Scientists from NASA’s Goddard Space Flight Center in Greenbelt, Maryland and international collaborators have unveiled a new method for mapping the location and size of trees that grow outside forests, discovering billions of trees in arid and semi-arid regions, and laying the foundation for more accuracy on the global measurement of carbon storage on Earth.
Using supercomputers and machine learning algorithms, the team mapped more than 1.8 billion trees across an area of more than 500,000 square miles, or 1,300,000 square kilometers, that included densities depending on precipitation and land use.
Mapping non-forest trees at this level of detail could take months or years using traditional analysis methods, compared to a few weeks for this study, the team said.
The use of high-resolution images and strong artificial intelligence represents a technical achievement for mapping and measuring these trees, and this study aims to be the first in a series of research papers that work to calculate the amount of carbon they store, which is vital information for understanding the carbon cycle on Earth and how it occurs, and how it changes over time. Time.
Carbon is one of the building blocks of all life on Earth, and this element cycles between the Earth, the atmosphere and the oceans through the carbon cycle.
Some natural processes and human activities release carbon into the atmosphere, while other processes remove it from the atmosphere and store it on the ground or in the ocean, and during this cycle trees and other green plants play the role of carbon pools, meaning that they use carbon for growth and store it outside the atmosphere in their stems, branches and leaves. And its roots.
Human activities, such as burning trees and fossil fuels or deforestation, releasing carbon into the atmosphere as carbon dioxide, and increasing concentrations of carbon dioxide in the atmosphere are a major cause of climate change.
Tucker and his colleagues at NASA, along with an international team, used commercial satellite images from DigitalGlobe, which were high enough to spot individual trees and measure the size of their crowns.
The images came from commercial satellites QuickBird-2, GeoEye-1, WorldView-2 and WorldView-3, as the team focused on areas of dry land, areas that receive less precipitation than evaporate from vegetation each year, including the arid southern side of the Sahara. The Greater Sahel, which extends across the semi-arid Sahel and into the humid subtropics of West Africa.
The team trained their computing algorithms to identify trees across a variety of terrain types, from deserts in the north to savannahs in the south.