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 This series includes information relating the version 2.0 products of Spatial BioCondition, which have superseded the version 1.0 products (https://portal.tern.org.au/metadata/TERN/40990eec-5cef-41fe-976b-18286419da0c,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.

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.

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.

<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>

<br>This dataset lists the plant communities from Rangeland sites across Australia described by the TERN Surveillance Monitoring team, using standardised AusPlots methodologies. <br /> <br> For each plant community, vegetation condition as well as the spatial extent of the community, are described using AusPlots Plot and physical descriptions, and Structural summary and homogeneity methods.<br /> Plant specimen, soil, basal area and structural information are also assessed at each site and form part of the TERN Surveillance Monitoring Program data collection.<br />

This data contains a list of all vascular plants surveyed in the Tumbarumba Wet Eucalypt site in 2015.