5. Rasters in Action, GIS kurs, GIS Data Formats, Design, and Quality, W1 Course Overview & Data Models and Formats, ...

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[MUSIC] Hello, everyone and welcome back. In this lesson I'm going to walkyou through some concepts of raster data that we discussed last time butwe'll see them in action in ARC map. We'll have a look at a coupleof key types of rasters. We'll look at raster value attributetables, raster cell alignment, and multi-band rasters. So first let's take a look at the tworasters on our screen right now. If we zoom in, we can seethe digital elevation model here or DEM and I think it might take a littlewhile for your eyes to adjust to it. But at some point it becomes prettyintuitive where the high values over here in the symbology are white andthe low values are dark and we can see this sort of mountainous, dendriticriver pattern coming in, where we have these high areas, and then these slopingdrainage networks coming out of them. Now, digital elevation models are reallyimportant, because they underlie so many other things. And, rasters are a great simple way torepresent digital elevation models. They're 3D information in a sort of,2D format and these are often the way that westart generating the 3D formats that we use in terrain models andsurface models and so we still consider a digital elevationmodel to be a terrain model of sorts. And if we keep zooming in, and I'll turnoff the layer below it to speed this up. At some point we get to the actualcell size of the raster and we can start to see the pixels. And this is a great demonstration of whywe use rasters for continuous information. And it's that they providethe sort of illusion that they are showing allof the detail of a surface. But in fact they are filled with discretevalues just packed in so closely next to each other that we have an effectivelycontinuous stream of information. But right in here all of theseare just individual pixels. We can see the pixel boundarieshere as the values change. This makes rasters great for anything thatvaries continuously across the landscape where our data actually lends Itself tocontinuous variation rather than sort of the vector model of these discreetpolygons or something like that. So we can do hazard maps with rasters,we can do climatic models with rasters, terrain models as we'relooking at right now. And so many other things lendthemselves to this format. And you'll kind of knowit when you see it. You'll start to get an intuitive sense forwhether data should be raster or vector at its core. That said it doesn't mean that rasterscan only be continuous information so this other raster right here,is discreet information, of a sort. And it's continuous in that they're tryingto have a continuous representation of the landscape. But it's discreet in that the integer values in the raster don't necessarilyhave 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 encodesfor 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 symbolizedby some color map here it's that actually the color values are assigned toeach value in the raster itself, so that we get something weactually kind of recognize. Where we're thinking that maybe theseroads, or these red areas are roads or urban areas and that this blue lookskind of like a river to me too. So it can help us intuitively see what's in this raster whichis again a land cover raster. So this makes fora great time to show about raster attribute tables which aresomething I haven't talked about before. Up until now raster haven't haveattribute tables because they're not feature classes. So let's take a look this raster hasan attribute table and I can open it. And it has an object ID field still andit has a value field and account field. And we noticed that thisraster only has 15 records for a raster that covers a huge areawith millions and millions of cells. So think for a second aboutwhat could be going on here. What it's doing is if I identifya value in here, I can get the color index here and let's pin thetable open so you can continue to see it. So I can get the color indexwhich is the value here. And then I can get the count. Now basically what it's doing it's givingus a record in the attribute table for every distinctive value inthe raster rather than every raster cell having a attribute table which wouldbe very prohibitive because it'd be so many records. We have a record for every valuein this discreetly valued raster. This provides a nice opportunity,though, because the values 11 and 21 don't mean anything to me forland cover. Those code for other values, and I happento have the other values that it codes for right here in this commaseparated values file. So, we can see that 11means open water and 12 means perennial ice or snow and so on. And we can join that in justlike we would with vector data in order to see what valuesthese rasters code for. So, let's do that now. If I right click and I go to joins and relates just like wewould with vector data. I go to join and I'll find that table. And select the value inthe raster's attribute table here. And the value in the form table,the land cover type CSV. And all click okay, andit completes the join. And now instead of just seeing the valueof the land cover, the coded value, I can actually in my attribute table havethis information about what those mean. So that's where rasterattribute tables are useful. It's usually with these notfully continuous rasters, these rasters with discrete values andwhere those values code for something that means something to us. You'll also notice something elsegoing on here which is that I selected cells in a raster so we can do that wecan select these developed areas too and create these selections. Unfortunately we can't do the samethings with those selections as we can with vector data. It's more of a highlighting it foryou to visually see it. We can't export the selected cells,we can't go use those only those, only those selected cells in ageoprocessing tool, or anything like that. What we'll go over later how toextract information from rasters, but it's not though the selection work flows. Okay solet's close the identify window here and collapse the table again andclear our selection. So now one thing that comes withthe vector attribute tables is that shape area that tells usthe area of each individual polygon. Now, since, again raster cells aren't polygons that's not necessarilya valid thing to hope for here. But what if we wanted the total areaof a particular set of values here. Or of just a particular value. Since we have the attribute table herewe 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 attributetable we'd add a field and I'll call it area of land cover. And I'm going to make it a doublebecause it could be a large number. And it pops up in the middle here atthe 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 youwould find the area of a raster. Basically, we need to knowhow many cells we have and multiply it by the area of the cells,right? So, in this case, we can find the areaof the cells, so let's cancel out for a second, and lets go to the land coverlayer 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'remultiplying 30 by 30 to get the area of one cell and then multiply it by the countto get the area of all of those cells. And what it's going to do is run forthat selected row, and it gives us that area ofthat set of cells here. So we have five billionsquare meters of open water. Okay now let's take a look at that cellalignment problem I mentioned last time. And let's zoom to a particular spot here. And we can see once wezoom in to the rasters, they're different cell sizes andthey're different cell alignments. So the land cover rasteris a 30 by 30 raster but the digital elevation model isa 10 by 10 approximately raster. So with these different cell sizes we getdifferent 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 combinethese rasters, we'd have a problem. So let's just look atthis a little closer. I'm going to bring up the imageanalysis window, I'll pin that here. And I'll select the top and I'm going touse the swipe tool and I'll go over here and that lets me kind of turn offthe top layer and show what's below it. So if we take a close look we cansee that once we get to that bigger cell in the land cover raster, we're stillnot quite done with these other cells. So, these cells right here,are touching that cell. So, we have three cells andthen another three cells. So, we have six cells touching it and,then seven and eight cells touching it, not including thenull and then, a ninth cell touching it. And we have this notquite aligned edge here. So it doesn't match upcompletely over here, so we have ambiguity in how to choose which cell value toassign to which other cell value. If we were trying to, say, add thesetogether or something, if I was trying to merge the values in the digitalelevation model with the land cover and some sort of model and use 30 meters we'dneed to decide some set of rules for how the digital elevation model's valuesget applied at that larger cell size. Most commonly it's either an average or it's whichever one is most dominant orit's whichever one is at ... [ Pobierz całość w formacie PDF ]

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