In this module,
we're gonna be working on a demonstration of how to analyze satellite imagery.
So go ahead and start up arc map if you haven't already.
I want to just show you a little bit about the data we're going to be working with.
You should have already downloaded this demo five data folder.
You should have a series of individual images here.
Glance at ETM_GLS2000_Band01, 02, 03, 04, 05, 06,07 and 08.
Then you should also have a shape file up here.
These are a few different quad sheets, sort
of a shape file that is the boundaries of the quad sheets for South Bend here.
So what you probably notice right away is all of the bands from this glance at ETM
satellite image are separate.
This is very commonly how you might download imagery.
So this is why I give them to you this way.
Because we need to learn how to stack them together first.
Before we can start to learn how to analyze them.
So if we add these in to arc map using our Add Data button up here,
we go into demo five.
Just select all eight of them.
Lead the quad sheets alone for the moment.
Hit Add. And what they're gonna come in as
is a series of independent grey scale images.
Each one representing the reflectivity of that particular band that the Landsat
satellite has sampled.
So to stack them together into a full color image is going to be our first task.
And you can see right it's doing now is it's building the pyramids for
all of these images, cuz although they're geo referenced.
I didn't include all of the sort of indexing and
statistics that get generated when it builds it in just for space issues.
So if you wanted to look at these in a little bit more detail,
you can zoom in and out on them.
So we have our band one it's going to be 255 is the high.
The low is 27 that's the reflectivity in one particular bend.
We can look through them all, but
you may begin to notice is that as you get further and further out.
It stops looking quite as much like a photograph would when you get to bend six.
Actually, if we zoom in a little bit,
we'll probably notice that bend six is at a lower spacial resolution.
It's a thermal band.
And seven is at the same.
And then when we get to Band08,
Band08 is actually a high resolution panchromatic image.
So it's going to be an even higher resolution
image than some of the other ones like seven is.
You can see that there's a lot more pixels in there.
So the very first thing we want to do, I'm just going to minimize these over here.
And just for ease of seeing them.
So you want to load up our image analysis toolbar and
this will give us the ability to create that stacked view, but
also begin to do some of the different satellite imagery analysis.
Techniques that we wanna work with through this module.
So if you don't already have it open you go to window,
image analysis to open that tool bar.
I'm gonna make mine just a little bit bigger because it's really important
that I, at this point when I start to do the layer stack
can see what the band numbers are.
These are pretty long file names.
So you wanna make sure that they're in the right order that they go
from one at the top to eight at the bottom.
And we wanna select all of them but eight.
And why is that?
Well, eight is at a higher spatial resolution than the rest of them.
So if we were to stack them altogether with Band08 in there,
we'd either need to artificially increase the spacial resolution
of all the other layers making an enormous file.
Or we need to downgrade Band08 to match the other ones and
there's really no reason to do that.
Band08 is some something independent of the other seven.
When we create the panchromatic image out of the other seven,
you want to leave Band08 separate.
So with bands one through seven correctly selected over there.
To do the layer stack,
all we need to do is hit this Composite Bands button right down here.
And now, what we've had generated for us, is roughly a color image.
Just like the ones we were playing around with in the earlier module
we dealt with raster imagery.
So we've created this nice false color image for ourselves.
Now the next thing we want to do is convert this into something we
can work with.
This is temporary.
We're interested in kind of South Bend, generally Notre Dame campus specifically.
You can see that your average roster or your average land side image covers
most of Northern Michigan, a little bit of Illinois.
I'm sorry, most of Northern Indiana.
Southern Michigan and a little bit of Illinois, in addition to Lake Michigan.
So we want to cut this down.
We want to click this image.
Crop this image to just be the area that we're interested in.
This is where if you go to Add Data, I gave you that
one little tiny shape file that's just the boundaries of the two South Bend quads.
So bring that in.
You can see that that's the area that we're entering interested in.
You could make this transparent.
Maybe with a red boundary if you wanted to.
But what we want to do is take that new composite image that we've done.
And cut it down so that it's just that little section right there.
So once I've added in this boundary file that we're gonna use.
First thing I want to do is select the features in there that I want to use.
So you can either do it with the select features, button right here that
will allow us to kind of drag across and get them highlighted and like that.
You could also open up the attribute table.
The only thing in there is those two so
you would just need to sort of drag down and select them like that.
Once that's in place just simply choose the layer that you want to crop.
In this case, the composite land set ETM.
And then it's this button right here, this Clip button.
So I did it twice by accident, just ignore the other one that I just removed.
And you can see, that once I turn off all the other layers.
I now have just that little section that I've been interested in.
So I want to do the same thing with Band08.
Band08 was that one that I didn't deal with anything yet with, so
I want to go down and find Band08.
Make sure my quads are turned on and selected with
Band08 selected in the image analysis window I'm going to choose this button.
Now I have one of Band08 as well.
So at this point I am done with all of my individual bands,
I'm done with the composite of the entire thing.
The only thing I want to do is I want to save these two little sections.
This Landsat_ETM Band08.
My individual clipped one there and
my clipped composite panchroma or my multi-spectral clipped one here.
So first I'm going to choose the Landsat, Band08 in the image analysis.
Choose this little Export button right here.
Again, it's given me all my information already.
It's in the right extent.
It's in the right spacial preference or spacial reference.
My cell size, this is a 15 meter resolution image, that's good.
I wanna make sure I've chosen the proper folder.
So Demo05_Data.
And I want to make sure that I'm giving it a useful name.
So in this case, I don't think Clip is as useful as South Bend would be.
So I'm gonna call it SouthBend_Landsat_ETMBand08.
I'm gonna go ahead and
say no, when I add it in, you can see that it's there right there.
It looks pretty much the same.
Now I just want to do the same thing with my other clipped, the composited.
So I'm gonna choose that.
Go to Export.
It's already sending it to the right folder.
I just wanna change my name to be SouthBend_Landsat_ETM.
And it thinks it's just Band11 and
I want to make sure it's Band01 through 07, so I know what's in there.
So now I have that one as well.
So what I want to do at this point, is I want to just go ahead and
remove all these other ones.
All my temporary stuff, all I really need to keep is the two South Bend
ones that I just saved permanently.
Could even get rid of the quad's shape file there, I don't need it any longer.
So I'm gonna choose those, right click, and go to Remove.
So it's these two images are gonna be the ones that I want to work with.
So I've cropped them.
I've done pretty much everything else with them
up to the point of wanting to do a couple of other things.
The first one is something called a pan sharpening.
We'll talk about this in the lecture a little bit.
So if I zoom in a little bit, you can see that what I have in bands one through
three there, what I have up here in the multi-spectral version,
is not as high of a spacial resolution as the black and white.
as the panchromatic Band08.
And Band08 is actually all of the reflectivity between bands one,
two, three, and four.
So I can use it to artificially sharpen those other bands.
Now the software is smart enough, that if I select both of these two layers.
One at a higher spacial resolution than the other, panchromatic at a higher
spacial resolution, the multispectral at a lower spacial resolution.
I now get this thing enabled right here, this little button right here,
which is called a resolution or a pansharpen or resolution merge.
So if I choose that, what it's done is I've created a new
temporary layer It has just the first four bands.
So if I were to go into Properties and go over to my symbology and
try to change bands, well it's thrown out five, six, and seven.
Because I don't have any information for them.
And it's created an image that is artificially sharpened there for me.
So it looks likes it's the same spatial resolution as the panchromatic Band08.
But it contains the first four bands of the multispectral bands one through seven.
Now if I want to save this permanently,
I can do it using this Export button right here.
And I, I think that name is sufficient, so I'm gonna call it Pansharp.
South Bend_Landsat [SOUND].
But in this case, it's only bands one through four.
And you can see that my cell size has gone down to 15 from 30.
So I'm just gonna bring that in.
It's now permanent.
Now it's very tempting, this is my permanent version here.
It's very tempting for a lot of people throughout the rest of these modules to
use the pansharpened one as the default image.
I wouldn't recommend that.
I would always use the original ones,
the pansharpened is really just sort of a fancy way of displaying the data.
If you're doing any sort of very quantitative analysis,
you do want to do that on the original bands, banded image, the bands one through seven.
So I'm just gonna turn off the pansharpened at this point.
So we now run this pansharpening on it.
And you saw that it shows to use bands one through four.
Now what if you had a panchromatic image that was only one band,
bands one through three?
Fortunately there are ways to set this in the image analysis toolbar.
And we do that with this button right here.
So we choose this thing, this will allow us to go to Pansharpen and
we can choose what method it's using, which bands it's using,
what are a few other different things that are happening in there.
So there's a few different other kind of versions that we could do.
And this is, this button, this tools in here are particularly important when we
start working with the vegetation indices.
So we're gonna just do that real quick now as our last part of
kind of basic satellite imagery analysis.
So if you remember there are vegetation indices,
we talked about them in the lecture for this module.
The most common one is the normalized
difference vegetation indices index and DVI.
And that's going to be the one we work with here.
Now again, you want to run this on your band one through seven.
You don't wanna run this on the pen sharpen necessarily.
And there are few different things we need to, to worry about first.
The most obvious is what band is near infrared and what band is red.
And we have to set that, so that it knows which ones to use.
So we do that in the tools up here.
So MDVI is gonna ask us what is the red band and what is the infrared band?
Well, for a Landsat ETM image, the red band is 3, the new infrared band is 4.
So its actual default is proper.
If you had, say, a glance at MSS, the older sensor.
I've shown you one of these earlier modules.
It would be two and four, would be the ones you want to use.
If you're dealing with an aster image, it's two and three.
Two is the red band, three is the near infrared.
But for this particular one the defaults three and four is proper.
Choose okay and then to run the NDVI, it's this once you have it selected
up here it's just the simple little maple leaf Canada flag looking button.
Now, it's gonna run it.
And what we end up with is something that makes some degree of intuitive sense.
The areas in red like that are areas that are there's very little vegetation.
If you go back and look at the original image.
Those are paved mostly.
Areas that are darker green are forests, denser vegetation.
These are mostly crop lands.
There are a few little forests in here that tend to be even darker.
Golf courses tend to be fairly obvious, the way they show up.
And the river itself is, is some of the lower values that we have.
It could be, could be all the way down to as low as it'll go.
In this case it is not quite that way because the river is relatively shallow.
So you're getting some reflectivity,
it's not absorbing everything that would be in there.
Now you notice probably that this has come.
Come up as minimized, that's because it does it sort of in a silly way.
We talked about how NDVI is usually from negative one to one, but
the way that it's done in ARC GIS goes from zero to 200.
Well let's also put in 256 possible values.
So there's even values that are higher than anything we would ever have.
So it's giving each individual color possible value its own color in there.
So, I don't necessarily find that quite as useful as it could be.
It's a little hard to figure out exactly what you're looking at, but
it does still give you the same data that a regular NDVI would do.
It's just scaled a little bit differently.
So part of the assignment for
this one is to experiment with running an NDVI on a different image.
There's a few different satellite images that were part of the data for
the module on roster data.
So we may want to experiment with that.
And those are a case where you would need to change what
band combinations are being looked at.
Otherwise, follow along with the demo notes to do the assignment itself.
And if you have any questions,
please do not hesitate to contact me.