Gridded near-surface (2 and 10 m) daily average wind datasets for Australia from 1975 to 2018 have been constructed by interpolating observational data collected by the Australian Bureau of Meteorology (BoM). The new datasets span Australia at 0.05 × 0.05° resolution with a daily time step. The datasets were constructed by blending observational data collected at various heights using local surface roughness information.

Ground layer vascular plant species identity and projective foliage cover (PFC) data were collected from four permanently marked 50x10 metre plots in north Queensland on a three monthly frequency for three years. Ten 0.5 square metre quadrats were used for sampling at each occasion at each site and the data pooled and averaged. Refer to Neldner, V.J., Kirkwood, A.B. and Collyer, B.S. (2004). Optimum time for sampling floristic diversity in tropical eucalypt woodlands of northern Queensland. The Rangeland Journal 26: 190-203 for more information. Note: Spreadsheet compiled in 2021 from original data collection records.

This product has been superseded and will not be processed from early 2023.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/dd359b61-3ce2-4cd5-bc63-d54d2d0e2509. An estimate of persistent green cover per season. 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 (dim) time series.

<p>This dataset shows the broad groups of crops grown in the main cropping areas of Queensland, for the winter and summer growing seasons from 1990 to the current year. The winter growing-season is defined as June to October, and the summer growing-season is November to May. The predicted group is stored in the attribute table (field 'CLASS'), along with the probability of the prediction (field 'P_CLASS', the larger this value, the more certain is 'CLASS').</p> <p>Each season has 2 maps: an end-of-season prediction and a mid-season prediction. The mid-season prediction is labelled "_vInterim" to indicate that it is based on a relatively short time series and should be used with caution. </p>

Version 1 of the Brigalow Belt Bioregion Spatial BioCondition dataset is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/rnqz-cn10.<br><br> Version 1 was an initial demonstration version. The version 1 data has been removed from publication to negate temporal comparisons between v1 (2019) and v2 (2021), as this is a future goal for the product but still in development phase. This was 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 was intended to represent predicted BioCondition for year 2019 rather than any single date.