The datasets in this series comprise predictions of biocondition for Queensland's Bioregions. The datasets are 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 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.

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 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.

<p> This data set provides the photosynthetic pathways for 4832 species recorded across plots surveyed by Australia’s Terrestrial Ecosystem Research Network (TERN) between 2011 and May 2022 (inclusive). TERN survey plots are 1&nbsp;ha (100 x 100&nbsp;m) permanently established sites located in a homogeneous area of terrestrial vegetation. At each plot, TERN survey teams record vegetation composition and structural characteristics and collect a range of plant samples using a point-intercept method. Species were assigned a photosynthetic pathway using literature and carbon stable isotope analysis of bulk tissue collected by TERN at the survey plots. </p><p>The data set is comprised of one data table that contains a list of each species and its photosynthetic pathway, and one metadata file which provides a data descriptor that defines data values and a list of all the peer-reviewed sources used to create this data set. </p> Version 1 (2020) included the photosynthetic pathways of 2428 species recorded across TERN plots surveyed between 2011 and 2017 (inclusive) and was originally published in 2020. Key updates in version 2 (2024) include an expanded species list and updated taxonomy were applicable </p>

This is Version 1 of the Southeast Queensland Bioregion Spatial BioCondition dataset. It is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/r976-1v85.<br><br> 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.

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.

Vertical plant profiles for the Australian continent were derived through integration of ICESat GLAS waveforms with ALOS PALSAR and Landsat data products. Co-registered Landsat Foliage Projected Cover (FPC) and ALOS PALSAR L-band HH and HV mosaics were segmented to generate objects with similar radar backscatter and cover characteristics. Within these, height, cover, age class and L-band backscatter characteristics were summarised based on the ICESat and Landsat time-series and ALOS PALSAR datasets.

This data contains a list of all vascular plants surveyed in the Great Western Woodlands site between 2013 - 2016.

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).