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Summarize terrain metrics by polygon zones

Source: R/summaries.R
summarize_terrain.Rd

Computes summary statistics for raster metrics inside polygons, buffers, or corridor features.

Usage

summarize_terrain(
  metrics,
  zones,
  fun = c("mean", "sd", "min", "max", "median"),
  na.rm = TRUE,
  exact = FALSE,
  ...
)

summarize_terrain_by_zone(
  metrics,
  zones,
  fun = c("mean", "sd", "min", "max", "median"),
  na.rm = TRUE,
  exact = FALSE,
  ...
)

Arguments

metrics

A metric raster stack.

zones

Polygon zones as sf, terra::SpatVector, or a local vector path.

fun

Summary functions. Supported values are "mean", "sd", "min", "max", "median", "sum", and "count".

na.rm

Logical. Remove missing values before summarizing.

exact

Logical. If TRUE, use exactextractr for exact raster-polygon intersections and coverage-fraction-weighted summaries. The optional package must be installed. Weighted count is an effective cell count and weighted sum is a coverage-fraction-weighted cell-value sum.

...

Additional arguments passed to extraction functions.

Value

A tibble with zone identifiers, zone attributes, and wide summary columns named metric_function.

Details

summarize_terrain() does not assume specific zones, depth ranges, or ecological labels. For distance-sensitive summaries, use zones and rasters in a projected CRS. With exact = TRUE, positive coverage_fraction values weight means, population standard deviations, sums, counts, and medians. Minimum and maximum are evaluated over intersected cells with positive coverage. The resulting exact mean is area-weighted when raster cells have equal area in the working CRS.

See also

summarize_depth_bands(), extract_terrain_points()

Examples

bathy <- read_bathy(blueterra_example("bathy"))
terrain <- derive_terrain(bathy, metrics = c("slope", "bpi"))
zones <- terra::vect(blueterra_example("zones"))
summarize_terrain(terrain, zones)
#> # A tibble: 3 × 23
#>   site_id site_name  feature_type source_name width_m height_m angle_deg zone_id
#>   <chr>   <chr>      <chr>        <chr>         <dbl>    <dbl>     <dbl>   <int>
#> 1 hitw    Hole-in-t… sampling_re… Hole In th…     300      300         0       1
#> 2 hoyo    El Hoyo    sampling_re… Hoyo Terra…     300      400       135       2
#> 3 slope   Slope Clip analysis_ex… Slope_clip…     NaN      NaN       NaN       3
#> # ℹ 15 more variables: slope_deg_mean <dbl>, slope_deg_sd <dbl>,
#> #   slope_deg_min <dbl>, slope_deg_max <dbl>, slope_deg_median <dbl>,
#> #   bpi_3x3_mean <dbl>, bpi_3x3_sd <dbl>, bpi_3x3_min <dbl>, bpi_3x3_max <dbl>,
#> #   bpi_3x3_median <dbl>, bpi_11x11_mean <dbl>, bpi_11x11_sd <dbl>,
#> #   bpi_11x11_min <dbl>, bpi_11x11_max <dbl>, bpi_11x11_median <dbl>