madChelsa | R Documentation |
Rasters of bioclimatic variables for an eastern portion of Madagascar from CHELSA version 2.1 in unprojected (WGS84) coordinates. Values represent averages across 1980-2010. Only these BIOCLIM variables are included:
* bio1
: Mean annual temperature (deg C)
* bio7
: Temperature annual range (hottest - coldest month temperature; deg C)
* bio12
: Total annual precipitation (mm)
* bio15
: Precipitation seasonality (unit-less)
An object of class SpatRaster
in unprojected (WGS84) coordinates.
Karger, D.N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., and Kessler, M. 2017. Climatologies at high resolution for the earth's land surface areas. Scientific Data 4:170122. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/sdata.2017.122")}
### vector data
###############
library(sf)
# For vector data, we can use data(*) or fastData(*):
data(madCoast0) # same as next line
madCoast0 <- fastData("madCoast0") # same as previous
madCoast0
plot(st_geometry(madCoast0))
madCoast4 <- fastData("madCoast4")
madCoast4
plot(st_geometry(madCoast4), add = TRUE)
madRivers <- fastData("madRivers")
madRivers
plot(st_geometry(madRivers), col = "blue", add = TRUE)
madDypsis <- fastData("madDypsis")
madDypsis
plot(st_geometry(madDypsis), col = "red", add = TRUE)
### raster data
###############
library(terra)
# For raster data, we can get the file directly or using fastData(*):
rastFile <- system.file("extdata/madElev.tif", package="fasterRaster")
madElev <- terra::rast(rastFile)
madElev <- fastData("madElev") # same as previous two lines
madElev
plot(madElev)
madForest2000 <- fastData("madForest2000")
madForest2000
plot(madForest2000)
madForest2014 <- fastData("madForest2014")
madForest2014
plot(madForest2014)
# multi-layer rasters
madChelsa <- fastData("madChelsa")
madChelsa
plot(madChelsa)
madPpt <- fastData("madPpt")
madTmin <- fastData("madTmin")
madTmax <- fastData("madTmax")
madPpt
madTmin
madTmax
# RGB raster
madLANDSAT <- fastData("madLANDSAT")
madLANDSAT
plotRGB(madLANDSAT, 4, 1, 2, stretch = "lin")
# categorical raster
madCover <- fastData("madCover")
madCover
madCover <- droplevels(madCover)
levels(madCover) # levels in the raster
nlevels(madCover) # number of categories
catNames(madCover) # names of categories table
plot(madCover)
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