GRMI- Flood Vulnerability Mapping Arcmap session (2021) Part2

[Music] so um i'm going to input my as logo logo fd which is the flow directory this is selected decide on column 2 row 2 column 202 which is this 7 column 2 can flow in eight different directions can flow in eight different directions just as they replies d8 eight directive r2 box then on our both special analysis um click on this and we'll see our through direction to flow direction to gauss-seidel flow direction too so we'll click on that so after that we input our um last reaction so like i said um [Music] earlier i don't usually dnr which is a 3x3 uses down three by three so matrix to calculate directional flow of the inner muscle outside of it so now it's telling you that the innermost cell can flow in eight different direction outer as outer which can flow out outside in eight different directions and like i said it's in the the flow is in a binary format it's in a binary format from two ways to possible to choose about seven view eight different directions so i know there is a positive is what's one so um one is one is uh the flow direction in the eastern direction the eastern direction the cell that is on that is directly east of our innermost cell so that is our cell one that is really we sell with our uh value one which which implies the eastwood flow from the middle side eastward flow from the green cell and this um data board goes in a clockwise direction so under the eastern florida the south eastward flow the south is also which is tourism which is two there you have the south southward floor which is four which is through this bottom there we have the south western flow which is trustworthy which is eight then the western flu which is tourist to power four 16 the north western flow tourism to power um 5 32 the north not the northern flow tourist about 664 and finally the north eastern florida which is just part 7 128 beautiful so other iranian analysis will be able to have a metal uh picture of what i just explained now using the dna algorithm i believe we are falling up to this extent yes so wait for it so we can see like i said earlier you did algorithm output eight different directions one two four eight sixteen thirty two six four one twenty eight like i said a banner format in the power of from zero to zero towards two raise to power seven which is one to one twentieth one yes simplifies what our eastward is for flow one separate our installed through and forward to responsibility our if you can see this place very well you notice that this falls in our south western direction i because of uh the number of uh possible flow uh through uh directional flow within uh our study area but i believe from our explanation we can be able to understand all the different flow uh directional flow that can possibly occur using our elevation data so i won't draw much on this also other entire process because we can also ask our questions for areas that is okay to us so after we do the information the next thing we are going to be doing is what what i call the flow accumulation analysis to do the flight mission analysis would go to our special our actual special analysis technology and what we through our mission so for example there are two the most important parameters or the full direction raster so the flow accumulation just shows us the areas that have high accumulation or i possibly eye accumulation of water within the study area based on the direction of fluid you know once areas water flow in a particular direction the accumulation the area with higher flow with more flow direction as the higher probability of having a higher flow occupation has regard as compared to areas with just little uh flow direction or direction of fluid as the case may be so uh out of that mission data group my flow my logo too and check out the results so um while this is this itself is because the region is black have its own uh floor present in this area it is shown as black while we can see that this region is right flower mission to be a bit more detailed to be a bit more detailed to show us the uh other flow uh other tree which is data contain gg flow operation immediate but i can't see now so that brings us to our next analysis which is which are going to be using don't do using the raster calculator raster calculator 2 so i'm going to be using the search tool yeah to get my assignment what i'm going to do is break my raster carbonyl now uh the mathematical operation i'm going to perform on this after application is leak logarithmic uh mathematical uh mathematical operation so we are going to bring convert this from pixel values of 54.7 to ah logarithmic value so like i said in my maths my answer calculator i'm going to be using what's on that this you see these are the operator keys and these are the these are the uh different uh operations mathematical operations that we can carry out so i'm going to go to my log space then uh also let me just refresh our mathematical uh knowledge you know log 10 of 10 raised to power one is one log ten of ten raised to part three is what three i mean that is not ten of ten of energies was one now three so log ten of ten to the power four it was four log ten of ten zero five it was five so i'll drag this this micro i can click on this and drive this into this uh into this um bracket here that's a lot of our logo proact so i'm going to specify my output to be what log logo [Music] look so with partners i will uh okay keep okay we can also you can see these however i believe we can all see these this is quite more detailed than the one we have i'm just going to check all this other all this other data so that you can as you'll see wow wow this is so so close so sweetness so so close okay all right so there i have my data and you can see the difference of tributaries what is not the final analysis what we're going to be doing since we are going to be uh telling our computer that the values we have is supers is um too much and would want it to exclude some values what was the exclusive value so that the data it tried to improve readability of our our data and also uh give our uh our analysis a more um intriguing uh intricate um result so i'm still going to use my raw stack upgrade but this time i'll be using doing the telling the computer to do a personal conditional statement that okay if it's what satisfies this condition run if you satisfy this condition wrong so for my uh for my master elastic calculator i'm going to use this contour the contour so let's clean my contour which you should know you cannot you know that our uh contour has a bracket two parentheses on the comma separating it so the first thing i'm going to do is if we bring in our log 10 data that very calculated from our cloud nation we bring heating then we use our greater than or equal to operator i'm going to be selecting a value of 2 and after that will not bring in this again so what this is telling us telling our um our calculator to do is that for every value greater than that it should only show us values that are greater than or equal to now these values that are lesser than two and uh and below should be ignored that is uh basically what the conditional uh tool um is uh telling our calculator so like you said you save it again logo this time around we will be using uh i personally received it as uh con for example analysis so that will get mixed up okay um so this is our this is our resulting data these are resulting that i'm going to change the volume so i can all see uh the stream network and out also with huge using a different color right now so let me just say so you can see uh so i believe common cities so basically here the region with higher flow accumulation is highlighted in this deep let me assure you so i can see what i'm talking about either why so this in itself is not the final result this just shows you the very first three networks that i would do are you able to get the final result we are going to do an analysis uh this one is what we stream other analysis basically what the human analysis was authors our stream segments based on uh uh the uh different classes identified using stroller technique what's this matter of classification it shows that two smaller is just two smaller segments meets to form a bigger treatment a bigger uh stream segments that a stream order of re stream order two when i do the analysis with this better so i'm going to improve my stream master to be my logo and also inputs after direction raster which is ah logo through d which is the flow direction so just to stream order i'm just quickly do that s3 technique okay okay okay right right we are on track okay i apologize for this for this time consumption it's fine it's actually very important and i think but those other team members that watching this video they will find it more interesting so so it's the best it's it's not just this night alone to serve us something that everybody can always look back to whenever they start walking so thanks so much because you can go ahead it's a privilege thank you so much so like like i explained earlier i told you this uh strange team order technique or classical or classification stream order classification says that 22 lower ice cream order that means a street segue stream what i won and segment and anna or stream other one connects they form a stream order uh uh stream of his stream segment i've been streaming that too so as you can see here this is one lower mother one two actually two converging to form it's two more diodes two camera c is stream other two so also we can also notice that streaming r2 and humana what i want to form ayashi kodak so we can also see from our class trimana one is allotted in undisputed to form together from what our stream would have formed so for this particular analysis we have four different stream orders four different stream orders one two three and four beautiful so the last analysis that i'm going to be doing to carry out to give us our final uh stream feature is our stream to future analysis this tutorial is basically converts our data from the raster format in towards the um future format which is our polyline format and this is done to enable us perform for analysis like buffering technique on our data so i'm going to open this to each to filter to remember our process as well as our toolbox specialist to ideology i'll write draw click on this so i'll input my stream master if you've been following my stream master you view my logo stream order now logo stream order which is distrib or order classification based on strata technique also i'm going to use my free direction which is uh my logo flow g so i'm going to add my segment output as logo stream to run it wait for the results okay beautiful so i'm going to uncheck this so i'm going to line features so this is our product of our just classified uh stream order so we can check uh the attribute table to see the different number of features and also to note number so in this pattern for this particular class story are based on analysis i have 800 two different stream segments that are inter inter-connected using what are the uh um the uh uh contour like i did and also the stream model so here are two different stream segments individual stream segments also from the grid code this grid code is is just from record you can just check um the statistics this report is uh our stream order is our stream order has done carried out by using our uh our strata method of stream of river uh audrey so we can see the minimum values was one why the maximum value is two do you understand i'll be there for it and the number of stream segments was eight hundred and two it's only around two so the sum and also the mean of value now so i'm going to close this now we can all see the distribution one is the most is is the frequency description one has the highest number that means one out over 400 different stream segments while four is the least i've been just below 50 below 50 uh stream segments you can see the distribution just like that one to two to three and two four and so on so i'm going to close this and also close my table so the next thing i'm going to do is ah now that i have my logo stream and also my big reverb the next analysis i'm going to do is the buffer analysis development analysis for our bubble of analysis um we are going to be using uh a particular radius for analysis based on r based on the uh floodless knowledge that we have we for this analysis we are not using a buffer extent or to create of our big regular big big uh river this is telling us that regions that are really two kilometer radius of our big river are susceptible to flooding and also this condition is based on one our influential knowledge and also based on several literatures that have been kind of in the past to do what are floodless mapping several regions have noted that oh when a particular flooding event occurs and for areas that are very very close to larger bodies like our river bedroom uh river ninja and some other big rivers uh uh people properties within two kilometer we don't have eyes successivity susceptibility to flooding as compared to regions from the river body like two point something kilometers and a boom so for this analysis i'm going to be using my buffer 2 my buffer 2 so what i'm just going to do is like i did earlier i'm going to use my buffer my also if um if you don't do the same to how can i if okay now you might you might ask if i don't do the search tool how can i navigate to my buffer too now it is simple don't panic what happened to this was my click on my add toolbox for my absolute boss i'm going to go to so let me just i can't see this um okay okay okay okay proximity [Music] meet the plastic world uh logo big river both buffer so this uh distance is very important value of field so for this particular analysis i'm going to use in linear induces value so i'm going to change this to kilometers since we already said that our analysis that we jobs that are we doing they took interviews of the bigger buffer are more successful to flooding so i'm going to come down select my uh buffer type for this particular analysis i'm going to use food then around also just click on dissolve right i'll click on all i will tell you how click on resolve in the next analysis why i choose the solve to just why i could choose all for myself so i just let this one and you see the results uh let me just go under this session i believe we all got it to this extent yes okay so is the result of my buffer um don't let me let me just show you the big river that i used for the classification all the time so that you don't get confused [Music] so we'll drag this right up so you can see the big rival is the big arrival so once if you just is going to be my smaller stream remember that i already did uh the raster supporting gun of my local stream so i'm going to be using the uh polyline features rastas features which is the logo stream for this analysis so now tutorial class was my logo stream buffer so for this particular time guys you need to start based on several digital past features and also artificial religious religions that are within 500 meters of these smaller sigma segments are highly subscribed to me so i used to fight foreign overlapping each other will be jointed by the buffer tool see specified that the dissolved perform to remove the [Music] out um hmm okay this is the result of my buffer so we can see how this this is so whenever i bring this in [Music] a picture so this is our stream segment and also remember every type of photo just so let me just select this and i'll check this so that we can have a clear picture so the file the last some of the last analysis is what our classification so basically this is what we are going to be doing in the next uh um segment of scientists but one can do the reclassification basically what is the classification classification is just an assignment of values based on user defined values now you are telling the computer that for my particular from this map i wanted to assign a value of within the class of maybe one two three where one represents low risk uh no flood risk to iphone risk or in a case three mile represents low for the flood risk why one million high flow risk dependent on the user defined values like i said these are defined values so for this my analysis the classification i'm going to be doing is the class of one to five but because of the clarity of the classification too you must ensure that all our input features must be in a raster format for the computer to be able to reclassify the values so what i'm going to do is this i'm going to convert my bigger buffer and my local stream buffer into what into raster so how do i do that what i'm just going to do is i click on my uh polygon remember my profile is going to rust so i can improve that message too and i click on polygons roster conversions [Music] buffer again this time small lower extreme buffer oh what is specified this we cannot hear you please or meet yourself yes so from right there analysis i did this after i did putting those roster uh before you go to rasta um conversion so this is what i got my let me uncheck this so you can see so this is the black is ah only contractor buffer of my bigger body why the um the stream to hdbf rasta is the holy ghost rasta of my smaller stream segments as seen here so this is the seven of the final phase so the final phase is like i said earlier is reclassification technique so what we are doing is we are going to reclassify our data based on the uh uh classification digital defined classification values like i said we are going to use it has a value of one to five one to five we had one signify the lowest and five sigma the highest flood risk vulnerability so what i'm going to do now is i'm going to reclassify each of my criteria um before this is very old note that we have six criterias now you have elevation we have the slope we have the landslide cover we have to wait for we have the um distance what are the two big revolutions are we got a big river and the distance to smaller river which is our stream stream buffer so the next thing i'm going to do is um go to for our actual box from outbox from our board select our special analyst two toolbox challenges to obsolete our special analysis then from that would go to reclass reclassify we classified two images followed three at least two box specialists uh two boxes uh journalists are actually going to specialize together to go to your classroom so we classify each uh value [Music] each criteria so the first classification i'm going to be doing is the logo here which is my elevation data so for our analysis yeah very careful now now for my opportunities five represents the ias risk for vulnerability while one represents the lowest risk for vulnerability based on my own analysis analysis so what i'm going to do is i'm going to classify to classify these values based on that so 5 elevation with um a blessed initial value of twenty eleven we have five which is the is you know performing events flooding uh water populates in uh area of low [Music] of water accumulating in this region of higher lower elevations water flows from higher ground to the background so same goes for my analysis i want to place more uh place more uh important on regions that have lower grounds that have lower elevation so like i said my value is what uh for i is five four four is one so i'm just going to reverse these values so given here if you try to let one one level five and this one one so uh i'll do that and i will save it as a class relief maybe one of foreign this is my classified either and there are one for elevation on five for lower elevation so let me just check so i'm going to do the same analysis and classify for my slope the same thing goes for slope so i might repeat this the same thing goes off goes for slope uh slope i asked loop ah a value of of um one why places with lower slope values are the value of five just using our uh elevation technique so once we close this i was like let me just close this chart and i'll show you the result so once you do that this is going to be the result of our slope our classified slope so you can see one i have places i have two values why are five five other places which is doing very well this is lower slope values so we don't reclass the slope the elevation let's look now we are going to the land island cover for the land is not recovered we are going to be classifying it since we have only three largest classes we are going to classify it based on three different values one five four and three so let me show you what i mean let me uh the two is easy okay you're classified too now remember that our landing this land cover that we did not use now we did we have three different values and while i was doing the classification at once and twenty four remember that ah from our attribute table i inputted what each class is one is whatever body six is not glute or balance transfers of medication now for our analysis we're on to paradise class because you know this is the most important class when it comes to surgeries because flood itself is what water so area where the average would give it a float a a flood risk value of five while built up is the second in consideration because we thought this is uh these are regions that are mostly affected by flood mutables are really excited by the flood because of both the different factors such as the kind of green pattern they are called the compact surface and the lights while position is the least affected because areas like densely vegetated have high infiltration rates because of one they are the kind of cover of the uh the vegetation and secondly because of the surrounding surrounding environment of the residence of east the since the water has been chopped by the uh vegetation cover the when it reaches the surface does not have enough capacity to run off so it just infiltrates into the ground in most cases so like i said i'm going to be assigning values to this one randomly so five remember beautiful and four and a position three so class length so we trust our slope elevation is so this is the classified land island cover i used it now so like i said three vegetation four view top and five resume very well the rival body so the next is my uh rainfall so i'm going to reclassify the rainfall based on my reform distribution like i did here so what go to your toolbox go to your channel analysis toolbox in class two blocks because fighting so for my analysis like i said i'm going to be classifying what my referral distribution i already have number of classes so areas with ira for distribution of 147 to one forty one four one thousand four eighty seven one twelve four to one five seven five value five for that of one thousand and forty twenty so wait for that so this is the result of our classification based on that so this five world is one so the next is i'm going to classify our uh um last two classes let me quickly quickly change this classifier to just one class i really missed it yeah just one class okay so we have just one class so we're classified like i said also both with class two books then we classify so so this is yellow one and hey we have to be very careful here since uh uh remember we're gonna start buffer our big rub offer is of high concentration that means regions that are within two kilometers to the river are of our highly considered regions for flood mapping so i don't change the value to 5. remember i want signifies blue wire 5 signifies i so just change that okay yes following yes so now that we are done with that okay this is our result this problem you can see fly five this is our uh classified value so we repeat the same process for this time which is our last class last but not least uh the stream feature that we just did stream to buffer future rasta remember that for us to be able to reclassify our muscular data from polyline to polygon so the same thing applies for you to since you are giving the conversation of five parameters to areas a particular channel stream channel is a value of five to delete that regions that are in this area are highly susceptible to risk to flooding so so you see oh all right so uh after you have all our classes uh other classes um okay just you can just group everything so that's when you're doing a classification you know select everything so aha so far whether or not we're going to so uh-huh so as i have applied your class your class um values where you're just going to check the uh elevation two hours to do the weighted value you go to active box we have to go because we go to the overlay to the specialists go to the overlay two box from the overlap but you see your weighted overlay two like i said i have two books special analysts two books overlay two boxes analysis book that you see we take good value so this is where i'm going to ground up to this session we will continue the session in another class thank you everyone for being a part of this uh thank you for the opportunity to start viewing our code section yes

Channel: Geohazards Risk Mapping Initiative

Views: 562

Rating: 5 out of 5

Keywords: gis, mapping, arcgis, arcmap, grmi, flood analysis, flood mapping, esri, volunteering, nigeria

Id: RluE6J7E_lQ

Length: 54min 33sec (3273 seconds)

Published: Fri Feb 12 2021