How drones help map Huon pines, the ancient giant trees of Tasmania

If a rare, old tree burns in a fire, but no one has ever marked it on a map, will we ever notice it?

Scientists don’t think we would. And that’s why a new project is being launched to “imprint” vulnerable trees and remove them from aerial images.

Key points:

  • A team of Tasmanian scientists has found a way to detect the “fingerprint” of the Huon pine among other tree and plant species, based on the way it reflects light.
  • Recent forest fires have highlighted the need for accurate mapping to help protect the most vulnerable species in Tasmania.
  • The technique needs to be tested on a larger scale, and the team wants to work with aircraft operators to map larger forest areas.

The need for the project became apparent after a lightning strike hit the rugged terrain of the Gell River region southwest of Tasmania in 2018, marking the start of a devastating forest fire season.

Subsequent fires, which started in the Gell River region, burned 210,000 acres, destroying several homes and requiring the evacuation of hundreds of people.

But 6% of the World Heritage area in Tasmania also caught fire.

A subsequent review found that the vast majority of the threatened vegetation in these areas had developed some fire-fighting capacity, four areas that burned contained old and vulnerable species and are expected to never recover.

Part of the challenge of protecting these rare, fire-vulnerable species comes from not knowing exactly where they all are.

Jayne Balmer is part of the team working to fix it.

Dr. Jayne Balmer, a senior ecologist with Tasmania’s Department of Natural Resources and Environment, says the 2019 wildfires highlighted the need for accurate mapping of the state’s Huon Pines. (ABC News: Matthew Growcott)

He is a senior ecologist in the Tasmanian Department of Natural Resources and Environment.

“We’re trying to improve the mapping of fire-sensitive communities in the Tasmanian World Heritage area … among those things are things like the Huon pine,” he said.

The Huon pine is the longest-lived tree species in Australia, with records showing that some giants have lived for thousands of years.

Like King Billy pine, pencil pine and beech, Huon pine species evolved before fire became an important part of the landscape.

One of the first photos of the Gell River fire, seen from an evacuation helicopter. (Provided by: Teagan Fone-Stephenson)

“These communities date back to their Gondwana-era ancestry: the Huon pine is about 68 million years old in terms of its evolutionary history,” said Dr. Balmer.

“They are very sensitive to fire and we need to know where they are in order to protect them.”

The mapping of vegetation in Tasmanian World Heritage areas began in 1990, and although some of the information is collected on foot, most of the mapping is based on interpretations of images taken from planes.

Huon Pine evolved before fire became a major force in its habitat. (Supplied by: Dr. Emiliano Cimoli)

But it is difficult to choose specific trees and plants from above.

“They all look green, so it’s a bit like‘ Where’s Wally ’from the air, just like when you look at aerial photos,” Dr. Balmer said.

“Conventional red, green, and blue visible spectrum aerial images clearly do not make any difference between the Huon pine and the species it encounters.

“You can choose it if you look closely and know roughly where it is, but when you don’t know where it is and you’re looking for a new one, it’s really a challenge.”

Professor Arko Lucieer is an expert in high-precision aerial prospecting. (ABC News: Owain Stia-James)

Dr. Balmer sought help from Tasmanian University professor Arko Lucieer, an expert in high-precision aerial studies.

Hyperspectral sensors are a special type of camera that can capture 170 bands of light within each image, instead of just the red, green, and blue that humans see, allowing researchers to gather images in the near infrared.

Because there are subtle differences in the way each tree and plant species reflects light, each has a kind of spectral “fingerprint.”

“So it is this unique spectral fingerprint of the Huon pine that we are trying to detect from the air and, with drone technology, to find the right combination of sensor parameters to achieve this,” said Professor Lucieer. who is the head of the study. Faculty of Geography, Planning and Space Sciences.

Professor Lucieer’s team is one of the few in the world to use hyperspectral scanners connected to drones. (Supplied by: Dr. Emiliano Cimoli)

They began by measuring the “fingerprint” of a Huon pine at the Tasmanian Botanic Gardens in Hobart, before taking the technology to the southwestern desert and flying over areas where they knew Huon pines were located.

The team also developed a computer algorithm to analyze each pixel of the mass of images they collected.

They discovered that using this algorithm, they could make Huon pines stand out among the other vegetation.

The algorithm is able to select Huon pins from the map. (ABC News)

“There are a handful of teams around the world working with hyperspectral remote sensing in drones, so it’s a very specialized area,” said Professor Lucieer.

“We were one of the first in the world to develop this ability in a drone.”

Vegetation mapping for Tasmanian World Heritage areas began in 1990. (Supplied by: Arko Lucieer)

But there are limits on where and how far drones can fly.

Now, they hope to work with state-owned aircraft operators to test the technique on a larger scale.

“The drones have allowed us to fly low and slow and test this technique, the next thing is to mount our sensors on a plane and fly much larger areas, so that we can map larger remote areas in the southwest … and produce a map of the Huon pines, “said Professor Lucieer.

In the future, the technique could be applied to map other species, allowing them to control the biodiversity of a region and the impacts of climate change, he said.

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Posted 15 hours ago 15 hours, Saturday, May 28, 2022 at 10:29 PM, updated 3 hours, 15 hours ago, Saturday, May 28, 2022 at 11:21 PM

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