Extract cell values at point locations
Arguments
- x
object of class
starsorstars_proxy- ...
passed on to aggregate.stars when geometries are not exclusively POINT geometries
- at
object of class
sforsfcwith geometries, or two-column matrix with coordinate points in rows, indicating where to extract values ofx- bilinear
logical; use bilinear interpolation rather than nearest neighbour?
- time_column
character or integer; name or index of a column with time or date values that will be matched to values of the first temporal dimension (matching classes
POSIXct,POSIXt,Date, orPCICt), inx, 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 .- interpolate_time
logical; should time be interpolated? if FALSE, time instances are matched using the coinciding or the last preceding time in the data cube.
- FUN
function used to aggregate pixel values when geometries of
atintersect with more than one pixel
Value
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.
Details
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.
Examples
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