Tree-level inventory summary — silv_tree

Computes individual tree-level dendrometric metrics, diametric classes, expansion factors, competition indices, and optional volume, biomass, and carbon predictions.

Usage

silv_tree_summary(
  data,
  diameter,
  height = NULL,
  plot_id = NULL,
  species = NULL,
  expan = NULL,
  plot_size = NULL,
  plot_shape = c("circular", "rectangular", "snfi"),
  dmin = 7.5,
  dmax = NULL,
  class_length = 5,
  include_lowest = TRUE,
  compute_bal = FALSE,
  predict_volume = FALSE,
  province = NULL,
  predict_biomass = FALSE,
  biomass_component = "tree",
  predict_carbon = FALSE
)

Arguments

data: A data frame or tibble with tree-level records.
diameter: Unquoted column name with the tree diameter (in cm).
height: Unquoted column name with the tree height (in m), optional.
plot_id: Unquoted column name with the plot identifier, optional.
species: Unquoted column name with the tree species identifier/name, optional.
expan: Unquoted column name with the expansion factor (trees/ha), optional.
plot_size: Numeric. Size of the sampling plot (radius in meters if circular, area in m² if rectangular).
plot_shape: Character. Shape of the sampling plot ("circular" or "rectangular"). Default is "circular".
dmin: Numeric. Minimum diameter for diametric classes (default: 7.5).
dmax: Numeric. Maximum diameter for diametric classes (default: NULL).
class_length: Numeric. Width of diametric classes (default: 5).
include_lowest: Logical. Whether to include lowest bound in classes (default: TRUE).
compute_bal: Logical. If TRUE and plot_id is supplied, computes BAL and BAS (default: FALSE).
predict_volume: Logical. If TRUE, predicts SNFI volume (vcc, vsc, iavc) using silv_predict_snfi_volume() (default: FALSE).
province: Unquoted column name or scalar string/integer with the province code/name for volume prediction.
predict_biomass: Logical. If TRUE, predicts tree biomass using silv_predict_biomass_auto() (default: FALSE).
biomass_component: Character. Tree component to predict for biomass (default: "tree").
predict_carbon: Logical. If TRUE, predicts tree carbon content using silv_predict_carbon_auto() (default: FALSE).

Value

The original data frame enriched with computed tree-level metrics.

Examples

library(dplyr)
silv_tree_summary(
  data       = inventory_samples,
  diameter   = diameter,
  height     = height,
  plot_id    = plot_id,
  species    = species,
  plot_size  = 10,
  compute_bal = TRUE
)
#> # A tibble: 162 × 11
#>    plot_id species diameter height dclass      g expan  g_ha slenderness   bal
#>      <int>   <int>    <dbl>  <dbl>  <dbl>  <dbl> <dbl> <dbl>       <dbl> <dbl>
#>  1       7      27     50.6   18.9     50 0.201   31.8  6.40        37.4 101. 
#>  2       7      27     57.2   19.8     55 0.257   31.8  8.18        34.6  62.6
#>  3       7      27     36.4   16.5     35 0.104   31.8  3.31        45.3 130. 
#>  4       7      27     46.4   18.5     45 0.169   31.8  5.38        39.9 125. 
#>  5       7      27     55.5   19.5     55 0.242   31.8  7.70        35.1  70.8
#>  6       7      27     59.5   17.7     60 0.278   31.8  8.85        29.7  45.1
#>  7       7      27     24.3   12.9     25 0.0464  31.8  1.48        53.1 134. 
#>  8       7      27     50.5   16.6     50 0.200   31.8  6.38        32.9 107. 
#>  9       7      27     55.3   19.3     55 0.240   31.8  7.65        34.9  78.5
#> 10       7      27     48.6   18.5     50 0.186   31.8  5.90        38.1 114. 
#> # ℹ 152 more rows
#> # ℹ 1 more variable: bas <dbl>