Vegetation Condition Predictions for Southeast Queensland Bioregion 2019

Aggregating spatial predictions of vegetation condition for biodiversity in the Southeast Queensland Bioregion for 2019. It was generated using a gradient boosting decision tree model trained on 17,000 sites and eight remote sensing datasets. The model predicts a BioCondition score based on differences in remote sensing data within homogeneous vegetation communities.

Use Cases

• Analyze spatial patterns of predicted BioCondition scores across the Southeast Queensland Bioregion.
• Validate the gradient boosting decision tree model's predictions against the 17,000 training sites of known vegetation community and condition state.
• Use the eight vegetation-specific remote sensing datasets as features to model vegetation condition as a function of differences in remote sensing space.

Strengths

• Model trained on 17,000 training sites of known vegetation community and condition state.
• Predictions based on eight vegetation-specific remote sensing datasets.
• Uses a gradient boosting decision tree model for prediction.

Limitations

• This Version 1 dataset has been superseded by a Version 2 dataset and removed from publication to prevent temporal comparisons.
• The dataset is an initial demonstration version and its development is described as still in a future phase.
• The dataset is provided in non-tabular formats (HTML, PNG, PDF), limiting direct analytical use.

Provenance

Source

Terrestrial Ecosystem Research Network's Data Discovery

Collection Method

Created using a gradient boosting decision tree model based on eight remote sensing datasets and 17,000 training sites.

Time Range

2019

Freshness

Last updated metadata indicates 2026-03-20, but the underlying data represents predictions for 2019 and is a superseded version.

Geography

Southeast Queensland Bioregion