This morning, I spoke to Pachama’s co-founder and CEO, Diego Saez Gil–a graduate of the Y combinator who brings machine learning to the complex tree planting business. Pachama ensures in brief that the planting comes to action when an organization claims that it offsets pollution by planting trees.
From an article in the Sanford Philosopher.
Saez Gil says, adding that the platform needs greater accountability for the carbon offset sector — which is gradually trading on trees. “We employ remote sensing to track and verify reports of forest carbon ventures, incorporating satellite images and Lidar information.
Last week, a platform that wishes to bring together the world’s various reforestation campaigns, among others the comparable Trillion Trees project, that aims at planting a trillion trees, received support from leaders sweet-talked by the World Economic Forum.
Critics have cautioned that the PR drive on forests is a distraction from the broader and harder challenge of attempting to reduce carbon emissions, and I am getting along with it. Tree growth seems to be a tediously lengthy process, so it will take longer to achieve the promised compensations, and even then, a wildfire, drought, or some other catastrophe can deny it. There’s a different problem, however.
“Today’s demand is greater than the certified supply of carbon offset tree cultivation,” says Saez Gil. The finding does not mean that there is scarce land to contain a trillion trees. The ETH Zurich has reported that 0.9 billion hectares of land are viable for reforestation worldwide and could hold 1.2 trillion trees and 205 billion tons of carbon.
The issue is the certification process, Saez Gil says, which evaluates how much carbon emits from a proposed reforestation program. It takes years to achieve the current process, which depends heavily on manual tests. Pachama tries to accelerate the system.
“Using Lidar imagery — from the use of drones or an aircraft— field tracks – that is information provided on the land from forestry services— we practice deep learning algorithms.” Such algorithms begin to learn that some color combinations, forms, and tree species have a certain carbon content, and we can start to determine carbon absorption rates, “said Saez Gil.
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