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 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 It is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/rnqz-cn10. 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.

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 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 document summarizes the potential sagebrush vegetation as well as the 2012 - 2019 sagebrush vegetation availability estimates on Greater Sage-Grouse Priority and Important Habitat Management Areas (PHMA and IHMA, respectively) within the Biologically Significant Units (BSUs) identified in the 2015 Greater Sage-Grouse Land Use Plans as maintained through 2021. BSUs are grouped by State and, along with PHMA and IHMA datasets, were provided by each individual planning area between May 2015 and February 2021. Sagebrush potential and availability were derived from LANDFIRE Biophysical Setting (BpS) and Existing Vegetation Type (EVT) data products, respectively, as described in the Greater Sage-Grouse Monitoring Framework. Updates to the EVT product from 2013 to 2015 were also performed as outlined in the Greater Sage-Grouse Monitoring Framework. All analyses were completed by the BLM’s Wildlife Habitat Spatial Analysis Lab at the National Operations Center.

This document summarizes the potential sagebrush vegetation as well as the 2012 - 2019 sagebrush vegetation availability estimates on Greater Sage-Grouse Priority and Important Habitat Management Areas (PHMA and IHMA, respectively) within the Biologically Significant Units (BSUs) identified in the 2015 Greater Sage-Grouse Land Use Plans as maintained through 2021. BSUs are grouped by State and, along with PHMA and IHMA datasets, were provided by each individual planning area between May 2015 and February 2021. Sagebrush potential and availability were derived from LANDFIRE Biophysical Setting (BpS) and Existing Vegetation Type (EVT) data products, respectively, as described in the Greater Sage-Grouse Monitoring Framework. Updates to the EVT product from 2013 to 2015 were also performed as outlined in the Greater Sage-Grouse Monitoring Framework. All analyses were completed by the BLM’s Wildlife Habitat Spatial Analysis Lab at the National Operations Center.

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. 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.