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, or astarsobject with geometry and temporal dimensions (vector data cube)- 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- resampling
character; resampling method; for method cubic or cubicspline, `stars_proxy` objects should be used and GDAL should have version >= 3.10.0
- sfc_attribute
character; if
atis of classstarsshould the aggregation be performed for the attribute geometry rather than the dimension geometry? IfNULL(default), the aggregation is performed at the dimension geometries, else the name of the attribute geometry to perform the aggregation on. If the given attribute geometry does not exist, the aggregation defaults to the dimension geometry.
Value
if at is of class matrix, a matrix with extracted values is returned;
if at is of class stars and a temporal dimension was passed to time_column,
a stars object with the original at dimensions
and the extracted values as attributes.
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
# Extraction on non-POINT geometries
poly = st_buffer(pnt, 1000)
st_extract(r, poly)
#> stars object with 2 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> L7_ETMs.tif 12.22199 61.95172 70.34698 69.55859 81.08313 112.3657
#> dimension(s):
#> from to refsys point
#> geometry 1 10 SIRGAS 2000 / UTM zone 25S FALSE
#> band 1 6 NA NA
#> values
#> geometry POLYGON ((299340.2 911494...,...,POLYGON ((290531.4 911147...
#> band NULL
# Extraction with time matching
rdate = c(r, r*2, along = "date")
dates = c(Sys.Date()-1, Sys.Date())
rdate = st_set_dimensions(rdate, "date", values = c(dates))
pntsf = st_sf(date = dates, geometry = pnt)
st_extract(split(rdate, "band"), pntsf) # POINT geometries
#> stars object with 2 dimensions and 6 attributes
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> X1 63 81.50 118.0 125.25 161.0 220
#> X2 46 68.00 96.5 108.60 136.0 202
#> X3 38 68.50 98.0 107.40 135.0 228
#> X4 13 47.75 75.5 82.50 113.5 174
#> X5 13 82.00 118.5 131.40 179.5 300
#> X6 12 48.00 85.0 97.50 146.0 256
#> dimension(s):
#> from to offset delta refsys point
#> geometry 1 10 NA NA SIRGAS 2000 / UTM zone 25S TRUE
#> date 1 2 2025-02-13 1 days Date NA
#> values
#> geometry POINT (298340.2 9114943),...,POINT (289531.4 9111471)
#> date NULL
polysf = st_buffer(pntsf, 1000)
st_extract(split(rdate, "band"), polysf, time_column = "date") # POLYGON geometries
#> Simple feature collection with 10 features and 7 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 287950.3 ymin: 9110189 xmax: 299340.2 ymax: 9120338
#> Projected CRS: SIRGAS 2000 / UTM zone 25S
#> X1 X2 X3 X4 X5 X6 date
#> 1 95.61855 87.97014 66.87460 13.91219 13.35652 12.22199 2025-02-13
#> 2 168.67356 145.05173 153.31092 133.18779 221.45680 171.79255 2025-02-14
#> 3 70.16835 59.61072 56.02305 77.30407 93.00078 58.88915 2025-02-13
#> 4 161.71081 138.51940 142.88153 141.05122 211.41852 157.77237 2025-02-14
#> 5 76.32885 66.93708 67.44537 77.82367 102.07587 68.78250 2025-02-13
#> 6 174.55556 155.56283 129.95569 68.87434 74.75926 53.56283 2025-02-14
#> 7 69.69241 57.50929 54.95356 66.84740 89.00159 59.73938 2025-02-13
#> 8 163.53261 144.08644 152.66253 148.69203 224.73137 167.08644 2025-02-14
#> 9 77.67062 62.33319 62.58754 53.40356 86.72064 66.59983 2025-02-13
#> 10 154.69930 124.51919 123.22393 107.94016 164.82381 124.12995 2025-02-14
#> geometry
#> 1 POLYGON ((299340.2 9114943,...
#> 2 POLYGON ((294918.4 9114415,...
#> 3 POLYGON ((294485.2 9118749,...
#> 4 POLYGON ((295440.9 9114839,...
#> 5 POLYGON ((296209 9118813, 2...
#> 6 POLYGON ((296048.9 9111189,...
#> 7 POLYGON ((290649.4 9116888,...
#> 8 POLYGON ((296730.3 9119338,...
#> 9 POLYGON ((289950.3 9111816,...
#> 10 POLYGON ((290531.4 9111471,...
vdc = st_sf(rdm = rnorm(20), polygons = st_buffer(st_sample(st_bbox(pnt), 20), 500),
geometry = rep(pnt, 2), date = rep(dates, each = 10)) |>
st_as_stars(dims = c("geometry", "date"))
(vdc_new = st_extract(split(rdate, "band"), vdc)) # stars vector data cube
#> stars object with 2 dimensions and 6 attributes
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> X1 63 81.50 118.0 125.25 161.0 220
#> X2 46 68.00 96.5 108.60 136.0 202
#> X3 38 68.50 98.0 107.40 135.0 228
#> X4 13 47.75 75.5 82.50 113.5 174
#> X5 13 82.00 118.5 131.40 179.5 300
#> X6 12 48.00 85.0 97.50 146.0 256
#> dimension(s):
#> from to offset delta refsys point
#> geometry 1 10 NA NA SIRGAS 2000 / UTM zone 25S TRUE
#> date 1 2 2025-02-13 1 days Date NA
#> values
#> geometry POINT (298340.2 9114943),...,POINT (289531.4 9111471)
#> date NULL
merge(vdc_new, name = "band")
#> stars object with 3 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> X1.X2.X3.X4.X5.X6 12 68 94.5 108.775 148.5 300
#> dimension(s):
#> from to offset delta refsys point
#> geometry 1 10 NA NA SIRGAS 2000 / UTM zone 25S TRUE
#> date 1 2 2025-02-13 1 days Date NA
#> band 1 6 NA NA NA NA
#> values
#> geometry POINT (298340.2 9114943),...,POINT (289531.4 9111471)
#> date NULL
#> band X1,...,X6
### Extraction applied to the geometries inside the vector data cube (cell values)
(vdc_new2 = st_extract(split(rdate, "band"), vdc,
sfc_attribute = "polygons")) # stars vector data cube
#> stars object with 2 dimensions and 6 attributes
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> X1 64.05389 81.78454 114.71922 121.18932 160.0073 193.3864
#> X2 52.09223 71.41825 96.37767 103.37781 134.0551 177.9421
#> X3 44.29948 68.55390 95.45427 100.92211 136.0219 157.0072
#> X4 13.16426 63.60088 76.40195 89.18981 130.5063 160.7764
#> X5 13.76653 94.78568 118.90647 133.22952 193.5465 217.5130
#> X6 12.76550 75.00908 95.18119 98.57301 133.8600 168.5140
#> dimension(s):
#> from to offset delta refsys point
#> geometry 1 10 NA NA SIRGAS 2000 / UTM zone 25S TRUE
#> date 1 2 2025-02-13 1 days Date NA
#> values
#> geometry POINT (298340.2 9114943),...,POINT (289531.4 9111471)
#> date NULL
merge(vdc_new2, name = "band")
#> stars object with 3 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> X1.X2.X3.X4.X5.X6 12.7655 73.93376 102.7202 107.7469 143.4256 217.513
#> dimension(s):
#> from to offset delta refsys point
#> geometry 1 10 NA NA SIRGAS 2000 / UTM zone 25S TRUE
#> date 1 2 2025-02-13 1 days Date NA
#> band 1 6 NA NA NA NA
#> values
#> geometry POINT (298340.2 9114943),...,POINT (289531.4 9111471)
#> date NULL
#> band X1,...,X6