and AI for mapping A team of international scientists, including Nasa researchers, are using deep learning to map trees and bushes in the drylands of West Africa and the southern Sahara desert

Tree-mendous: researchers use HPC

Deep learning on the Blue Waters supercomputer aims to develop a better understanding of tree coverage and more accurately calculate how much carbon they store. In an interview in The Guardian, Martin

Brandt, assistant professor of geography at the University of Copenhagen, and an international study’s lead author to find trees in arid parts of West Africa, said they found ‘quite a few hundred million’ in areas they did not expect to find any. ‘Most maps show these areas as

basically empty’, stated Brandt. ‘But they’re not empty. Our assessment suggests a way to monitor trees outside of forests globally, and to explore their role in mitigating degradation, climate change and poverty.’ Mapping non-forest trees across a wide area and then calculating how much carbon they store is crucial to understanding Earth’s carbon cycle, its changes over time, and how it may affect climate.

Using powerful supercomputers – such as Blue Waters at the National Center for Supercomputing Applications, University of Illinois – and machine learning algorithms, the team mapped the crown diameter – the width of a tree when viewed from above – of more than 1.8 billion trees across an area of more than 1,300,000km2

. The researchers also mapped how tree crown diameter, 16 Scientific Computing World Winter 2021

coverage, and density varied depending on rainfall and land use. Mapping non-forest trees at this level of detail would take months or years with traditional analysis methods, the team said, compared to a few weeks for this study. The use of very high-resolution imagery and powerful AI represents a technology breakthrough for mapping and measuring these trees. The study – published in Nature – is

intended to be just the first in a series of papers on this key environmental issue. It would previously have taken years to

calculate, but the use of machine learning allowed the researchers to map the trees in just a few weeks. The team used 1.5 million node hours on Blue Waters, which is the largest use of machine learning methods on the supercomputer to date.

“There are important ecological processes, not only inside, but outside forests too”

Conservation experts working to

mitigate climate change and other environmental threats have targeted deforestation for years, but these efforts do not always include trees that grow outside forests, said Compton Tucker, senior biospheric scientist in the Earth sciences division at Nasa Goddard Space Flight Center. He also noted that many current methods for studying trees’ carbon content only include forests, not

trees that grow individually or in small clusters. Trees and other green vegetation

are carbon ‘sinks,’ meaning they use carbon for growth and store it out of the atmosphere in their trunks, branches, leaves and roots. Human activities, like burning trees and fossil fuels or clearing forested land, release carbon into the atmosphere as carbon dioxide, and rising concentrations of atmospheric carbon dioxide are the main cause of climate change.

Not only could these trees be

significant carbon sinks, but they also contribute to the ecosystems and economies of nearby human, animal and plant populations. However, many current methods for studying trees’ carbon content only include forests, not trees

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