Calculates a bunch of forest metrics
silv_summary.RdSummarize 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
- 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]`
- 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
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")
)
#> $dclass_metrics
#> # A tibble: 57 × 9
#> plot_id species dclass height ntrees ntrees_ha h0 dg g_ha
#> <int> <int> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 7 27 50 18 3 95.5 19.7 57.9 18.8
#> 2 7 27 55 17.6 5 159. 19.7 57.9 37.8
#> 3 7 27 35 16.5 1 31.8 19.7 57.9 3.06
#> 4 7 27 45 14.6 2 63.7 19.7 57.9 10.1
#> 5 7 27 60 19.1 3 95.5 19.7 57.9 27
#> 6 7 27 25 12.9 1 31.8 19.7 57.9 1.56
#> 7 7 27 120 20.9 1 31.8 19.7 57.9 36
#> 8 8 28 55 15.5 1 31.8 17.5 57.6 7.56
#> 9 8 28 60 19.5 1 31.8 17.5 57.6 9
#> 10 8 81 10 5.87 3 95.5 6.39 16.2 0.75
#> # ℹ 47 more rows
#>
#> $group_metrics
#> # A tibble: 14 × 15
#> plot_id species d_mean d_median d_sd dg h_mean h_median h_sd h_lorey
#> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7 27 54.7 55 19.2 57.9 17.4 17.6 1.92 18.1
#> 2 8 28 57.5 55 2.5 57.6 17.5 15.5 2 17.7
#> 3 8 81 15 15 6.12 16.2 6.29 5.87 0.525 6.43
#> 4 8 83 14.3 10 4.95 15.1 5.67 6.10 0.495 5.41
#> 5 8 294 14 15 2 14.1 6.74 7.12 0.770 7.07
#> 6 10 27 86.7 85 16.5 88.2 27.7 28.4 4.03 28.5
#> 7 10 72 41.2 35 6.50 41.8 13.4 10.8 2.75 13.2
#> 8 10 81 13.5 15 3.20 13.9 7.38 7.64 0.379 7.50
#> 9 10 83 15 15 3.16 15.3 6.4 6.30 0.155 6.33
#> 10 53 27 41.8 40 11.5 43.4 21.3 19.3 5.11 23.0
#> 11 189 81 20.7 20 6.51 21.7 11.5 12.6 2.26 12.4
#> 12 189 82 17.3 15 3.73 17.7 10.2 9.33 1.30 9.85
#> 13 189 83 14.5 15 3.34 14.9 9.18 9.08 0.751 9.26
#> 14 189 84 20.2 20 6.72 21.3 11.6 11.2 1.93 11.6
#> # ℹ 5 more variables: h0 <dbl>, ntrees <int>, ntrees_ha <dbl>, g_ha <dbl>,
#> # spacing <dbl>
#>