The Statewide Landcover and Trees Study (SLATS) monitors woody vegetation extent and changes in Queensland using Sentinel-2 satellite imagery as its primary tool. This dataset provides annual summaries of woody vegetation clearing and regrowth from the 2018–19 reporting period onward, aligning with an updated Sentinel-2-based methodology introduced in 2018. <br></br> The data is presented as annual time series summaries, with each year’s data corresponding to a nominal August-to-August reporting period. Summary statistics are provided at the state-wide scale, as well as for administrative boundaries, natural resource management regions and divisions, and other authoritative datasets. <br></br> This multi-year dataset includes data from the 2018–19 onwards SLATS reporting periods. It supersedes and is not directly comparable with SLATS data published for reporting periods up to and including 2017–18, due to a methodological change. Note that regrowth was not reported in 2018–19; values for regrowth in that year are represented as zero in the dataset.

<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>

The seasonal dynamic reference cover method product compares the current ground cover level of each pixel to a reference pixel based on the historical timeseries and is available for Queensland from 1987 to present. It is 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.<br> This product is based upon the JRSRP Fractional Cover 3.0 algorithm.

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/fe9d86e1-54e8-4866-a61c-0422aee8c699. The seasonal fractional ground cover product shows the proportion of bare ground, green and non-green ground cover and is derived directly from the seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. However, the seasonal fractional cover product does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% woody vegetation. Currently, this is an experimental product which has not been fully validated.

<p>Tree structural characteristics are collected at the centre of a site, usually in conjunction with the Statewide Landcover and Trees Study (SLATS) star transect field data. The basal wedge is first used to identify a sample of trees then direct measurements are taken of each tree, which constitute the tree structural characteristics.Tree structural measurements have been collected at several locations across Australia (including the formally known AusCover Supersites) to relate field-based measurements to satellite data products, such as Landsat-derived ground cover estimates.</p> <p>Data can be downloaded from https://field.jrsrp.com/ by selecting the combination Field and Tree Structure.</p>

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>

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.

These Seasonal Ground Cover Statistics datasets provide long-term statistical summaries derived from the sentinel2-based seasonal ground cover v3 product, calculated separately for each ground cover fraction. Two distinct product types are available, differentiated by their seasonal aggregation and statistical content. <br></br> 1. All-Seasons Percentile Summary (Product Code: aj6) This product summarises the 5th and 95th percentiles across all seasons for each ground cover fraction. It is delivered as a 2-band image, capturing the overall long-term minimum and maximum percentiles across the full time series. <br></br> 2. Seasonal Statistics per Fraction (Product Code: aj7) For each season and ground cover fraction, a separate raster image is generated for the full time series of available imagery. Each image includes the following statistical layers: 5th percentile (minimum), Mean, Median, 95th percentile (maximum), Standard deviation, and Observation count. <br></br>

<p data-renderer-start-pos="234">The Seasonal Ground Cover Summary Statistics datasets provide long-term statistical summaries derived from the seasonal ground cover v3 data, calculated separately for each fraction. Two distinct product types are available, differentiated by their seasonal aggregation and statistical content.</p> <ol start="1" data-indent-level="1"> <li> <p data-renderer-start-pos="533">Seasonal Statistics per Fraction (Product Code: dpi)<br />For each season and ground cover fraction, a separate raster image is generated for the full time series of available imagery. Each image includes the following statistical layers: include:<br />band 1 &ndash; 5th percentile minimum;<br />band 2 &ndash; mean value for pixel over full time series;<br />band 3 &ndash; median value for pixel over full time series;<br />band 4 &ndash; 95th percentile maximum;<br />band 5 &ndash; Standard deviation - the temporal standard deviation of the full time-series;<br />band 6 &ndash; Count - the number of observations statistics for that pixel are based on.</p> </li> <li> <p data-renderer-start-pos="1126">All-Seasons Percentile Summary (Product Code: dph)<br />This product summarises the 5th and 95th percentiles across all seasons for each ground cover fraction. It is delivered as a 2-band image, capturing the overall long-term minimum and maximum percentiles across the full time series (currently 1990-2020).</p> </li> </ol> <p data-renderer-start-pos="1435">Version 4 update: Dataset filenames have been revised to now include fraction and season tags, replacing multiple stage codes. Related products are grouped under a single code for improved clarity and usability. Additionally, band values are now expressed as percentages (0&ndash;100) to match the parent seasonal ground cover dataset, rather than using the previous percent + 100 scaling.</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.