Calculates a bunch of forest metrics — silv

Summarize forest inventory data calculating most typical variables

Usage

silv_summary(
  data,
  diameter,
  height,
  plot_size,
  .groups = NULL,
  plot_shape = "circular",
  dmin = 7.5,
  dmax = NULL,
  class_length = 5,
  include_lowest = TRUE,
  which_h0 = "assman",
  which_spacing = "hart"
)

Arguments

data

A tibble of inventory data

diameter

A column with inventory diameters

height

A column with inventory heights

plot_size

The size of the plot. See silv_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_dominant_height

which_spacing

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

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

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 × 15] (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 ...