Machine learning used to map gamba grass from space

Friday, 27 November, 2020

Machine learning used to map gamba grass from space

Researchers from CSIRO, Charles Darwin University and The University of Western Australia (UWA) have developed a machine-learning approach that detects invasive gamba grass from high-resolution satellite imagery. Their work has been published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Gamba grass, originally from Africa, is listed as a Weed of National Significance, and is one of five introduced grass species that pose extensive and significant threats to Australia’s biodiversity. The perennial grass can grow to 4 m in height and forms dense tussocks that can burn as large, hot fires late in the dry season.

Mapping where gamba grass occurs is essential to managing it effectively, but northern Australia is so vast and remote that on-the-ground mapping and even airborne detection of the weed is too labour-intensive. So the researchers turned to high-quality satellite imagery and developed a technique that could help detect and prioritise gamba grass for management.

CSIRO’s Dr Shaun Levick said the research team used field data to ‘train’ a machine-learning model to detect gamba grass from high-resolution, multispectral satellite imagery. They then commissioned the WorldView-3 satellite to capture very high-resolution imagery across 16 spectral bands for an area of 205 km2 near Batchelor in the Northern Territory — an area of dense gamba grass infestation.

The wide range of spectral data allowed the team to use factors unseen to the human eye, such as leaf moisture levels and chlorophyll content, to differentiate between gamba grass and native grass species. Under optimum conditions, the method can detect gamba grass presence with about 90% accuracy.

Dr Natalie Rossiter-Rachor, of Charles Darwin University, said the project drew on extensive on-ground research into the life cycle of gamba grass to help achieve such accurate detection rates. She explained, “We knew that gamba grass tends to stay green longer into the dry season than native grasses, so we timed the capture of the satellite imagery for this period.

“Understanding the ecology of the problem was essential to informing the remote sensing and machine-learning solution to the problem.”

The project, funded by the Australian Government’s National Environmental Science Program under the Northern Australia Environmental Resources Hub, is part of a larger effort to detect and map gamba grass throughout the north. According to Dr Levick, “Our longer-term goal is to move to a system where we can use free, open-access imagery to map gamba grass. We want to develop a technique that is accessible to anyone and that can help improve land management in northern Australia.”

UWA’s Associate Professor Samantha Setterfield added that accurate maps of where gamba grass occurs are essential to control the spread of the weed, saying, “Mapping gamba grass using satellite imagery unlocks the potential to frequently map large areas so we can get a better picture of where gamba grass is across northern Australia, and how quickly it is spreading.

“Managers can then target areas that are the highest priority for control, such as biodiversity-rich areas or culturally important sites.”

Image caption: Gamba grass can grow up to 4 m high and forms dense tussocks which can burn as large, hot fires late in the dry season. Image credit: NESP Northern Australia Hub.

Please follow us and share on Twitter and Facebook. You can also subscribe for FREE to our weekly newsletters and bimonthly magazine.

Related News

AI diagnoses prostate cancer as accurately as specialists

AI systems can diagnose prostate cancer biopsies with the same level of accuracy as specialist...

Computer vision system analyses cells in microscopy videos

The system allows automatic analysis of biomedical videos captured by microscopy in order to...

AI imaging tech diagnoses COVID-19 in minutes

The technique uses an AI process known as deep convolutional neural network, an algorithm...

  • All content Copyright © 2022 Westwick-Farrow Pty Ltd