The globcoverVN package contains land cover data for Vietnam from the
GlobCover project. It is
made of a rasterLayer object that can be loaded with the getgcvn
function and of the SpatialPolygonsDataFrame provinces object that
contains, for each province, the percentage of land cover of each type.
Below we describe in detail these data and their use.
You can install globcoverVN from
GitHub with:
# install.packages("devtools")
devtools::install_github("epix-project/globcoverVN", build_vignettes = TRUE)Once installed, you can load the package:
library(globcoverVN)The raster file can be loaded by the getgcvn function:
landcover <- getgcvn()
#> Loading required package: raster
#> Loading required package: spThe code of the different categories of land cover can be seen with the
legend method:
show_legend(landcover)
#> 11 : Post-flooding or irrigated croplands (or aquatic)
#> 14 : Rainfed croplands
#> 20 : Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)
#> 30 : Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)
#> 40 : Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)
#> 50 : Closed (>40%) broadleaved deciduous forest (>5m)
#> 60 : Open (15-40%) broadleaved deciduous forest/woodland (>5m)
#> 70 : Closed (>40%) needleleaved evergreen forest (>5m)
#> 100 : Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m)
#> 110 : Mosaic forest or shrubland (50-70%) / grassland (20-50%)
#> 120 : Mosaic grassland (50-70%) / forest or shrubland (20-50%)
#> 130 : Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5m)
#> 140 : Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)
#> 150 : Sparse (<15%) vegetation
#> 160 : Closed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish water
#> 170 : Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water
#> 190 : Artificial surfaces and associated areas (Urban areas >50%)
#> 200 : Bare areas
#> 210 : Water bodies
#> 220 : Permanent snow and iceThe output can also be assigned to an data frame:
leg <- show_legend(landcover)
#> 11 : Post-flooding or irrigated croplands (or aquatic)
#> 14 : Rainfed croplands
#> 20 : Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)
#> 30 : Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)
#> 40 : Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)
#> 50 : Closed (>40%) broadleaved deciduous forest (>5m)
#> 60 : Open (15-40%) broadleaved deciduous forest/woodland (>5m)
#> 70 : Closed (>40%) needleleaved evergreen forest (>5m)
#> 100 : Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m)
#> 110 : Mosaic forest or shrubland (50-70%) / grassland (20-50%)
#> 120 : Mosaic grassland (50-70%) / forest or shrubland (20-50%)
#> 130 : Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5m)
#> 140 : Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)
#> 150 : Sparse (<15%) vegetation
#> 160 : Closed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish water
#> 170 : Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water
#> 190 : Artificial surfaces and associated areas (Urban areas >50%)
#> 200 : Bare areas
#> 210 : Water bodies
#> 220 : Permanent snow and iceAnd then:
str(leg)
#> 'data.frame': 20 obs. of 3 variables:
#> $ landtype: chr "Post-flooding or irrigated croplands (or aquatic)" "Rainfed croplands" "Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)" "Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)" ...
#> $ code : num 11 14 20 30 40 50 60 70 100 110 ...
#> $ color : chr "#AAF0F0" "#FFFF64" "#DCF064" "#CDCD66" ...This raster file can be plotted as follow:
colors <- leg$color
plot(landcover, col = colors, legend = FALSE, axes = FALSE)
l <- length(colors)
x <- ceiling(l / 2)
sel1 <- 1:x
sel2 <- (x + 1):l
leg1 <- leg$code[sel1]
leg2 <- leg$code[sel2]
col1 <- colors[sel1]
col2 <- colors[sel2]
legend("left", legend = leg1, fill = col1, bty = "n")
legend("right", legend = leg2, fill = col2, bty = "n")
show_legend(landcover)
#> 11 : Post-flooding or irrigated croplands (or aquatic)
#> 14 : Rainfed croplands
#> 20 : Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)
#> 30 : Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)
#> 40 : Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)
#> 50 : Closed (>40%) broadleaved deciduous forest (>5m)
#> 60 : Open (15-40%) broadleaved deciduous forest/woodland (>5m)
#> 70 : Closed (>40%) needleleaved evergreen forest (>5m)
#> 100 : Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m)
#> 110 : Mosaic forest or shrubland (50-70%) / grassland (20-50%)
#> 120 : Mosaic grassland (50-70%) / forest or shrubland (20-50%)
#> 130 : Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5m)
#> 140 : Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)
#> 150 : Sparse (<15%) vegetation
#> 160 : Closed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish water
#> 170 : Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water
#> 190 : Artificial surfaces and associated areas (Urban areas >50%)
#> 200 : Bare areas
#> 210 : Water bodies
#> 220 : Permanent snow and iceIn addition to this RasterLayer resource, the package also provides
SpatialPolygonsDataFrame resources that contains the polygons of the
provinces of Vietnam at different points in time together with the
proportions of the different categories of land cover for each province,
in the form of attributes. These SpatialPolygonsDataFrame resources
can be retrieved thanks to the getlandcover function:
prov2008 <- getlandcover(2008)
str(prov2008@data)
#> 'data.frame': 63 obs. of 41 variables:
#> $ province: chr "An Giang" "Ba Ria - Vung Tau" "Bac Giang" "Bac Kan" ...
#> $ 11 : num 0.911396 0.03667 0.007003 0.000273 0.321547 ...
#> $ 14 : num 0.0213 0.37934 0.23353 0.00627 0.19674 ...
#> $ 20 : num 0.01063 0.07835 0.12403 0.00878 0.13278 ...
#> $ 30 : num 0.00514 0.01746 0.19113 0.0605 0.0666 ...
#> $ 40 : num 0.0021 0.15572 0.20113 0.46956 0.00809 ...
#> $ 50 : num 7.96e-05 1.02e-03 2.73e-04 1.82e-05 4.96e-04 ...
#> $ 60 : num 1.54e-03 6.52e-03 1.55e-03 4.56e-03 7.63e-05 ...
#> $ 70 : num 0.000424 0.006371 0.014393 0.040316 0.022924 ...
#> $ 100 : num 0.000769 0.047807 0.034197 0.040535 0.065225 ...
#> $ 110 : num 1.06e-04 0.00 4.55e-05 3.64e-05 1.87e-03 ...
#> $ 120 : num 5.30e-05 0.00 4.55e-05 3.64e-05 2.52e-03 ...
#> $ 130 : num 0.0137 0.1982 0.1794 0.3686 0.0437 ...
#> $ 140 : num 0.00289 0.03414 0.00487 0 0.1364 ...
#> $ 150 : num 0 0.000535 0 0 0 ...
#> $ 160 : num 0 0 0 0 0 ...
#> $ 170 : num 0 0.021545 0 0 0.000267 ...
#> $ 190 : num 0 0.00199 0 0 0 ...
#> $ 200 : num 7.96e-05 2.58e-03 9.09e-05 0.00 1.91e-04 ...
#> $ 210 : num 0.029756 0.011721 0.008276 0.000546 0.000572 ...
#> $ 220 : num 0 0 0 0 0 ...
#> $ 11_pop : num 0.8196 0.1236 0.0213 0.0104 0.3604 ...
#> $ 14_pop : num 0.0408 0.3648 0.447 0.0333 0.2064 ...
#> $ 20_pop : num 0.0454 0.1517 0.2086 0.0491 0.1534 ...
#> $ 30_pop : num 0.0127 0.065 0.1987 0.1036 0.0537 ...
#> $ 40_pop : num 0.00326 0.05868 0.03871 0.38485 0.00533 ...
#> $ 50_pop : num 1.67e-04 9.14e-04 3.03e-04 1.72e-05 3.71e-04 ...
#> $ 60_pop : num 0.001807 0.007032 0.003118 0.002707 0.000123 ...
#> $ 70_pop : num 0.000239 0.002357 0.001826 0.032272 0.014596 ...
#> $ 100_pop : num 0.000376 0.02007 0.008279 0.051629 0.040319 ...
#> $ 110_pop : num 7.09e-05 0.00 3.06e-05 1.33e-05 1.39e-03 ...
#> $ 120_pop : num 1.83e-05 0.00 7.30e-06 3.07e-05 1.58e-03 ...
#> $ 130_pop : num 0.0192 0.0814 0.0588 0.3318 0.0343 ...
#> $ 140_pop : num 0.0123 0.04755 0.00652 0 0.12702 ...
#> $ 150_pop : num 0 0.0013 0 0 0 ...
#> $ 160_pop : num 0 0 0 0 0 ...
#> $ 170_pop : num 0 0.006013 0 0 0.000152 ...
#> $ 190_pop : num 0 0.0267 0 0 0 ...
#> $ 200_pop : num 0.003619 0.035835 0.00019 0 0.000237 ...
#> $ 210_pop : num 0.040463 0.007022 0.006633 0.000406 0.00072 ...
#> $ 220_pop : num 0 0 0 0 0 0 0 0 0 0 ...The variables whose names end with _pop correspond to percentages of
land cover when correcting for the local human population density as
available from the worldpopVN package. The user interested in seeing
how this is computed can have a look at the data_creation script in
the data-raw folder of the package sources. This script makes use of
the lcsummary and lcpopsummary non-exported functions that are in
the lcsummary.R and lcpopsummary.R files in the R folder of the
package sources. One can verify that the percentages of land cover sum
to 1:
rowSums(prov2008@data[, 2:21])
#> 1 6 2 3 4 5 7 8 9 10 11 12 13 14 17 15 16 18 19 20 21 22 25 26 27
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 23 24 28 30 29 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 51 52 53 54 55 56 57 58 59 60 61 62 63
#> 1 1 1 1 1 1 1 1 1 1 1 1 1
rowSums(prov2008@data[, 22:41])
#> 1 6 2 3 4 5 7 8 9 10 11 12 13 14 17 15 16 18 19 20 21 22 25 26 27
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 23 24 28 30 29 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 51 52 53 54 55 56 57 58 59 60 61 62 63
#> 1 1 1 1 1 1 1 1 1 1 1 1 1You can also check that, for example, in Vietnam, there is no “Permanent snow and ice”:
sum(prov2008@data$`220`)
#> [1] 0.0006297298
sum(prov2008@data$`220_pop`)
#> [1] 0.0001935054This data can be visualize this way as for the example show below for
the “Post-flooding or irrigated croplands (or aquatic)”. Let’s first
make a palette of colors form RColorBrewer:
n <- 9
pal <- RColorBrewer::brewer.pal(n, "Blues")Let’s find a classes intervals definition:
library(classInt)
tmp <- classIntervals(prov2008@data$`11`, n = n, style = "quantile")
plot(tmp, pal = pal, main = NA)
Once we’re satisfied with the class interval definition we can plot the map:
plot(prov2008, col = findColours(tmp, pal))
Or, with another definition of the classes intervals:
tmp <- classIntervals(prov2008@data$`11`, n = n, style = "jenks")
plot(tmp, pal = pal, main = NA)
plot(prov2008, col = findColours(tmp, pal))
The same thing, but with values weighted by the local human population density:
tmp <- classIntervals(prov2008@data$`11_pop`, n = n, style = "jenks")
plot(tmp, pal = pal, main = NA)
plot(prov2008, col = findColours(tmp, pal))
As another example, let’s see the urban areas:
tmp <- classIntervals(prov2008@data$`190_pop`, n = n, style = "jenks")
#> Warning in classIntervals(prov2008@data$`190_pop`, n = n, style = "jenks"):
#> n greater than number of different finite values\nn reset to number of
#> different finite values
#> Warning in classIntervals(prov2008@data$`190_pop`, n = n, style = "jenks"):
#> n same as number of different finite values\neach different finite value is
#> a separate class
plot(tmp, pal = pal, main = NA)
plot(prov2008, col = findColours(tmp, pal))
Which, again, makes quite some sense (apart maybe for Cao Bang and Hai Phong?).