Calculates the dominant height — silv_stand_dominant

Calculates the dominant height using the Assman equation or the Hart equation

Usage

silv_stand_dominant_height(
  diameter,
  height,
  ntrees = NULL,
  which = c("assman", "hart")
)

Arguments

diameter: Numeric vector of diameters or diameter classes
height: Numeric vector of tree heights
ntrees: Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree
which: The method to calculate the dominant height (see details)

Value

A numeric vector

Details

The dominant height \(H_0\) is the mean height of dominant trees, which is less affected than overall mean height by thinning or other treatments.

Assman: calculates the \(H_0\) as the mean height of the 100 thickest trees per hectare
Hart: calculates the \(H_0\) as the mean height of the 100 tallest trees per hectare

When ntrees = NULL, the function will assume that each diameter and height belongs to only one tree. If you have data aggregated by hectare, you'll use the number of trees per hectare in this argument.

References

Assmann, E. (1970) The principles of forest yield study: Studies in the organic production, structure, increment, and yield of forest stands. Pergamon Press, Oxford.

Examples

## calculate h0 for inventory data grouped by plot_id and species
library(dplyr)
inventory_samples |>
mutate(dclass = silv_tree_dclass(diameter)) |>
  summarise(
    height = mean(height, na.rm = TRUE),
    ntrees = n(),
    .by    = c(plot_id, species, dclass)
  ) |>
  mutate(
    ntrees_ha = silv_density_ntrees_ha(ntrees, plot_size = 10),
    h0        = silv_stand_dominant_height(dclass, height, ntrees_ha),
    .by       = c(plot_id, species)
  )
#> # A tibble: 57 × 7
#>    plot_id species dclass height ntrees ntrees_ha    h0
#>      <int>   <int>  <dbl>  <dbl>  <int>     <dbl> <dbl>
#>  1       7      27     50  18         3      95.5 19.7 
#>  2       7      27     55  17.6       5     159.  19.7 
#>  3       7      27     35  16.5       1      31.8 19.7 
#>  4       7      27     45  14.6       2      63.7 19.7 
#>  5       7      27     60  19.1       3      95.5 19.7 
#>  6       7      27     25  12.9       1      31.8 19.7 
#>  7       7      27    120  20.9       1      31.8 19.7 
#>  8       8      83     20   5.10      3      95.5  5.15
#>  9       8      83     10   6.10      4     127.   5.15
#> 10       8      28     55  15.5       1      31.8 17.5 
#> # ℹ 47 more rows