Inventory class

Description

A list containing forest inventory data summaries. It includes data by diametric class (dclass_metrics) and by plot and species (group_metrics).

Usage

Inventory(dclass_metrics = data.frame(), group_metrics = data.frame())

Arguments

Value

An S7 Inventory object, which contains the inventory data.

SampleSize class

Description

A list containing the summary data of silv_sample_size. It includes input data (sampling_opts) and output data (sampling_res)

Usage

SampleSize(sampling_res = list(), sampling_opts = list())

Arguments

Value

An S7 SampleSize object

Biomass models

Description

Biomass models available in silviculture package. If you would like to suggest new models, please open a new issue.

Usage

biomass_models

Format

A tibble

Calculates dominant height

Description

Calculates dominant height

Usage

calc_dominant_height(nmax, ntress, height)

Arguments

Value

A numeric vector

Calculates number of plots using Student's T

Description

Calculates number of plots using Student's T

Usage

calc_n_simple(students_t, max_n, cv, max_error)

Arguments

Value

A length-one numeric vector

Forest inventory samples

Description

Inventory data from Spanish National Forest Inventory

Usage

inventory_samples

Format

A tibble

Calculate Forest Fraction Cover from LiDAR Data

Description

This function calculates the forest fraction cover (Fcov) from LiDAR data. The Fcov in LiDAR is defined as the proportion of first returns above a specified height threshold (default: 5 meters) relative to the total number of first returns.

Usage

lid_fcov(z, rn, th = 5)

Arguments

Value

A numeric value representing the forest fraction cover, which is the proportion of first returns with heights greater than 5 meters.

Examples

# Example data
z <- c(2, 6, 10, 4, 15)
rn <- c(1, 1, 2, 1, 1)

# Calculate forest fraction cover
lid_fcov(z, rn)

LiDAR-derived Height Diversity Index (LHDI)

Description

LiDAR metric that calculates the LiDAR Height Diversity Index, which can be used in lidR *_metrics functions

Usage

lid_lhdi(z, interval = 0.5)

Arguments

Value

numeric

References

Listopad, C. M. C. S., Masters, R. E., Drake, J., Weishampel, J., & Branquinho, C. (2015). Structural diversity indices based on airborne LiDAR as ecological indicators for managing highly dynamic landscapes. Ecological Indicators, 57, 268–279. doi:10.1016/j.ecolind.2015.04.017

Examples

1 + 1 ## TODO

Plot an object

Description

Generic for plotting objects.

Usage

plot(x, ...)

Arguments

Value

Usually called for side-effects (producing a plot).

Calculates Basal Area

Description

Calculates Basal Area in square meters.

Usage

silv_basal_area(diameter, ntrees = NULL, units = "cm")

Arguments

Details

The function uses the next formula:

G = \frac{\pi}{40000} \cdot D^2

where G is the basal area in m^2, and D is the diameter in the units specified in the function. It is recommended to use the squared mean diameter calculated with silv_sqrmean_diameter

Value

A numeric vector

Examples

## calculate G for inventory data grouped by plot_id and species
library(dplyr)
inventory_samples |>
mutate(dclass = silv_diametric_class(diameter)) |>
  summarise(
    height = mean(height, na.rm = TRUE),
    ntrees = n(),
    .by    = c(plot_id, species, dclass)
  ) |>
  mutate(
    ntrees_ha = silv_ntrees_ha(ntrees, plot_size = 10),
    dg        = silv_sqrmean_diameter(dclass, ntrees_ha),
    g         = silv_basal_area(dclass, ntrees_ha),
    .by       = c(plot_id, species)
  )

## calculate individual basal area
silv_basal_area(c(23, 11, 43.5, 94))

Calculate Tree Biomass

Description

Computes the biomass of a tree species using species-specific allometric equations (in kg).

Usage

silv_biomass(
  diameter = NULL,
  height = NULL,
  ntrees = NULL,
  species = NULL,
  component = "stem",
  model = "ruiz-peinado-2012",
  return_rmse = FALSE,
  quiet = FALSE
)

Arguments

Details

The function estimates biomass using validated allometric models available in the dataset biomass_models. The available models include:

• ruiz-peinado-2011: Developed for softwood species in Spain.

• ruiz-peinado-2012: Developed for hardwood species in Spain.

Users can check the list of supported species and their corresponding components in biomass_models.

If you would like to suggest additional models, please open a new issue on GitHub.

Value

A numeric vector of biomass values (in kg). If return_rmse = TRUE, returns the RMSE instead.

Examples

# Calculate biomass for a single tree
silv_biomass(
  diameter = 45,
  height   = 22,
  species  = "Pinus pinaster",
  model    = "ruiz-peinado-2011"
)

Classify diameters in classes

Description

Classifies the measured diameters into classes of a specified length

Usage

silv_diametric_class(
  diameter,
  dmin = 7.5,
  dmax = NULL,
  class_length = 5,
  include_lowest = TRUE,
  return_intervals = FALSE
)

Arguments

Value

A numeric vector

Examples

library(dplyr)
inventory_samples |>
  mutate(dclass = silv_diametric_class(diameter))

Calculates the dominant height

Description

Calculates the dominant height using the Assman equation or the Hart equation

Usage

silv_dominant_height(diameter, height, ntrees = NULL, which = "assman")

Arguments

Details

The dominant height H_0 is the mean height of dominant trees, which is less affected than overall mean height by thinning or other treatments.

• Assman: calculates the H_0 as the mean height of the 100 thickest trees per hectare

• Hart: calculates the H_0 as the mean height of the 100 tallest trees per hectare

When ntrees = NULL, the function will assume that each diameter and height belongs to only one trees. If you have data aggregated by hectare, you'll use the number of trees per hectare in this argument.

Value

A numeric vector

References

Assmann, E. (1970) The principles of forest yield study: Studies in the organic production, structure, increment, and yield of forest stands. Pergamon Press, Oxford.

Examples

## calculate h0 for inventory data grouped by plot_id and species
library(dplyr)
inventory_samples |>
mutate(dclass = silv_diametric_class(diameter)) |>
  summarise(
    height = mean(height, na.rm = TRUE),
    ntrees = n(),
    .by    = c(plot_id, species, dclass)
  ) |>
  mutate(
    ntrees_ha = silv_ntrees_ha(ntrees, plot_size = 10),
    h0        = silv_dominant_height(dclass, height, ntrees_ha),
    .by       = c(plot_id, species)
  )

Calculates Lorey's Height

Description

Tree's mean height weighted by basal area

Usage

silv_lorey_height(height, g, ntrees = NULL)

Arguments

Details

The function calculates Lorey's mean height according to:

h_L = \frac{\sum n_i g_i h_i}{\sum n_i g_i}

When ntrees is not provided (i.e. ntrees = NULL) the formula is simply the weighted mean of the provided heights and basal areas:

h_L = \frac{\sum g_i h_i}{\sum g_i}

Value

A numeric vector

Examples

## Calculate Lorey's Height by plot and species
library(dplyr)
inventory_samples |>
  mutate(g = silv_basal_area(diameter)) |>
  summarise(
    lh  = silv_lorey_height(height, g),
    .by = c(plot_id, species)
  )

Calculates number of trees per hectare

Description

Calculates number of trees per hectare for a given plot size and shape

Usage

silv_ntrees_ha(ntrees, plot_size, plot_shape = "circular")

Arguments

Value

A numeric vector

Examples

library(dplyr)
## Circular plot of 10 meters radius
inventory_samples |>
  count(plot_id, species) |>
  mutate(
    ntrees_ha = silv_ntrees_ha(n, plot_size = 10)
  )

## Rectangular plot of 10x15 meters
inventory_samples |>
  count(plot_id, species) |>
  mutate(
    ntrees_ha = silv_ntrees_ha(
      n,
      plot_size = c(10, 15),
      plot_shape = "rectangular"
     )
  )

Calculates sample size for a random sampling inventory

Description

Calculates sample size for a random sampling inventory

Usage

silv_sample_size(
  x,
  plot_size = 100,
  total_area = 150000,
  max_error = 0.05,
  conf_level = 0.95,
  max_iter = 1000,
  quiet = FALSE
)

Arguments

Value

SampleSize object

Examples

## pilot inventory measuring 4 plots of 25x25 meters
## total forest area 15 ha
## measured variable (x): basal area per hectare
silv_sample_size(
  x          = c(33, 37.5, 42, 35.2),
  plot_size  = 25 * 25,  # squared plot of 25x25
  total_area = 15 * 1e4, # 15 ha
  max_error  = 0.05,
  conf_level = 0.95,
  max_iter   = 100
)

Hart or Hart-Becking spacing index

Description

Calculates the Hart Index or the Hart-Becking Index for even-aged stands

Usage

silv_spacing_index(h0, ntrees, which = "hart")

Arguments

Details

The spacing index can be used to determine whether a thinning is needed or not, and also to determine how intense it should be.

• Hart Index: it assumes even-aged stands with square planting pattern.

• Hart-Brecking Index: it assumes triangular planting pattern.

Value

A numeric vector

References

Assmann, E. (1970) The principles of forest yield study: Studies in the organic production, structure, increment, and yield of forest stands. Pergamon Press, Oxford.

Examples

library(dplyr)
## Calculate spacing index for each plot
inventory_samples |>
  summarise(
    h0     = silv_dominant_height(diameter, height),
    ntrees = n(),
    .by    = plot_id
  ) |>
  ## calculate number of trees per hectare
  mutate(ntrees_ha = silv_ntrees_ha(ntrees, plot_size = 14.1)) |>
  mutate(spacing = silv_spacing_index(h0, ntrees_ha))

Calculates the squared mean diameter

Description

Calculates the squared mean diameter

Usage

silv_sqrmean_diameter(diameter, ntrees = NULL)

Arguments

Value

A numeric vector

Examples

## calculate dg for inventory data grouped by plot_id and species
library(dplyr)
inventory_samples |>
mutate(dclass = silv_diametric_class(diameter)) |>
  summarise(
    height = mean(height, na.rm = TRUE),
    ntrees = n(),
    .by    = c(plot_id, species, dclass)
  ) |>
  mutate(
    ntrees_ha = silv_ntrees_ha(ntrees, plot_size = 10),
    h0        = silv_dominant_height(dclass, height, ntrees_ha),
    dg        = silv_sqrmean_diameter(dclass, ntrees_ha),
    .by       = c(plot_id, species)
  )

## calculate dg for a vector of diameters
silv_sqrmean_diameter(c(12.5, 23.5, 14, 16, 18.5))

Calculates a bunch of forest metrics

Description

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

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

Value

an S7 Inventory list with 2 tibbles

Examples

silv_summary(
  data      = inventory_samples,
  diameter  = diameter,
  height    = height,
  plot_size = 10,
  .groups   = c("plot_id", "species")
 )

Calculate Tree Volume

Description

This function calculates the volume of a tree or logs using different formulas: Pressler, Huber, Smalian, and Newton. The appropriate diameter and height parameters must be provided depending on the selected formula.

Usage

silv_volume(
  diameter_base = NULL,
  diameter_top = NULL,
  diameter_center = NULL,
  diameter = NULL,
  height = NULL,
  formula = "pressler",
  ntrees = NULL
)

Arguments

Value

A numeric value representing the tree volume.

Examples

silv_volume(diameter_base = 30, height = 20, formula = "pressler")
silv_volume(diameter_center = 25, height = 15, formula = "huber")
silv_volume(diameter_base = 30, diameter_top = 20, height = 20, formula = "smalian")

Calculates weighted mean

Description

Calculates weighted mean

Usage

weighted_median(var, wt)

Arguments

Value

A length-one numeric vector

Calculates weighted standard deviation

Description

Calculates weighted standard deviation

Usage

weighted_sd(var, wt)

Arguments

Value

A length-one numeric vector