CODEWITHSUNDEEP
rrasterr-sfr-stars
Leprechault
Leprechault

Reputation: 1823

sf and stars: polygonize categorical raster

I'd like to draw the raster countour (l) just only for the "target" categorical in x raster without considering NA values. I try to do:

# Packages
library(stars)
library(sf)

#Vectorizing a raster object to an sf object
tif = system.file("tif/L7_ETMs.tif", package = "stars")
x = read_stars(tif)[, 1:50, 1:50, 1:2]
x[[1]] = round(x[[1]]/5)
x[[1]] = ifelse(x[[1]]<10,NA,"target")
str(x[[1]])

#Polygonizing
l =  st_contour(x)
plot(l[1])
Error in CPL_write_gdal(mat, file, driver, options, type, dims, from,  : 
  Not compatible with requested type: [type=character; target=double].

But, doesn't work. Please, any help with it?

Thanks in advance,

Alexandre

Upvotes: 1

Views: 323

Answers (1)

Jonathan V. Sol&#243;rzano
Jonathan V. Sol&#243;rzano

Reputation: 5620

There are several errors with your script, first, st_contour is indicating that it is not compatible with the character type (referring to the "target" string you set in the raster). Secondly, I would suggest using the breaks argument inside st_contour to set the target value for which you wish to obtain the contours. Additionally, you might want to use x[rule] <- NA to mask certain values in the raster. I made other modifications to your code that might help:

# Let's stay with only the first band, indicated in the final dimension
x = read_stars(tif)[, 1:50, 1:50, 1]
x = round(x/5)
# Calculate the min and max of the raster values
purrr::map(x, min)
# 10
purrr::map(x, max)
# 28
# Mask values lower than 10
# However, this does not make any change, because the lowest value is 10
x[x<10] <- NA
# Take a look at the image
plot(x)

# Obtain the contours
l =  st_contour(x, 
                # Remove NA
                na.rm = T,
                # Obtain contour lines instead of polygons
                contour_lines = TRUE,
                # raster values at which to draw contour levels
                breaks = 12)
# Plot the contours
plot(l)

Upvotes: 4

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