Let's explore band6 which is the thermal infrared band. It looks dark because I haven't enhanced it. So, let's do something a little different with this. If I go to properties and symbology, this is actually measuring thermal energy or heat energy that's being emitted from the surface as opposed to light that's being reflected from the sun. So, it might be fun or interesting or useful to give it a color scheme that reflects that. So, let's try blue to red, but we want to invert this because blue we want to be cool color or cool areas red to lights, high to warm colors. So, I'll use the Invert option there and click okay. I'll choose standard deviations for my enhancements, click okay. So, what this is showing me now are different amounts of thermal energy being emitted from the surface. So, I think it's interesting to examine this and see that the urbanized areas are very warm. The water of course as we would expect as much cooler, but the land areas above the urban areas, the more mountainous areas, are cooler as well. So, this gives us an idea about the urban heat island effect and the fact that different types of materials may be storing heat or emitting heat differently. So, that would be fun to explore. If want to explore this a bit further, we can have a bit more fun and open the image analysis window. I'll dock that. And so this gives us some quick access to some tools that people often use when they're doing analysis, interpretation, or exploring satellite imagery. There's some fun tools in here. So, I'm just going to show you some of them. So, for example, we might be interested in looking at the amount of vegetation that exists in this image. I know for a fact, that we could use band3 along with band4 to create what's known as a Normalized Difference Vegetation Index. That's just a way of looking at the difference between band3 and band4 which is essentially the red band and the near-infrared band because vegetation reflects a lot in the near-infrared and not a lot in the red and so when you see the difference between the two and you normalize that, which is basically just a way of kind of standardizing the numbers, then you have a quick mathematical method of being able to identify areas with more vegetation or less. That's a quick explanation but I just want to show you how this works. So, the NDVI uses band3 and band4. If I go to the image analysis listing here of my bands, and I had band3 and band4, and I select both of them, so that they're both active, let's say. You'll notice that this little maple leaf tool becomes active and I can click on that and that creates an NDVI image. So, there it is up there in the table of contents. This is a standard color scheme that's often used are traditionally used. You can use other ones but it's sort of the convention where areas with more vegetation are green, areas with less vegetation or this kind of reddish, orange. So, now we have values that are in index that allow us to analyze or interpret how much vegetation there is in particular area. It's a very common method NDVI, Normalized Difference Vegetation Index. So, now that we have this, we can zoom to raster resolution, and what that does is it's matching the resolution of the screen that I'm displaying my image on to the image itself so that one pixel on the screen equals one cell from my image. This is just a way of showing it as sort of the maximum amount of detail. In other words, if you zoom in any farther than this, you're not going to see any extra information. So, this is as good as it'sgoing to get. So, that's what the little tool for and it's handy to know that it's there. So, what if we wanted to compare my NDVI to my thermal band. So, I'll turn off the one in between. So, we can use this tool called the Swipe Layer Tool and if we activate that, we can swipe back and forth between two layers. So, we have the NDVI layer on top, the thermal layer underneath and we can interactively just compare the two to see what might be similar or different between the two and I'm hoping what you're noticing is that there are areas that have more vegetation are cooler and the areas with less vegetation are warmer and this definitely plays into this idea of the urban heat island effect. The fact that urban areas are warmer, and also that if you live in a neighborhood that has more vegetation, you will tend to be cooler, which is a nice thing to know. In the summer months especially, is that you can maybe save some money on energy, on air conditioning if you have more trees planted in your neighborhood, just little pitch there to plant more trees. So, I just wanted you to be aware of the swipe tool. It's really interesting to be able to do this, you can also do it top to bottom, swipe this way, and you can do this with any two layers to compare them. If we go back to our thermal band and check the spatial resolution, go to the source tab, you'll see that it has a spatial resolution of 60 meters as opposed to the other ones that are 30 meters. Because there's less energy being emitted then reflected, it's harder to pick up. The sensors have to have a larger area in order to be able to collect enough radiation in order to be able to register or sense it properly. So, in other words, you need bigger cells in order to be able to sense that heat energy. That's why thermal bands tend to have a lower spatial resolution or bigger cells than the multispectral bands. Now, what if we want to look at something with a higher spatial resolution such as the panchromatic band. We can add that in. That is band8 from Landsat seven. If we zoom in a bit here, you should notice that we have more detail with our panchromatic band. So, why is that? Let's check the spatial resolution here. So, this has a resolution of 15 meters. So, we've gone from 30 meters for multispectral to 60 meters for the thermal down to 15 meters for our panchromatic. These are all on the Landsat seven satellite. There different sensors that provide different image products depending on what it is we're interested in. So, I'm going to turn off some of these other ones. Now, one of the nice things that you can do is something called pansharpening and that is an attempt to try and get the best of both worlds. The idea here is that we're going to have as kind of our base, this panchromatic image at 15 meters resolution, but we're going to take the color information from our color composite, from our multispectral data and attach it to the panchromatic and basically try to simulate what we're seeing from our, say a false color image through the panchromatic. It's like if you want to think of it like copying or transferring the color information from a lower resolution but more spectral information to a higher resolution panchromatic. We can do this in the image analysis window. We just have to select our panchromatic band and our composite bands or composite file and then go down here and then select the pan sharpen tool. So, now what we have, it's automatically gone to a default of a natural color image but we can change that to, let's say four, three, two, and you should notice that there's more detail now than there was before because we are looking at a pan sharpen version of this. So, it's taking the information from the composite bands applied it to the panchromatic to, so we're still at the panchromatic level, we're still at 15 meter resolution, but we're able to see things with more color and get more information by using this pan sharpen version. So, let's compare that to the 30-meter version using the swipe tool. So, I'll just move this up so that they are next to each other, not that I have to do this, but for some reason it makes me feel better. So, now I am going to do the swipe tool between my two layers. So, what I hope that you will see here is that we have the lower resolution underneath and the higher resolution on top, and that you're actually seeing that one has more detail than the other. So, that's definitely showing it. You can see it especially with the boats in the water, a little bit with the buildings downtown. That the pan sharpen version is giving us the same amount of color really but a little bit more detail in terms of the objects that we're able to see on the ground. We zoom in a bit more. I wonder if this will help. Yep, definitely. You can see that the pan sharpen version definitely has more detail than the original multispectral version. So, I hope this helps you to see what's possible using imagery inside of ArcMap. There's quite a few tools that are available to do remote sensing work inside of ArcMap. I've really just kind of scratch the surface here. So, I encourage you to get your own data, load it into ArcMap and see what you can do.
