domvar: Function to compute the dominant stand-level variable based...

In biometrics: Functions and Datasets for Forest Biometrics and Modelling

Function to compute the dominant stand-level variable based on a sample plot data.

Description

Computes the so-called dominant stand-level variable, corresponding to the average of a tree-level variable for the 100 largest sorting-tree-level diameter trees in 1-ha.

Usage

domvar(
  data = data,
  var.int = var.int,
  var.sort = var.sort,
  plot.area = plot.area
)

Arguments

data	data frame having the tree list of a sample plot.
var.int	column name with the tree-level variable of interest (e.g., height).
var.sort	column name with the tree-level variable for defining the
plot.area	column name having the plot area, in square meters.

Details

The original function was written by Dr Oscar García for computing top height, and the corresponding reference is provided. Nevertheless, several changes were applied, thus the current function provide a broader application. Regardless, the function aims to calculate a "dominant" stand-level variable by taking into account the plot area. Thus, requires having a dataframe having both the variable of interest (e.g., height) and the sorting variable used for the computation (e.g., diameter) for all trees in a sample plot, as well as, the plot area.

Value

The main output is the calculated dominant stand-variable for the given sample plot.

Author(s)

Christian Salas-Eljatib.

References

- Garcia O, Batho A. 2005. Top height estimation in lodgepole pine sample plots. Western Journal of Applied forestry 20(1):64-68.

Examples

##Creates a fake dataframe
set.seed(45)
x <- round(rnorm(20,mean=45,sd=10),1); y=round(1.3+35*(1-exp(-.07*x)),1)
df<-data.frame(dap=x,atot=y)
head(df)
datana::descstat(df)
##Using the domvar function
domvar(data=df,var.int="atot",var.sort="dap",plot.area=500)

biometrics documentation built on April 4, 2025, 12:35 a.m.