createBBNH {ssExtra}R Documentation

Create a “ssBigBAF” object for “Big BAF” Sampling Northern Hardwood Simulations

Description

This routine is used for the specific purpose of creating a “ssBigBAF” object for a synthetic northern hardwoods stand. The object created can then be used in Monte Carlo simulation experiments to look more closely at the mechanics of “Big BAF” sampling.

Usage

createBBNH(extents = c(x = 178, y = 178),
           cellSize = 1,
           bufferWidth = 18,
           units = "metric",
           baf.ct = 4,
           baf.bb = 10,
           startSeed = 355,
           ...)

Arguments

extents

The extents of raster cells in x and y (from an origin at (0,0)) for the four “bufferedTract” objects that will be created to hold the different sampling surfaces.

cellSize

The cell size in meters (for units = "metric") or feet (for units = "English").

bufferWidth

The width of the buffer (see “bufferedTract” for more information). Please note that this should be large enough to include the half-width of the largest inclusion zone for any sampling method tested; i.e., in the case of “Big BAF” sampling, the largest inclusion zone for the baf.ct basal area factor.

units

“metric” or “English”.

baf.ct

The basal area factor for the count sample.

baf.bb

The basal area factor for the volume sample.

startSeed

A seed for the random number generator. See initRandomSeed for more details.

...

Arguments that are passed on to makePop, the sampSurf constructor and ssBigBAF constructor. Please see these routines for the different arguments that they accept.

Details

The default values in the arguments to this routine will duplicate the northern hardwood sampling surfaces for one pair of basal area factors that are used in Gove et al (2020). A quick look at the R code in this routine will show the necessary steps to make “ssBigBAF” objects for other forest types. There is also some discussion of this in the package user's guide vignette.

Value

An object of class “ssBigBAF”, invisibly.

Note

It may take some quick calculations to determine the width of the buffer required to hold the largest inclusion zone. And it may have to be iterated as well. But it is quite simple really.

Author(s)

Jeffrey H. Gove

References

Gove, J. H., Gregoire,T. G., Ducey, M. J., and Lynch, T. B. 2020. A Note on the Estimation of Variance for Big BAF Sampling. Forest Ecosystems, Submitted.

See Also

initTract, makePop, drawTreePop, and the “ssBigBAF” class.

Examples

#
# use the defaults...
#
## Not run: 
ssBB.nh = createBBNH()

## End(Not run)

[Package ssExtra version 0.1-2 Index]