[MUSIC] Hello everyone and welcome back. In this lesson I'm going to show you a quick demo of a GIS project, something that you may encounter in the real world. The goal here is to give you an idea of how these things work and the kinds of things you might do day to day as a GIS analyst. The emphasis here isn't on exactly how I'm doing things. I'm not going to walk you through everything and explain all the concepts in this one. I'm just going to show you a basic workflow and hopefully give you an idea of how this all works. So, let's proceed. Currently what you're looking at is a satellite image from a satellite called Landsat that's flown by the US Geological Survey. And what it does is it circles the Earth and covers the same spots about every 16 days. So it's a good measure of how things change on the Earth. And the area we're looking at here is a segment of the California coast just above San Francisco. You can see the San Francisco Bay peeking out right here. The reason I'm showing you this area is because a couple major fires just occurred in this area over the last summer. California has been experiencing a really unprecedented drought and wildfire risk had never been higher, and some really devastating fires whipped through these areas, and you can see them in the satellite image. And we'll zoom in and take a look. The particular fire we're going to look at is called the Valley Fire. And it's a little hard to make out here, but this area here is sort of the fire boundary. And you can see this kind of reddish, purplish area that seems to not have as much green vegetation as some of the surrounding areas. That's the fire. What we're looking at in this image is your typical satellite image. It's visible light, the kinds of things that you can see with your own eye. And we just saw that it's a little hard to make out the fire boundary with just visible light. The good thing is that satellites have a number of other sensors that pick up a lot of other information when they're flying over the earth, and using this information we can better visualize the fire and its impacts. While we can still see only visible light, we can convert some of that data into the colors that we can see using GIS so that we can sort of adapt and get a sense for what it looks like and I'm going to turn on an image now that does that. This image here is what's called a color infrared or a false color infrared image. And instead of being our typical red green and blue light that we can see, this visualizes near infrared light, the light that's just past red on the electromagnetic spectrum as red. And then it visualizes red as green and green as blue, which is a little counter intuitive at first, but we see some patterns pop out immediately. First, the fire really peeks out here. Not a whole lot of near infrared light coming off of that fire, but a lot coming off from the vegetated areas and even the urban areas around it. We also see another fire scar peeking out right here that I didn't mention to you before. To help you get a sense for what vegetation normally looks like with satellite imagery, you can see these agricultural fields over here reflect a lot of near infrared light back to that satellite to pick up. So what we're doing here is kind of the first basic use of GIS. We're loading up data and viewing it in a couple different contexts, and just learning things about it from there. What we learned is that the fire zone is devegetated and you can see it from satellite imagery. Another thing we could do without doing a whole lot of specialized analysis is get a rough sense of the fire's area just interactively here. So I can use the measure tool and just click around the approximate boundary here. And see that the fire was about 400 square kilometers in area, which is a very large fire. And then if I wanted to, I could click around this other fire and get a sense for how these two areas compare. So now that we've explored this area a little bit, I want to do some analysis on this data and I'm going to use a set of tools called geo-processing tools to do that. These are where the real investigative power of GIS comes out. And over here in Arc Toolbox, I have a tool that we made called calculate NDVI. And NDVI is just a measure of plant health that we can get from aerial and satellite images. And if I run that, I'll close the measure tool first. If I run that, it gives me a couple options and it's asking for the near infrared raster, which we'll learn what rasters are soon. And it's asking for the red raster which is right here. And it's asking me for an output location. It's giving me a default, I'll just accept that for this demo. If I click OK, It's going to run and work on combining these rasters together for me and giving me the output of NDVI, that vegetation index. So it's complete and I'll close the box. Now what we're looking at isn't too different from what we were just looking at in a lot of ways. We still have the same sorts of things bright and the same sorts of things dark, just in different colors now. But what we've done is we've created a continuous scale from negative one to one, of is it healthy vegetation, where negative one is no, it's not healthy vegetation, and one is yes, it is healthy vegetation, and everything in between. And we're symbolizing those as colors on our screen, so we can see that the dark areas, the lakes, are not healthy vegetation. This makes sense. And we can see that the crops look like they're healthy vegetation. They're nearly white, which means that their value is very close to one, and they're probably healthy vegetation. In between, we have this kind of gray mud in here, where the fires are. And let's see what the value there is. And if I right click on it, I can Identify. And it brings up this pane over here, and it tells me that the pixel value is 0.178. And that's pretty consistent with bare earth. It's not a vegetated area. So at this point we don't really have any answers that we didn't have before just looking at the false color infrared imagery. But what we do have is we have this data set that we can then continue to analyze in the context of our other data and pass through other tools. And we've effectively reclassifed everything to provide this new meaning of is it healthy vegetation. And that's where we could begin another analysis. Let's take a look at one direction we might want to take that analysis. When an area loses its vegetation in a fire, it becomes much more likely that dirt is going to end up in the waterways during the next big rainstorm. So we might want to know what watersheds are affected by this fire so that we can then go look up or create other information that helps us understand the impacts of this fire on ecosystems, on people, and on everything around it. To get that started kind of in a simple manner but visually, I'm going to turn on a layer of sub watersheds covering the state of California, and we'll just see the boundaries here. And let's take a look at this one really quickly because it seems to be mostly encompassed by the fire. So it's probably going to have a lot of impact from that fire. Again, I can right click on it too and Identify and see a bunch of different attributes of this watershed and that its name is Rocky Creek and Cash Creek. I can also see it's Huck12ID here. That's a unique identifier that I could then go use to look up information about this watershed in other databases. I might want to do things like look at the local soil and slope information for this watershed to see if there are populated areas at risk for landslides. I might also look at a database of fish or other aquatic species information to see what animals and plants or other organisms in the water are going to be impacted if a lot of sediment ends up in the waterway in a rainstorm. Really, there are so many directions that you could take an analysis like this, but I want to show you something that is just the starting point for an analysis. But that shows you the visual way that you assess data and, also, the automated processing way that you can assess data in ArcGIS. We'll leave it there for this lesson, but by the end of this course you're going to be able to understand and accomplish similar types of things to what we did here, in terms of visualizing and processing your geographic data. So that's it for now. In this lesson I showed you what one basic type of GIS analysis might look like. There are so many other types of analysis you can do with GIS, whether you're working with business analytics or working for utility. But here's one example to help you conceptualize the kind of work that you might do as a GIS analyst. In the next lesson we'll cover some fundamental terms you need to know to get started working with GIS. I'll see you there.
