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My name is Compton James Tucker,
and I am a scientist at the

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Goddard Space Flight Center.
We're very interested to improve

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our knowledge of the carbon
cycle globally. Where is carbon

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going in vegetation? And how
long does it persist? In the

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study, we use a large volume of
commercial satellite data,

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hundreds of thousands of
commercial satellite images at

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the 50 centimeter scale, to map
trees to identify trees in a

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semi-arid region, from the
Atlantic Ocean to the Red Sea in

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Africa, what we actually mapped
were tree crowns.

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We then use our tree crown data
to make predictions from the

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allometry, which was also
collected on the same region.

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And the data are very important.
The the processing code is

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important, the training data is
important. The allometry is

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important. And then
understanding the results that

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come out of those four
components. In the study, the

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study has been in the works
since 2015, or 2016. I started

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five or six years ago, draining
the archive of all of the data

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from Africa. This has taken me
three or four years to get all

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the data. Secondly, Ankit, who's
one of our team members, as a

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graduate student in computer
science, he wrote our processing

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code. And it's a highly
optimized neural net code, it

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works very well. He worked on
that for two or three years,

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then you need the training data
to go with the processing code.

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When you use machine learning or
artificial intelligence, you

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need to train on something so
you have confidence that that's

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what you're measuring. Training
data is where you go out and you

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select all different types of
trees. And they have to have a

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green tree crown and an
associated shadow to be a tree.

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And Martin Brandt did this over
three or four months, and

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selected 89,000 or 90,000
individual trees, it's a heroic

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effort. Now there are people
like Pierre Hiernaux, one of our

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co-authors who go out and they
sample trees and they measure

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the tree crown, they then cut
the tree down, they then measure

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the volume of leaves in the tree
crown. The same for the wood and

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the same for the roots. And so
we then convert the tree crown

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data which we measure into the
predicted leaf mass or carbon,

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the root carbon and the wood
carbon of every individual tree.

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You know, individual tree crown
is probably the highest

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resolution you're gonna get. And
like knowing the exact number of

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trees, and also when they have
leaves throughout the year is

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going to be really, really
important for improving our

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climate models.

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Then you put all this together,
and you run out on a

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supercomputer. So we would run
the data of this way, run it

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that way, then you take the
results. That's really the fun

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part of seeing what you did, how
well you did it, and what it can

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be used for.

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So the viewer is an important
tool for NGOs that are

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interested in understanding if
the tree restoration programs

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have paid off, but it can also
be used for the local farmer who

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would be interested in knowing
how many trees are standing on

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the fields, and are they alive,
are they dead,

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etc. With a viewer you can zoom
into individual trees and see

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how much carbon is there and the
leaves and the wood and the

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roots and the specific location
of that tree. Or you can

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aggregate the data up to an area
of 100 meters by 100 meters or

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one hectare. We plan to expand
our work next to Australia and

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then maybe to Eastern Africa,
Southern Africa, Central Asia,

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and possibly other arid and semi
arid areas.