I use decision theory and operations research to improve the outcomes achieved for biodiversity from conservation management. Ecological systems are incredibly complex, and changing how those systems interact and evolve can have unexpected implications. I build mathematical models of coupled ecological, land-use, and economic systems to understand the mechanisms driving success, failure, and efficiency of management actions. Carefully, transparently, and defensibly planning management and policy interventions while acknowledging those complexities and the associated risks helps achieve better outcomes for the environment and society.
That’s the aim of new research just published by ACEMS’ researchers in the Journal of Big Data. Led by Jacinta Holloway Brown from ACEMS at QUT, the researchers developed a new statistical method to predict forest cover in satellite images where portions of the image are blocked by cloud cover. Not only that, the new method also calculates a probability to show how confident the prediction is.
Invited talks, refereed proceedings and other conference outputs
Holloway, J., Mengersen KL., & Helmstedt K. J.
(2018). Spatial and machine learning methods of satellite imagery analysis for Sustainable Development Goals. IAOS-OECD Better Statistics for Better Lives.