In 1972, the Landsat mission launched its
first satellite into orbit, ushering in a revolutionary new era of Earth observation.
More than 50 years and 8 satellites later,
the Landsat mission has collected an immense amount of data that's proven an invaluable
resource to scientists studying the complexities of our planet.
The incredible abundance of data provides
insights, but it can also pose a daunting challenge for researchers to extract and analyze
information -- Landsats 8 & 9 alone each gather close to a terabyte
of data per day.
Enter artificial intelligence.
AI's popularity has taken off in recent
years with new tools that allow users to generate imagery, transcribe audio, and even compose
music at the click of a button.
This is nothing new for the scientific community, however, which have been using artificial
intelligence methods for decades.
When it comes to working with Landsat data, one of the most popular AI tools is machine
learning.
Machine learning is a subset of AI that can train computer programs to recognize patterns
and analyze imagery, skills that prove exceptionally useful in the application of Landsat data.
In fact, when combined with Landsat, machine
learning models have led to a number of advances across a variety of scientific fields, granting
further insight into our planet's past, present and future.
One of the major challenges of working with
satellite imagery like Landsat can actually be found up in the sky - clouds.
Obscuring Earth's surface and casting shadows
that reduce visibility, a cloudy day can be a downright nuisance when it comes to analyzing
certain
satellite imagery.
Accurately detecting these pesky clouds would be a tall task for any one human, but a piece
of cake for a computer.
In 2019, researchers from Oregon State University constructed a deep convolutional neural network
model, a machine learning tool that excels at recognizing patterns in imagery.
With the help of existing Landsat 8 data,
they taught their neural network to automatically detect clouds in satellite imagery with an
amazing 97.1% accuracy rate.
The researchers believe, in the future, this machine learning technique could even be harnessed
to identify clouds across the entire Landsat 8 archive.
Machine learning's benefits don't just end when the clouds clear - down on the ground,
there's plenty to keep an eye on.
Our planet's one constant is change - Earth's surface is perpetually evolving due to human
and natural forces.
Landsat's ability to track these changes over time has proven to be an incredible asset
to the scientific community, especially when used in concert with machine learning.
For example, researchers from the University
of Texas at Austin used Landsat data with a random forest classifier, yet another type
of machine learning tool that combines multiple decision trees to make predictions.
Using data from Landsats 4-8, they used
the classifier to map the changes in land-use in northwestern Belize from the 1980's
to the present.
The results showed that tropical forests and wetlands that don't have a designated protection
status are increasingly vulnerable to deforestation due to Belize's expanding industrial agriculture.
By combining these new advances in machine
learning with Landsat's capacity for looking back in time, researchers believe this approach
makes it possible to provide robust estimates of deforestation in Belize going forward.
Wildfires
As climate change drives our planet's temperatures higher, so does the prevalence of extreme
events that put ecosystems at risk.
Wildfires across the globe have seen an increase in frequency and intensity - Australia is
no stranger to these types of fires - the 2019-2020 bushfire season was one of the most
destructive on record.
Using satellite imagery to pinpoint when and where wildfires are burning can be an important
tool for assessing damage and preventing future fires.
Researchers at the University of Western Australia
developed a new machine learning approach with the help of Landsat 8 data
to generate
a 16-year history of wildfire severity in the eucalyptus forests of the continent's
southwest.
They fed data gathered by satellites including Landsat 8 from 2005-2020 into a supervised
classifier, a type of machine learning algorithm that learns to classify data based on labeled
examples provided during training.
By teaching the algorithm with examples from the past, this method of machine learning
could be used to predict the severity of future wildfires - critical data that could aid in
the management and conservation of Australia's extensive eucalyptus forests.
These were just a few examples of the remarkable ways the
fusion of Landsat data with machine learning tools are unlocking new possibilities
for understanding our planet.
This collaboration has already led to significant advances in agriculture, forestry, urban planning,
climate change research and more.
As satellite data, such as Landsat's, becomes increasingly available and machine learning
techniques continue to improve, so too will the potential for applications in additional
fields critical to the health of our planet's ecosystems.
In tackling the complex challenges of today
and tomorrow, the partnership between Landsat data and machine learning will
be vital to help people make better decisions to protect our planet.