Two QUT researchers from the School of Biology and Environmental Science have been awarded ARC Early Career Industry Fellowships.
A project to develop an innovative and potentially commercialisable technology to improve the accuracy of satellite-based soil carbon modelling has been awarded a $355,000 ARC Early career Industry Fellowship.
Dr Trung Nguyen, from the School of Biology and Environmental Science, and industry partner Agrimix will develop technology that integrates ground data, drones, satellites, Eddy covariance CO2 flux towers, soil carbon (C) models, and artificial intelligence (AI).
The project’s aim is to provide an accurate and cost-effective solution to quantification of soil C changes to unlock potentially large carbon offsets in Australia and the world’s rangelands.
The Australian Emissions Reduction Fund has set up soil C offset programs to monetise the adoption of innovative grazing management practices that improve soil carbon.
“Grazing management provides significant opportunities to combat climate change through biological C sequestration,” Dr Nguyen said.
“Our technology will enable and incentivise graziers to reduce the industry’s greenhouse gas emissions, which account for 70 per cent of those by the agricultural industry.”
Dr Daniella Teixeira, from the School of Biology and Environmental Science, has received a $474,000 ARC Early Career Industry Fellowship to investigate eco-acoustic technologies to transform ecological monitoring that will provide robust data to inform conservation efforts.
Dr Teixeira is partnering with Bush Heritage Australia to develop new acoustic metrics to measure biodiversity from individual species through to whole communities.
“Eco-acoustic devices have enormous potential to improve ecological monitoring because they can capture high-resolution data on numerous ecological attributes with relatively little effort,” Dr Teixeira said.
“To advance the utility of eco-acoustic methods, we urgently need sound data metrics that are informed by ecology and linked to conservation goals for species and ecosystems.
“Our aim is to address the challenge of analysing eco-acoustic data for metrics that capture multiple levels of biodiversity at ecologically meaningful scales to provide detailed data that align with the monitoring objectives for conservation.
“We will use advanced machine learning, combined with detailed knowledge of animal ecology, to create tools to monitor biodiversity in ways that are directly relevant to management.”
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