Swath Elevation Profile Statistics — swath_profile • geoprofiler

Calculate swath-profile values perpendicular to a straight baseline. The distance between samples and the number of samples can be specified, see arguments k and dist. Values of the swath-profile are extracted from a given raster file, see argument raster. CRS of raster and points have to be the same.

Usage

swath_profile(
  profile,
  raster,
  k = 1,
  dist,
  crs = terra::crs(raster),
  method = c("bilinear", "simple")
)

Source

The algorithm is a modified version of "swathR" by Vincent Haburaj (https://github.com/jjvhab/swathR).

Arguments

profile

either a sf object or a matrix(ncol=2, nrow=2) with x and y coordinates of beginning and end point of the baseline; each point in one row

column 1: x coordinates (or longitudes)
column 2: y coordinates (latitudes)

raster

Raster file ("SpatRaster" object as loaded by terra::rast())

k

integer. number of lines on each side of the baseline

dist

numeric. distance between lines

crs

character. coordinate reference system. Both the raster and the profile are transformed into this CRS. Uses the CRS of raster by default.

method

character. method for extraction of raw data, see terra::extract(): default value: "bilinear"

Value

list.

swath: matrix. Statistics of the raster measured along the lines
data: list of numeric vector containing the data extracted from the raster along each line
lines: swath lines as "sf" objects

Details

The final width of the swath is: \(2k \times \text{dist}\).

See also

swath_stats()

Examples

# Create a random raster
r <- terra::rast(ncol = 10, nrow = 10, xmin = -150, xmax = -80, ymin = 20, ymax = 60, crs = "WGS84")
terra::values(r) <- runif(terra::ncell(r))

# Create a random profile
profile <- data.frame(lon = c(-140, -90), lat = c(55, 25)) |>
  sf::st_as_sf(coords = c("lon", "lat"), crs = "WGS84")
swath_profile(profile, r, k = 2, dist = 1)
#> $swath
#>       distance      mean    median        sd       min       max quantile25
#> lyr.1       -2 0.4810259 0.4917692 0.1280879 0.1868466 0.6924040  0.4069518
#> lyr.1       -1 0.4889792 0.4924963 0.1245893 0.1868466 0.6924040  0.4109120
#> lyr.1        0 0.4610791 0.4717171 0.1190467 0.1868466 0.6219876  0.4042503
#> lyr.1        1 0.4563076 0.4524081 0.1166132 0.1868466 0.6219876  0.4195053
#> lyr.1        2 0.4572422 0.4524081 0.1128143 0.1868466 0.6219876  0.4348879
#>       quantile75
#> lyr.1  0.5741303
#> lyr.1  0.5741303
#> lyr.1  0.5481505
#> lyr.1  0.5210006
#> lyr.1  0.5210006
#> 
#> $data
#> $data$lyr.1
#>  [1] 0.3874607 0.4369957 0.3373393 0.3983100 0.4998237 0.4155935 0.4924963
#>  [8] 0.1868466 0.5821908 0.4910261 0.4917692 0.6150751 0.6219876 0.6924040
#> [15] 0.5660698
#> 
#> $data$lyr.1
#>  [1] 0.3874607 0.4369957 0.5421774 0.3983100 0.4998237 0.4155935 0.4924963
#>  [8] 0.1868466 0.5821908 0.4910261 0.4062304 0.6150751 0.6219876 0.6924040
#> [15] 0.5660698
#> 
#> $data$lyr.1
#>  [1] 0.4327801 0.3874607 0.4369957 0.5421774 0.3983100 0.4998237 0.4524081
#>  [8] 0.4924963 0.1868466 0.5821908 0.4910261 0.4062304 0.6150751 0.6219876
#> [15] 0.2653878 0.5660698
#> 
#> $data$lyr.1
#>  [1] 0.4327801 0.5562058 0.4369957 0.5421774 0.3983100 0.4998237 0.4524081
#>  [8] 0.4924963 0.1868466 0.4468624 0.4910261 0.4062304 0.6150751 0.6219876
#> [15] 0.2653878
#> 
#> $data$lyr.1
#>  [1] 0.4327801 0.5562058 0.4369957 0.5421774 0.5836794 0.4998237 0.4524081
#>  [8] 0.4924963 0.1868466 0.4468624 0.4910261 0.4062304 0.4437255 0.6219876
#> [15] 0.2653878
#> 
#> 
#> $lines
#> Simple feature collection with 5 features and 0 fields
#> Geometry type: LINESTRING
#> Dimension:     XY
#> Bounding box:  xmin: -141.029 ymin: 23.28501 xmax: -88.97101 ymax: 56.71499
#> Geodetic CRS:  WGS 84
#>                         geometry
#> 1 LINESTRING (-141.029 53.285...
#> 2 LINESTRING (-140.5145 54.14...
#> 3   LINESTRING (-140 55, -90 25)
#> 4 LINESTRING (-139.4855 55.85...
#> 5 LINESTRING (-138.971 56.714...
#>