How to calculate the land area in R

Question

Basically, I calculated the probability distribution of the global model of the ASCII, for example: gdpm. gdpmvalues ​​are between 0 and 1.

Then I imported the local map from the form file:

shape <- file.choose()  
map <- readOGR(shape, basename(file_path_sans_ext(shape)))

The next step, I rasterized gdpmand cropped using a 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 have a cropped and reclassified bitmap, now I need to summarize the earth cover, that is, for each level, I want to calculate its share of the area in each region of the local map. (I do not know how to describe this 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)

, , tab . , ? ?

, ( , ):

(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")

:

r <- as(r, "SpatialPixelsDataFrame")
r <- as.data.frame(r)
colnames(r) <- c("value", "x", "y")

, , ggplot2, ?

Answer 1

, .
:

r <- raster(system.file("external/test.grd", package="raster"))
plot(r)

, , :

r <- r / 1000
r[r>1,] <- 1

:

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)

, ?
, . :

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

, 40 x 40 , CRS.

.

app <- res(r)[1] * res(r)[2] # area per pixel

table(r2[]) * app
#      1       2       3       4       5 
# 124800 2800000 1310400  486400  243200 

, , SO

Answer 2

loki , , . , angular (/) ()

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)

1.

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

2. angular ( )

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

, - :

# example polygons
a <- rasterToPolygons(aggregate(r, 25))

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    

2

x <- rasterize(a, r2)
z <- crosstab(x, r2)
z <- cbind(z, area = z[,3] * prod(res(r2)))

:

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

, lon/lat, prod (res (r)), . , , .

. Raster *. :

 image(r2)
 plot(r2)
 spplot(r2)

 library(rasterVis); 
 levelplot(r2)

:

 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)