How to compute land cover area in R - r

Question

Basically, I computed a global distribution probability model in the form of ASCII, say:
gdpm. gdpm's values are all between 0 and 1.
Then I imported a local map from shape file:
shape <- file.choose()
map <- readOGR(shape, basename(file_path_sans_ext(shape)))
The next step, I rasterized gdpm, and cropped using the local map:
ldpm <- mask(gdpm, map)
Then, I reclassified this continuous model into a discrete model (I divided the model into 6 levels):
recalc <- matrix(c(0, 0.05, 0, 0.05, 0.2, 1, 0.2, 0.4, 2, 0.4, 0.6, 3, 0.6, 0.8, 4, 0.8, 1, 5), ncol = 3, byrow = TRUE)
ldpmR <- reclassify(ldpm, recalc)
I've got a cropped and reclassified raster, now I need to summarize land cover, that is, to each level, I want to calculate its proportion of area in each region of the local map. (I don't know how to describe it in terminology). I found and followed an example (RobertH):
ext <- raster::extract(ldpmR, map)
tab <- sapply(ext, function(x) tabulate(x, 10))
tab <- tab / colSums(tab)
But I'm not sure if it works, since the output of tab is confusing.
So how to compute land cover area correctly? How can I apply the correct method within each polygon?
My original data is too large, I can only provide an alternative raster (I think this example should apply a different reclassify matrix):
Example raster
Or you can generate a test raster (RobertH):
library(raster)
s <- stack(system.file("external/rlogo.grd", package="raster"))
writeRaster(s, file='testtif', format='GTiff', bylayer=T, overwrite=T)
f <- list.files(pattern="testtif_..tif")
I also have a question about plotting a raster:
r <- as(r, "SpatialPixelsDataFrame")
r <- as.data.frame(r)
colnames(r) <- c("value", "x", "y")
I do this conversion to make a raster plot-able with ggplot2, is there a more concise method?

Answer 1

loki's answer is OK, but this can be done the raster way, which is safer. And it is important to consider whether the coordinates are angular (longitude/latitude) or planar (projected)
Example data
library(raster)
r <- raster(system.file("external/test.grd", package="raster"))
r <- r / 1000
recalc <- matrix(c(0, 0.05, 0, 0.05, 0.2, 1, 0.2, 0.4, 2, 0.4, 0.6, 3, 0.6, 0.8, 4, 0.8, 2, 5), ncol = 3, byrow = TRUE)
r2 <- reclassify(r, recalc)
Approach 1. Only for planar data
f <- freq(r2, useNA='no')
apc <- prod(res(r))
f <- cbind(f, area=f[,2] * apc)
f
# value count area
#[1,] 1 78 124800
#[2,] 2 1750 2800000
#[3,] 3 819 1310400
#[4,] 4 304 486400
#[5,] 5 152 243200
Approach 2. For angular data (but also works for planar data)
a <- area(r2)
z <- zonal(a, r2, 'sum')
z
# zone sum
#[1,] 1 124800
#[2,] 2 2800000
#[3,] 3 1310400
#[4,] 4 486400
#[5,] 5 243200
If you want to summarize by polygons, you can do something like this:
# example polygons
a <- rasterToPolygons(aggregate(r, 25))
Approach 1
# extract values (slow)
ext <- extract(r2, a)
# tabulate values for each polygon
tab <- sapply(ext, function(x) tabulate(x, 5))
# adjust for area (planar data only)
tab <- tab * prod(res(r2))
# check the results, by summing over the regions
rowSums(tab)
#[1] 124800 2800000 1310400 486400 243200
Approach 2
x <- rasterize(a, r2)
z <- crosstab(x, r2)
z <- cbind(z, area = z[,3] * prod(res(r2)))
Check results:
aggregate(z[, 'area', drop=F], z[,'Var2', drop=F], sum)
Var2 area
#1 1 124800
#2 2 2800000
#3 3 1310400
#4 4 486400
#5 5 243200
Note that if you are dealing with lon/lat data you cannot use prod(res(r)) to get the cell size. In that case you will need to use the area function and loop over classes, I think.
You also asked about plotting. There are many ways to plot a Raster* object. The basic ones are:
image(r2)
plot(r2)
spplot(r2)
library(rasterVis);
levelplot(r2)
More tricky approaches:
library(ggplot2) # using a rasterVis method
theme_set(theme_bw())
gplot(r2) + geom_tile(aes(fill = value)) +
facet_wrap(~ variable) +
scale_fill_gradient(low = 'white', high = 'blue') +
coord_equal()
library(leaflet)
leaflet() %>% addTiles() %>%
addRasterImage(r2, colors = "Spectral", opacity = 0.8)

Answer 2

Seems like you can get the area by the number of pixels.
Let's start with a reproducible example:
r <- raster(system.file("external/test.grd", package="raster"))
plot(r)
Since, the values in this raster are in another range than your data, let's adapt them to your values:
r <- r / 1000
r[r>1,] <- 1
Afterwards, we apply your reclassification:
recalc <- matrix(c(0, 0.05, 0, 0.05, 0.2, 1, 0.2, 0.4, 2, 0.4, 0.6, 3, 0.6, 0.8, 4, 0.8, 1, 5), ncol = 3, byrow = TRUE)
r2 <- reclassify(r, recalc)
plot(r2)
Now, how do we get the area?
Since you are working with a projected raster, you can simply use the number of pixels and the raster resolution. Therefore, we first need to check the resolution and the map units of the projection:
res(r)
# [1] 40 40
crs(r)
# CRS arguments:
# +init=epsg:28992
# +towgs84=565.237,50.0087,465.658,-0.406857,0.350733,-1.87035,4.0812 +proj=sterea
# +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000
# +y_0=463000 +ellps=bessel +units=m +no_defs
Now, we know that we are dealing with pixels of 40 x40 meters, since we have a metric CRS.
Let's use this information to calculate the area of each class.
app <- res(r)[1] * res(r)[2] # area per pixel
table(r2[]) * app
# 1 2 3 4 5
# 124800 2800000 1310400 486400 243200
For the plotting of georeferenced rasters, I would like to refer you to an older question here on SO