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.

This product has been superseded and will not be processed from early 2023.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.

<p>The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover across a season. It is a spatially explicit raster product that predicts vegetation cover at medium resolution (30&nbsp;m per-pixel) for each 3-month calendar season across Australia from 1987 to the present. The green and non-green fractions may include a mix of woody and non-woody vegetation. </p> <p>A 3 band (byte) image is produced:</p> <p>band 1 – bare ground fraction (in percent),</p> <p>band 2 - green vegetation fraction (in percent),</p> <p>band 3 – non-green vegetation fraction (in percent).</p> <p>The no data value is 255.</p>

An estimate of persistent green cover per season across Australia from 1989 to the present season, minus 2 years. This is intended to estimate the portion of vegetation that does not completely senesce within a year, which primarily consists of woody vegetation (trees and shrubs), although there are exceptions where non-woody cover remains green all year round. It is derived by fitting a multi-iteration minimum weighted smoothing spline through the green fraction of the seasonal fractional cover (dp1) time series. A single band image is produced: persistent green vegetation cover (in percent). The no data value is 255.

The acquisition of sunphotometer measurements are critical to capture vital data on atmospheric properties during airborne hyperspectral imaging campaigns as well as for measurements coinciding with the overpass of satellite sensors. The atmospheric properties measured are used in atmospheric correction of the remotely sensed image data. This data is primarily for input into atmospheric correction systems. It may also prove of use in validation of aerosol products such as MOD04 and the reflectance change method developed as part of CRC-SI project 4.1 which may be integrated into the Auscover 19 band reflectance product processing. It can also be used to check methods that produce water vapour directly from the data (SODA). The MicroTops instruments referred to here capture solar radiance data in 5 wavelengths which are used to extract information on aerosol optical thickness and water vapour content. These two key parameters of interest are used as inputs for the atmospheric correction of remotely sensed image 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.

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>

TERN worked together with Airborne Research Australia (ARA) to deliver airborne hyperspectral and lidar data for a number of selected homogenous 5 km x 5 km field sites across several locations in Australia (formally known as the AusCover Supersites). A Riegl Q560 Lidar, a SPECIM AisaEAGLE II hyperspectral scanner (VNIR) and a SPECIM AisaHAWK hyper-spectral scanner were mounted in underwing pods of ARA's ECO-Dimona research aircraft VH-EOS, each one together with its own navigation and altitude system. The spatial resolution of the Airborne hyperspectral data is 0.5m and Airborne LiDAR is 0.3m. Details on the data acquisition for each site is summarized <a href="https://dap.tern.org.au/thredds/fileServer/landscapes/remote_sensing/airborne_validation/metadata/data_report/5_AcquisitionOfData.pdf">here</a>.

The Seasonal Fractional Cover Summary Statistics datasets provide long-term statistical summaries derived from the seasonal fractional cover v3 product, calculated separately for each fraction. 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.

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.