Brigalow Belt Bioregion Spatial BioCondition, 2021, Version 2.0 

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. 

We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging. 

Spatial BioCondition (SBC) is a mapping framework that aligns with Queensland’s Regional Ecosystem (RE) and BioCondition frameworks, as it integrates site-based vegetation condition assessment methods and remote sensing (RS) to provide predictions of the condition of vegetation for biodiversity across most terrestrial ecosystems of a bioregion. There is an increasing requirement for new vegetation information to support current and emergent drivers in natural resource management. The SBC framework has been developed to support reforms to the Queensland Vegetation Management Act 1999 that aim to provide more holistic reporting on vegetation extent and condition in Queensland. Following version 1.0, this version predicts the condition of vegetation for biodiversity in 2021 for the Brigalow Belt bioregion. 

The Spatial BioCondition (SBC) 2021 version 2.0 dataset was produced by the Queensland Herbarium and Biodiversity Science and the Remote Sensing Sciences business units in the Queensland Department of Environment, Science and Innovation.

The pixel values in SBC dataset represent the predicted condition of vegetation for biodiversity in 2021. The range is 0-100, where lower values indicate poorer condition. No Data is represented by a value of 255.

No Data include areas where:
a. Regional Ecosystems with insufficient training and reference data to apply the framework;
b. Marine, intertidal, native grassland, sedgeland, forbland and predominantly unvegetated ecosystems defined in RE preclearing;
c. Urban, suburban, commercial, and industrial areas including intensive use lakes, estuaries, canals, dams and reservoirs as defined by Queensland Land Use Mapping dataset (https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={BE30CE16-B1B9-48B1-BF21-DBE70597FA93})

Condition of vegetation for biodiversity may be influenced by agricultural practices, grazing land management, fire regimes and wildfire, urban development, incursion of invasive 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 suite 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 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 remote sensing space) from the 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 with sufficient training and reference data 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. Bands 1 and 3 show the lower and upper boundary of the 90% prediction interval, respectively. This prediction interval provides a likely range in which the true value of the prediction will be.