There are different wavelengths or bands that are available for different sensors. So, there might be a blue band, a green band, a red band, and there maybe six of those or even more bands that you can possibly work with. But when you are displaying those bands inside the software on a computer monitor or a screen, you only have three colors to work with, red, green, and blue. And so I just want to explain a little bit about how you can take these multiple bands and combine them in different ways to be able to visualize different things and to make sure that that's clear for you when you want to do it yourself. So this is a great diagram from NASA where I think they do a good job of explaining that we have these three different bands. So you can think of them as whatever. It can be one, two and three or it can be any band combination. And then you have the colors that are available to you to display on the monitor. And so that's blue, green and red. So just like a television, or the screen on your phone, that's what they have to work with in terms of displaying any kind of color combination. Is it's going to be a combination of the brightness levels of red, green, and blue. And when it does that, they're able to let you see or perceive different colors than just red, green, or blue based on those combinations. So if you've ever heard of RGB, so that's a common term that's used for red, green, and blue. That's what a screen uses, a TV, anything. So when you want to display your image, you can only tell it to use red, green, and blue, and you can assign those to the bands that you want to see. When you do that, that's referred to as a color composite. Okay, so for an ArcMap, we zoom in a little bit here, you can see that we have a landsat imag, and what's important to see here is that we have red, green, and blue listed. Those are the colors that can be displayed on the monitor, that's what's on the left. What's on the right here are the bands from the satellite image. So we're going to assign one band to red, another band to green, and another band to blue and then see what that looks like. So here what we've done is we've assigned the red band to be displayed as red by the screen. We've assigned the green band to green and the blue band to blue, and what that gives us is something that's referred to as a natural color image. So this mimics or simulates what it would look like if you were just there looking at it, or if you just had a regular photograph. In other words, the colors are assigned in way that you would expect to see naturally. So that's an actual term is a natural color image, so red looks like red. Green looks like green. Blue looks like blue. However, we don't have to constrain ourselves to only seeing images that way. For example, here what we've done is we've assigned the near-infrared band to red, the red band to green, and the green band to blue. Now this may seem a bit confusing at first but it's an incredibly useful thing to be able to know how to do, why? Because if you can assign other bands to those colors and understand how that works you can extract more information whether it's visually or quantitatively from the data that you working with. So for example here, this image, we can see patterns a little more clearly, especially with things with vegetation than we would be able to see with just a natural color image. Anytime you assign bands to colors, other than in the natural color sequence, red to red, green to green, blue to blue, this is referred to as a false color image. So in this case, what I'm doing here, this is a classic color combination that stimulates the way infrared film works before digital sensors came along. And so this is referred to as a false color infrared combination or false color infrared image. And so, this is not the only other one that's available. There's all kinds of them but this is a very popular one. One because it's something that people are used to looking at, it's a convention. And another is that it's really good for separating out different types of land cover. So just to get used to interpreting this, if you have a look at the ravine's here in Toronto. This is the Don Valley ravine system here. You can see that is fairly bright red, then we have a park over here that's bright red, you have these areas that are red, the red that is sticking out. Why is that? Why are they so red? Near infrared light is being shown as red. So if we have an area in our image that's bright red, that means a lot of near infrared light is being reflected and being sensed. Vegetation reflects near infrared light really well. So where we have red areas, that means we have high values of near infrared light being recorded, which means there's a lot of it being reflected. And we now things like vegetation have high near infrared reflectance. Then, when we see red on there we can say that's probably vegetation, okay? Or, by conversely, if we see areas that are darker, that's because near infrared light absorbs that wavelength. And so that's going to end up looking like a darker color. So this goes to this idea of spectral signatures is that these different combinations can be visualized by assigning red, green, and blue on the screen to these different bank combinations and being able to assign those colors and be able to interpret things to be able to identify different kind of land covers based on that. There's all kinds of combinations that are possible. I would recommend that you have a look at this blog post from Esri. It's actually from 2013, but it's still perfectly relevant. It's for Landsat 8. And I encourage you to experiment with these. It's really easy to assign these different colors and just try them out. You can do it very quickly. And so for example here, you can see with Landsat 8 the numbering's a little bit different than what I just showed you. So natural color is 4 3 2, but that's still red, green and blue. We have false color, that's 7 6 4. You have color for vegetation, 5 4 3, land water, 5 6 4. So all you need to know is what number refers to which band for Landsat 8, and then how to assign each band to each screen color, red, green or blue. And then when you do that for the particular image you have, the data you have, you'll be able to see or highlight or enhance the different types of land cover that are useful for your particular purpose.