<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).

<p> Digital Cover Photography (DCP) upward-looking images are collected at least twice per year to capture vegetation cover at Calperum SuperSite. These images can be used to estimate Leaf area index (LAI), Crown Cover or Foliage Projective Cover (FPC). The images are captured at the times of estimated maximum and minimum LAI.</p> <p> The Calperum Mallee SuperSite was established in 2011 and is located on Calperum Station with research plots located in mallee woodland (burnt in 2014), Callitris woodland and a river floodplain (recovering from extensive grazing), consisting of black box, river red gum and lignum. The core 1 ha plot is located in mallee woodland. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/calperum-mallee-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>

Vertical plant profiles for the Australian continent were derived through integration of ICESat GLAS waveforms with ALOS PALSAR and Landsat data products. Co-registered Landsat Foliage Projected Cover (FPC) and ALOS PALSAR L-band HH and HV mosaics were segmented to generate objects with similar radar backscatter and cover characteristics. Within these, height, cover, age class and L-band backscatter characteristics were summarised based on the ICESat and Landsat time-series and ALOS PALSAR datasets.

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.

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.

RSMA measures change in the relative contributions of photosynthetic vegetation (PV, or GV green vegetation), non-photosynthetic vegetation (NPV) and soil reflectance compared to a baseline date. These spectral changes correspond to changes in fractional cover relative to the baseline date. Full details on the RSMA method are presented in Okin (2007). One of the key advantages of the RSMA, its insensitivity to changes in soil spectra, is a result of the fact that it does not require us to know the soil reflectance profile for a region. This strength is also the cause of a major weakness in RSMA. Since the measure is relative to a baseline date, and the absolute cover levels for every pixel are unknown at the baseline, the RSMA does not convey the absolute cover levels at any other point in time. However, if the absolute cover levels are known at any point in time, it is theoretically possible to convert the RSMA to absolute relative spectral mixture analysis (ARSMA).<br> 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 bird's-eye-view. Therefore, dense canopy may prevent observation of significant soil exposure.

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.

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/TERN/169dbb12-846f-4536-9dab-e31378d16b41. 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.

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.