thin {rconifers} | R Documentation |
Reduces the expansion factors for tree records in sample.data
list object, to the specification of the user.
thin( x,control=list(type=2,target=50.0,target.sp=NULL ) )
x |
an of type sample.data . |
control |
a list that controls how the sample is thinned. See details. |
0 | Thin trees to a target sdi, does not remove shrubs (not used directly) |
1 | Proportional thin for selected tree species, does not remove shrubs |
2 | Proportional thin across all tree species |
3 | Thin from below, by dbh, all species |
4 | Thin from below, by dbh, species identified with sp.code |
swo
and smc
). For proportional thinning
(i.e. type=2 or 3), the species must be set to NULL
(i.e. sp=NULL)The default thinning regime is to remove 50 percent of all trees across all diameter classes.
For thinning from below, the trees are ranked in increasing diameter, and stems are removed (i.e. expf=0.0) for all stems until the target percent removal has been achieved or all stems have been removed.
The function prints out a diagnostic statement reporting the number of stems removed and the total basal area removed during the operation.
To reduce the shrubs, use a type 4 thinning, which thins from below, by dbh, which here can be zero, for a single species (i.e. sp="COCO")
The sp element of the control list is examined only is the thinning
type is one (1) (DO_EXPF_SP_THIN
) or four (4)
(DO\_EXPF_SP_THIN_FROM_BELOW
), otherwise the argument is ignored and
assumed to be zero, which includes all species.
The function returns a list object of type sample.data
.
Jeff D. Hamann jeff.hamann@forestinformatics.com,
Martin W. Ritchie mritchie@fs.fed.us
Ritchie, M.W. 2008. User's Guide and Help System for CONIFERS: A Simulator for Young Conifer Plantations Version 4.10. See http://www.fs.fed.us/psw/programs/ecology_of_western_forests/projects/conifers/
calc.max.sdi
,
impute
,
plants
,
plots
project
,
rand.seed
,
rconifers
,
sample.data
,
set.species.map
,
set.variant
,
smc
,
summary.sample.data
,
swo
,
thin
library( rconifers ) ## set the variant to the SWO variant set.variant( 0 ) # load the Southwest-Oregon species coefficients into R as a data.frame object data( swo ) # set the species map sp.map <- list(idx=swo$idx, fsp=swo$fsp, code=as.character(swo$code), em=swo$endemic.mort, msdi=swo$max.sdi, b=swo$browse.damage, m=swo$mechanical.damage, gwh=swo$genetic.worth.h, gwd=swo$genetic.worth.d) set.species.map( sp.map ) ## grow the data that was originally swo in the smc variant # load and display CONIFERS example plots data( plots ) print( plots ) # load and display CONIFERS example plants data( plants ) print( plants ) # randomly remove 10 crown.width observations to test # the impute function blanks <- sample( 1:nrow( plants ), 10, replace=FALSE ) plants[blanks,]$crown.width <- NA # create the sample.data list object sample.3 <- list( plots=plots, plants=plants, age=3, x0=0.0 ) class(sample.3) <- "sample.data" # fill in missing values sample.3.imp <- impute( sample.3 ) # print the maximum stand density index for the current settings print( calc.max.sdi( sample.3.imp ) ) # print a summary of the sample print( sample.3.imp ) # now, project the sample forward for 20 years # with all of the options turned off sample.23 <- project( sample.3.imp, 20, control=list(rand.err=0,rand.seed=0,endemic.mort=0,sdi.mort=0)) ## print the projected summaries print( sample.23 ) ## plot the diagnostics from the fit a linear dbh-tht model ## before thinning opar <- par( mfcol=c(2,2 ) ) plot( lm( sample.23$plants$tht ~ sample.23$plants$dbh ) ) par( opar ) ## thin the stand to capture mortality, redistribute growth, ## and possibly generate revenue ## Proportional thin for selected tree species, does not remove shrubs sample.23.t1 <- thin( sample.23, control=list(type=1, target=50.0, target.sp="DF" ) ) print( sample.23.t1 ) ## Proportional thin across all tree species sample.23.t2 <- thin( sample.23, control=list(type=2, target=50.0 ) ) print( sample.23.t2 ) ## Thin from below, by dbh, all species sample.23.t3 <- thin( sample.23, control=list(type=3, target=50.0 ) ) print( sample.23.t3 ) ## Thin from below, by dbh for species "PM" sample.23.t4 <- thin( sample.23, control=list(type=4, target=50.0, target.sp="PM" ) ) print( sample.23.t4 ) ## plot the diagnostics from the fit a linear dbh-tht model ## after proportional thinning opar <- par( mfcol=c(2,2 ) ) plot( lm( sample.23.t2$plants$tht ~ sample.23.t2$plants$dbh ) ) par( opar ) ## print the differences, by species print( sp.sums( sample.23.t4 ) - sp.sums( sample.23 ) )