Queensland Brigalow Belt Bioregion Spatial BioCondition, 2019, Version 1.0

The Spatial BioCondition 2019 version 1.0 dataset (SBC) was produced by the Queensland Herbarium and Biodiversity Science and the Remote Sensing Sciences business units in the Queensland Department of Environment and Science.

The pixel values in SBC dataset represent the predicted condition of vegetation for biodiversity in 2019. The range is 0-100, where lower values indicate poorer condition. No data is represented by a value of 255. No data include:

• Regional Ecosystems (RE) with insufficient training and reference data to apply the framework
• Marine, intertidal, native grassland and predominantly unvegetated ecosystems defined in RE preclearing
• Urban, suburban, commercial, and industrial areas as defined by the Queensland Land Use Mapping dataset (https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid=%7B273F1E50-DD95-4772-BD6C-5C1963CAA594%7D).

Condition of vegetation for biodiversity may be influenced by agricultural practices, grazing land management, inappropriate fire regimes, urban development, incursion of non-native species, industrial logging, and mining. Queensland has a site-based vegetation condition assessment framework ‘BioCondition’ which assesses the relative capacity of an ecosystem to support the suite of species expected to occur in its relatively undisturbed (reference) state. This is measured using a set of compositional, structural, and functional vegetation attributes which are compared against a reference. The greater the difference from the reference state the worse the condition. The reference state characteristics (the benchmark) are derived from a set of sites in the same vegetation community that are known to be in the best available condition. SBC moves the assessment of vegetation condition for biodiversity from a site-based approach to a predictive modelling approach that can be applied at the regional or state scale. It is based on the premise that the greater the difference (measured as distance in multi-dimensional RS space) from the RS reference, the worse the condition. The model is developed using the remote sensing datasets as predictor variables and training sites with known RE and condition state as the response variable. The resulting model is applied to all vegetated areas to produce predictions of condition.

The dataset comprises three bands. Band 2 is the predicted BioCondition score 0-100, with higher values representing better vegetation condition for biodiversity. B1 and B3 show the upper and lower boundary of the 90% prediction interval, that is the likely range in which the true value of the prediction will be.

Data Creation
This version was created using the Spatial BioCondition modelling workflow.
The model uses the following datasets: Sentinel 2 based green and bare fractional cover statistics (2017-2019); Landsat derived fractional cover for the 2019 dry season; Sentinel 2 NDVI derived phenological metrics; Regional ecosystem pre-clearing dataset - version12.2; Selected vegetation field survey data held in departmental databases.
The final model output was clipped to the IBRA7 Brigalow Belt bioregion boundary, and the following areas were masked: The pre-clearing extent of: natural grasslands; predominantly unvegetated ecosystems; regional ecosystems extra to the Brigalow Belt and Southeast Queensland bioregions defined by version 12.2 regional ecosystem mapping; Built environments and infrastructure (urban, suburban, commercial and industrial areas) defined by Queensland land use mapping.
Masked areas, pixels without predictions, and pixels outside the bioregion are classified as No Data (DN = 255). Band 1 is the 5^th of the prediction interval, Band 2 is the predicted BioCondition score and Band 3 is the 95^th percentile of the prediction interval.