WEBVTT FILE

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In 1972,

2
00:00:03.770 --> 00:00:06.573
the first Landsat satellite
was launched into orbit,

3
00:00:07.107 --> 00:00:10.510
ushering in a revolutionary
new era of earth observation.

4
00:00:11.211 --> 00:00:13.780
More than 50 years
and eight satellites later,

5
00:00:14.080 --> 00:00:15.815
the Landsat program
has collected

6
00:00:15.815 --> 00:00:17.417
an immense amount of data

7
00:00:17.417 --> 00:00:19.252
that's proven
an invaluable resource

8
00:00:19.252 --> 00:00:20.086
to scientists

9
00:00:20.086 --> 00:00:22.689
studying the complexities
of our planet's surface.

10
00:00:23.223 --> 00:00:24.290
The abundance of data

11
00:00:24.290 --> 00:00:26.493
provides insights,
but it can also pose

12
00:00:26.493 --> 00:00:27.594
a daunting challenge

13
00:00:27.594 --> 00:00:30.363
for researchers to extract
and analyze information.

14
00:00:30.697 --> 00:00:33.166
Landsat 8 and 9 alone each gather

15
00:00:33.166 --> 00:00:35.502
close to a terabyte of data per day.

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00:00:36.236 --> 00:00:38.471
Enter Artificial Intelligence.

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00:00:38.471 --> 00:00:41.341
AI's popularity has taken off
in recent years

18
00:00:41.641 --> 00:00:42.475
with new tools

19
00:00:42.475 --> 00:00:45.845
that allow users to generate
imagery, transcribe audio,

20
00:00:46.046 --> 00:00:48.715
and even compose music
at the click of a button.

21
00:00:49.549 --> 00:00:51.818
This is nothing new
for the scientific community,

22
00:00:51.818 --> 00:00:53.486
however, which have been using

23
00:00:53.486 --> 00:00:55.889
artificial intelligence
methods for decades.

24
00:00:56.356 --> 00:00:56.856
When it comes

25
00:00:56.856 --> 00:00:59.926
to working with Landsat data,
one of the most popular A.I.

26
00:00:59.926 --> 00:01:01.861
tools is machine learning.

27
00:01:01.861 --> 00:01:03.930
Machine learning is a subset of AI

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00:01:04.130 --> 00:01:04.764
that can train

29
00:01:04.764 --> 00:01:05.832
computer programs

30
00:01:05.832 --> 00:01:08.935
to recognize patterns
and analyze imagery, skills

31
00:01:08.935 --> 00:01:10.503
that prove
exceptionally useful

32
00:01:10.503 --> 00:01:12.806
in the application of Landsat data.

33
00:01:12.806 --> 00:01:14.841
In fact, when combined with Landsat

34
00:01:14.841 --> 00:01:16.609
machine learning models have led

35
00:01:16.609 --> 00:01:18.545
to a number of advances across a variety

36
00:01:18.545 --> 00:01:20.013
of scientific fields,

37
00:01:20.013 --> 00:01:21.347
granting further insight

38
00:01:21.347 --> 00:01:24.050
into our planet's
past, present and future.

39
00:01:24.851 --> 00:01:26.653
One of the major challenges
of working

40
00:01:26.653 --> 00:01:27.654
with satellite imagery

41
00:01:27.654 --> 00:01:30.924
like Landsat can actually be
found up in the sky,

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clouds

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00:01:32.559 --> 00:01:34.394
obscuring Earth's surface
and casting

44
00:01:34.394 --> 00:01:36.396
shadows
that reduce visibility.

45
00:01:36.396 --> 00:01:38.431
A cloudy day
can be a downright nuisance

46
00:01:38.431 --> 00:01:41.000
when it comes to analyzing
certain satellite imagery.

47
00:01:41.434 --> 00:01:44.170
Pinpointing these clouds
helps to improve data quality

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by removing
noise and artifacts,

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making it easier
to detect changes over time.

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00:01:49.309 --> 00:01:50.910
But while
accurate cloud detection

51
00:01:50.910 --> 00:01:53.246
across a massive dataset
such as Landsat

52
00:01:53.313 --> 00:01:54.948
would be a tall
task for any one

53
00:01:54.948 --> 00:01:57.417
human, it's a piece of cake
for a computer.

54
00:01:57.951 --> 00:02:01.287
In 2019, researchers
from Oregon State University

55
00:02:01.387 --> 00:02:03.256
constructed
a deep convolutional

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00:02:03.256 --> 00:02:04.958
neural network model

57
00:02:04.958 --> 00:02:06.759
a machine learning tool
that excels

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00:02:06.759 --> 00:02:08.928
at recognizing patterns
in imagery.

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00:02:08.928 --> 00:02:11.231
With the help of existing
Landsat 8 data,

60
00:02:11.464 --> 00:02:12.732
they taught their neural network

61
00:02:12.732 --> 00:02:15.335
to automatically detect clouds
in satellite imagery

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00:02:15.401 --> 00:02:18.872
with an amazing 97.1%
accuracy rate.

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00:02:19.672 --> 00:02:21.508
The researchers
believe in the future

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00:02:21.508 --> 00:02:22.909
cloud detection algorithms

65
00:02:22.909 --> 00:02:24.777
like this one
could even be harnessed

66
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to identify clouds across the
entire Landsat 8 archive.

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00:02:30.683 --> 00:02:32.485
Machine learning's
benefits don't just end

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00:02:32.485 --> 00:02:34.687
when the clouds
clear - down on the ground

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00:02:34.721 --> 00:02:36.656
there's plenty
to keep an eye on.

70
00:02:36.656 --> 00:02:38.791
Our planet's one
constant is change.

71
00:02:39.192 --> 00:02:39.893
Earth's surface

72
00:02:39.893 --> 00:02:43.329
is perpetually evolving due
to human and natural forces.

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00:02:43.997 --> 00:02:46.366
Landsat its ability
to track these changes over

74
00:02:46.366 --> 00:02:47.333
time has proven

75
00:02:47.333 --> 00:02:49.869
to be an incredible asset
to the scientific community,

76
00:02:50.470 --> 00:02:53.206
especially when used in
concert with machine learning.

77
00:02:53.840 --> 00:02:56.643
For example, researchers
from the University of Texas

78
00:02:56.643 --> 00:02:59.646
at Austin used Landsat data
with a random force

79
00:02:59.646 --> 00:03:01.981
classifier, yet
another type of machine

80
00:03:01.981 --> 00:03:03.249
learning tool that combines

81
00:03:03.249 --> 00:03:05.552
multiple decision trees
to make predictions.

82
00:03:06.252 --> 00:03:08.521
Using data from Landsats 4
through 8,

83
00:03:08.821 --> 00:03:11.524
They used the classifier
to map changes in land use

84
00:03:11.524 --> 00:03:14.661
in northwestern Belize
from the 1980s to the present.

85
00:03:16.029 --> 00:03:18.464
The results showed that
tropical forests and wetlands

86
00:03:18.464 --> 00:03:20.767
that don't have
a designated protection status

87
00:03:21.000 --> 00:03:23.836
are increasingly vulnerable
to deforestation due

88
00:03:23.836 --> 00:03:26.406
to Belize's expanding
industrial agriculture.

89
00:03:27.040 --> 00:03:28.942
By combining
these new advances in machine

90
00:03:28.942 --> 00:03:29.943
learning with Landsat

91
00:03:29.943 --> 00:03:31.911
to capacity
for looking back in time,

92
00:03:31.911 --> 00:03:33.680
researchers
believe in the future,

93
00:03:33.680 --> 00:03:35.248
this approach would make it
possible

94
00:03:35.248 --> 00:03:38.251
to provide robust estimates
of deforestation in Belize.

95
00:03:41.454 --> 00:03:44.190
As climate change drives our
planet's temperatures higher,

96
00:03:44.457 --> 00:03:44.991
so does the

97
00:03:44.991 --> 00:03:48.428
prevalence of extreme events
that put ecosystems at risk.

98
00:03:48.995 --> 00:03:49.996
Wildfires across

99
00:03:49.996 --> 00:03:52.865
the globe have increased
in frequency and intensity.

100
00:03:53.199 --> 00:03:56.002
Australia is no stranger
to these types of fires.

101
00:03:56.202 --> 00:03:58.771
The 2019-2020 bushfire season

102
00:03:58.771 --> 00:04:00.840
was one of the most
destructive on record.

103
00:04:01.341 --> 00:04:03.142
Using satellite imagery
to pinpoint

104
00:04:03.142 --> 00:04:05.044
when and where
wildfires are burning

105
00:04:05.044 --> 00:04:06.145
could be an important tool

106
00:04:06.145 --> 00:04:08.281
for assessing damage
in future fires.

107
00:04:09.349 --> 00:04:11.217
Researchers
at the University of Western

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00:04:11.217 --> 00:04:13.019
Australia
have developed a new machine

109
00:04:13.019 --> 00:04:15.521
learning approach with
the help of Landsat 8 data

110
00:04:16.022 --> 00:04:19.158
to generate a 16-year
history of wildfire severity

111
00:04:19.158 --> 00:04:21.761
in the eucalyptus forests
of the continent's southwest.

112
00:04:22.762 --> 00:04:26.132
They fed data gathered by
satellites, including Landsat 8

113
00:04:26.132 --> 00:04:29.802
from 2005 to 2020
into a supervised classifier

114
00:04:29.869 --> 00:04:31.571
a type of machine
learning algorithm

115
00:04:31.571 --> 00:04:32.772
that learns to classify

116
00:04:32.772 --> 00:04:35.808
data based on labeled examples
provided during training.

117
00:04:36.776 --> 00:04:39.245
By teaching the algorithm
with examples from the past.

118
00:04:39.279 --> 00:04:41.481
This method of machine
learning could be used

119
00:04:41.481 --> 00:04:43.683
to predict the severity
of future wildfires.

120
00:04:43.950 --> 00:04:44.884
Critical data

121
00:04:44.884 --> 00:04:47.053
that could aid in the
management and conservation

122
00:04:47.053 --> 00:04:49.722
of Australia's
extensive eucalyptus forests.

123
00:04:50.690 --> 00:04:52.992
These are just a few examples
of the remarkable ways

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00:04:52.992 --> 00:04:55.728
Landsat data and machine
learning tools are unlocking

125
00:04:55.728 --> 00:04:57.964
new possibilities
for understanding our planet.

126
00:04:58.731 --> 00:05:00.233
This combination
has already led

127
00:05:00.233 --> 00:05:02.235
to significant advances
in agriculture,

128
00:05:02.268 --> 00:05:04.570
forestry,
urban planning, climate change

129
00:05:04.570 --> 00:05:06.372
research and more

130
00:05:06.372 --> 00:05:08.908
as more satellite data
becomes available and machine

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00:05:08.908 --> 00:05:11.110
learning techniques
continue to improve.

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So too will the potential

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00:05:12.445 --> 00:05:14.580
for applications
in additional fields

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critical to the health
of our planet's ecosystems.

135
00:05:17.517 --> 00:05:18.084
In tackling

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00:05:18.084 --> 00:05:20.386
the complex challenges
of today and tomorrow,

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00:05:20.553 --> 00:05:22.622
the blending of Landsat data
and machine

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learning will be vital to help
people

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00:05:24.357 --> 00:05:26.626
make better decisions
to protect our planet.

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00:08:30.243 --> 00:08:31.878
In 1972, the

141
00:08:31.878 --> 00:08:34.780
Landsat mission launched its
first satellite into orbit,

142
00:08:35.147 --> 00:08:37.950
ushering in a revolutionary
new era of earth

143
00:08:37.950 --> 00:08:39.619
observation.

144
00:08:42.121 --> 00:08:43.623
The Landsat mission
has collected

145
00:08:43.623 --> 00:08:45.091
an immense amount of data

146
00:08:45.091 --> 00:08:45.625
that's proven

147
00:08:45.625 --> 00:08:47.627
an invaluable resource
to scientists

148
00:08:47.627 --> 00:08:50.096
studying the complexities
of our planet's surface.

149
00:08:50.563 --> 00:08:53.266
The incredible abundance
of data provides insights,

150
00:08:53.466 --> 00:08:55.401
but it can also pose
a daunting challenge

151
00:08:55.401 --> 00:08:58.170
for researchers to extract
and analyze information.

152
00:08:58.738 --> 00:09:01.307
Landsat eight and nine alone,
each gather

153
00:09:01.307 --> 00:09:03.543
close to a terabyte of data
per day.

154
00:09:04.310 --> 00:09:08.214
Enter artificial intelligence

155
00:09:09.649 --> 00:09:10.516
with new tools

156
00:09:10.516 --> 00:09:13.653
that allow users to generate
imagery, transcribe audio,

157
00:09:13.786 --> 00:09:16.656
and even compose music
at the click of a button.

158
00:09:17.523 --> 00:09:19.892
This is nothing new
for the scientific community,

159
00:09:19.892 --> 00:09:21.327
however, which have been using

160
00:09:21.327 --> 00:09:23.863
artificial intelligence
methods for decades.

161
00:09:24.497 --> 00:09:26.399
When it comes to working
with Landsat data.

162
00:09:26.399 --> 00:09:27.800
One of the most popular A.I.

163
00:09:27.800 --> 00:09:29.802
tools is machine learning.

164
00:09:29.802 --> 00:09:32.038
Machine
learning is a subset of A.I.

165
00:09:32.071 --> 00:09:33.573
that can train
computer programs

166
00:09:33.573 --> 00:09:36.642
to recognize patterns
and analyze imagery skills

167
00:09:36.642 --> 00:09:38.210
that prove exceptional useful

168
00:09:38.210 --> 00:09:40.513
in the application of Landsat
data.

169
00:09:40.513 --> 00:09:42.548
In fact,
when combined with Landsat

170
00:09:42.848 --> 00:09:44.984
machine learning models
have led to a number

171
00:09:44.984 --> 00:09:47.820
of advances across a variety
of scientific fields,

172
00:09:48.120 --> 00:09:50.656
granting further insight
into our planet's past,

173
00:09:50.756 --> 00:09:52.858
present and future.

174
00:09:52.858 --> 00:09:54.627
One of the major challenges
of working

175
00:09:54.627 --> 00:09:56.529
with satellite imagery
like Landsat

176
00:09:56.529 --> 00:10:00.032
can actually be
found up in the sky, clouds

177
00:10:00.466 --> 00:10:02.401
obscuring the Earth's surface
and casting

178
00:10:02.401 --> 00:10:04.103
shadows
that reduce visibility.

179
00:10:04.103 --> 00:10:06.305
A cloudy day
can be a downright nuisance

180
00:10:06.305 --> 00:10:08.874
when it comes to analyzing
certain satellite imagery.

181
00:10:10.042 --> 00:10:11.310
Accurately detecting these

182
00:10:11.310 --> 00:10:14.347
pesky clouds would be a tall
task for any one human,

183
00:10:14.580 --> 00:10:18.150
but a piece of cake
for a computer.

184
00:10:25.992 --> 00:10:29.161
In 2019, researchers
from Oregon State University

185
00:10:29.161 --> 00:10:31.263
constructed
a deep convolutional neural

186
00:10:31.263 --> 00:10:32.565
network model

187
00:10:32.565 --> 00:10:33.699
a machine learning tool

188
00:10:33.699 --> 00:10:36.268
that excels at recognizing
patterns in imagery.

189
00:10:36.836 --> 00:10:39.171
With the help of existing
Landsat eight data,

190
00:10:39.305 --> 00:10:40.539
they taught
their neural network

191
00:10:40.539 --> 00:10:43.309
to automatically detect clouds
and satellite imagery

192
00:10:43.309 --> 00:10:46.979
with an amazing 97.1%
accuracy rate.

193
00:10:47.713 --> 00:10:49.682
The researchers
believe in the future,

194
00:10:49.682 --> 00:10:51.150
this machine learning
technique

195
00:10:51.150 --> 00:10:53.352
could even be harnessed
to identify clouds

196
00:10:53.352 --> 00:10:55.955
across the entire Landsat
eight archive.

197
00:10:57.456 --> 00:11:00.026
Machine learning benefits
don't just end when the clouds

198
00:11:00.026 --> 00:11:01.594
clear down on the ground.

199
00:11:01.594 --> 00:11:03.529
There's plenty
to keep an eye on.

200
00:11:03.529 --> 00:11:05.731
Our planet's one
constant is change.

201
00:11:06.098 --> 00:11:06.799
Earth's surface

202
00:11:06.799 --> 00:11:10.302
is perpetually evolving due
to human and natural forces.

203
00:11:10.836 --> 00:11:13.706
Landsat its ability to track
these changes over time

204
00:11:13.939 --> 00:11:15.641
has proven to be
an incredible asset

205
00:11:15.641 --> 00:11:17.309
to the scientific community,

206
00:11:17.309 --> 00:11:20.046
especially when used in
concert with machine learning.

207
00:11:20.680 --> 00:11:23.416
For example, researchers
from the University of Texas

208
00:11:23.416 --> 00:11:25.317
at Austin
used the Landsat data

209
00:11:25.317 --> 00:11:27.520
with a random forest
classifier,

210
00:11:27.520 --> 00:11:30.289
yet another type of machine
learning tool that combines

211
00:11:30.289 --> 00:11:32.558
multiple decision trees
to make predictions.

212
00:11:33.092 --> 00:11:35.461
Using data from Landsat four
through eight.

213
00:11:35.695 --> 00:11:38.664
They used the classifier
to map the changes in land use

214
00:11:38.664 --> 00:11:41.934
in northwestern Belize
from the 1980s to the present.

215
00:11:42.902 --> 00:11:45.337
The results showed that
tropical forests and wetlands

216
00:11:45.571 --> 00:11:47.640
don't have
a designated protection status

217
00:11:47.807 --> 00:11:50.409
or are increasingly vulnerable
to deforestation

218
00:11:50.643 --> 00:11:53.212
due to Belize's expanding
industrial agriculture.

219
00:11:53.846 --> 00:11:55.748
By combining
these new advances in machine

220
00:11:55.748 --> 00:11:57.283
learning with Landsat
to capacity

221
00:11:57.283 --> 00:11:59.485
for looking back in time,
researchers

222
00:11:59.485 --> 00:12:01.387
believe this approach
makes it possible

223
00:12:01.387 --> 00:12:04.623
to provide robust estimates
of deforestation in Belize.

224
00:12:04.623 --> 00:12:06.192
Going forward,

225
00:12:08.294 --> 00:12:10.563
as climate change drives
our planet's temperatures

226
00:12:10.563 --> 00:12:11.197
higher,

227
00:12:11.197 --> 00:12:12.231
so does the prevalence

228
00:12:12.231 --> 00:12:14.767
of extreme events
that put ecosystems at risk.

229
00:12:15.234 --> 00:12:17.870
Wildfires across
the globe has seen an increase

230
00:12:17.870 --> 00:12:20.039
in frequency and intensity.

231
00:12:20.039 --> 00:12:22.508
Australia is no stranger
to these types of fires.

232
00:12:22.842 --> 00:12:23.843
The 2019

233
00:12:23.843 --> 00:12:25.411
2020 bushfire season

234
00:12:25.411 --> 00:12:27.513
was one of the most
destructive on record.

235
00:12:27.847 --> 00:12:29.749
Using satellite imagery
to pinpoint

236
00:12:29.749 --> 00:12:31.851
when and where
wildfires are burning

237
00:12:31.851 --> 00:12:32.985
can be an important tool

238
00:12:32.985 --> 00:12:35.688
for assessing damage
and preventing future fires.

239
00:12:36.288 --> 00:12:38.891
Researchers at the University
of Western Australia

240
00:12:43.095 --> 00:12:46.398
to generate a 16 year
history of wildfire severity

241
00:12:46.398 --> 00:12:49.235
in the eucalyptus forests
of the continent's southwest.

242
00:12:49.935 --> 00:12:51.203
They said data gathered by

243
00:12:51.203 --> 00:12:54.974
satellites, including Landsat
eight from 2005 to 2020

244
00:12:55.107 --> 00:12:57.810
into a supervised classifier,
a type of machine

245
00:12:57.810 --> 00:12:59.912
learning algorithm
that learns to classify

246
00:12:59.912 --> 00:13:02.882
data based on labeled examples
provided during training

247
00:13:03.849 --> 00:13:06.252
by teaching the algorithm
with examples from the past.

248
00:13:06.285 --> 00:13:08.020
This method of machine
learning could be used

249
00:13:08.020 --> 00:13:10.189
to predict the severity
of future wildfires.

250
00:13:10.389 --> 00:13:11.857
Critical data
that could aid in

251
00:13:11.857 --> 00:13:13.425
the management
and conservation

252
00:13:13.425 --> 00:13:15.861
of Australia's
extensive eucalyptus forests.

253
00:13:17.463 --> 00:13:19.965
These were just a few examples
of the remarkable ways

254
00:13:20.199 --> 00:13:23.102
the fusion of Landsat data
with machine learning tools

255
00:13:23.269 --> 00:13:24.937
are unlocking
new possibilities

256
00:13:24.937 --> 00:13:26.872
for understanding our planet.

257
00:13:26.872 --> 00:13:28.474
This collaboration
has already led

258
00:13:28.474 --> 00:13:31.343
to significant advances
in agriculture, forestry,

259
00:13:31.377 --> 00:13:34.079
urban planning, climate change
research and more

260
00:13:34.680 --> 00:13:36.982
as satellite data
such as Landsat becomes

261
00:13:36.982 --> 00:13:38.951
increasingly available
and machine

262
00:13:38.951 --> 00:13:40.986
learning techniques
continue to improve.

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00:13:40.986 --> 00:13:43.389
So too will
the potential for applications

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00:13:43.389 --> 00:13:44.423
in additional fields

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critical to the health
of our planet's ecosystems.

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In tackling

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the complex challenges
of today and tomorrow,

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the partnership
between Landsat data

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and machine
learning will be vital to help

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people make better decisions
to protect our planet.