WEBVTT FILE

1
00:00:01.590 --> 00:00:06.180
My name is Compton James Tucker,
and I am a scientist at the

2
00:00:06.180 --> 00:00:10.560
Goddard Space Flight Center.
We're very interested to improve

3
00:00:10.590 --> 00:00:15.270
our knowledge of the carbon
cycle globally. Where is carbon

4
00:00:15.270 --> 00:00:19.410
going in vegetation? And how
long does it persist? In the

5
00:00:19.410 --> 00:00:23.220
study, we use a large volume of
commercial satellite data,

6
00:00:24.840 --> 00:00:27.990
hundreds of thousands of
commercial satellite images at

7
00:00:27.990 --> 00:00:32.880
the 50 centimeter scale, to map
trees to identify trees in a

8
00:00:32.880 --> 00:00:36.510
semi-arid region, from the
Atlantic Ocean to the Red Sea in

9
00:00:36.510 --> 00:00:40.260
Africa, what we actually mapped
were tree crowns.

10
00:01:13.210 --> 00:01:17.800
We then use our tree crown data
to make predictions from the

11
00:01:17.830 --> 00:01:21.100
allometry, which was also
collected on the same region.

12
00:01:21.460 --> 00:01:26.260
And the data are very important.
The the processing code is

13
00:01:26.260 --> 00:01:29.710
important, the training data is
important. The allometry is

14
00:01:29.710 --> 00:01:32.500
important. And then
understanding the results that

15
00:01:32.500 --> 00:01:38.410
come out of those four
components. In the study, the

16
00:01:38.410 --> 00:01:43.990
study has been in the works
since 2015, or 2016. I started

17
00:01:43.990 --> 00:01:48.550
five or six years ago, draining
the archive of all of the data

18
00:01:48.550 --> 00:01:53.050
from Africa. This has taken me
three or four years to get all

19
00:01:53.050 --> 00:02:00.250
the data. Secondly, Ankit, who's
one of our team members, as a

20
00:02:00.250 --> 00:02:05.080
graduate student in computer
science, he wrote our processing

21
00:02:05.080 --> 00:02:08.560
code. And it's a highly
optimized neural net code, it

22
00:02:08.560 --> 00:02:12.670
works very well. He worked on
that for two or three years,

23
00:02:12.730 --> 00:02:16.420
then you need the training data
to go with the processing code.

24
00:02:16.450 --> 00:02:20.290
When you use machine learning or
artificial intelligence, you

25
00:02:20.290 --> 00:02:24.880
need to train on something so
you have confidence that that's

26
00:02:24.880 --> 00:02:27.460
what you're measuring. Training
data is where you go out and you

27
00:02:27.460 --> 00:02:31.900
select all different types of
trees. And they have to have a

28
00:02:31.900 --> 00:02:35.170
green tree crown and an
associated shadow to be a tree.

29
00:02:35.800 --> 00:02:39.490
And Martin Brandt did this over
three or four months, and

30
00:02:39.490 --> 00:02:45.490
selected 89,000 or 90,000
individual trees, it's a heroic

31
00:02:45.490 --> 00:02:53.560
effort. Now there are people
like Pierre Hiernaux, one of our

32
00:02:53.560 --> 00:02:56.590
co-authors who go out and they
sample trees and they measure

33
00:02:56.590 --> 00:03:00.430
the tree crown, they then cut
the tree down, they then measure

34
00:03:00.430 --> 00:03:05.560
the volume of leaves in the tree
crown. The same for the wood and

35
00:03:05.560 --> 00:03:10.480
the same for the roots. And so
we then convert the tree crown

36
00:03:10.480 --> 00:03:16.060
data which we measure into the
predicted leaf mass or carbon,

37
00:03:16.270 --> 00:03:21.250
the root carbon and the wood
carbon of every individual tree.

38
00:03:22.020 --> 00:03:26.790
You know, individual tree crown
is probably the highest

39
00:03:27.810 --> 00:03:36.570
resolution you're gonna get. And
like knowing the exact number of

40
00:03:36.570 --> 00:03:41.460
trees, and also when they have
leaves throughout the year is

41
00:03:41.460 --> 00:03:45.000
going to be really, really
important for improving our

42
00:03:45.000 --> 00:03:45.900
climate models.

43
00:03:47.130 --> 00:03:50.430
Then you put all this together,
and you run out on a

44
00:03:50.430 --> 00:03:54.270
supercomputer. So we would run
the data of this way, run it

45
00:03:54.270 --> 00:03:58.290
that way, then you take the
results. That's really the fun

46
00:03:58.290 --> 00:04:02.190
part of seeing what you did, how
well you did it, and what it can

47
00:04:02.190 --> 00:04:02.790
be used for.

48
00:04:03.950 --> 00:04:08.720
So the viewer is an important
tool for NGOs that are

49
00:04:08.720 --> 00:04:13.700
interested in understanding if
the tree restoration programs

50
00:04:13.730 --> 00:04:17.990
have paid off, but it can also
be used for the local farmer who

51
00:04:17.990 --> 00:04:22.070
would be interested in knowing
how many trees are standing on

52
00:04:22.250 --> 00:04:25.220
the fields, and are they alive,
are they dead,

53
00:04:25.400 --> 00:04:29.300
etc. With a viewer you can zoom
into individual trees and see

54
00:04:29.300 --> 00:04:33.050
how much carbon is there and the
leaves and the wood and the

55
00:04:33.050 --> 00:04:38.240
roots and the specific location
of that tree. Or you can

56
00:04:38.240 --> 00:04:42.350
aggregate the data up to an area
of 100 meters by 100 meters or

57
00:04:42.350 --> 00:04:48.170
one hectare. We plan to expand
our work next to Australia and

58
00:04:48.170 --> 00:04:52.160
then maybe to Eastern Africa,
Southern Africa, Central Asia,

59
00:04:52.610 --> 00:04:56.840
and possibly other arid and semi
arid areas.