So this module's gonna build on the second module where we focused on raster data.
And in this case we're gonna be talking quite a bit about
one specific type of raster data, and that's satellite imagery.
And a little bit about how we can begin to analyze it and how it might be useful for
different tasks in GIS.
So this is all based on the electromagnetic spectrum
which we addressed in some of the earlier modules as well.
Where we have multiple different wavelengths of light, some visible some
not visible, that we can use to analyze certain types of land cover.
And different satellite sources sample different ones of these.
Now, it's important to remember, and I'm just gonna hit this again as a review,
is that what we're dealing with here is reflectivity.
So, the color of an object is controlled by how much light it reflects in each
wavelength.
So, some reflect certain wavelengths, some will absorb certain wavelengths.
So, something that looks blue to us is actually reflecting blue light, and
absorbing everything else, and the same thing's true in other spectra as well.
And when we're talking about satellite imagery, this is what they're usually
measuring, this is what satellites are usually measuring.
And this is all part of the concept of remote sensing.
And in this case the remote sensing is really just something that senses energy
remotely, whether it's actively doing this by sending out a signal and
then seeing what comes back to it.
Something like a radar or lidar, or whether it's doing it passively,
whether it is working off of light that's come from the sun and
has balanced off the surface and then coming back to sensor.
Something like a digital camera without a flash or
a lot of these satellite imagery sources.
Now other terms that I just wanna make sure that are fresh coming from previous
modules are the concepts of panchromatic and multi spectral imagery, or
panchromatic and multi spectral images.
Where panchromatic is cross different colors, panchroma, essentially a black and
white photograph sampling either all of the visible spectra of light or
a little bit more, the reflectivity in those spectra.
and multi spectral image is essentially multi bands like a color image where we're
sort of using small swaths to recreate the image on a computer monitor.
Now in the specifics of satellite imagery, the nature of
different satellite sources are usually defined by different types of resolution.
So we've talked about spatial resolution already, so
the area on the ground represented by each pixel, but there's a few other types of
resolution that are important for understanding satellite imagery as well.
Things like temporal resolution, spectral resolution, radiometric resolution.
Each of which I'm gonna go over in a little bit more detail,
but then there are few others that I'm not.
Things like view angle resolution that might control how many different angles
the camera, the sensor on the satellite can look at.
So spatial resolution, we've already gone over.
Essentially, it's defined as the length in real world coordinates
of one side of each square pixel in the image.
So a ten meter resolution image would, would have ten
meters on the side of each pixel, would be 100 square meters covered by each one.
Five meter would be five meters on each side, cover 25 square meters.
This makes a lot of difference for how detailed the image might look.
As you see here from the examples of several different satellites and
sort of what their resolution might be, ranging anywhere from 30 meter,
90 meter down to 60 centimeters and sub meter resolution.
When we're talking about tempo resolution, this is essentially the number of days
between overhead passes at the same location.
So we think about these satellites that's orbiting the earth,
its the number of days that it's gonna take for
that satellite to get back to going back over the exact same spot on the globe.
These can be anywhere from two weeks, a month, down to some that circle very, very
rapidly, that orbit very rapidly and might cover the same spot every, twice a day.
Now, that's not to say that every single satellite covers every point on the Earth
systematically.
Some of the orbital patterns don't really cross over the poles as well.
Spectral Resolution, in this case, it's the number, spacing, and
width of the sampled bands of wavelengths of light that we have.
So when we're thinking about some of these sampling red, green, and blue, and
then often the NIR infrared.
It's essentially how many bands there are that are being sampled and
what the width of them is.
A higher resolution in this case would mean
more precision in the representation of different spectral signatures.
So if we wanted to figure out what the signature of a particular type of land
cover is, whether it be forests or concrete or
something like that, we would have more data available to be able to figure
out what the specific signature of that would be.
This is just an example of a few of these from the Landsat satellite, different
wavelengths that are being sampled, there's seven of them in this case.
Radiometric resolution in this case what we're talking about is the number of
possible data values reported by the sensor.
So when we talked in the earlier modules about eight bit resolution,
where we have two to the eight values, 256 possible values, zero to 255.
That's a measure of the radiometric resolution of a sensor,
that means there's 256 possible values that you could have.
Some of them have 16 bit resolution which have little over 65,000 possible values,
some of them even have more than that.
So it's a measure of how many different
possible values there are in the image itself.
The value that we then have for
this we usually call either the digital number of the brightness value.
Now I'm gonna turn to talking about some of the different imagery sources that
we're gonna work with, particularly the ones we're gonna work with in the demo for
this particular module.
But before that I wanna just address the the idea of orthoimagery as well.
Orthoimages are, essentially, true color images, so
it's a photograph with any distortions caused by tilts in the camera,
or topography of the land that have been removed from them.
So, as they've gone through the projection process, the geo referencing process, and
a little bit beyond.
Now, these are really common, that you'll see, for the base layer of a lot of maps.
This is what you see in Google Earth, and
they usually come from either aerial photographs or satellites.
And they're available for the entire country through the USGS EarthExplorer
which I'll show the link to in a little while here.
But the first satellite I wanna talk about is Is one of the more common one's for
pan chromatic imagery and this is the SPOT satellite.
It samples at there have been a few different incarnations of it.
The one's were gonna be working with in In the demonstration are pre 1999,
and they're freely available through the USGS.
And these are panchromatic images at ten meter resolution.
So they're pretty high resolution to have for that time period.
The newer SPOT satellites are all commercially available and
they sample at a higher spatial resolution.
And usually two bands in the visible spectrum and then two in the NIR infrared.
This is fairly common that they won't sample in the blue band because there's
not a whole lot of nature that's blue but not green.
So it's usually, if they're going to skip one in the visible spectrum
it's usually the blue band.
So this is what a SPOT image looks like.
You can see that it is panchromatic and it is relatively high resolution.
Now the next satellite I wanna talk about is the Landsat satellite.
This is the longest running Earth observing satellite, it's a US project.
And it's actually been a series of now eight different satellites.
With different sensors that are pretty similar between them.
They do orbits about every 16 days and almost all the Landsat data
is freely available for academic uses through the USGS.
So it's a really, really powerful satellite both because it's been
around for, from since the early 1970s sampling in a pretty systematic way,
but also because it's all available pretty freely.
So there are two main sensors that Landsat has used.
The early one is the multi spectral sensor, the Landsat MSS.
And it sampled at a 90 meter resolution.
Four different bands, two in the visible spectrum, two in the NIR infrared.
And then the more common one that we see today is the Landsat TM and ETM sensor.
They're very similar, the only real difference is the ETM added
a panchromatic sensor at a 15 meter resolution,
whereas all the other multi spectral bands are at 30 meter resolution.
The samples red, green, blue in the visible spectrum,
it's three in NIR infrared and one in the thermal infrared.
So this an older Landsat image,
this is from the MSS sensor, you can see that it still shows us that very
distinct color pattern that we associate with vegetation in satellite imagery.
Because it doesn't have all three visible light bands,
it's kind of hard to recreate true color easily with it.
But we can do it with some post processing techniques.
And then the more recent Landsat is a higher spatial resolution and
we can show it as true color as well if we want to.
Now one of the neat thing about Landsat imagery is something called
the global land survey.
And these are standardized, treated Landsat imagery for the entire globe
at five different times throughout the tenure of the Landsat satellite.
Now why is this important to have a treated and
standardized surface like this?
Well the Landsat satellites go along little swaths.
So you have time period you have different days that might be right next to each
other, images from different days.
And because all these imagery are measuring reflectivity,
percent of light being reflected back in certain bands, it can change based on
whether there's lights on, whether it's at night, all sorts of different reasons.
So, whether there's cloud cover, so, it's important that they are then standardize,
so that they might be relatively the same, but
now they're absolutely the same as well.
So, they match up better that way.
And so, if you're gonna do anything in a continental, or even a state level,
it's important to try to use these standardized ones.
Now another satellite that we're gonna work with is the ASTER satellite.
This is the advanced space borne thermal emission and reflection radiometer.
And this is a project that's been around since 1999 as a joint US Japanese project.
The ASTER satellite has a very high spectral resolution,
it samples 14 different bands, both in the NIR infrared,
short wave infrared, and thermal infrared, and in addition to visible light as well.
We can see here that it doesn't sample in blu,e so
we have two bands in visible spectrum and then two and then the infrared.
And one other kind of neat thing about Landsat is there's actually two band
three's.
There's a sensor facing straight down and one's facing off to the back and
what this allows the satellite to do is create a measure of topography as well.
You can use those two sensors that have different view angles to
recreate topography.
And we'll talk about that a little bit more in a later module.
So these are just a few examples of what ASTER imagery might look like.
It does have a higher spatial resolution as well, 15 meters, so
it is a very useful compliment to some of the other satellite data sources.
That there a few others that we are not going to work with directly
in the demonstration, but I think you are probably useful to know about.
The Moda satellite is one of these, it is a medium resolution,
about 250 meters up to one kilometer resolution.
But it does have 36 spectral bands that it samples, so it actually gives you quite
a lot of data on a larger scale in many, many different wavelengths of light.
The AVHRR is the Advanced Very High Resolution Radiometer.
This is a one kilometer resolution image or satellite that's,
orbits very, very rapidly, twice daily, and
is very useful for like broad scale weather patterns and things like that.
We also have a few others, things like declassified spy satellites.
There's been a few processes of declassifying these which have
extremely high spatial resolution usually, but they're entirely panchromatic and they
have relatively limited coverage for the time periods that they were being used.
And then there are also the modern commercial satellites as well.
Things like Quickbird and Digitalglobe, which are the base maps for Google Earth,
and Google Maps.
And these have a very, very high spacial resolution, 1.6 meters,
multispectral, and about 60 centimeters panchromatic.
So there really are a lot of these commercially available sources as well.
Just gonna show a few examples of what these look like.
This is MODIS data, see it's good on a country or continental scale.
The AVHRR gives us very good measures of things like this hurricane.
And then our commercial data and declassified data as well.
Now one thing I just wanna stress here is that there are a lot of companies out
there that will offer commercial processing and
download of a lot of these satellite imagery sources.
Almost all the imagery we've discussed here,
unless I've distinctly said commercial, is available.
It's freely available for academic purposes.
So the notable exception is this commercial Quickbird satellites and
things like that, and some of the SPOT imagery.
And this USGS EarthExplorer is a very useful tool for finding imagery and
being able to download it with registration.
So I encourage you to explore there and find if there's any of these data sources
that might be useful for any projects that you have.
Now I want to talk a little bit about some of the uses of satellite imagery as well,
particularly the ones that we're going to focus on in the demonstration for
this module.
Now the first of these is what we call Vegetation Index.
In the earlier modules we've talked about how that NIR infrared
band of light is very, very good at distinguishing vegetation.
And a Vegetation index is a way of standardizing this into a synthetic image
layer that sort of gives us an idea of the health and density of any vegetation.
So it's a new layer that provides a kind of unique
information not necessarily found in the individual bands.
And this is usually done by looking at the relationship
between the NIR reflectivity NIR infrared and the reflectivity in the red band.
And that's because things that are reflecting very strongly in
the NIR infrared but
not very strongly in the red are probably vegetation, and healthy vegetation.
Things that are reflecting very strongly in both or
about the same in both, are probably something else.
So there's several different vegetation indices that we can use, most are kind of
functionally equivalent and use some combination of red and NIR infrared.
But the most common one and the one we're gonna focus on is something called
the NDVI or Normalized Difference Vegetation Index.
And it's essentially a scaled version of the relationship between those two bands,
where the values are ranging from negative one to one.
So well vegetated areas are gonna have significantly higher NIR infrared.
So it's gonna end up in a value that's closer to one.
Things that are about the same between them are gonna end up closer to zero or
possibly slightly negative values.
Things like water or rocky, dry, sparse vegetation are gonna have much lower
values because their reflectivity in the red and NIR infrared are about equal.
So this is an example of one of these.
I should point out here that the program we're gonna be using for this,
RGIS, gives us the measurements in a slightly different way.
Instead of being scaled from negative one to one, they're scaled from zero to 200,
the data are exactly the same.
Just anything below 100 is usually not particularly vegetated.
Anything above 100 is getting increasingly dense in vegetation.
So the colors that we're seeing here,
things that are red are zero or lower, 100 or lower.
And these are things like the city of South Bend, the urban areas.
Any places where there's just simply not much vegetation.
Crop fields and forests tend to be somewhere in between the lighter and
the darker greens.
And there are probably a few little spots in there that are blue.
That would be very, very dense water.
The river doesn't necessarily show up just simply because it's relatively shallow so
there's some reflectivity coming off the bottom as well.
Now we can find similar sorts of indices for various geological measures as well.
Because different minerals reflect differently in some of the higher
wavelengths of light as well.
And this is gonna depend a lot on the satellite and the data that you have.
These have been kind of standardized for Landsat, so
there are a few different indices that we could calculate here.
We can look and say iron oxides or clay minerals or other types of iron minerals,
based on looking at the combinations of different bands.
Now the last thing that we can also do is something that's called a resolution merge
or a pan sharpening.
And this is essentially taking a high spatial resolution pan chromatic image and
using it to artificially sharpen a lower resolution multi spectral image.
So if you think about the fact that that pan chromatic image
covers all of the visible spectrum and
sometimes into the NIR infrared it's got the overall reflectivity in there.
That can be used to artificially increase the resolution of
the other image of the multispectral image.
So this really only works if they're from about the same time or
exactly the same time.
Most a lot of the satellites will have a pan chromatic band that can be used to
artificially sharpen the multispectural data that comes from it.
So this is a really pretty common task and
its something we're going to work with a little bit here.
So that's a review of the different sources of satellite imagery.
When you might want to use some of the different ones.
What some of the different satellites are good for.
And some of the different techniques that we can use.
In the following module we're gonna focus a little bit more
on creating vector data and when we might want to use some of these
processes to actually digitize parts of maps or parts of satellite data.