To understand how much carbon the Earth can store, and how it changes over time, scientists need to count a bewildering number of trees, and track their growth over time. Incredibly, NASA is now using supercomputers to do exactly that.
Scientists from NASA recently partnered with an international team of researchers to map a test area of trees in west Africa using high-resolution satellite images - more than 1.8 billion trees that are found outside of forests, over a swath of more than a half million square miles.
The team used one of the fastest supercomputers in the world (Blue Waters at the University of Illinois) to perform a “deep learning”. They found they could not only count trees that satellites had failed to see before, but they could begin to assess the carbon storage potential of those trees at the same time.
Much of the world’s effort to assess large numbers of trees has focused on well-forested regions. This is why the NASA team sought to focus on isolated trees in drylands and semi-arid regions in West Africa - for a fuller picture.
“These dry areas are white on maps - they are basically masked out because normal satellites just don’t see the trees,” said lead author Martin Brandt in a statement. “They see a forest, but if the tree is isolated, they can’t see it. Now we’re on the way to filling these white spots on the maps. And that’s quite exciting.”
To train the machine-learning algorithms, Brandt, an assistant professor of geography at the University of Copenhagen, marked nearly 90,000 trees spanning different terrains personally - giving the software different shapes and shadows to learn from, and published their new study in Nature.
With the right training in place, a job that may have taken trained eyes several years to complete took only a few weeks for artificial intelligence.
The team was able to map the crown diameter (the width of a tree viewed from above) of 1.8 billion trees spanning an area of more than 500,000 square miles. They also compared the variability in tree coverage and density under different rainfall patterns - information the team plans on comparing with upcoming tree height and biomass data to identify carbon storage potential.
In the future, assessments of this kind will more effectively track deforestation around the world for conservationists. The overhead data from one year will also be compared to later years for scientists to assess whether conservation efforts are working or not.
Improving our ability to spot trees with satellite images - and to gauge their carbon storage potential - will enable climate scientists to make accurate global measurements of carbon storage on land. It will be one of the vital tools we need at a time when storing excess carbon is becoming ever more crucial.