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    This product has been superseded and will not be processed from early 2023.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/23881. The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover, created from a time series of Sentinel 2 imagery. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (10 m per-pixel) for each 3-month calendar season. The green and non-green fractions may include a mix of woody and non-woody vegetation. This model was originally developed for Landsat imagery, but has been adapted for us with Sentinel-2 imagery to produce a 10 m resolution equivalent product.

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    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 m), Sentinel-2 (10 m) and Earth-i (1 m).

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    The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Sentinel-2 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. The seasonal surface reflectance is of the 6 TM-like bands (Blue, Green, Red, NIR, SWIR1, SWIR2), all at 10 m resolution. This dataset is intended to be a 10 m equivalent of the Landsat surface reflectance, using only Sentinel-2. The two 20m bands are resampled using cubic convolution. The pixel values are scaled reflectance, as 16-bit integers. To retrieve physical reflectance values, the pixel values should be multiplied by 0.0001.

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    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 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.

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    The data set is a statewide annual composite of fire scars (burnt area) derived from all available Landsat 5, 7 and 8 images acquired over the period January to December using time series change detection. Fire scars are automatically detected and mapped using dense time series of Landsat imagery acquired over the period 1987 - present. In addition, from 2013, products have undergone significant quality assessment and manual editing. The automated Landsat fire scar map products covering the period 1987-2012 were validated using a Landsat-derived data set of over 500,000 random points sampling the spatial and temporal variability. On average, over 80% of fire scars captured in Landsat imagery have been correctly mapped with less than 30% false fire rate. These error rates are significantly reduced in the edited 2013-2016 fire scar data sets, although this has not been quantified. <br> For the 2016 annual fire scar composite, the manual editing stage incorporated Landsat and Sentinel 2A imagery (resampled to match Landsat spatial resolution), allowing for increased cloud-free ground observations, and an associated reduction in the number of missed fires (not quantified). Sentinel 2A images were primarily used to map fire scars that were otherwise undetectable in the Landsat sequence due to cloud cover/Landsat revisit time. Additionally, Landsat-7 SLC-Off imagery (affected by striping) was excluded from the 2016 annual composite. It is expected that these modifications should result in improved mapping accuracy for the 2016 period.<br> A new fire scar detection algorithm has been developed, with a new edited product implemented in 2021.

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    This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the Southeast Queensland Bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on eight vegetation specific remote sensing (RS) datasets and 17,000 training sites of known vegetation community and condition state. Condition score was modelled as a function of the difference in the RS space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for year 2019 rather than any single date.

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    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.

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    This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the brigalow belt bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on eight vegetation specific remote sensing (RS) datasets and 17,000 training sites of known vegetation community and condition state. Condition score was modelled as a function of the difference in the RS space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for year 2019 rather than any single date.

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    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/23884 ">Seasonal ground cover - Landsat, JRSRP algorithm Version 3.0, Australia Coverage</a> which is based on a different satellite sensor.

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    The monthly blended ground cover product is a spatially explicit raster product that shows the proportion of bare ground, green and non-green ground cover at medium resolution (30&nbsp;m per-pixel) for each calendar month. It is derived directly from both the Landsat-based fractional cover product and the Sentinel-2-based fractional cover product by Queensland's Remote Sensing Centre. A 3 band (byte) image is produced: band 1 - bare ground fraction (in percent), band 2 - green vegetation fraction (in percent), band 3 - non-green vegetation fraction (in percent). The no data value is 255. This product is derived from the <a href="https://portal.tern.org.au/metadata/TERN/8d3c8b36-b4f1-420f-a3f4-824ab70fb367 ">Monthly blended fractional cover - Landsat and Sentinel-2, JRSRP algorithm Version 3.0, Queensland coverage</a>