Environmental data

We turn environmental data into robust projections that can be used for impact and risk assessments and to develop policy guidelines.

The Challenge

Turning data into information that supports decision making

Data from environmental monitoring, remote sensing and the climate are all examples of big data sets creating new challenges for statisticians.

How should we design monitoring systems to collect environmental data? How can we analyse data collected over space and time, or integrate data from different data sets? Can we have confidence that our climate forecasts are robust?

Our Response

New data, new science, new insights

We create informatics tools to analyse complex data, revealing insights that underpin robust decision making in the face of future uncertainty and risk. We work at the interface between data science and applications in agribusiness and environment.

We are developing innovative statistical methods that:

  • help design monitoring systems for river health
  • integrate data from different sources
  • reconcile global climate models with observations of climate and weather and quantify the reliability of our predictions
  • predict the future frequency of extreme events like floods
  • find ways forward when data is missing or incomplete, such as estimating whale numbers in the Southern Ocean based on surveys from the air and at sea
  • assess changes in land cover over time from remote sensing images
  • assess risk to waterways from new coal and gas mines in Australia.

Case studies


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