My name is Compton James Tucker, and I am a scientist at the
Goddard Space Flight Center. We're very interested to improve
our knowledge of the carbon cycle globally. Where is carbon
going in vegetation? And how long does it persist? In the
study, we use a large volume of commercial satellite data,
hundreds of thousands of commercial satellite images at
the 50 centimeter scale, to map trees to identify trees in a
semi-arid region, from the Atlantic Ocean to the Red Sea in
Africa, what we actually mapped were tree crowns.
We then use our tree crown data to make predictions from the
allometry, which was also collected on the same region.
And the data are very important. The the processing code is
important, the training data is important. The allometry is
important. And then understanding the results that
come out of those four components. In the study, the
study has been in the works since 2015, or 2016. I started
five or six years ago, draining the archive of all of the data
from Africa. This has taken me three or four years to get all
the data. Secondly, Ankit, who's one of our team members, as a
graduate student in computer science, he wrote our processing
code. And it's a highly optimized neural net code, it
works very well. He worked on that for two or three years,
then you need the training data to go with the processing code.
When you use machine learning or artificial intelligence, you
need to train on something so you have confidence that that's
what you're measuring. Training data is where you go out and you
select all different types of trees. And they have to have a
green tree crown and an associated shadow to be a tree.
And Martin Brandt did this over three or four months, and
selected 89,000 or 90,000 individual trees, it's a heroic
effort. Now there are people like Pierre Hiernaux, one of our
co-authors who go out and they sample trees and they measure
the tree crown, they then cut the tree down, they then measure
the volume of leaves in the tree crown. The same for the wood and
the same for the roots. And so we then convert the tree crown
data which we measure into the predicted leaf mass or carbon,
the root carbon and the wood carbon of every individual tree.
You know, individual tree crown is probably the highest
resolution you're gonna get. And like knowing the exact number of
trees, and also when they have leaves throughout the year is
going to be really, really important for improving our
climate models.
Then you put all this together, and you run out on a
supercomputer. So we would run the data of this way, run it
that way, then you take the results. That's really the fun
part of seeing what you did, how well you did it, and what it can
be used for.
So the viewer is an important tool for NGOs that are
interested in understanding if the tree restoration programs
have paid off, but it can also be used for the local farmer who
would be interested in knowing how many trees are standing on
the fields, and are they alive, are they dead,
etc. With a viewer you can zoom into individual trees and see
how much carbon is there and the leaves and the wood and the
roots and the specific location of that tree. Or you can
aggregate the data up to an area of 100 meters by 100 meters or
one hectare. We plan to expand our work next to Australia and
then maybe to Eastern Africa, Southern Africa, Central Asia,
and possibly other arid and semi arid areas.