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.

Field spectroradiometer measurements have been collected at several locations across Australia (formally known as the AusCover Supersites) to relate field based measurements to satellite data products, such as Landsat and MODIS NBAR products. Collected in collaboration with multiple government and research-based institutions.

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.

The SLATS star transect field dataset has been compiled as a record of vegetative and non-vegetative fractional cover as recorded in situ according to the method described in <a href="https://www.researchgate.net/publication/236022381_Field_measurement_of_fractional_ground_cover">Muir et al (2011)</a>. The datasets are a combination of vegetation fractions collected in three strata - non-woody vegetation including vegetative litter near the soil surface, woody vegetation less than 2 metres, and woody vegetation greater than 2 metres - at homogeneous areas of approximately 1 hectare. This dataset is compiled from a variety of sources, including available sites from the ABARES ground cover reference sites database.

<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>

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.

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.

<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>

The Sentinel-2 seasonal fractional ground cover product shows the proportion of bare ground, green and non-green ground cover and is derived directly from the Sentinel-2 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 (10&nbsp;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, the persistent green product has only been produced at 30&nbsp;m pixel resolution based on Landsat imagery, resulting in this Sentinel-2 seasonal ground cover product having a medium 30&nbsp;m pixel resolution also. This is an experimental product which has not been fully validated. This product is similar to the <a href="https://portal.tern.org.au/metadata/TERN/fe9d86e1-54e8-4866-a61c-0422aee8c699 ">Seasonal ground cover - Landsat, JRSRP algorithm Version 3.0, Australia Coverage</a> which is based on a different satellite sensor.

These Seasonal Fractional Cover Summary Statistics datasets provide long-term statistical summaries derived from the <a href="https://portal.tern.org.au/metadata/TERN/13810293-c6b5-442b-bfcd-817700738e0d">sentinel2-based seasonal fractional cover v3 product</a>, calculated separately for each fraction. <br></br> For each 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.