Calculates the dominant height

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

Usage

silv_dominant_height(diameter, height, ntrees = NULL, which = "assman")

Arguments

diameter

Numeric vector with diameter classes

height

Numeric vector with averaged heights by diameter class

ntrees

Optional. Numeric vector with number of trees per hectare. Use this argument when you have aggregated data by diametric classes (see details).

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 trees. 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_diametric_class(diameter)) |>
  summarise(
    height = mean(height, na.rm = TRUE),
    ntrees = n(),
    .by    = c(plot_id, species, dclass)
  ) |>
  mutate(
    ntrees_ha = silv_ntrees_ha(ntrees, plot_size = 10),
    h0        = silv_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