Landsat 8: Atmospheric Correction and Band Rationing Using ArcGIS

hi there thank you for joining me today we're going to be doing some remote sensing within the ACMA environment I do have Landsat scene but it's a raw digital number so we're going to be doing today is to learn how to convert digital numbers to reflectance value so basically we're going to be doing an atmospheric correction and after that we're going to run some band raishin in like an idea and NDVI the NDVI stands for normalized difference vegetation index and NDVI stands for the normalized difference built-up index so with these two band ratios which were able to in NDVI which we were able to distinguish between the greenness or the thickness of a vegetation cover and nd BIA allows us to easily identify built-up areas so it distinguishes between built-up areas and other land covers and this can be useful in terms of making decisions when you're doing some land use land cover map classification and I have been able to go to the metadata for the particular Landsat scene I have to copy out some of this information that we'll be needing for a conversion so the formula for the DN values to reflectance values is the band specific multiplicative band multiplied by the raw digital numbers that's the band specific itself so if you convert in blue band for instance so you're going to impute your blue band at this point and you're gonna add the reflectance additive band number which is all available in the metadata and I have copied it out here already for all of us just to make life easy for us but by the way you're gonna have this metadata file if you have downloaded the Landsat scene this is the metadata file and all of this information that's in my Microsoft Word we're just copied out from this file so we're going to be needing some elevation to correct for the Sun elevation and this is the solar elevation itself and this is the sign for the solar vision so if we go back to my formula up here this is the formula for correcting the Sun angle so after running this formula I want to correct for the Sun angle at that particular day so I can be able to effectively correct my data more accurately for the particular data it was taken so because I know the Sun elevation for that particular day which is given in my meter data I can correct for the song elevation this is in a solar angle basically for that particular day so let's go to app map I have a Landsat scene like I said earlier for somewhere in this world and this is the scene showing a three-to-one band combination which is the true color just the way we normally see things and like to see color so I go to 4/3 which is basically the false color combination the color and this helps us to easily identify vegetation so all the red areas we see our vegetation and this color is this green blue you see basically the built-up areas and the decorate our wetland vegetation and let's get on so I do have my band 4 here which is the it corresponds to band red the red band in Landsat 8 data and band 5 corresponds to near-infrared and band 7 corresponds to shock wave infrared so if we just go back to the formula the NDVI needs two bands which is the near-infrared and the red and the NDVI the built up index needs to land as well which is the shock wave infrared actually I should change this shutter wave infrared and yes so shut wave infrared - no infrared / shock wave infrared plus near-infrared but before we do this we want to get the reflectance value which will need this information so while we need to reflect this value is because we don't want to confuse the T cells we do have pixels within the scene of different digital number values but these are just all digital numbers have been cutting directly from the satellite sensor and you must have gone through several complications and errors like atmospheric problems cloud cover scattering and atmosphere and opportunity that the data was taking off set of things might cause some misinterpretation from the earth to the satellite sensor so in order to not perfectly correct it to a large extent corrected so that we can be able to derive more useful information from that we're going to be running some algorithms using the data we have from the metadata file so you do an atmospheric correction so let's get started what we have here in the in the Microsoft server I do have a bad software would have been dealing with for some days now for it sounds bad and we're just going to be putting in this values into raster calculator so what I do have here is raster calculator I do have it here but normally you want to go into the upper toolbox or go to the search and type in raster calculator I just like it easy so I have customized it and put it up here so I do have my raster calculator you can remember our formula our formula says the bank specific multiplicative value multiplied by the digital number so so we're gonna start off with the bank red so the if I just go back to sorry on my other screen and looking at what I have from the I'm just gonna bring it here I'm looking at what I have so this is what I have and it says my multiplicative value is zero point zero zero two and my additive values minus 0.1 I'm just gonna go back to app map and put that in so I'm gonna start up with appearances put a bracket and I'm gonna say zero point zero zero zero zero two which is the specific bar multiplicative factor multiplied by my digital number which is the red it's so the red band plus my specific band additive which is minus zero point one so I'm going to put another bracket here and plus minus 0.1 close bracket divided by the solar angle which is this so I'm just gonna copy this so this is the sign from my son elevation so I'm correcting all at once for the atmospheric correction and at the same time for the solar elevation of that particular day and I'll go back and paste my sign from the solar elevation I'm just gonna let it go to the default geodatabase and I'm gonna call this red underscore wrap or reflectance so I know it's a it's been collected for reflectance oops where's that okay you put one more bracket here and okay hopefully it should work and now we do have the reflectance value for the red band and normally it should rain between minus 1 to 1 actually with the father it diverges from zero to hide the value and in that means there's a high reflectance within that band and for red band normally the built-up areas and the barrels reflect higher than vegetation so the vegetation here looks very dark which should probably be between the lower values and I'm just going to involve my raster calculator one more time Oh second time out of many vegetable brackets and the multiplicative factor actually is the same thing for all the bands here you see so I'm just gonna still do basically just about the same thing so it's gonna be zero point zero zero zero zero two let's fly by this time I'm gonna be putting an infrared band and close parentheses plus minus 0.1 close parentheses divided by I'm just going to paste my sign for the solar elevation very good to the default really trees I'm gonna call this and I like our for the infrared underscore rec or reflections and then okay that hopefully reflectance value for my new infrared and it's ranging between minus one and one with anything above zero basically the zero point want you to be higher reflected during that spectral wavelength I'm going to bring up my West capitulate one more time go into Swiss parentheses there point zero two zero zero to x this time around to chat wave infrared and clinic closed parentheses minus 0.1 loss parentheses divided by my son elevation they'll go to the default database again and I'm gonna rename this has to be by artificial wave infrared underscore re f reflectance and okay and hopefully I now you have atmospherically corrected bands for red near-infrared and shut wave infrared so what I'm gonna do here is just click on this hold and shift click on this and remove this just to walk walk in a clean way so I have less things in my table of contents I'm not that confused so I still do have my composite bag and now I have my three bands basically that I'm gonna be using to do some valuation and so basically I've been able to achieve my first objective which is the digital numbers to reflectance and I'm gonna go on to run some burn duration in like NV VI and NDVI and we will see what it looks like and how to be able to integrate these two other analysis so now I know the formula for the NDVI is named for red - red divided by name for a + red so I can still just do that within the raster calculator or in my other video I show how to run nd here using the image analysis window which is right here if you don't have you can go to Windows image analysis and it's quite easy to do it because you can just click on the button once and it runs but I don't want to use it again because I used it in my other video just when it is a different way of doing it because are you guys who are watching me so what I'm gonna do here normally you want u nder okay foundation in your doing to come up with floating values also we're gonna let up just you know that we wanted to be in floating points well I've done a couple of times just by putting just directly and what's fine but just be on the safe side today and just want to stick with you guys I like to do it this way too so I'm gonna let know that I want it to be a floating so I'm gonna say floating double-click on floats here if you come here in this tool set so you're gonna find floating yeah you need to click on it and it comes with this two parentheses around it and I'm gonna say yeah infrared so now I'm walking with the atmospherically corrected bands so I am hoping to get more useful and more accurate information from this so near infrared - read the parentheses put the division sign that's float again and I'm gonna say no infrared + rec gonna go to default geodatabase change its name to NDVI okay perfect we now have NDVI model and not gonna talk about it now I'm just gonna quickly wander the second band rationing of certain comebacks all of them and we're gonna run for the NDVI which is the normalized difference water index I want it still to be in a floating point so I'm going to say floating but this time around I'm not using the red I'm using the shuttle waiting for a driver so I'm gonna say that we enough for it - near-infrared / loading again shall wave infrared + name infrared let it go to the deep will do database game I'm just going to rename this to Ain de Bie and then okay that hopefully that's good and normalize difference you have index model and basically what I can do now is I can take all of this out nice and clean so I'm left with this two layers so I'm just gonna symbolize this to me to look better and see how things have been represented spatially so earlier I said NDB is normally useful for identifying the greenness of area and healthy vegetation so if I just go to the I just double click on the NDVI layer actually and it lets me go into the layer properties and I go into the symbology tab and I can do all different kind of stuffs with this but there's just a quick analysis so I'm just gonna let it okay I'll just show you guys the classified actually you couldn't do in two different classes based on how you wanna look at your data but now I'm just gonna leave it as stretched and get a nicer color maybe like green to dark green I like using this for vegetation actually and you're able to see the differences in the colors and gives us a better idea of how the vegetation within this area doing if we look closely into this area we can see how this is actually a farmland it looks much more greener than this for a bit so this could be this could be pop probably crops they're growing and it's also useful to put keep in mind the particular date that you're latina for the Landsat data so if you're looking at a wet season you're probably hoping to see more crops growing rather than if you're looking at the dry season I don't know where you I'm just world I do cold seasons wet and dry people say winter and summer and I do have the NDB right here I'm just gonna do the same template but keep in mind NDB is meant to help us identify areas of buildings so maybe you're doing an analysis to determine number of houses in a particular area or you're trying to include the model where you want to be able to differentiate between agriculture of cultural area and built-up areas so this can also be very useful for you in terms of making decisions and I could click on this and go to the layer properties and symbology tab you can do the same thing like classify based in classes I'm gonna leave it stretched I'm gonna do this number I'm gonna do something funky I'm gonna use this one yes and the higher values expected to be the NDVI values actually which are going to be the built-up areas so basically all of this powerful colors my higher reflectance value within the normalized difference built up index which means this areas are built up areas and probably this few bits here you see I I am not very much convinced these are built up areas which probably be a problem of mixed pixels when you're dealing with remote sensing data you do get a lot of that next Bissell's and other steps and this will also be bare soil due to the similarity in the spectral reflectance in the built-up properties in pairs for Indian land sometimes you seem to get similar spectral signatures actually so basically this is what I just wanted to show during this video so I've been able to deal with this first problem digital numbers to reflectance which is just basically spirit corrections I was able to correct for spawn angle and we're able to run NDVI and EDI and there are a lot of stuffs you can do with these two models in terms of making a decision in town planning and a lot of people use NDVI for agricultural monitoring and other stuffs so thank you for watching 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Channel: Usman Buhari

Views: 78,480

Rating: 4.8986354 out of 5

Keywords: Landsat 8 (Satellite), ArcMap (Software), Atmospheric Correction, ArcGIS

Id: PknESopCwjA

Length: 19min 21sec (1161 seconds)

Published: Wed Sep 03 2014