# 5. vector-raster conversions, reprojection, warping

#### Edzer Pebesma

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

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))
    ## stars object with 2 dimensions and 1 attribute
    ## attribute(s):
    ##                    Min.  1st Qu. Median     Mean  3rd Qu.     Max. NA's
    ## dens [1/km^2] 0.2545128 1.225654 1.9322 3.345956 3.825793 21.24795 4808
    ## dimension(s):
    ##   from  to offset delta                 refsys point x/y
    ## x    1 162 123830  5000 NAD83 / North Carolina FALSE [x]
    ## y    1  61 318256 -5000 NAD83 / North Carolina FALSE [y]
    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)
    ## Simple feature collection with 2500 features and 2 fields
    ## Geometry type: POINT
    ## Dimension:     XY
    ## Bounding box:  xmin: 288790.5 ymin: 9119350 xmax: 290187 ymax: 9120747
    ## Projected CRS: SIRGAS 2000 / UTM zone 25S
    ## First 10 features:
    ##    L7_ETMs.tif.V1 L7_ETMs.tif.V2                 geometry
    ## 1              14             11 POINT (288790.5 9120747)
    ## 2              14             11   POINT (288819 9120747)
    ## 3              13             10 POINT (288847.5 9120747)
    ## 4              12              9   POINT (288876 9120747)
    ## 5              12             10 POINT (288904.5 9120747)
    ## 6              12             10   POINT (288933 9120747)
    ## 7              12             10 POINT (288961.5 9120747)
    ## 8              12             10   POINT (288990 9120747)
    ## 9              13             10 POINT (289018.5 9120747)
    ## 10             13             10   POINT (289047 9120747)

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

    st_as_sf(x, as_points = TRUE, merge = FALSE, long = TRUE)
    ## Simple feature collection with 5000 features and 2 fields
    ## Geometry type: POINT
    ## Dimension:     XY
    ## Bounding box:  xmin: 288790.5 ymin: 9119350 xmax: 290187 ymax: 9120747
    ## Projected CRS: SIRGAS 2000 / UTM zone 25S
    ## First 10 features:
    ##    band L7_ETMs.tif                 geometry
    ## 1     1          14 POINT (288790.5 9120747)
    ## 2     1          14   POINT (288819 9120747)
    ## 3     1          13 POINT (288847.5 9120747)
    ## 4     1          12   POINT (288876 9120747)
    ## 5     1          12 POINT (288904.5 9120747)
    ## 6     1          12   POINT (288933 9120747)
    ## 7     1          12 POINT (288961.5 9120747)
    ## 8     1          12   POINT (288990 9120747)
    ## 9     1          13 POINT (289018.5 9120747)
    ## 10    1          13   POINT (289047 9120747)

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)
    ## Simple feature collection with 2500 features and 2 fields
    ## Geometry type: POLYGON
    ## Dimension:     XY
    ## Bounding box:  xmin: 288776.3 ymin: 9119336 xmax: 290201.3 ymax: 9120761
    ## Projected CRS: SIRGAS 2000 / UTM zone 25S
    ## First 10 features:
    ##    L7_ETMs.tif.V1 L7_ETMs.tif.V2                       geometry
    ## 1              14             11 POLYGON ((288776.3 9120761,...
    ## 2              14             11 POLYGON ((288804.8 9120761,...
    ## 3              13             10 POLYGON ((288833.3 9120761,...
    ## 4              12              9 POLYGON ((288861.8 9120761,...
    ## 5              12             10 POLYGON ((288890.3 9120761,...
    ## 6              12             10 POLYGON ((288918.8 9120761,...
    ## 7              12             10 POLYGON ((288947.3 9120761,...
    ## 8              12             10 POLYGON ((288975.8 9120761,...
    ## 9              13             10 POLYGON ((289004.3 9120761,...
    ## 10             13             10 POLYGON ((289032.8 9120761,...

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
    ## stars object with 2 dimensions and 1 attribute
    ## attribute(s):
    ##              Min. 1st Qu. Median    Mean 3rd Qu. Max.
    ## L7_ETMs.tif     7       9     11 11.2548      12   28
    ## dimension(s):
    ##          from   to                     refsys point
    ## geometry    1 2500 SIRGAS 2000 / UTM zone 25S  TRUE
    ## band        1    2                         NA    NA
    ##                                                       values
    ## geometry POINT (288790.5 9120747),...,POINT (290187 9119350)
    ## band                                                    NULL

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
    ## stars object with 2 dimensions and 1 attribute
    ## attribute(s):
    ##                    Min.  1st Qu. Median     Mean  3rd Qu.     Max. NA's
    ## dens [1/km^2] 0.2545128 1.225654 1.9322 3.345956 3.825793 21.24795 4808
    ## dimension(s):
    ##   from  to                       refsys point                         values
    ## x    1 162 +proj=laea +lat_0=34 +lon... FALSE [162x61] -2210936,...,-1371611
    ## y    1  61 +proj=laea +lat_0=34 +lon... FALSE      [162x61] 90646,...,538200
    ##   x/y
    ## x [x]
    ## y [y]
    ## curvilinear grid
    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
    ## stars object with 2 dimensions and 1 attribute
    ## attribute(s):
    ##                    Min.  1st Qu. Median     Mean  3rd Qu.     Max.  NA's
    ## dens [1/km^2] 0.2545128 1.225654 1.9322 3.344844 3.825793 21.24795 36155
    ## dimension(s):
    ##   from  to   offset delta                       refsys x/y
    ## x    1 380 -2188108  2098 +proj=laea +lat_0=34 +lon... [x]
    ## y    1 171   494920 -2098 +proj=laea +lat_0=34 +lon... [y]
    plot(nc.new)

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