Description Usage Arguments Details Value Warning See Also Examples
This function calculates the aboveground biomass (or other tree components) of a given tree based on published allometric equations. Users need to provide a table (i.e. dataframe) with DBH (cm), parsed species Latin names, and site(s) coordinates. The biomass of all trees in one (or several) censuses can be estimated using this function.
1 2 3 4 5 6 7 8 9 10  get_biomass(
dbh,
genus,
coords,
species = NULL,
new_eqtable = NULL,
wna = 0.1,
w95 = 500,
nres = 10000
)

dbh 
a numeric vector containing tree diameter at breast height (dbh) measurements, in cm. 
genus 
a character vector (same length as dbh), containing the genus (e.g. "Quercus") of each tree. 
coords 
a numeric vector of length 2 with longitude and latitude (if all trees were measured in the same location) or a matrix with 2 numerical columns giving the coordinates of each tree. 
species 
a character vector (same length as dbh), containing the
species (e.g. "rubra") of each tree. Default is 
new_eqtable 
Optional. An equation table created with the

wna 
a numeric vector, this parameter is used in the 
w95 
a numeric vector, this parameter is used in the 
nres 
number of resampled values. Default is "1e4". 
allodb
estimates AGB by calibrating a new allometric equation for each
taxon (arguments genus
and species
) and location (argument coords
) in
the userprovided census data. The new allometric equation is based on a set
of allometric equations that can be customized using the new_eqtable
argument. Each equation is then given a weight with the weight_allom()
function, based on: 1) its original sample size (numbers of trees used to
develop a given allometry), 2) its climatic similarity with the target
location, and 3) its taxonomic similarity with the target taxon (see
documentation of the weight_allom()
function). The final weight attributed
to each equation is the product of those three weights. Equations are then
resampled with theresample_agb()
funtion: the number of samples per
equation is proportional to its weight, and the total number of samples is
provided by the argument nres
. The resampling is done by drawing DBH values
from a uniform distribution on the DBH range of the equation, and estimating
the AGB with the equation. The couples of values (DBH, AGB) obtained are then
used in the function est_params()
to calibrate a new allometric equation,
by applying a linear regression to the logtransformed data. The parameters
of the new allometric equations are then used in the get_biomass()
function
by backtransforming the AGB predictions based on the userprovided DBHs.
A "numeric" vector of the same length as dbh, containing AGB value (in kg) for every stem.
The function can run into some memory problems when used on large datasets (usually several hundred thousand observations).
weight_allom()
, new_equations()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  # Estimate biomass of all individuals from the Lauraceae family at the SCBI
# plot
lau < subset(scbi_stem1, Family == "Lauraceae")
lau$agb < get_biomass(lau$dbh, lau$genus, lau$species,
coords = c(78.2, 38.9)
)
lau
# Estimate biomass from multiple sites (using scbi_stem1 as example with
# multiple coord)
dat < scbi_stem1[1:100, ]
dat$long < c(rep(78, 50), rep(80, 50))
dat$lat < c(rep(40, 50), rep(41, 50))
dat$biomass < get_biomass(
dbh = dat$dbh,
genus = dat$genus,
species = dat$species,
coords = dat[, c("long", "lat")]
)
dat

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