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.

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.

This data contains a list of all vascular plants surveyed in the Mitchell Grass Rangelands site between 2018 to 2022.

This data contains a list of all vascular plants surveyed in the Gingin Banksia Woodlands site in 2018.

This dataset contains bird occurrence data collected at the Boyagin Wandoo Woodlands site from 2018 - 2019.

The dataset contains maps of total % C₃ and C₄ plant cover, proportional C₃ and C₄ vegetation (relative to combined C₃ and C₄ cover), and vegetation δ¹³C isoscape (stable carbon isotope values) across Australia. Data are centered on year 2015. We used vegetation and land-use rasters to categorize grid-cells (100 m²) into woody (C₃), native herbaceous (C₃ and C₄), and herbaceous cropland (C₃ and C₄) cover. TERN Ecosystem Surveillance field surveys and environmental factors were regressed to predict native C₄ herbaceous cover. These layers were combined and a δ¹³C mixing model was used to calculate site-averaged δ¹³C values.

This product provides locations of areas affected by fire including the approximate day of burning. Inputs are daily day time observations from MODIS sensors on Terra and Aqua. Observations are atmospherically corrected and the resulting time series is investigated for sudden changes in reflectance, persistent over multiple days. Variations in observation and illumination geometry are taken into account through application of a kernel driven Bidirectional Reflectance Distribution Function (BRDF) model.

<br>This release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer using eddy covariance techniques. Data were processed using PyFluxPro (v3.4.7) as described by Isaac et al. (2017). PyFluxPro produces a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER).</br> <br>The flux station is located at Rosebank Station, approximately 11&nbsp;km south-east of Longreach in Queensland. The site is arid tussock grassland with a variety of grass species including <em>Astrebla lappacea</em> and <em>Astrebla squarrosa</em> over black vertosol soil that supports sheep and beef cattle grazing. Traditional owners at this site are the Iningai people.

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.

This data contains a once-off general structural description according to the National Vegetation Information System (NVIS) level 5 for the core 1 hectare plot in the Mitchell Grass Rangeland site in 2018. Dominant growth form, cover, height and species (up to 5 species in order of dominance) for up to 3 sub-stratum per traditional strata (Ground, Mid and Upper).