Extract summarized values from raster with points and a buffer radius

For simplicity, it is assumed that the coordinate systems of the points and the raster are the same.

Usage

extract_at_buffer(
  points = NULL,
  surf = NULL,
  radius = NULL,
  id = NULL,
  qsegs = 90L,
  func = "mean",
  kernel = NULL,
  bandwidth = NULL,
  extent = NULL,
  max_cells = 2e+07,
  ...
)

extract_at_buffer_flat(
  points = NULL,
  surf = NULL,
  radius = NULL,
  id = NULL,
  qsegs = NULL,
  func = "mean",
  kernel = NULL,
  bandwidth = NULL,
  max_cells = 2e+07,
  ...
)

extract_at_buffer_kernel(
  points = NULL,
  surf = NULL,
  radius = NULL,
  id = NULL,
  qsegs = NULL,
  func = stats::weighted.mean,
  kernel = NULL,
  bandwidth = NULL,
  max_cells = 2e+07,
  ...
)

Arguments

points: sf/SpatVector object. Coordinates where buffers will be generated.
surf: SpatRaster object or file path(s) with extensions that are GDAL-compatible. A raster from which a summary will be calculated
radius: numeric(1). Buffer radius. Here we assume circular buffers only
id: character(1). Unique identifier of each point.
qsegs: integer(1). Number of vertices at a quarter of a circle. Default is 90L.
func: a function taking a numeric vector argument.
kernel: character(1). Name of a kernel function One of "uniform", "triweight", "quartic", and "epanechnikov"
bandwidth: numeric(1). Kernel bandwidth.
extent: numeric(4) or SpatExtent. Extent of clipping vector. It only works with points of character(1) file path. When using numeric(4), it should be in the order of c(xmin, xmax, ymin, ymax). The coordinate system should be the same as the points.
max_cells: integer(1). Maximum number of cells in memory. See exactextractr::exact_extract for more details.
...: Placeholder.

Value

a data.frame object with mean value

Note

When Sys.setenv("CHOPIN_FORCE_CROP" = "TRUE") is set, the raster will be cropped to the extent of the polygons (with snap = "out"). To note, the function is designed to work with the exactextractr package. Arguments of exactextractr::exact_extract are set as below (default otherwise listed):

force_df = TRUE
stack_apply = TRUE
max_cells_in_memory = 2e8
progress = FALSE

Author

Insang Song geoissong@gmail.com

Examples

library(terra)
rrast <- terra::rast(nrow = 100, ncol = 100)
terra::crs(rrast) <- "EPSG:5070"
terra::values(rrast) <- rgamma(1e4, 4, 2)
rpnt <- terra::spatSample(rrast, 100L, as.points = TRUE)
rpnt$pid <- sprintf("id_%03d", seq(1, 100))
extract_at_buffer(rpnt, rrast, 4, "pid")
#>        pid     mean
#> 1   id_001 1.651610
#> 2   id_002 1.375360
#> 3   id_003 1.869488
#> 4   id_004 1.879870
#> 5   id_005 1.830806
#> 6   id_006 2.203978
#> 7   id_007 2.025010
#> 8   id_008 2.104624
#> 9   id_009 2.044772
#> 10  id_010 1.882280
#> 11  id_011 1.506968
#> 12  id_012 2.049565
#> 13  id_013 1.596740
#> 14  id_014 2.027810
#> 15  id_015 1.891467
#> 16  id_016 2.026121
#> 17  id_017 1.724052
#> 18  id_018 1.845675
#> 19  id_019 1.516506
#> 20  id_020 1.870031
#> 21  id_021 1.701714
#> 22  id_022 2.313477
#> 23  id_023 2.263182
#> 24  id_024 1.995274
#> 25  id_025 2.431489
#> 26  id_026 2.374461
#> 27  id_027 2.080226
#> 28  id_028 1.700923
#> 29  id_029 1.962064
#> 30  id_030 2.107451
#> 31  id_031 2.165406
#> 32  id_032 2.240682
#> 33  id_033 2.274774
#> 34  id_034 2.040657
#> 35  id_035 1.396226
#> 36  id_036 1.964290
#> 37  id_037 1.866187
#> 38  id_038 1.559619
#> 39  id_039 1.822197
#> 40  id_040 1.903492
#> 41  id_041 2.117386
#> 42  id_042 1.392489
#> 43  id_043 2.339543
#> 44  id_044 2.585882
#> 45  id_045 1.813985
#> 46  id_046 2.156444
#> 47  id_047 1.653794
#> 48  id_048 1.688204
#> 49  id_049 1.952219
#> 50  id_050 2.010062
#> 51  id_051 2.029783
#> 52  id_052 2.473734
#> 53  id_053 2.347674
#> 54  id_054 1.847772
#> 55  id_055 1.885386
#> 56  id_056 2.378431
#> 57  id_057 2.591648
#> 58  id_058 2.317759
#> 59  id_059 1.790030
#> 60  id_060 1.946267
#> 61  id_061 1.878067
#> 62  id_062 1.625792
#> 63  id_063 1.669429
#> 64  id_064 2.041649
#> 65  id_065 2.333508
#> 66  id_066 1.969423
#> 67  id_067 1.440952
#> 68  id_068 2.481989
#> 69  id_069 1.950975
#> 70  id_070 1.949170
#> 71  id_071 1.800481
#> 72  id_072 1.727038
#> 73  id_073 1.856413
#> 74  id_074 2.165578
#> 75  id_075 2.798059
#> 76  id_076 2.008407
#> 77  id_077 1.824913
#> 78  id_078 2.024015
#> 79  id_079 1.740571
#> 80  id_080 2.040131
#> 81  id_081 2.149531
#> 82  id_082 2.495682
#> 83  id_083 2.375884
#> 84  id_084 1.835961
#> 85  id_085 1.960443
#> 86  id_086 2.293348
#> 87  id_087 2.268371
#> 88  id_088 2.319497
#> 89  id_089 1.744779
#> 90  id_090 1.748024
#> 91  id_091 2.193364
#> 92  id_092 2.249387
#> 93  id_093 2.230290
#> 94  id_094 2.054080
#> 95  id_095 2.208818
#> 96  id_096 1.823460
#> 97  id_097 1.687319
#> 98  id_098 1.775530
#> 99  id_099 1.922610
#> 100 id_100 2.047888