[MUSIC] Hello, everyone and welcome back. In this lesson I'm going to walk you through some concepts of raster data that we discussed last time but we'll see them in action in ARC map. We'll have a look at a couple of key types of rasters. We'll look at raster value attribute tables, raster cell alignment, and multi-band rasters. So first let's take a look at the two rasters on our screen right now. If we zoom in, we can see the digital elevation model here or DEM and I think it might take a little while for your eyes to adjust to it. But at some point it becomes pretty intuitive where the high values over here in the symbology are white and the low values are dark and we can see this sort of mountainous, dendritic river pattern coming in, where we have these high areas, and then these sloping drainage networks coming out of them. Now, digital elevation models are really important, because they underlie so many other things. And, rasters are a great simple way to represent digital elevation models. They're 3D information in a sort of, 2D format and these are often the way that we start generating the 3D formats that we use in terrain models and surface models and so we still consider a digital elevation model to be a terrain model of sorts. And if we keep zooming in, and I'll turn off the layer below it to speed this up. At some point we get to the actual cell size of the raster and we can start to see the pixels. And this is a great demonstration of why we use rasters for continuous information. And it's that they provide the sort of illusion that they are showing all of the detail of a surface. But in fact they are filled with discrete values just packed in so closely next to each other that we have an effectively continuous stream of information. But right in here all of these are just individual pixels. We can see the pixel boundaries here as the values change. This makes rasters great for anything that varies continuously across the landscape where our data actually lends Itself to continuous variation rather than sort of the vector model of these discreet polygons or something like that. So we can do hazard maps with rasters, we can do climatic models with rasters, terrain models as we're looking at right now. And so many other things lend themselves to this format. And you'll kind of know it when you see it. You'll start to get an intuitive sense for whether data should be raster or vector at its core. That said it doesn't mean that rasters can only be continuous information so this other raster right here, is discreet information, of a sort. And it's continuous in that they're trying to have a continuous representation of the landscape. But it's discreet in that the integer values in the raster don't necessarily have a relation to each other. Ten isn't more than five in this case, and 20 isn't more than ten. Instead each value in this raster encodes for a specific type of land cover. And it has a color map baked in, so in particular it's not that the raster is just being symbolized by some color map here it's that actually the color values are assigned to each value in the raster itself, so that we get something we actually kind of recognize. Where we're thinking that maybe these roads, or these red areas are roads or urban areas and that this blue looks kind of like a river to me too. So it can help us intuitively see what's in this raster which is again a land cover raster. So this makes for a great time to show about raster attribute tables which are something I haven't talked about before. Up until now raster haven't have attribute tables because they're not feature classes. So let's take a look this raster has an attribute table and I can open it. And it has an object ID field still and it has a value field and account field. And we noticed that this raster only has 15 records for a raster that covers a huge area with millions and millions of cells. So think for a second about what could be going on here. What it's doing is if I identify a value in here, I can get the color index here and let's pin the table open so you can continue to see it. So I can get the color index which is the value here. And then I can get the count. Now basically what it's doing it's giving us a record in the attribute table for every distinctive value in the raster rather than every raster cell having a attribute table which would be very prohibitive because it'd be so many records. We have a record for every value in this discreetly valued raster. This provides a nice opportunity, though, because the values 11 and 21 don't mean anything to me for land cover. Those code for other values, and I happen to have the other values that it codes for right here in this comma separated values file. So, we can see that 11 means open water and 12 means perennial ice or snow and so on. And we can join that in just like we would with vector data in order to see what values these rasters code for. So, let's do that now. If I right click and I go to joins and relates just like we would with vector data. I go to join and I'll find that table. And select the value in the raster's attribute table here. And the value in the form table, the land cover type CSV. And all click okay, and it completes the join. And now instead of just seeing the value of the land cover, the coded value, I can actually in my attribute table have this information about what those mean. So that's where raster attribute tables are useful. It's usually with these not fully continuous rasters, these rasters with discrete values and where those values code for something that means something to us. You'll also notice something else going on here which is that I selected cells in a raster so we can do that we can select these developed areas too and create these selections. Unfortunately we can't do the same things with those selections as we can with vector data. It's more of a highlighting it for you to visually see it. We can't export the selected cells, we can't go use those only those, only those selected cells in a geoprocessing tool, or anything like that. What we'll go over later how to extract information from rasters, but it's not though the selection work flows. Okay so let's close the identify window here and collapse the table again and clear our selection. So now one thing that comes with the vector attribute tables is that shape area that tells us the area of each individual polygon. Now, since, again raster cells aren't polygons that's not necessarily a valid thing to hope for here. But what if we wanted the total area of a particular set of values here. Or of just a particular value. Since we have the attribute table here we can actually answer that question of how much open water is there. What's the area of the open water? So just like in a vector attribute table we'd add a field and I'll call it area of land cover. And I'm going to make it a double because it could be a large number. And it pops up in the middle here at the end of the original attribute table, not with the joined values. And I'm going to go to field calculator. And think for a second how you would find the area of a raster. Basically, we need to know how many cells we have and multiply it by the area of the cells, right? So, in this case, we can find the area of the cells, so let's cancel out for a second, and lets go to the land cover layer here, go to properties, and we can see that the cell size is 30 by 30, so it's 30 meters to each side. So if we go back to the field calculator, we can put in the count here. And then put in multiply it by the area, which is 30 by 30. So really what we're doing is we're multiplying 30 by 30 to get the area of one cell and then multiply it by the count to get the area of all of those cells. And what it's going to do is run for that selected row, and it gives us that area of that set of cells here. So we have five billion square meters of open water. Okay now let's take a look at that cell alignment problem I mentioned last time. And let's zoom to a particular spot here. And we can see once we zoom in to the rasters, they're different cell sizes and they're different cell alignments. So the land cover raster is a 30 by 30 raster but the digital elevation model is a 10 by 10 approximately raster. So with these different cell sizes we get different cell alignments and already we can see that their slightly off, if this looks like it's one pixel here and then we have these pixels overlaying it. Imagine if we needed to combine these rasters, we'd have a problem. So let's just look at this a little closer. I'm going to bring up the image analysis window, I'll pin that here. And I'll select the top and I'm going to use the swipe tool and I'll go over here and that lets me kind of turn off the top layer and show what's below it. So if we take a close look we can see that once we get to that bigger cell in the land cover raster, we're still not quite done with these other cells. So, these cells right here, are touching that cell. So, we have three cells and then another three cells. So, we have six cells touching it and, then seven and eight cells touching it, not including the null and then, a ninth cell touching it. And we have this not quite aligned edge here. So it doesn't match up completely over here, so we have ambiguity in how to choose which cell value to assign to which other cell value. If we were trying to, say, add these together or something, if I was trying to merge the values in the digital elevation model with the land cover and some sort of model and use 30 meters we'd need to decide some set of rules for how the digital elevation model's values get applied at that larger cell size. Most commonly it's either an average or it's whichever one is most dominant or it's whichever one is at the center of the target cell so whichever one would be at the center of the land cover cell. Okay, the last thing I want to do is I want to show you a multi-band raster so if we switch data frames here, imagery is a multi band raster. And we can see that over on the left here. We have band 1, band 2, and band 3 in this one raster. And if we go to properties and symbology. We can assign those bands to different channels. So we can actually view red light as blue, and blue light as green, if we want to. Which, has valid use cases. But, for now, just see that we can select any of the bands in this multi-band raster. There's actually a fourth band, which is near infra-red light, in this case, that we assign to make it visible. So, we could say, take this band in this raster, and display it as red, green, or blue light on our screen. And when we display light captured by a sensor as red light, as red, and light capture by sensor as green light as green, and light captured by a blue sensor as blue, we get, visible imagery as we expect, but we can start to play with this to take multiple strings of information and make them visible in a multi-band raster. But really mostly what I want you to take away from this is that there's one raster, that has multiple layers of information built inside of it. And that we can then take those and display them in different ways, but that they're available as different streams of information for analysis as well. This might be confusing still but we'll talk about imagery in a later class, it should become much clearer. If we were to zoom in here, we'd still see the cell size here but we don't see three streams of information, we see one because our eyes see red, green, and blue light combined so when it puts them out appropriately as red, green, and blue light we just see things we normally see. Okay, that's it for this lecture. In this lecture we went through some characteristics of rasters, from digital elevation models as continuous rasters to land covered data set as a discrete raster, and we looked at raster attribute tables and raster cell sizes and overlaps, and then at multiband rasters as well. I hope that helps you better conceptualize what raster data is and some potential uses for it. See you next time.