dem_comparisonPapadopoulos Alexandros

January 12, 2018

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First thing first, package ‘raster’ in needed for geographical data analysis and modeling.library(raster)

## Warning: package 'raster' was built under R version 3.3.3

## Warning: package 'sp' was built under R version 3.3.3

With the ‘raster’ library tif layer can create RasterLayers. In this case f and h parameters are the DEMsmade by us and p is reference DEM we want to compare them to.f <- 'S1A_20151217_20151229.tif'DEM_s1_20152117_20151229 = raster(f)

h <- 'S1A_0170209_20170221.tif'DEM_s1_20170209_20170221 = raster(h)

p <- 'Rio-Patra_Ktim_5m_UTM34N.tiff'DEM_ktimatologiou0 = raster(p)

In order to make pixel by pixel computations, the DEMs have to be of the same extent and resolution.The ‘extent’ function returns the Extent of one object (here one of the made DEMs because it is smallerthan the reference). ‘Crop’ returns the geographic subset of the reference object, as specified by the Extentobject. Finally, ‘projectRaster’ projects the values of the made DEMs to a new object with the projectionand resolution of the reference DEM. The following three plots demonstrate the topography of the are foreach DEM.e <- extent(DEM_s1_20152117_20151229)DEM_ktimatologiou <- crop(DEM_ktimatologiou0, e)DEM_s1_20152117_20151229_asKtim <- projectRaster(DEM_s1_20152117_20151229, DEM_ktimatologiou)DEM_s1_20170209_20170221_asKtim <- projectRaster(DEM_s1_20170209_20170221, DEM_ktimatologiou)

par(mfrow=c(1,3))plot(DEM_ktimatologiou, main = "DEM_ktimatologiou")plot(DEM_s1_20152117_20151229_asKtim, main = "DEM_s1_20152117_20151229")plot(DEM_s1_20170209_20170221_asKtim, main = "DEM_s1_20170209_20170221")

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Then the comparison between made DEMs and reference DEM in made by their subtraction. Then, two plotsshow the differences between each made DEM and the reference one. Green pixels show that the referenceDEM had higher values that the made one and red pixels show that the reference DEM had smaller valuesthat made one. Plotting spacially their differences patterns may be identified, in this case the DEMs shouldbe re-evaluated and find the cause for the patterns.diff2015 = DEM_ktimatologiou - DEM_s1_20152117_20151229_asKtimdiff2017 = DEM_ktimatologiou - DEM_s1_20170209_20170221_asKtim

par(mfrow=c(1,2))plot(diff2015, main = "Diff=DEM_ktimatologiou - DEM_s1_20152117_20151229")plot(diff2017, main = "Diff=DEM_ktimatologiou - DEM_s1_20170209_20170221")

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Usefuls histograms can also be generated. The first three represent the probability of values for each DEM.The other two histograms show the probability of difference between the reference and the made DEMs.par(mfrow=c(1,3))hist(DEM_ktimatologiou, prob=T, main="DEM_ktimatologiou", xlab="")hist(DEM_s1_20152117_20151229_asKtim, prob=T, main="DEM_s1_20152117_20151229_asKtim", xlab="")hist(DEM_s1_20170209_20170221_asKtim, prob=T, main="DEM_s1_20170209_20170221_asKtim", xlab="")

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20par(mfrow=c(2,1))hist(diff2015, prob=T, breaks=40,main="Diff=DEM_ktimatologiou - DEM_s1_20152117_20151229", xlab="")hist(diff2017, prob=T, breaks=40,main="Diff=DEM_ktimatologiou - DEM_s1_20170209_20170221", xlab="")

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With the code underneath the standard deviation of the differences (reference - made DEMs) is computed:2015 case = 50.611

2017 case = 43.273standard_deviation15 = sd(c(as.matrix(diff2015)),na.rm=T)standard_deviation17 = sd(c(as.matrix(diff2017)),na.rm=T)

Root Mean Square Error (RMSE) between sim and obs, in the same units of sim and obs, with treatment ofmissing values. RMSE gives the standard deviation of the model prediction error. A smaller value indicatesbetter model performance.library(hydroGOF)

## Warning: package 'hydroGOF' was built under R version 3.3.3

## Warning: package 'zoo' was built under R version 3.3.3DEM_1_matrix = c(as.matrix(DEM_ktimatologiou))DEM_2_matrix = c(as.matrix(DEM_s1_20152117_20151229_asKtim))rmseA = rmse(DEM_1_matrix,DEM_2_matrix)rmseA

## [1] 57.67353DEM_1_matrix = c(as.matrix(DEM_ktimatologiou))DEM_2_matrix = c(as.matrix(DEM_s1_20170209_20170221_asKtim))rmseB = rmse(DEM_1_matrix,DEM_2_matrix)rmseB

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## [1] 54.91381

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