Raster Reclassify

well welcome back to GIS analysis at the University of Alaska Fairbanks in this session we're going to reclassify our test roster as a reminder our test roster has values ranging from 1 to 100 so basically each pixel is a value of 1 through 100 we could symbolize our roster so if we go to properties let's classify our roster and then if we click on the classify button interval size of 10 and then let's assign a color rent so green to red so 1 through 10 will be this color green 10 through 20 will be that color green all the way up to 90 to 100 will be this color red so it's easy to classify a roster in terms of symbology but what we might want to know is well how many pixels have values from 90 to a hundred how many pixels have values from 50 to 60 so to do that we can use the reclassify tool so the reclassify tool will output a new raster and it will group pixels based on ranges of values so examples of reclassifying you may want to reclassify an elevation raster into elevation zones of 100 meter elevation zones or you might want to reclassify a land cover raster into a raster where one is a vegetated pixel 0 is a non vegetated pixel or you might want to reclassify an air pollution raster where zero is healthy pixels and one are unhealthy pixels so we can do this all using the reclassify tool so our input raster obr test raster and then if we click on the classify button there's many methods we can classify by so let's choose define interval and then our inner row will be ten so then values that have from one to ten will become in our output values of one and then values from ninety to a hundred in our output will become values of ten so we'll create a new output roster and let's name this raster raster interval ten and then just okay so that creates a brand new raster so then we know that there's ten pixels in the first zone there's ten pixels in the last zone now six months from now we may forget well what is the value one represent what does a value five represent what is a value ten represent so it's always a good idea to after you reclassify run the zonal statistics as table to get the minimum value and the maximum value from the original raster in this table so our zones will be the raster that was output by the reclassified tool and then what we want to know is for each value in this output from the reclassified tool what is the original minimum and maximum pixel value so then the value of one ranges from one to ten the value of two ranges from eleven to twenty the value of ten ranges from 91 to 100 so then our final step is we'll use the join field tool to transfer this information to our raster attribute table so we're going to transfer the minimum and the maximum to this table using the key field value and value so we're going to transfer from our table to our roster and we're going to base that on the key field value and value and then what we want to transfer is the minimum and the maximum and then just okay so now six months from now we'll be able to determine that okay a value 1 in this roster is representing from our original roster all the pixels that range from 1 to 10 and a value 10 in this roster is representing all the pixels that range from 91 to 100 in the original roster so let's say we have our original test roster and we want to create a new raster representing low medium and high we're low is values let's say one standard deviation less than the mean and high our values one standard deviation above the mean so we can use the reclassify tool and then classify by standard deviation so this will be the value that's within one standard deviation of the mean so let's give it a code of one if it's not that value and a code of three if it's above that value so two will represent it's within one standard deviation of the mean these classes represent it's less than one standard deviation from the mean and these classes will represent it's one standard deviation above the mean so then let's output that as a raster named low medium and high so then we can add a field to our raster attribute table to describe what the values 1 2 & 3 represent so we'll add a text field and let's give it a length of 16 and let's name that class so this represents all the pixels that were in class low so enclosed in double quotes we can give it a class low and then repeat the process for the medium row and for the high row and then once again six months from now we may not know okay what does low really represent in terms of the original pixel values so once again we'll run zonal statistics as table and then join field to get the original minimum and maximum values in this raster attribute table so once again our owns are from this raster and we're asking about the pixel values in her original raster and returned the minimum and maximum pixel values for each of these three zones so that creates our table and then we simply use the join field based on value to transfer the minimum and the maximum back to our arrest or attribute table so we're gonna join field based on value and value and then we're gonna transfer the minimum and the maximum to this raster attribute table so then six months from now we'll know that lo represents values between 1 and 35 in our original raster medium represents values between 36 and 65 high represents all those values 66 and above so then we could add that information in our table of contents so here we could instead of 1 we could say low and then we could actually include the values so it's up to you as the user for example you could say low is 1 to 35 or we can use for example high is greater than 65 so then we can see in our table contents exactly what we mean by low medium and high

Channel: GIS Analysis NRM435 University of Alaska Fairbanks

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Rating: 5 out of 5

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Id: goZbzyBkFAA

Length: 8min 26sec (506 seconds)

Published: Fri Mar 16 2018