In order to somewhat counteract the CO2 \u00a0<\/sub>emissions they produce, various major corporations globally purchase what are known as carbon credits. Carbon credits legally enable companies to emit certain quantities of CO2 <\/sub>as long as the greenhouse gas is neutralised in another form, such as by planting or not cutting down trees.<\/p>\n
However, it is especially hard to track the billions of trees worldwide as millions are planted and felled each year, meaning that accurately quantifying the amount of carbon they sequester is challenging. Therefore, determining if carbon credits are an adequate and effective method to combat emissions is unclear.<\/p>\n
To overcome this, the research team pioneered a method that analysed 9.9 billion trees in the semiarid Sahel, a vast belt of land stretching across Northern Africa from the Atlantic Ocean to the Red Sea \u2013 an area of roughly ten million square kilometres.<\/p>\n
Their technique will enable experts to count the number of trees in large land areas precisely, and much carbon is sequestered within each tree.<\/p>\n
The team investigated Africa’s Sahel region by utilising 300,000 very-high-resolution satellite images from NASA, which were then reviewed and amalgamated in a mosaic that shows the number of trees from above.<\/p>\n
The researchers then trained one of NASA’s supercomputers using Artificial Intelligence (AI) to identify all trees individually.<\/p>\n
Professor Christian Igel, from the University of Copenhagen’s Department of Computer Science, commented: “Our study demonstrates that deep learning techniques can revolutionise the global mapping of individual trees and their biomass.<\/p>\n
“Our artificial neural networks learn to extract complex patterns from large amounts of satellite images, allowing for more accurate and efficient identification of individual trees and subsequent estimation of their biomass.”<\/p>\n
The results showed that the nearly ten billion trees in the Sahel region currently store 840,000,000 tons of carbon \u2013 a number based on the weight of individual trees.<\/p>\n
The innovative method will potentially help to control the effect of climate credits and will illustrate if various tree planting restoration projects are actually effective at curbing emissions.<\/a><\/p>\n