Extract cell values at point locations
object of class stars or stars_proxy
passed on to aggregate.stars when geometries are not exclusively POINT geometries
object of class sf or sfc with geometries, or two-column matrix with points in rows, indicating where to extract x
logical; use bilinear interpolation rather than nearest neighbour?
character or integer; name or index of a column with time or date values that will be matched to values of the dimension "time" in x, after which this dimension is reduced. This is useful to extract data cube values along a trajectory; see https://github.com/r-spatial/stars/issues/352 .
logical; should time be interpolated? if FALSE, time instances are matched using the coinciding or the last preceding time in the data cube.
function used to aggregate pixel values when geometries of at intersect with more than one pixel
if at is of class matrix, a matrix with extracted values is returned;
otherwise: if x has more dimensions than only x and y (raster), an
object of class stars with POINT geometries replacing x and y raster
dimensions, if this is not the case, an object of sf with extracted values.
points outside the raster are returned as NA values. For
large sets of points for which extraction is needed, passing a matrix as
to at may be much faster than passing an sf or sfc object.
tif = system.file("tif/L7_ETMs.tif", package = "stars")
r = read_stars(tif)
pnt = st_sample(st_as_sfc(st_bbox(r)), 10)
st_extract(r, pnt)
#> stars object with 2 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> L7_ETMs.tif 12 60.75 74 72.51667 87 150
#> dimension(s):
#> from to refsys point
#> geometry 1 10 SIRGAS 2000 / UTM zone 25S TRUE
#> band 1 6 NA NA
#> values
#> geometry POINT (298340.2 9114943),...,POINT (289531.4 9111471)
#> band NULL
st_extract(r, pnt) %>% st_as_sf()
#> Simple feature collection with 10 features and 6 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 288950.3 ymin: 9111189 xmax: 298340.2 ymax: 9119338
#> Projected CRS: SIRGAS 2000 / UTM zone 25S
#> L7_ETMs.tif.V1 L7_ETMs.tif.V2 L7_ETMs.tif.V3 L7_ETMs.tif.V4 L7_ETMs.tif.V5
#> 1 97 88 67 14 13
#> 2 82 66 74 49 107
#> 3 66 54 46 73 79
#> 4 80 68 69 77 117
#> 5 87 85 104 87 120
#> 6 90 83 65 13 13
#> 7 63 46 38 65 83
#> 8 110 101 114 74 150
#> 9 80 68 74 54 110
#> 10 80 65 65 44 84
#> L7_ETMs.tif.V6 geometry
#> 1 12 POINT (298340.2 9114943)
#> 2 82 POINT (293918.4 9114415)
#> 3 42 POINT (293485.2 9118749)
#> 4 86 POINT (294440.9 9114839)
#> 5 79 POINT (295209 9118813)
#> 6 12 POINT (295048.9 9111189)
#> 7 50 POINT (289649.4 9116888)
#> 8 128 POINT (295730.3 9119338)
#> 9 88 POINT (288950.3 9111816)
#> 10 71 POINT (289531.4 9111471)
st_extract(r[,,,1], pnt)
#> Simple feature collection with 10 features and 1 field
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 288950.3 ymin: 9111189 xmax: 298340.2 ymax: 9119338
#> Projected CRS: SIRGAS 2000 / UTM zone 25S
#> L7_ETMs.tif geometry
#> 1 97 POINT (298340.2 9114943)
#> 2 82 POINT (293918.4 9114415)
#> 3 66 POINT (293485.2 9118749)
#> 4 80 POINT (294440.9 9114839)
#> 5 87 POINT (295209 9118813)
#> 6 90 POINT (295048.9 9111189)
#> 7 63 POINT (289649.4 9116888)
#> 8 110 POINT (295730.3 9119338)
#> 9 80 POINT (288950.3 9111816)
#> 10 80 POINT (289531.4 9111471)
st_extract(r, st_coordinates(pnt)) # "at" is a matrix: return a matrix
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 97 88 67 14 13 12
#> [2,] 82 66 74 49 107 82
#> [3,] 66 54 46 73 79 42
#> [4,] 80 68 69 77 117 86
#> [5,] 87 85 104 87 120 79
#> [6,] 90 83 65 13 13 12
#> [7,] 63 46 38 65 83 50
#> [8,] 110 101 114 74 150 128
#> [9,] 80 68 74 54 110 88
#> [10,] 80 65 65 44 84 71