Compute tree aboveground biomass (AGB) based on allometric equations

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.

Usage

get_biomass(
  dbh,
  genus,
  coords,
  species = NULL,
  new_eqtable = NULL,
  wna = 0.1,
  w95 = 500,
  nres = 10000
)

Arguments

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 NULL, when no species identification is available.

new_eqtable

Optional. An equation table created with the new_equations() function.

wna

a numeric vector, this parameter is used in the weight_allom() function to determine the dbh-related weight attributed to equations without a specified dbh range. Default is 0.1.

w95

a numeric vector, this parameter is used in the weight_allom() function to determine the value at which the sample-size-related weight reaches 95% of its maximum value (max=1). Default is 500.

nres

number of resampled values. Default is "1e4".

Value

A "numeric" vector of the same length as dbh, containing AGB value (in kg) for every stem.

Details

allodb estimates AGB by calibrating a new allometric equation for each taxon (arguments genus and species) and location (argument coords) in the user-provided 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 log-transformed data. The parameters of the new allometric equations are then used in the get_biomass() function by back-transforming the AGB predictions based on the user-provided DBHs.

Warning

The function can run into some memory problems when used on large datasets (usually several hundred thousand observations).

See also

weight_allom(), new_equations()

Examples

# 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
#> # A tibble: 1,209 × 7
#>    treeID stemID   dbh genus   species Family        agb
#>     <int>  <int> <dbl> <chr>   <chr>   <chr>       <dbl>
#>  1      2      2  2.37 Lindera benzoin Lauraceae  0.731 
#>  2      2  31195  1.07 Lindera benzoin Lauraceae  0.0989
#>  3      1  31194  1.6  Lindera benzoin Lauraceae  0.272 
#>  4     13  36481  1.34 Lindera benzoin Lauraceae  0.174 
#>  5     15  31208  1.27 Lindera benzoin Lauraceae  0.152 
#>  6     13     13  1.54 Lindera benzoin Lauraceae  0.247 
#>  7     13  31206  1.37 Lindera benzoin Lauraceae  0.184 
#>  8     15     15  1.55 Lindera benzoin Lauraceae  0.251 
#>  9     16  38297  1.1  Lindera benzoin Lauraceae  0.106 
#> 10     17     17  6.82 Lindera benzoin Lauraceae 10.4   
#> # ℹ 1,199 more rows

# 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
#> # A tibble: 100 × 9
#>    treeID stemID   dbh genus species Family       long   lat biomass
#>     <int>  <int> <dbl> <chr> <chr>   <chr>       <dbl> <dbl>   <dbl>
#>  1   2695   2695  1.41 Acer  negundo Sapindaceae   -78    40  0.183 
#>  2   1229  38557  1.67 Acer  negundo Sapindaceae   -78    40  0.282 
#>  3   1230   1230  1.42 Acer  negundo Sapindaceae   -78    40  0.187 
#>  4   1295  32303  1.04 Acer  negundo Sapindaceae   -78    40  0.0842
#>  5   1229  32273  2.47 Acer  negundo Sapindaceae   -78    40  0.766 
#>  6     66  31258  2.19 Acer  negundo Sapindaceae   -78    40  0.564 
#>  7   2600   2600  1.81 Acer  negundo Sapindaceae   -78    40  0.347 
#>  8   4936   4936  1.35 Acer  negundo Sapindaceae   -78    40  0.164 
#>  9   1229  36996  2.05 Acer  negundo Sapindaceae   -78    40  0.476 
#> 10   1005   1005  1.41 Acer  negundo Sapindaceae   -78    40  0.183 
#> # ℹ 90 more rows