ForestElementsR (version 2.0.1)

d_100: Dominant Diameter d100

Description

The dominant diameter d100 was conceptuated by Ernst Assmann and Friedrich Franz in order obtain a mean diameter value for those trees which usually dominate a stand throughout its whole life.

Usage

d_100(d, n_rep_ha)

Value

The dominant diameter d100 value resulting from the input data

Arguments

d

vector of diameter values to calculate the dominant diameter d_100 of

n_rep_ha

vector of representation numbers per ha for each diameter in d. Must have the same length as d or the length 1 (in which case it is recycled to the length of d). Otherwise, the function terminates with an error.

Details

The d100 is defined as the quadratic mean diameter of the hundred thickest trees per ha. If there are only 100 trees or less on one ha, the d100 is the same as the quadratic mean diameter d_q. While the d100 is well defined and useful in monospecific stands, it is less so in mixed stands.

See Also

Other stand diameters: d_dom_weise(), d_q()

Examples

# A sample of trees from an angle count sample, where each
# tree represents a basal area of 4 m²/ha
d_cm <- c(12, 13, 25, 27, 28, 26, 26.1, 32, 35, 31, 42)
n_rep_ha <- 4 / ((d_cm / 100)^2 * pi / 4) # representation number of each tree
d_100(d_cm, n_rep_ha)
d_q(d_cm, n_rep_ha) # quadratic mean diameter for comparison


# Typical application to a set of single tree data grouped by survey
# time and species
# (note that everyone is applying d_100 mixed stands, but you should do it
# only, if you know exactly what you are doing)
library(dplyr)
oldopt <- options(fe_spec_lang = "eng") # display colloquial species names
# for d_100 in mixed stands, we require species shares
spec_shares <- species_shares(
  mm_forest_1_fe_stand_spatial,
  tree_filter = !removal, # include remaining trees only
  method = "ba_wd"
)
# extract the tree data to allow insights into the mechanics
trees <- mm_forest_1_fe_stand_spatial$trees |> filter(!removal)
# join with the shares
trees |>
  left_join(spec_shares) |>
  group_by(species_id, time_yr) |>
  summarise(
    n_ha     = round(sum(n_rep_ha)),
    d_q      = d_q(dbh_cm, n_rep_ha), # For comparison
    d_100_cm = d_100(dbh_cm, n_rep_ha / species_share)
  ) |>
  print(n = Inf)
options(oldopt) # set species name display to previous value