R: Basal Area Increment (Outside In)

bai.out {dplR} | R Documentation

## Basal Area Increment (Outside In)

### Description

Convert multiple ring-width series to basal area increment (i.e., ring area) going from the bark to the pith.

### Usage

    bai.out(rwl, diam = NULL)

### Arguments

`rwl` | a `data.frame` with series as columns and years as rows such as that produced by `read.rwl`
`diam` | an optional `data.frame` containing two variables. If present, variable one (`series` in the example below) gives the series ID as either `character`s or `factor`s. These must exactly match `colnames(rwl)`. Variable two (`diam` in the example below) gives the diameter of the tree (in mm) up to the outermost ring (e.g., the diameter of the tree where the core was taken minus the thickness of the bark). If `diam` is `NULL` then the diameter is taken as twice the sum of the widths for each series (column) in `rwl`.

### Details

This converts ring-width series (mm) to ring-area series (mm squared) (aka basal area increments) based on the diameter of the tree and the width of each ring moving towards the pith of the tree. It is related to `bai.in`, which calculates each ring area starting from the inside of the tree and working outward. Both methods assume a circular cross section (Biondi 1999). See the references below for further details.

### Value

A `data.frame` containing the ring areas for each series with column names, row names and dimensions of `rwl`.

### Note

DendroLab website: http://dendrolab.org/

### Author(s)

Code by Andy Bunn based on work from DendroLab, University of Nevada Reno, USA. Patched and improved by Mikko Korpela.

### References

Biondi, F. (1999) Comparing tree-ring chronologies and repeated timber inventories as forest monitoring tools. Ecological Applications, 9(1), 216–227.

Biondi, F. and Qeadan, F. (2008) A theory-driven approach to tree-ring standardization: Defining the biological trend from expected basal area increment. Tree-Ring Research, 64(2), 81–96.

### See Also

`bai.in`

### Examples

    library(graphics)
    library(utils)
    ## Not run:
    library(stats)
    ## Toy
    n <- 100
    ## Make three fake tree-ring series to show that these funcs work on rwl objects
    base.series <- 0.75 + exp(-0.2 * 1:n)
    rwl <- data.frame(x1 = base.series + abs(rnorm(n, 0, 0.05)),
                      x2 = base.series + abs(rnorm(n, 0, 0.05)),
                      x3 = base.series + abs(rnorm(n, 0, 0.05)))

    ## The inside out method
    foo <- bai.in(rwl = rwl)
    ## The outside in method
    bar <- bai.out(rwl = rwl)

    ## Identical
    head(bar)
    head(foo)

    ## End(Not run)
    ## Use gp data
    data(gp.rwl)
    data(gp.dbh)
    ## dbh (minus the bark) from cm to mm
    gp.dbh2 <- gp.dbh[, 1:2]
    gp.dbh2[, 2] <- (gp.dbh[, 2] - gp.dbh[, 3]) * 10
    bar <- bai.out(rwl = gp.rwl, diam = gp.dbh2)
    bar.crn <- chron(bar)
    yrs <- time(bar.crn)
    plot(yrs, bar.crn[, 1], type = "n",
         xlab = "Year", ylab = expression(mm^2))
    lines(yrs, bar.crn[, 1], col = "grey", lty = "dashed")
    lines(yrs, ffcsaps(bar.crn[, 1], nyrs = 32), col = "red", lwd = 2)

[Package dplR version 1.7.1 Index]