the fraction of the ground covered by green foliage. Each pixel in map 2 shows two pieces of information. The first is a classification of whether the vegetation is forest or not. The pixels classified as forest are attributed with the second piece of information: the foliage projective cover. Each pixel in map 3 is a class that provides information on the classification accuracies of the woody extent. These maps are derived from Landsat.

For some time, Remote Sensing Sciences, has produced Foliage Projective Cover (FPC) using a model applied to Landsat surface reflectance imagery, calibrated by field observations. An updated model was developed which relates field measurements of FPC to 2-year time series of Normalized Difference Vegetation Index (NDVI) computed from Landsat seasonal surface reflectance composites. The model is intended to be applied to Landsat and Sentinel-2 satellite imagery, given their similar spectral characteristics. However, due to insufficient field data coincident with the Sentinel-2 satellite program, the model was fitted on Landsat imagery using a significantly expanded, national set of field data than was used for the previous Landsat FPC model fitting. The FPC model relates the field measured green fraction of mid- and over-storey foliage cover to the minimum value of NDVI calculated from 2-years of Landsat seasonal surface reflectance composites. NDVI is a standard vegetation index used in remote sensing which is highly correlated with vegetation photosynthesis. The model is then applied to analogous Sentinel-2 seasonal surface reflectance composites to produce an FPC image at Sentinel-2 spatial resolution (i.e. 10 m) using the radiometric relationships established between Sentinel-2 and Landsat in Flood (2017). This is intended to represent the FPC for that 2-year period rather than any single date, hence the date range in the dataset file name. The dataset is generally expected to provide a reasonable estimate of the range of FPC values for any given stand of woody vegetation, but it is expected there will be over- and under-estimation of absolute FPC values for any specific location (i.e. pixel) due to a range of factors. The FPC model is sensitive to fluctuations in vegetation greenness, leading to anomalies such as high FPC on irrigated pastures or locations with very green herbaceous or grass understoreys. A given pixel in the FPC image, represents the predicted FPC in the season with the least green/driest vegetation cover over the 2-year period assumed to be that with the least influence of seasonally variable herbaceous vegetation and grasses on the more seasonally stable woody FPC estimates. The two-year period was used partly because it represents a period relative to tree growth but was also constrained due to the limited availability of imagery in the early Sentinel-2 time series. The FPC dataset is constrained by the woody vegetation extent dataset for the FPC year.

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/24070. Two fractional cover decile products, green cover and total cover, are currently produced from the historical timeseries of seasonal fractional cover images. These products compare, at the per-pixel level, the level of cover for the specific season of interest against the long term cover for that same season. For each pixel, all cover values for the relevant seasons within a baseline period (1988 to 2013) are classified into deciles. The cover value for the pixel in the season of interest is then classified according to the decile in which it falls.

<p>Hemispherical photography has been collected across Australia to characterise plant canopy cover and structure, and to study leaf area index. Hemispherical photography is a technique for quantifying plant canopies via photographs captured through a digital camera with hemispherical or fisheye lens. Such photographs can be captured from beneath the canopy, looking upwards, (orientated towards zenith) or above the canopy looking downwards. These measurements have typically been collected in conjunction with the Statewide Landcover and Trees Study (SLATS) star transects field data together with plant canopy analysers such as LAI-2200 and CI-110.</p> <p>Data can be downloaded from https://field.jrsrp.com/ by selecting the combination Field and Hemispheric imagery. Photographs can be accesed through the right-hand side panel, or by finding the file_loc attribute in the csv file. </p>

Digital Cover Photography (DCP) upward-looking images will be collected up to twice per year to capture vegetation cover at Boyagin Wandoo Woodland SuperSite. These images can be used to estimate Leaf area index (LAI), Crown Cover or Foliage Projective Cover (FPC). The Boyagin Wandoo Woodland SuperSite was established in 2017 in Wandoo Woodland, which is surrounded by broadacre farming. About 80% of the overstorey cover is <em>Eucalyptus accedens</em>. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/boyagin-wandoo-woodland-supersite/ . Digital Hemispheric Photography (DHP) has also been collected at Boyagin SuperSite.

<p>Digital Cover Photography (DCP) upward-looking images are collected up to three times per year to capture vegetation cover at Samford Peri-Urban SuperSite. These images can be used to estimate Leaf Area Index (LAI), Crown Cover or Foliage Projective Cover (FPC). </p><p> The Samford Peri-Urban SuperSite was established in 2010 in remnant fringe eucalypt forest, near urban development in the Samford Valley. The upper storey is dominated by <em>Corymbia intermedia</em>, <em>Eucalyptus siderophloia</em> and <em>Lophostemon suaveolens</em>. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/samford-peri-urban-supersite/ . </p><p> Other images collected at the site include photopoints, phenocam time-lapse images taken from fixed overstorey cameras, panoramic landscape and ancillary images of fauna and flora.</p>

Statewide composite of fire scars (burnt area) derived from all available Sentinel-2 images acquired over Queensland. It is available in both monthly and annual composites. Fire scars have been mapped using an automated change detection method, with supplementary manual interpretation. This data contains both automated and manually edited data.

<p>Digital Hemispherical Photography (DHP) upward-looking images are collected twice per year to capture vegetation and crown cover at the Warra Tall Eucalypt SuperSite. These images are used to estimate Leaf area index (LAI). The images are captured at the times of estimated maximum and minimum LAI. </p> <p> The Warra Tall Eucalypt SuperSite was established in 2012 and is located in a stand of tall, mixed-aged <em>Eucalyptus obliqua</em> forest (1.5, 77 and &gt;250 years-old) with a rainforest / wet sclerophyll understorey and a dense man-fern (<em>Dicksonia antarctica</em>) ground-layer. The site experienced a fire in January 2019, which consumed the ground layer and killed a high proportion of the understorey trees but stimulated dense seedling regeneration. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/warra-tall-eucalypt-supersite/. </p><p> Other images collected at the site include photopoints, phenocam time-lapse images taken from fixed under and overstorey cameras, panoramic landscape and ancillary images of fauna and flora. </p>

This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product here <a href="https://portal.tern.org.au/metadata/24072">Seasonal dynamic reference cover method - Landsat, JRSRP algorithm version 3.0, Queensland Coverage</a>. The seasonal dynamic reference cover method images are created using a modified version of the dynamic reference cover method developed by <a href="https://doi.org/10.1016/j.rse.2012.02.021">Bastin et al (2012)</a>. This approach calculates a minimum ground cover image over all years to identify locations of most persistent ground cover in years with the lowest rainfall, then uses a moving window approach to calculate the difference between the window's central pixel and its surrounding reference pixels. The output is a difference image between the cover amount of a pixel's reference pixels and the actual cover at that pixel for the season being analysed. Negative values indicate pixels which have less cover than the reference pixels. <br> The main differences between this method and the original method are that this method uses seasonal fractional ground cover rather than the preceding ground cover index (GCI) and this method excludes cleared areas and certain landforms (undulating slopes), which are considered unsuitable for use as reference pixels.