Map Algebra Functionality and Advanced Raster Calculator Calculations

in previous tutorials I'll talked about basic raster calculations that we can perform on raster data also look at different functionalities such as you know overlay operations extracts and other types of things that we can do in this tutorial we're going to look specifically at more advanced map algebra or raster calculator calculator operations that can be performed within ArcGIS the raster calculator or map algebra as it's called now is an extension of the spatial analyst toolbar and if we look on our you know click on our toolbox functionality we'd see a toolbar something docked there right here and I see this spatial analyst I'm gonna talk specifically about this raster calculator calculator here and I can double click on it well when you click on this you can see unable to execute the selected tool and basically that means it's good it's an extension so if I go to customize extensions these the different extensions that we have depending upon your level of licensure we have different licenses that allow different functionalities I can click on spatial analyst and it allows me to run that license now so now I can double click on raster calculator and you can see what I have here it looks a lot like our field calculator kind of like our field calculator when we calculate different attributes for your vector data but we can see the different types of raster data that we're looking at here I'm looking at an area here of northwestern Durham and this is just a topo quad so this is I think 15 15 minute topo quad so an eighth of a degree by any that would agree but we can see this here I've got elevations here yeah and you can see the low is fifty point two six up to two twenty six point five nine three I've got National Land cover dataset so I can zoom in and we can see this land cover data set at 30 meter pixel resolution and last but not least I've got some imagery you know I'll talk about this a little bit later here from the North Carolina one map server and we'll talk about how to extract data this but you can see the red green blue this is your true color imagery that we're looking at here I want to check this because it's sitting on a server over in Raleigh it gets a little bit big to extract zoom zoom in zoom out especially on a Wi-Fi network I'm gonna zoom into my layer here now when I click on my raster calculator I'm just gonna mention a couple different types of calculations that we can run and this is just gonna complement some of the previous lectures that we're going to look at we can see here our de M northwestern Durham state plane this is our digital elevation model here the high is 226 the low is 50 I'm going to assume that this is in this is in meters and I want to convert this to feet so we're just going to talk about some of the different calculations that we can run on this I can click on DD m times 3.28 and remember when we create we want a brand new raster query calculation or whatnot it's gonna create a brand new layer remember on our folder you I'm gonna go to my temp I think I have a working tutorial output we're gonna just call this yeah feet click ok these are some other types of raster calculations that I can do so you can see it's just been multiplied by a factor of 3.28 so I got our high elevations of 743 feet or low elevations down here towards Jordan leg of 164 feet above sea level if I click on the properties you can see my source it's about 6 5 7 Meg it's a floating point this talks about some of the basic attributes or some of the properties attached to this this is in state plane I think we have some we have some cell sizes here and this is a 9 meter to about 10 meter 10 meter resolution here so this is a pretty decent data here and I think this is extracted at one point in time from some of your you know some of your DM data from the USGS so these are some types of calculations that we can do there are other types of calculations too well we're gonna generate boolean output so I might want to look at something like the Durham Northwest quad remember like we said before if you've worked with national land cover data set this is extracted from Landsat data and it looks so it looks at the Anderson classification scheme so 11 is wetland 21 22 23 24 these are some type of developed 41 42 43 are some type of forests and I think there's obviously more categories than this here but we can extract out these categories that give the values of you know 0 or 1 false are true in this case I'm just gonna find everything that's 24 so our Northwest Durham quad now in this case I'm going to use the double equals to sign double equal to sign mean is equal to in computer programming language equal to means take on the value if you don't see an equal to sign here it takes on the value of so x equals 3 basically means that some variable we're gonna call X takes on the takes on the value of x + 3 equals that means something totally different in this case we're going to look at 24 and I'm just gonna say category click okay now you can see everything here with the value of one is in category 24 anything in zero is not so these are a couple of simple queries that we can run let's get more complex I want to find anything that's 21 22 23 or 24 we have these parentheses it's really important that when we generate these compound queries we use parentheses so we can work inside out so I've got these parentheses and I have this line right here this line means or the ampersand means and so now what we're gonna look at I want to see that Northwest Durham quad equals 21 and the parentheses or northwest Durham equals 22 or and what the restaurant quad equals 23 finally or Northwest Durham quad equals 24 I'm gonna and I'm actually gonna let's see okay so basically we're looking at anything that's 21 or 22 or 23 or 24 a lot of times people run into some of these logical errors where they use the and this pick salt when I forget if I click on with any one of these pixels it can only have one possible value so it can't be 21 and 22 at the same time typically when we work with raster data there's only one attribute attached to it I'm gonna click OK and I'm gonna run it here all right so now we can see these particular values that are going to be ones are going to be that satisfy this query and if I right mouse click open up my attribute field or open up my attribute table you'll see how the attribute table looks different so there's 90 1921 zeroes falses 80 1638 truths that are going to be some type of developed low intensity high intensity developed okay and we can calculate percentages from these or whatnot okay if I really wanted to I can look at my statistics find the sums and all that other good stuff too last thing that I'm gonna do is create a compound query now I want to find areas that are prone to flooding and these are basically going to be areas that satisfied one in this prior query and are less than 300 feet in elevation okay we're looking for areas that are flooding runoff impermeable surfaces that are low elevation where areas where water wants to run do this again now my raster calculator and I can start to combine this category with this one or I can just cut and paste this thing back in now these are going to be compound queries so I want to generate these queries so I want to find everything that's going to be 21 22 23 or 24 and I also want to see where the elevation is less than or equal to I'll say 300 in this case 300 okay so you can see the reason why I put the parentheses to the left one more parenthesis is that it wants to be generated it wants to be generated explicitly or separately of the DM so I want to generate these separately you know so all these are going to be combined separately and then combine together and then combined with the less than 300 feet I'm taking run this and see what we have here okay so all the ones and I can change the color on these I can make these hollow and I can zoom in right here if I wanted to okay so these areas have low elevation okay these areas in blue we can see they're 21 22 23 or 24 we don't care which one because we just use that or command and then they have low elevation okay they have low elevation right here and you can see those are indicated by my darker areas for my DMS and if I really want to now I can superimpose my imagery on top look at these particular areas and these might be areas that are prone to you know flooding or runoff or whatever we're looking at here we really wanted to we can set these up to from my display sending it up to be I don't know 50 percent transparency make some high-quality maps out of these but we know when we look at this category one 21 to 24 it needs to be less than the number of pixels that are available in this because we're doing the and it's a combination when I go back here I've just got a couple other things that I'll mention the parentheses are extremely important the double equal to sign is something we haven't seen before with raster data this tilde is a conditional not I haven't used it that often and then we have the less than or equal to signs here we also have a number of mathematical functions we have something called the conditional just make sure you follow directions on how to run that conditional basically if the elevation is less than 300 make it zero otherwise make it one so there's lots of other ways that we can do this we're set it to no data or no values or nulls and then we have things like absolute values or exponents you know convert data to floats because you can see I've got some floating values over here in the left while I have some categorical categorical values here then your whole gamut of your trigonometric functions so this is a really powerful tool I just wanted to mention a few things about it before we worked on some of these in our class

Channel: DEEGSNCCU

Views: 12,878

Rating: 4.9223299 out of 5

Keywords: NCCU, Map algebra, Raster calculator

Id: PIXxuvx3Rys

Length: 13min 35sec (815 seconds)

Published: Thu Sep 06 2018