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library(raster)
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#Get list of tile names and add full pathname to beginning
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Tiles <- list.files("DEM/asterGdem/N59to81_W20toE19", pattern="^ASTGTM.*_dem.tif$")
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Tiles<- paste("DEM/asterGdem/N59to81_W20toE19/", Tiles, sep="")
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6
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# datavalues: 0= water, -9999= nodata
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#Create new vectors for name of tile and accompanying % of cells= ocean and non-landmass
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# IF % OCEAN AND % NON-LAND ARE VERY DIFFERENT USE % NON-LAND FOR QC CHECK, IF THEY
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# ARE THE SAME, % OCEAN IS FASTER TO CALCULATE SO USE THIS
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l<- length (Tiles)
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Names<- c(1:l)*NA
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PctNotLand<- c(1:l) * NA
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PctOcean<- c(1:l)*NA
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#Fill vectors with % of each tiles where pixel value = 0 (water) OR 0 & -9999 (water or nodata)
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for (i in 1:l){
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Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)
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PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))
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PctNotLand[i]<- (count(raster(Tiles[i]),0)+count(raster(Tiles[i]),-9999))/ncell(raster(Tiles[i]))
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}
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data.frame (Names, PctOcean,PctNotLand)
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#Use the above calculation to calculate % landmass for each tile and compare total over all tiles
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# in folder to % landmass of USGS coverage for same area.
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29
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# Requires aggregating aster tiles to match pixel size of USGS coverage:
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LandAgg<- c(1:l) * NA
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Counts_Agg<- c(1:l)*NA
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for (i in 1:l){
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Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)
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LandAgg[i]<- ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]
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}
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a<-sum(LandAgg)
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40
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#Calculate % landmass in USGS coverage and compare:
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AstRast<- raster("DEM/asterGdem/N59to81_W20toE19/w020n90/W020N90_clipped.dem")
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b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value
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47
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1-(b/a) #Difference= 4.49%
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#------------------------------------------------------------------
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#after first try, anytime PctNotLand was >= 99%, so too was PctOcean and visa versa
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# All subsequent runs will only calc. PctOcean to save time
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51
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Tiles <- list.files("DEM/asterGdem/N59to81_E20to59", pattern="^ASTGTM.*_dem.tif$")
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Tiles<- paste("DEM/asterGdem/N59to81_E20to59/", Tiles, sep="")
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54
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l<- length (Tiles)
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Names<- c(1:l)*NA
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PctNotLand<- c(1:l) * NA
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PctOcean<- c(1:l)*NA
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for (i in 1:l){
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Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)
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PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))
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}
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data.frame (Names, PctOcean)
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LandAgg<- c(1:l) * NA
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Counts_Agg<- c(1:l)*NA
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for (i in 1:l){
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Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)
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LandAgg[i]<- ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]
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print( paste(round(100*i/l),"%",sep=""), quote=FALSE )
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}
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a<-sum(LandAgg)
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AstRast<- raster("DEM/asterGdem/N59to81_E20to59/e020n90/E020N90_clipped.dem")
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b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value
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1-(b/a) #Difference= 2.21%
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#-----------------------------------------------------------------------
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Tiles <- list.files("DEM/asterGdem/N59to81_E60to99", pattern="^ASTGTM.*_dem.tif$")
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Tiles<- paste("DEM/asterGdem/N59to81_E60to99/", Tiles, sep="")
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81
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l<- length (Tiles)
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Names<- c(1:l)*NA
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PctNotLand<- c(1:l) * NA
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PctOcean<- c(1:l)*NA
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for (i in 1:l){
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Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)
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PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))
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}
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90
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data.frame (Names, PctOcean)
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92
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LandAgg<- c(1:l) * NA
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Counts_Agg<- c(1:l)*NA
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for (i in 1:l){
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Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)
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LandAgg[i]<- ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]
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}
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99
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100
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a<-sum(LandAgg)
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AstRast<- raster("DEM/asterGdem/N59to81_E60to99/USGS_ErosDEM_N59to81E60to99/E060N90_clipped.dem")
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b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value
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1-(b/a) # Datasets differ by 2.27%
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cellStats(AstRast,max)
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cellStats(AstRast,min)
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count(AstRast, NA)
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#-----------------------------------------------------------------------
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Tiles <- list.files("DEM/asterGdem/N59to81_E100to139", pattern="^ASTGTM.*_dem.tif$")
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Tiles<- paste("DEM/asterGdem/N59to81_E100to139/", Tiles, sep="")
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l<- length (Tiles)
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Names<- c(1:l)*NA
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PctNotLand<- c(1:l) * NA
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PctOcean<- c(1:l)*NA
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for (i in 1:l){
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Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)
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PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))
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}
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data.frame (Names, PctOcean)
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LandAgg<- c(1:l) * NA
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Counts_Agg<- c(1:l)*NA
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for (i in 1:l){
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Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)
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LandAgg[i]<- ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]
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print( paste(round(100*i/l),"%",sep=""), quote=FALSE )
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}
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a<-sum(LandAgg)
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AstRast<- raster("DEM/asterGdem/N59to81_E100to139/e100n90/E100N90_clipped.dem")
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b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value
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1-(b/a) #Difference= 3.1%
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#-----------------------------------------------------------------------
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Tiles <- list.files("DEM/asterGdem/N59to81_E140to179", pattern="^ASTGTM.*_dem.tif$")
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Tiles<- paste("DEM/asterGdem/N59to81_E140to179/", Tiles, sep="")
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139
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l<- length (Tiles)
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Names<- c(1:l)*NA
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PctNotLand<- c(1:l) * NA
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PctOcean<- c(1:l)*NA
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for (i in 1:l){
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Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)
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PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))
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}
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data.frame (Names, PctOcean)
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LandAgg<- c(1:l) * NA
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Counts_Agg<- c(1:l)*NA
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for (i in 1:l){
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Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)
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LandAgg[i]<- ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]
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print( paste(round(100*i/l),"%",sep=""), quote=FALSE )
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}
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a<-sum(LandAgg)
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AstRast<- raster("DEM/asterGdem/N59to81_E140to179/e140n90/E140N90_clipped.dem")
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b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value
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1-(b/a) #Difference= 3.4%
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#-----------------------------------------------------------------------
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Tiles <- list.files("DEM/asterGdem/N59to81_W60to21", pattern="^ASTGTM.*_dem.tif$")
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Tiles<- paste("DEM/asterGdem/N59to81_W60to21/", Tiles, sep="")
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l<- length (Tiles)
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Names<- c(1:l)*NA
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PctNotLand<- c(1:l) * NA
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PctOcean<- c(1:l)*NA
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for (i in 1:l){
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Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)
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PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))
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}
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data.frame (Names, PctOcean)
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176
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LandAgg<- c(1:l) * NA
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Counts_Agg<- c(1:l)*NA
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for (i in 1:l){
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Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)
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LandAgg[i]<- ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]
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print( paste(round(100*i/l),"%",sep=""), quote=FALSE )
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}
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184
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185
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a<-sum(LandAgg)
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AstRast<- raster("DEM/asterGdem/N59to81_W60to21/w060n90/W060N90_clipped.dem")
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b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value
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1-(b/a) #Difference= 3.03%
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#-----------------------------------------------------------------------
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Tiles <- list.files("DEM/asterGdem/N59to81_W180to141", pattern="^ASTGTM.*_dem.tif$")
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191
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Tiles<- paste("DEM/asterGdem/N59to81_W180to141/", Tiles, sep="")
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l<- length (Tiles)
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Names<- c(1:l)*NA
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195
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PctNotLand<- c(1:l) * NA
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196
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PctOcean<- c(1:l)*NA
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197
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for (i in 1:l){
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Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)
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199
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PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))
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200
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}
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201
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202
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data.frame (Names, PctOcean)
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203
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204
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LandAgg<- c(1:l) * NA
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205
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Counts_Agg<- c(1:l)*NA
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206
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for (i in 1:l){
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207
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Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)
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208
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LandAgg[i]<- ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]
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209
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print( paste(round(100*i/l),"%",sep=""), quote=FALSE )
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210
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}
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211
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212
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a<-sum(LandAgg)
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213
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AstRast<- raster("DEM/asterGdem/N59to81_W180to141/w180n90/W180N90_clipped.dem")
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214
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b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value
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215
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1-(b/a) #Difference= 2.87%
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216
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#-----------------------------------------------------------------------
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217
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Tiles <- list.files("DEM/asterGdem/N59to81_W140to101", pattern="^ASTGTM.*_dem.tif$")
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218
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Tiles<- paste("DEM/asterGdem/N59to81_W140to101/", Tiles, sep="")
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219
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220
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l<- length (Tiles)
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221
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Names<- c(1:l)*NA
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222
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PctNotLand<- c(1:l) * NA
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223
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PctOcean<- c(1:l)*NA
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224
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for (i in 1:l){
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225
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Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)
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226
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PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))
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227
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}
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228
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229
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data.frame (Names, PctOcean)
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230
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231
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LandAgg<- c(1:l) * NA
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232
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Counts_Agg<- c(1:l)*NA
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233
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for (i in 1:l){
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234
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Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)
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235
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LandAgg[i]<- ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]
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236
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print( paste(round(100*i/l),"%",sep=""), quote=FALSE )
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237
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}
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238
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239
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a<-sum(LandAgg)
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240
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AstRast<- raster("DEM/asterGdem/N59to81_W140to101/w140n90/W140N90_clipped.dem")
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241
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b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value
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242
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1-(b/a) #Difference= 3.84%
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243
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#-----------------------------------------------------------------------
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244
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Tiles <- list.files("DEM/asterGdem/N59to81_W100to61", pattern="^ASTGTM.*_dem.tif$")
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245
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Tiles<- paste("DEM/asterGdem/N59to81_W100to61/", Tiles, sep="")
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246
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247
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l<- length (Tiles)
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248
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Names<- c(1:l)*NA
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249
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PctNotLand<- c(1:l) * NA
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250
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PctOcean<- c(1:l)*NA
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251
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for (i in 1:l){
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252
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Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)
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253
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PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))
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254
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}
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255
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256
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data.frame (Names, PctOcean)
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257
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|
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258
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LandAgg<- c(1:l) * NA
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259
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Counts_Agg<- c(1:l)*NA
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260
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for (i in 1:l){
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261
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Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)
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262
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LandAgg[i]<- ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]
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263
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print( paste(round(100*i/l),"%",sep=""), quote=FALSE )
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264
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}
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265
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266
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a<-sum(LandAgg)
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267
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AstRast<- raster("DEM/asterGdem/N59to81_W100to61/w100n90/W100N90_clipped.dem")
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268
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b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value
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269
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1-(b/a) #Difference= 7.21% (lot's of coastline in this set of tiles!)
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270
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#-------------------------------------------------------------------
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271
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c<- raster("DEM/asterGdem/N59to81_E60to99/USGS_ErosDEM_N59to81E60to99/E060N90.DEM")
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272
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a<- raster("DEM/asterGdem/N59to81_E60to99/USGS_ErosDEM_N59to81E60to99/E060N90_clipped.dem")
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273
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b<- raster(nrow=82800, ncol=144000)
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274
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d<- raster("DEM/asterGdem/N59to81_E60to99/ASTGTM_N59E060_dem.tif")
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275
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s<- resample(a,b,method='ngb')
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276
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disaggregate(a,30)
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277
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e<-aggregate(d,30)
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278
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279
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count(e, -9999)
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280
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count (e,0)
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