Calculates a bunch of forest metrics — silv

Summarize forest inventory data calculating most typical variables

Usage

silv_summary(
  data,
  diameter,
  height,
  plot_size = NULL,
  .groups = NULL,
  plot_shape = c("circular", "rectangular", "snfi"),
  dmin = 7.5,
  dmax = NULL,
  class_length = 5,
  include_lowest = TRUE,
  which_h0 = "assman",
  which_spacing = "hart",
  volume = NULL,
  volume_units = "dm3",
  biomass = NULL,
  carbon = NULL,
  predict_volume = FALSE,
  province = NULL,
  species = NULL,
  predict_biomass = FALSE,
  biomass_component = "tree",
  predict_carbon = FALSE
)

Arguments

data

A tibble of inventory data

diameter

Numeric vector of diameters or diameter classes

height

Numeric vector of tree heights

plot_size

The size of the plot. See silv_density_ntrees_ha()

.groups

A character vector with variables to group by (e.g. plot id, tree species, etc)

plot_shape

The shape of the sampling plot. Either circular or rectangular

dmin

The minimum inventory diameter in centimeters

dmax

The maximum inventory diameter in centimeters. Values that are greater than dmax are included in the greatest class

class_length

The length of the class in centimeters

include_lowest

Logical. If TRUE (the default), the intervals are [dim1, dim2). If FALSE, the intervals are (dim1, dim2]

[dim1, dim2). If FALSE, the intervals are (dim1, dim2]: R:dim1,%20dim2)%60.%20If%20FALSE,%20the%20intervals%20are%20%60(dim1,%20dim2

which_h0

The method to calculate the dominant height. See silv_stand_dominant_height()

which_spacing

A character with the name of the index (either hart or hart-brecking). See silv_density_hart()

volume

Unquoted column name with individual tree volume, optional.

volume_units

Character. Units of the individual tree volume ("dm3" or "m3"). Default is "dm3".

biomass

Unquoted column name with individual tree biomass (kg), optional.

carbon

Unquoted column name with individual tree carbon (kg), optional.

predict_volume

Logical. If TRUE, predicts tree volume using silv_predict_snfi_volume() (default: FALSE).

province

Unquoted column name or scalar string/integer with the province code/name.

species

Unquoted column name with tree species identifier.

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 using silv_predict_carbon_auto() (default: FALSE).

Value

an S7 Inventory list with 2 tibbles

Details

The function calculates many inventory parameters and returns two tibbles:

dclass_metrics: metrics summarized by .groups and diametric classes
group_metrics: metrics summarized by .groups

Volume is reported at stand level in \(m^3/\text{ha}\) (v_ha). Biomass and carbon are reported at stand level in \(t/\text{ha}\) (w_ha, c_ha).

Examples

silv_summary(
  data      = inventory_samples,
  diameter  = diameter,
  height    = height,
  plot_size = 10,
  .groups   = c("plot_id", "species")
 )
#> <silviculture::Inventory>
#>  @ dclass_metrics: tibble [57 × 9] (S3: tbl_df/tbl/data.frame)
#>  $ plot_id  : int [1:57] 7 7 7 7 7 7 7 8 8 8 ...
#>  $ species  : int [1:57] 27 27 27 27 27 27 27 28 28 81 ...
#>  $ dclass   : num [1:57] 50 55 35 45 60 25 120 55 60 10 ...
#>  $ height   : num [1:57] 18 17.6 16.5 14.6 19.1 ...
#>  $ ntrees   : int [1:57] 3 5 1 2 3 1 1 1 1 3 ...
#>  $ ntrees_ha: num [1:57] 95.5 159.2 31.8 63.7 95.5 ...
#>  $ h0       : num [1:57] 19.7 19.7 19.7 19.7 19.7 ...
#>  $ dg       : num [1:57] 57.9 57.9 57.9 57.9 57.9 ...
#>  $ g_ha     : num [1:57] 18.75 37.81 3.06 10.12 27 ...
#>  @ group_metrics : tibble [14 × 16] (S3: tbl_df/tbl/data.frame)
#>  $ plot_id    : int [1:14] 7 8 8 8 8 10 10 10 10 53 ...
#>  $ species    : int [1:14] 27 28 81 83 294 27 72 81 83 27 ...
#>  $ d_mean     : num [1:14] 54.7 57.5 15 14.3 14 ...
#>  $ d_median   : num [1:14] 55 55 15 10 15 85 35 15 15 40 ...
#>  $ d_sd       : num [1:14] 19.16 2.5 6.12 4.95 2 ...
#>  $ dg         : num [1:14] 57.9 57.6 16.2 15.1 14.1 ...
#>  $ h_mean     : num [1:14] 17.42 17.5 6.29 5.67 6.74 ...
#>  $ h_median   : num [1:14] 17.64 15.5 5.87 6.1 7.12 ...
#>  $ h_sd       : num [1:14] 1.924 2 0.525 0.495 0.77 ...
#>  $ h_lorey    : num [1:14] 18.1 17.67 6.43 5.41 7.07 ...
#>  $ h0         : num [1:14] 19.65 17.5 6.39 5.15 7.12 ...
#>  $ ntrees     : int [1:14] 16 2 8 7 5 6 4 10 5 19 ...
#>  $ ntrees_ha  : num [1:14] 509.3 63.7 254.6 222.8 159.2 ...
#>  $ g_ha       : num [1:14] 134.31 16.56 5.25 4 2.5 ...
#>  $ spacing    : num [1:14] 22.6 71.6 98.1 130.2 111.3 ...
#>  $ slenderness: num [1:14] 30.1 30.4 38.8 37.5 47.7 ...
#>  @ groups        : chr [1:2] "plot_id" "species"