credit: Tim Macuga, QUT.
Original Published Date: 
Tuesday, June 29, 2021

Full article issued by the Queensland University of Technology (QUT).

ARC-supported researchers at the QUT-based ARC Centre of Excellence for Mathematical and Statistical Frontiers have developed a new machine learning mathematical system that helps to identify and detect changes in biodiversity, including land clearing, when satellite imagery is obstructed by clouds.

Using statistical methods to quantify uncertainty, the research team analysed available satellite images of an 180km square area in central south-east Queensland.

The region is home to many native species including the critically endangered northern hairy-nosed wombat and the vulnerable greater glider, and the area mainly consists of forest, pasture, and agricultural land.

Dr Jacinta Holloway-Brown says measuring changes in forest cover over time is essential to track and preserve habitats and is a key sustainable development goal by the United Nations and World Bank to manage forests sustainably.

'Satellite imagery is important as it is too difficult and expensive to frequently collect field data over large, forested areas,' Dr Holloway-Brown says.

The research involved calculating two simulated types of clearing events, clear felling which involves removing all trees from the area and burning to prepare for future growth and, secondly, tree thinning which involves only removing trees from the area, leaving smaller shrubs, grassland, and pasture behind.

By simulating clouds, the researchers, which includes Australian Laureate Professor Kerrie Mengersen and Discovery Early Career Researcher Award (DECRA) recipient Dr Kate Helmstedt, could 'test the limits' of the method and know how well or not it could predict what was underneath the clouds.

The results showed the method accurately detected simulated land cover change under both clear felling and tree thinning.

Photo credit: 

Professor Kerrie Mengersen, Dr Jacinta Holloway-Brown, and Dr Kate Helmstedt. Photo credit: Tim Macuga, ARC Centre of Excellence for Mathematical & Statistical Frontiers.