Bio-physical Predictors of Coastal Land Use Change in the Great Barrier Reef, 1999-2009

Four key bio-physical predictors, including slope and rainfall, were identified as the strongest drivers of land use change along the Queensland coast. This dataset contains raster files from the NERP TE 9.4 project, modeling transitions between 18 land use classes at 50m and 500m resolution. The analysis was conducted by the Australian Ocean Data Network using a neural network model trained on data from 1999 and 2009.

Use Cases

• Modeling land use transition probabilities based on bio-physical suitability maps.
• Ranking the importance of environmental factors like slope and rainfall for explaining coastal development.
• Training machine learning models for land use change prediction using the described neural network methodology.
• Analyzing spatial patterns of land use change between two time points (1999 and 2009) in a defined coastal zone.

Strengths

• Analysis identifies the 4 strongest bio-physical predictors from a wider set of variables.
• Data is provided at a high spatial resolution of 50 meters, resampled to 500 meters.
• Model was trained and tested on subsets of the data, providing an estimate of prediction accuracy.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect geographic bias inherent to the Queensland coastal region and the specific 1999-2009 time period.

Provenance

Source

Australian Ocean Data Network, from the NERP TE 9.4 project (JCU).

Collection Method

Analysis using the IDRISI Land Use Change Modeler with a multi-layer perceptron neural network on rasterized QLUMP land use data.

Time Range

1999 to 2009

Freshness

Last updated 2026-07-13 14:48:33.351186; freshness should be verified.

Geography

Great Barrier Reef coastal zone, Queensland, Australia.