The Area of Applicability (AOA) describes the area to which a predictive model can reliably be applied, based on the predictor space covered by the underlying training data. It was evaluated following the approach proposed by Meyer and Pebesma (2021).<br></br> The JRSRP seasonal surface reflectance composites between winter 2014 and winter 2024 were used as a proxy for the range of representative surface reflectance values likely to be encountered across the continent under varying environmental conditions from which fractional cover predictions are made. The AOA of the FCv3 model was computed for each seasonal surface reflectance composite, then summarised as a frequency map representing the proportion of seasons that a location was outside the AOA.<br></br> For each state, five files are provided: an annual product summarising the AOA across all seasons, and four showing seasonal AOA frequencies for summer, autumn, winter, and spring.<br></br>

Digital Hemispheric Photography (DHP) upward-looking images are collected up to three times 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 Cover Photography was also collected at Boyagin from 2019.

The MODIS Land Condition Index (LCI) is an index of total vegetation cover (green and non-photosynthetic vegetation ), and so is also an index of soil exposure. The LCI is a normalised difference index based on MODIS bands in the mid-infrared portion of the spectrum. The index is produced from 500-m MODIS nadir BRDF adjusted reflectance (NBAR) data. As with all products derived from passive remote sensing imagery, this product represents the world as seen from above. Therefore, the cover recorded by this product represent what would be observed from a birds-eye-view. Therefore, dense canopy may prevent observation of significant soil exposure.

<p>Digital Hemispherical Photography (DHP) upward-looking images were collected annually to capture vegetation and crown cover at Whroo Dry Eucalypt SuperSite. These images can be used to estimate Leaf area index (LAI), Crown Cover or Foliage Projective Cover (FPC). </p><p> The site was established in 2010 in box woodland dominated by <em>Eucalyptus microcarpa</em> (grey box) and <em>eucalyptus leucoxylon</em> (yellow gum). For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/whroo-dry-eucalypt-supersite/. </p><p> Other images collected at the site include photopoints, phenocam time-lapse images taken from fixed overstorey cameras and ancilliary images of fauna and flora. </p>

The woody vegetation extent for Queensland is attributed with an estimated age in years since the last significant disturbance. The method uses a sequential Conditional Random Fields classifier applied to Landsat time series starting 1988 to predict woody cover over the time period. A set of heuristic rules is used to detect and track regrowing woody vegetation in the time series of woody probabilities and record the approximate start and end dates of the most recent regrowth event. Regrowth detection is combined with the Statewide Land and Trees Study (SLATS) Landsat historic clearing data to provide a preliminary estimate of age since disturbance for each woody pixel in the woody extent. The 'last disturbance' may be due to a clearing event or other disturbance such as fire, flood, drought-related death etc. Note that not all recorded disturbances may result in complete loss of woody vegetation, so the estimated age since disturbance does not always represent the age of the ecosystem. The age since disturbance product is derived from multiple satellite image sources and derived products which represent different scales and resolutions: Landsat (30&nbsp;m), Sentinel-2 (10&nbsp;m) and Earth-i (1&nbsp;m).

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&nbsp;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 here <a href="https://portal.tern.org.au/metadata/TERN/de2d53ec-1c00-46ac-bd01-d253ab0f2eb2">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.

<p>The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Landsat TM/ETM+/OLI 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.</p>

<p>Digital Cover Photography (DCP) upward-looking images were collected annually to capture vegetation cover at the TERN Karawatha Peri-Urban SuperSite. These images can be used to estimate Leaf area index (LAI), Crown Cover or Foliage Projective Cover (FPC). </p><p> The Karawatha Peri-Urban SuperSite was established in 2007 and decommissioned in 2018. The site was located in Eucalypt forest at Karawatha Forest. For additional site information, see https://deims.org/f15bc7aa-ab4a-443b-a935-dbad3e7101f4 . </p><p> Other images collected at the site include photopoints and ancilliary images of fauna and flora. </p>

<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>