biometrics (version 1.0.3)

domvar: Function to compute a 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 nref.ha largest sorting-tree-level diameter trees in 1-ha.

Usage

domvar(data, varint, varsort, plot.area, ndom.ha = 100)

Value

The main output is the calculated dominant stand-variable for the given sample plot. The unit of the computed variable is the same as the one used as variable of interest.

Arguments

data

the tree-list dataframe of a sample plot, having at least column varint.

varint

The column name of the data having the tree-level variable of interest (e.g., "toth"). Can be entered as the actual name, without the need of using quotation marks.

varsort

The column name of the data having the tree-level variable to be used as reference (e.g., "dbh") for defining the sorting variable of interest. If there is only data for the varint column, colum varsort can be the same as in varint.

plot.area

A numeric value of the plot area in m\(^{2}\). Notice that in a tree list, the plot area is also a column, thus, the option plot.area can also be the column name where the surface of the plot is keept. In such a case, the area to be used for the computation is the average of the plot.

ndom.ha

It is the number of trees/ha used as reference. By default ndom.ha is set to 100..

Author

Christian Salas-Eljatib.

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, to make 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.

References

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

Examples

# Dataframe to be used
df<-biometrics::eucaplot2
#' ?eucaplot2
#' head(df)
datana::descstat(df[,c("dap","atot")])
#' # Using the domvar function
domvar(data=df,varint = "atot",varsort = "atot",plot.area = 500)
domvar(data=df,varint = "atot",varsort = "dap",plot.area = 500)
domvar(data=df,varint = "atot",varsort = "dap",plot.area = 500,ndom.ha = 50)