Fill raster holes in R or Grass GIS

Question

Sample data

x <- raster(x=matrix(data=1:36, nrow=6), xmn=-1000, xmx=1000, ymn=-100, ymx=900)
x[c(8, 15, 16, 17, 22, 25, 26, 30, 31)] <- NA
plot(x)

The problem

How do I distinguish (algorithmically) the holes in the raster i.e., the area bounded by cells c(15:17, 22) from the other gaps that are not holes (i.e., the rest of the empty cells)?

This would make it possible to do operations only on the hole / island regions of the raster, fill holes with a custom value etc etc.

The actual rasters have around 30000 holes and therefore speed is important. I am interested in both R and Grass GIS solutions. Many thanks for your help, much appreciated !

Answer 1

What about polygonizing? For speed I don't know what it will worth, but you could:

x[!is.na(values(x))]<-1
plot(x)
x[is.na(values(x))]<-0

hole <- rasterToPolygons(x, fun=NULL, n=4, na.rm=TRUE, digits=12, dissolve=T)

Now you have to make your singlepart polygons to multipart:

hole2 <- SpatialPolygons(lapply(hole@polygons[[1]]@Polygons, function(xx) Polygons(list(xx),ID=round(runif(1,1,100000000)))))

plot(hole2, add=T)

Now you find the "real" holes, which are the ones not touching the border

around <- as(extent(x), "SpatialLines")
touch_border <- gIntersects(around, hole2, byid=T) 
extract(x, hole2[!touch_border,],cellnumbers=T)

That gives you the cells number by hole. It found the cell "8" which you're not saying it's a hole so I'm not sure if it's correct, but it must be very close!

If it's slow in R, do the same algorithm in GRASS (mostly the rasterToPolygons call)

Answer 2

Here a solution without polygonization: (it's not elegant, but it works). However you have to reclassify your holes/island into values (i.e. 999) and all other non island to NA. Like this:

x <- raster(x=matrix(rep(NA,36), nrow=6), xmn=-1000, xmx=1000, ymn=-100, ymx=900)
x[c(8, 15, 16, 17, 22, 25, 26, 30, 31)] <- 999

plot(x)

Now I use the clump() function to check if there are island and the cool thing about that function is, that it also returns IDs of these island:

#Get Islands with IDs
cl <- clump(x,directions=8)
plot(cl)

I then create a dataframe out of the frequencies of the islands (this is just to get the ID of each island)

freqCl <- as.data.frame(freq(cl))

#remove the (row) which corresponds to the NA values (this is important for the last step)
freqCl <- freqCl[-which(is.na(freqCl$value)),]

Check if any island touches a border:

#Check if the island touches any border and therefore isn't a "real island" (first and last column or row)

noIslandID <- c()
#First row
if(any(rownames(freqCl) %in% cl[1,])){
  eliminate   <- rownames(freqCl)[rownames(freqCl) %in% cl[1,]]
  noIslandID  <- append(noIslandID, eliminate)
}
#Last row
if(any(rownames(freqCl) %in% cl[nrow(cl),])){
  eliminate  <- rownames(freqCl)[rownames(freqCl) %in% cl[nrow(cl),]]
  noIslandID <- append(noIslandID, eliminate)
}
#First col
if(any(rownames(freqCl) %in% cl[,1])){
  eliminate   <- rownames(freqCl)[rownames(freqCl) %in% cl[,1]]
  noIslandID  <- append(noIslandID, eliminate)
}
#Last col
if(any(rownames(freqCl) %in% cl[,ncol(cl)])){
  eliminate   <- rownames(freqCl)[rownames(freqCl) %in% cl[,ncol(cl)]]
  noIslandID  <- append(noIslandID, eliminate)
}

Eliminate those island which touch a border:

noIslandID <- unique(noIslandID)
IslandID   <- setdiff(rownames(freqCl), noIslandID)

In a last step, assign a 1 to every "real island" from the inital raster:

for(i in 1:length(IslandID)) {
  x[cl[]==as.numeric(IslandID[i])] <- 1
}

plot(x)

Answer 3

In this case, a cell is in a hole if it is bounded by at least one non-missing value in each of the four cardinal directions. You can test this using the code below, which also replaces the missing values with 99.

x2 <- x                        ## Create a copy of the raster
notNA <- !is.na(as.matrix(x))  ## Matrix identifying cells that are not NA

nr <- nrow(x) ## Number of rows
nc <- ncol(x) ## Number of columns

for(i in 2:(nr-1)) {       ## Loop over rows, ignoring first and last
    for(j in 2:(nc-1)) {   ## Loop over columns, ignoring first and last
        if(notNA[i,j]) ## Skip cell if not NA
            next
        ## For NA cells, determine if non-NA cell exists in each cardinal direction
        any.north <- any(notNA[1:(i-1),j])
        any.south <- any(notNA[(i+1):nr,j])
        any.west <- any(notNA[i,1:(j-1)])
        any.east <- any(notNA[i,(j+1):nc])
        if(any.north & any.south & any.west & any.east)
            x2[i,j] <- 99 ## If cell is in hole, repalce with new value
    }
}

Here's the new raster:

New raster with holes filled by the value 99