Hi, and welcome back to Remote Sensing Foundations.
This week we are gonna talk about the instruments
that carry out remote sensing.
Those instruments are called sensors.
At the end of the week,
I hope that you'll be able to describe
some common sensors that we use in remote sensing
and also be able to define how they differ.
I first wanted to review what we covered last week.
So if you remember last week we started off
by introducing particle physics
and explaining how that relates to remote sensing.
We also touched upon the electromagnetic spectrum
and talked about how radiation
differs in terms of wavelength and frequency
and how there's a lot of different sources
of electromagnetic radiation,
including the sun,
which is one of the most important sources
of energy when it comes to remote sensing.
We also talked about how solar radiation going from the sun
to the earth back to the sensor,
can be sort of lost at different points of that path.
So energy from the sun can be scattered
or absorbed as it goes from the sun to the earth,
and it can also be scattered
and absorbed going from the earth back to the sensor.
And then finally, we talked about spectral signatures
and how different objects or land covers on the earth
can emit light that has quite different
percent reflectances at different wavelengths.
And those spectral signatures can help us identify
or define those objects when we are looking at imagery.
Remote sensing instruments or sensors
measure what travel back from the object
or the earth's surface to the sensor.
This figure depicts many but not close to all of the tools
that we humans utilize to remotely sense our world.
Though we won't dive into each one of these tools,
I wanted to put this here
to show you how many different kinds of tools there are
and how big of an opportunity there is
to use remotely sense data
to understand different aspects of the earth's surface.
You can also start to imagine that these tools
can be used in conjunction with one another
or in conjunction with other kinds
of non-remotely sense data
to gain a deeper understanding of the earth's surface.
So for example, you could use data from satellites,
with data from meteorological balloons.
You could use data from drones with soil sampling.
A lot of agricultural applications do that.
You could use underwater acoustic or sonar sampling
with fish DNA sampling.
Actually, one of my first internships in undergrad
was using sonar videos of fish swimming at a certain point
in a stream in Maryland.
And I would watch those and in those videos
and estimate the population size of this fish species.
And then we would match that data
with data that we collected from the field.
So these are just a few examples
of what you can do with remotely sensed data.
As you can see, the possibilities are endless.
And start to think, I know it's early in the semester,
but start to think what you might wanna do
your final project on.
What kind of a remotely sense data
or questions are most interesting to you.
In this class,
we're gonna focus primarily on satellite missions
and the data that we get from them.
And we're gonna learn how to process, interpret
and analyze the imagery that comes from those satellites.
In order to discuss different satellite sensors,
we must understand the characteristics that define them.
One important characteristic is spatial resolution,
which is essentially the size of a pixel
when the sensor's data is mapped.
Another way to understand this is,
it's the size of the smallest area
that can be separately recorded as an entity on an image.
Spatial resolution is often expressed in meters,
but it can also be expressed in finer
or coarser measurements as well.
I've seen some satellite data
that has a resolution of 60 centimeters,
which is pretty incredibly small.
That's a really high-resolution image.
In the bottom maps, I'm showing two different images
that are the same area or location,
but they have different resolutions.
You can see that the left image
has a lower resolution, spatial resolution,
meaning that its pixel sizes are bigger.
And the right image has a higher spatial resolution
corresponding to smaller pixel sizes.
High resolution data is great 'cause it's more specific
and it's easier to see objects or features on the landscape.
So in the right image, it's pretty clear to see
that the light-colored pixels represent buildings.
But in the left image it's not as clear
that those light pixels are buildings.
But high-resolution data is not always the best choice.
Low resolution, spatial resolution imagery
definitely has its pros.
So for example, it's not as data intensive
as high-resolution imagery.
For example, if you're doing an analysis
over a really large area, for example,
the entire state of Vermont,
you don't really need super high-resolution imagery.
The high-resolution imagery, it may be way too intensive,
it may take forever to run your analyses.
The files may be really huge.
So in that case, it may be more advantageous
to have lower resolution imagery that's easier to manage,
but where you can still see everything that you need to see.
Another characteristic of sensors is Spectral Resolution.
Spectral resolution indicates the sensitivity of a sensor
to distinguish between a range of frequencies
or define wavelength intervals.
Most applicably, we use spectral resolution
to describe the number of spectral bands
that a sensor can measure.
So for example, those bands may be red, green,
blue, near infrared.
On the top right of this slide,
I've shown some examples of different spectral resolutions
that sensors or satellites may have.
So panchromatic means that that sensor can measure one band,
and it's usually visualized as just black and white.
Color means that it can measure three different bands,
so red, green, blue.
And those bands can be combined
to create a true color image.
So that would basically look like...
If you took a normal camera
and took a photo of the landscape,
it would look like true color.
Multispectral means
that sensor is starting to measure some bands
that are outside of the visible range,
so near infrared or coastal blue.
And that can help you distinguish
between more objects in the landscape.
And then hyperspectral
means that hundreds of bands are measured,
so those are quite advanced sensors.
This figure at the bottom just shows some examples
of different spectral band sets that a sensor might have.
So for example, at the bottom is a four band subset,
a sensor that has red, green, blue, and near infrared.
A lot of the earlier sensors had these four bands.
And then at the top you can see a more
complex band set.
So this satellite, for example, would have eight bands,
coastal blue, blue, green, yellow, red, a red edge,
and then two different near infrareds.
So that provides you more information,
more spectral information,
and can help you more specifically distinguish
between items on the landscape.
All three of these rows in this figure
would be multi-spectral, right?
Because they're not just color...
They all have near infrared, which is non-visible,
but they're not hyperspectral
because they don't have hundreds of bands.
Another type of resolution that's important to,
of distinguished between different satellites
is Temporal Resolution,
which is also known as the revisit period
or the length of time it takes for a satellite
to complete an entire orbit cycle.
So this is basically the time it takes for a satellite
to return to approximately the same location in space.
So for example,
it captures imagery over Burlington, Vermont.
Captures imagery everywhere else on earth
and then comes back to take another image of Burlington.
For example, Landsat satellites,
which we'll talk about in a few slides,
they have a revisit period of 16 days.
Revisit periods are dependent on orbital speeds
of the satellite as well as the type of orbit
and what the global coverage of the instrument is.
Coverage refers to not just how much of the earth it images,
but also it refers to the swath width and the overlap.
So swath width is the width of the imagery
that the satellites collect.
And then overlap refers to the overlap
between adjacent images.
So as you can imagine,
if a sensor has a low swath width
or a high overlap between two adjacent,
you know, images, then it may take it more time
to complete that orbit cycle.
One fun fact is that some sensors,
like the sensors that are aboard
the International Space Station,
they don't actually measure the entire planet
because of the orbit path that they take.
There's a lot of reasons why it's valuable
to have low temporal resolution or in other words,
to get imagery that is close together in time.
For example, a lot of regions in the world,
like in the tropics, they're really cloudy.
And so the more times that a satellite flies over
and captures imagery,
the higher a chance there is that you'll get an image
when it's not cloudy.
You may also want imagery close together in time
if you're studying short-lived phenomena.
So for example, I've studied the effects of hurricanes
in the tropics,
and for those events we really wanna get
an image right before the hurricane
and right after the hurricane.
There's also a lot of reasons why we might want
data from specific seasons.
So for example, if we are studying corn production
and we wanna get images each year in the Midwest
when corn production is at its peak,
it's important that the satellite flies over
frequently enough that we can capture that moment every year
at that approximate time.
Finally, the last type of resolution
to describe satellites is Radiometric Resolution.
Radiometric resolution is basically the sensor's sensitivity
to the magnitude of electromagnetic energy
that it's measuring.
Another way to describe this is its bit depth,
or how many levels of data can be collected.
This kind of resolution can be kind of complicated
to wrap your head around
if you haven't worked in data science
or computer science before,
but I think this figure describes it well.
So a low radiometric resolution
might only have two levels of information
that it can collect.
So that would be like a binary data collection.
So it can, for example, distinguish zeros and ones,
but if you have four levels of data you can collect,
you can collect zeros, one, twos, and threes,
so then you have four different levels.
Eight levels is more information,
16 levels is even more information.
I wanted to also describe bit depth
for those of us that may not be familiar with this concept.
So bit depth is basically the number to the power of two.
So two bits means there's four levels of data
that that sensor can collect.
Eight bits means there's 256 levels of data
that that sensor can collect.
So obviously, if a sensor has a higher bit depth,
then that data that it can collect is more specific.
So just to summarize the four different kinds of resolution
that I just discussed,
I found this figure really helpful
in distinguishing between these.
So spatial resolution, again,
is referring to this pixel size.
Temporal resolution is referring
to how frequently imagery is collected.
Spectral resolution is how many bands
or wavelength ranges that a sensor can detect.
And then radiometric resolution
is how specific is the data that it's collecting,
how many levels of data is being collected.
Another way to define satellite sensors
is whether they're Passive or Active.
So passive sensors rely on natural energy.
They record energy that is naturally reflected
or emitted from the earth's surface.
So this is energy from the sun
that travels down to the earth
and is emitted from the earth's surface.
The downside of passive sensors
is that they can only detect energy
when it is naturally occurring.
So they can't really capture imagery at night
because there's not energy being emitted back.
Landsat sensors, which again we'll discuss in a second,
these are the most well-known examples
of a passive sensor
and they've mapped the Earth's surface for over 40 years.
Active sensors are the other main category of a sensor type,
and they're known for using artificial forms of energy
that they emit and they send this energy
to the object that they're detecting
and then they measure the response
or how much energy they received back.
And then perhaps also the timing of the energy
that they received back.
Active remote sensing
or sensors can transmit light or waves
and they can control the wavelength,
frequency, power, and duration of the signal.
So there's a lot of different ways
that they can control the energy.
Using this method, they can measure distance, elevation
or atmospheric conditions.
And they're unique to passive sensors
and that they can be used at night
or in different weather conditions.
For example, many types of signals that active sensors use,
for example, microwaves can penetrate cloud covers.
One common type of active sensor,
which I'll talk about at the end of this presentation
is called LiDAR, which stands for
Light Detection And Ranging.
And this is a really useful type of active sensor
in Earth system modeling.
As I mentioned, Landsat satellites
are a really common type of passive sensor
that we use a lot in remote sensing analysis.
Beginning in the 1960s,
the US began an effort to develop and launch
the first civilian Earth observation satellite.
And this is where Landsat was born.
There have been nine Landsat missions to date
with the first beginning in 1972
that was the launch of Landsat One.
And then Landsat Nine was the last Landsat
that was launched in 2021.
And it replaces Landsat Seven,
which was in orbit but had some complications,
which we'll talk about at a later date.
Here's a summary of the different Landsat missions
and how they differ.
So Landsat's One, Two, and Three were the first
and they had a spatial resolution of 80 meters
and a spectral resolution of four bands.
So they had red, green and blue bands
and near infrared as well.
And they had a revisit time of 18 days.
Landsat Four and Five were slightly more advanced,
so their spatial resolution improved a lot.
It was 30 meters, their spectral resolution was seven bands,
and the revisit time decreased to 16 days.
Landsat Six unfortunately failed at launch.
It never made it to orbit.
So RIP, that was in 1993.
And then Landsat Seven
was basically what Landsat Six was supposed to be
with a few improvements.
It had a spatial resolution of 30 meters, again,
a spectral resolution of eight bands,
and a revisit time of 16 days.
Landsat Eight then improved even more.
Spatial resolution, again, 30 meters.
It added some new bands including the coastal blue bands,
so it had 11 bands.
Revisit time of 16 days.
And then finally Landsat Nine, which is pretty brand new.
It's pretty similar to the Landsat Eight,
but it has a higher bit depth,
so a higher radiometric resolution.
Again, spatial resolution of 30 meters,
spectral resolution 11 bands and revisit time of 16 days.
I really love this video showing the Landsat legacy.
So all of the different Landsat missions
and how long they were in orbit.
You can see that some of these Landsat satellites
have lasted literal decades in space,
whereas some of the earlier ones lasted
from five to 10 years.
It's also interesting to see
which of these satellites are still in orbit.
So Landsat Seven, Eight, and Nine are in orbit,
though again, Landsat Seven's data is kind of questionable.
Again, as we'll discuss later.
You can also see the projected lifetimes
of Landsat Eight and Nine.
So they project that Landsat Eight
should be in orbit until around 2030
and Landsat Nine hopefully up until 2040 and beyond.
I really love this figure showing
the different spectral resolutions
of the different Landsat satellites.
So again, as you can see, Landsat One, Two and Three
had four bands,
so they had a relatively low spectral resolution.
Landsat Four and Five had seven bands.
Landsat Seven advanced to eight bands.
And then Landsat Eight and Nine
have the highest spectral resolutions, they have 11 bands.
So again, you can just see that
as we've advanced these satellites
with you know each and every one,
we've increased the spectral resolutions.
Finally, I wanted to talk about
two other important passive sensors
that we will use a lot in remote sensing.
So the first is MODIS which stands
for Moderate Resolution Imaging Spectralradiometer.
This is basically two satellites,
the Aqua and the Terra satellites.
They have differing spatial resolution,
so 250 meter, 500 meter, 1,000 meter.
Again, it's termed the moderate resolution,
so it doesn't claim to be high resolution.
But where these satellites are really advanced
is that they have a spectral resolution of 36 bands
and they have a revisit time of one to two days.
So that is pretty darn high.
They were launched in 1999 and 2002
and also replaced in 2011.
And this satellite is also really useful
because they make a bunch of summary data sets
like cloud free imagery every eight days
or a vegetation index every eight days.
So they're really useful
to use in larger area analysis.
And then another passive sensor
that I really love and use a lot is Sentinel-Two.
Again, this is two active satellites.
They use two satellites to increase the revisit time.
It has a really relatively high spatial resolution
compared to other satellites in orbit, so 10 meters.
Three times, as you know,
three times higher spatial resolution
as the Landsat satellites
which have a 30 meter spatial resolution.
Their spectral resolution is 13 bands
and their revisit time is five days.
And Sentinel-Two is pretty new.
It was launched in 2015 and 2017.
As I mentioned earlier,
LiDAR is a common type of Active Sensor,
meaning that it uses artificial or non-natural energy forms
to measure the Earth's surface.
LiDAR stands for Light Detection And Ranging.
I also wanna point out
that LiDAR is a type of method in remote sensing.
It doesn't actually refer to a specific sensor.
Lots of sensors have LiDAR capabilities.
In a LiDAR system,
light is rapidly admitted from a sensor,
from a laser in a sensor,
kind of like a laser light strobe.
And this light travels from the sensor to the ground
and then it reflects off of objects like trees or buildings
and the reflected energy returns to the LiDAR sensor
where it's recorded.
And that sensor can measure the time it took
for the light to travel to the ground and back.
And use that to calculate the distance
that the light traveled
and therefore, can measure elevation.
So LiDAR is really useful in understanding,
for example, the heightened density of vegetation,
building elevations in city,
the elevation of the Earth's terrain.
And therefore, data acquired from LiDAR instruments
is super helpful in agricultural sciences.
For example, if you wanna measure the height of your crops,
LiDAR can also help measure forest composition
and it help estimate how much carbon
there may be in a forest.
LiDAR is also really valuable in hard to reach places
such as in the marine sciences or in rescue missions.
One really fun application of LiDAR
is that it helped us uncover more information
about a civilization in Guatemala called Tikal.
So Tikal is located in an area
that is really densely forested.
And so while archeologists
have been trying to study this civilization
in this region for decades,
the research was hindered by just the sheer size of it
and also the dense canopy.
But using LiDAR, researchers were able to understand
what was going on under the canopy.
And you can see in this map here,
a large network of this city.
So through using LiDAR,
we realized that Tikal was much bigger
than we originally understood.
If you're interested in this topic,
I recommend that you pause the video
and you can read this infographic by "National Geographic."