5. vector-raster conversions, reprojection, warping

In stars: Spatiotemporal Arrays, Raster and Vector Data Cubes

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png")
suppressPackageStartupMessages(library(sf))
knitr::opts_chunk$set(fig.height = 4.5)
knitr::opts_chunk$set(fig.width = 6)
ev = TRUE

This vignette shows how stars object can be moved from vector and raster representations.

Rasterizing an sf vector object

library(stars)
system.file("gpkg/nc.gpkg", package = "sf") %>%
  read_sf() %>%
  st_transform(32119) -> nc
nc$dens = nc$BIR79 / units::set_units(st_area(nc), km^2)
(nc.st = st_rasterize(nc["dens"], dx = 5000, dy = 5000))
plot(nc.st)

The algorithm used is the GDAL rasterize utility, all options of this utility can be passed to st_rasterize. The geometry of the final raster can be controlled by passing a target bounding box and either the raster dimensions nx and ny, or pixel size by the dx and dy parameters.

Vectorizing a raster object to an sf object

stars objects can be converted into an sf object using st_as_sf. It has a number of options, depending on whether pixels represent the point value at the pixel center, or small square polygons with a single value.

We will work again with the landsat-7 6-band image, but will select the first band and round the values:

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)

Polygonizing

In case raster cells reflect point values and we want to get a vector representation of the whole field, we can draw contour lines and export the contour sets (only available when the GDAL version is at least 2.4.0):

l =  st_contour(x, contour_lines = TRUE, breaks = 11:15)
plot(l[1], key.pos = 1, pal = sf.colors, lwd = 2, key.length = 0.8)

Exporting to points

Alternatively, we can simply export all the pixels as points, and get them either as a wide table with all bands per point, and no replicated POINT geometries:

st_as_sf(x, as_points = TRUE, merge = FALSE)

or as a long table with a single attribute and all points replicated:

st_as_sf(x, as_points = TRUE, merge = FALSE, long = TRUE)

as we can see, an additional attribute band now indicates which band is concerned.

Exporting to polygons

Alternatively, we can export to polygons and either get a single polygon per pixel, as in

st_as_sf(x[1], as_points = FALSE, merge = FALSE)

or merge polygons that have identical pixel values;

p = st_as_sf(x, as_points = FALSE, merge = TRUE)

When plotted with boundaries, we see the resolved boundaries of areas with the same pixel value:

plot(p)

A further option connect8 can be set to TRUE to use 8 connectedness, rather than the default 4 connectedness algorithm. In both cases, the polygons returned will often be invalid according to the simple feature standard, but can be made valid using lwgeom::st_make_valid.

Switching between vector and raster in stars objects

We can convert a raster dimension to a vector dimension while keeping other dimensions as they are in a stars object by

x.sf = st_xy2sfc(x, as_points = TRUE)
x.sf

which also requires setting the as_points arguments as in st_as_sf.

Reprojecting a raster

If we accept that curvilinear rasters are rasters too, and that regular and rectilinear grids are special cases of curvilinear grids, reprojecting a raster is no longer a "problem", it just recomputes new coordinates for every raster cell, and generally results in a curvilinear grid (that sometimes can be brought back to a regular or rectilinear grid). If curvilinear grid cells are represented by coordinates of the cell center, the actual shape of a grid cell gets lost, and this may be a larger effect if grid cells are large or if the transformation is stronger non-linear.

An example of the reprojection of the grid created above is

nc.st %>% st_transform("+proj=laea +lat_0=34 +lon_0=-60") -> nc.curv
nc.curv
plot(nc.curv, border = NA, graticule = TRUE)

where it should be noted that the dimensionality of the grid didn't change: the same set of raster cells has been replotted in the new CRS, but now in a curvilinear grid.

Warping a raster

Warping a raster means creating a new regular grid in a new CRS, based on a (usually regular) grid in another CRS. We can do the transformation of the previous section by first creating a target grid:

nc %>% st_transform("+proj=laea +lat_0=34 +lon_0=-60") %>% st_bbox() %>%
    st_as_stars() -> newgrid

and then warping the old raster to the new

nc.st %>% st_warp(newgrid) -> nc.new
nc.new 
plot(nc.new)

This new object has a regular grid in the new CRS, aligned with the new x- and y-axes.

stars documentation built on May 29, 2024, 8:59 a.m.