Queensland Southeast Queensland Bioregion Spatial BioCondition, 2019, Version 1.0 

Version 1 of the Southeast Queensland Bioregion Spatial BioCondition dataset is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/r976-1v85.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. 

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, 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 in Queensland. 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. This initial version provides predictions of the condition of vegetation for biodiversity in 2019 for the southeast Queensland bioregion. 

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 Program 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.