Version 1 of the Southeast Queensland Bioregion Spatial BioCondition dataset is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/r976-1v85.<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 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 was intended to represent predicted BioCondition for year 2019 rather than any single date.

The dataset consists of species identity and projective foliage cover (PFC) of ground layer vascular plants from five sites located near Mareeba, in northern Queensland. The sites are located in eucalypt communities with altitudes ranging from 380 to 840 m. Data have been collected annually since 1992, in April and May, i.e. during the annual peak of plant species richness. At each site, data collection is carried out using ten 0.5 m² quadrats deployed within a permanently marked 50 x 10 m plot. For each quadrat, all plant species visible above ground are identified and sampled. PFC data for each species from the ten quadrats are averaged. Any additional species occurring within the 50 x 10 m plot is also recorded and assigned a PFC of 0.1% (Neldner and Butler, 2021).

The QBEIS survey database (formerly CORVEG) contains ecosystem physical and vegetation characteristics, including structural and floristic attributes as well as descriptions of landscape, soil and geologic features, collected at study locations across Queensland since 1982. The resulting survey database provides a comprehensive record of areas ground-truthed during the regional ecosystems mapping process and a basis for future updating of mapping or other relevant work such as species modelling.<br /><br /> Only validated survey data is made publicly available and all records of confidential taxa have been masked from the dataset. Data is accessible from the TERN Data Infrastructure, which provides the ability to extract subsets of vegetation, soil and landscape data across multiple data collections and bioregions for more than 100 variables including basal area, crown cover, growth form, stem density and vegetation height.

This is a series comprises of vegetation condition predictions for biodiversity for the bioregions of Queensland. The datasets were created using a gradient boosting decision tree (GBDT) model based on 10 vegetation-specific remote sensing (RS) datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date. This series includes information relating the version 2.0 products of Spatial BioCondition, which have superseded the version 1.0 products (https://portal.tern.org.au/metadata/TERN/40990eec-5cef-41fe-976b-18286419da0c, https://portal.tern.org.au/metadata/TERN/2c33325c-1dd5-4674-918a-1cd5bfc1a6e3). Spatial BioCondition is not suitable for the measurement of changes in condition over time, and direct comparisons of predictions between versions 1.0 and 2.0 are not advised.

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 10 vegetation-specific remote sensing datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date.

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 10 vegetation-specific remote sensing datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date.

This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the Central Queensland Coast bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on 10 vegetation-specific remote sensing datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date.

This is Version 1 of the Brigalow Belt Bioregion Spatial BioCondition dataset. It is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/rnqz-cn10.<br><br> 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.