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 This series includes information relating the version 2.0 products of Spatial BioCondition, which have superseded the version 1.0 products (https://portal.tern.org.au/metadata/TERN/40990eec-5cef-41fe-976b-18286419da0c,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

<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 set is the result of the investigation on the response of littoral and floodplain vegetation and soil moisture flux to weir pool raising in 2015. The data was collected over 18 months between August 2015 and December 2016- before, during and after the weir pool levels were raised. The data set contains information on Tree Condition including crown extent and density, bark form, epicormic growth and state, reproduction, crown growth, leaf die off and damage, and mistletoe. Leaf Water Potential, taken predawn and in the middle of the day. Plant Area Index/Canopy Cover measurements using hemispherical photos. Soil Chemistry measurements- total soil moisture (gravimetric water content; %), soil suction (or soil matric potential), Electrical Conductivity and soil pH.

<p> TERN Ecosystem Surveillance is a plot-based field monitoring platform that tracks the direction and magnitude of change in Australia’s environments. Information on soils and vegetation is collected according to standardized, widely endorsed and consistent protocols across all plots, and includes the collection of soil and vegetation samples for subsequent analysis.</p> <p>Data collected by TERN is stratified across the entire continent to ensure adequate coverage of major Australian ecosystems, and measures are repeated at least once a decade, with the aim to establish replicate plots throughout the ecosystem types existing within Australia’s Major Vegetation Groups (MVG’s). Additional plots located in key environmental transition zones will be re-measured every five years.</p> <p>TERN users include researchers, land managers and policy-makers who require access to terrestrial ecosystem attributes collected over time from continental scale to field sites at hundreds of representative locations. TERN provides model-ready data that enables users to detect and interpret changes in ecosystems. In addition, TERN curates The TERN Australia Soil and Herbarium Collection with over 150,000 vegetation and soil samples (and associated contextual environmental data) freely available to loan on request.</p> <p>TERN’s world-class surveillance monitoring infrastructure will support long-term ecological inventory, environmental monitoring, environmental prediction, reporting and assessment, and underpin decisions about our greatest environmental challenges.</p> <p>Occurrence records can be accessed through the <a href="https://www.ala.org.au/">Atlas of Living Australia</a>.</p>