Package 'silviculture'

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

---

Carbon content models

Description

Carbon content percentages per biomass component available in the silviculture package, compiled from Díeguez-Aranda et al. (2009) and Montero et al. (2005). If you would like to suggest new models, please open a new issue.

Usage

carbon_models

Format

A tibble with 264 rows and 13 variables:

article_id

Character. Short identifier of the source article (e.g. "montero-2005", "dieguez-aranda-2009").

title

Character. Full title of the source article.

doi_url

Character. DOI URL of the source article.

country

Character. Country where the study was conducted.

region

Character. Region within the country.

species

Character. Scientific name of the tree species.

biomass_group

Character. Biomass group ("AGB" or "BGB").

tree_group

Character. Sub-group within the biomass group.

tree_component

Character. Tree component (e.g. "stem", "bark", "thick branches", "small branches", "twigs", "leaves", "needles", "roots").

carbon_percentage

Numeric. Carbon content as a percentage (0-100). Multiply by the component biomass and divide by 100 to obtain carbon mass in the same units. NA where not reported in the source.

r2

Numeric. Coefficient of determination of the original model, if reported.

rmse

Numeric. Root mean square error of the original model, if reported.

obs

Character. Additional observations from the source article.

References

Díeguez-Aranda, U., Rojo Alboreca, A., Castedo-Dorado, F., Álvarez González, J.G., Barrio-Anta, M., Crecente-Campo, F., González González, J.M., Pérez-Cruzado, C., Rodríguez Soalleiro, R., López-Sánchez, C.A., Balboa-Murias, M.A., Gorgoso Varela, J.J., Sánchez Rodríguez, F. (2009). Herramientas selvícolas para la gestión forestal sostenible en Galicia. Xunta de Galicia.

Montero, G., Ruiz-Peinado, R., Muñoz, M. (2005). Producción de biomasa y fijación de CO2 por los bosques españoles. Monografías INIA: Serie Forestal n.º 13. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Madrid.

---

Biomass equations for 2 species in Castille and León (Spain)

Description

Allometric equations adjusted for Quercus petraea, and Pinus sylvestris in Castille and León (Spain)

Usage

eq_biomass_cudjoe_2024(species, component = "AGB", return_rmse = FALSE)

Arguments

species	A character string specifying the scientific name of the tree species. It can be a column name if all the species are included in this model. See Details for available species.
component	A character string specifying the tree component for biomass calculation (e.g., "stem", "branches"). See Details.
return_rmse	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.

Details

There are three species options in this model:

• Quercus petraea

• Pinus sylvestris

• Mixed: stands with Quercus petraea and Pinus sylvestris

The tree components include some AGB components:

• leaves: only for P. sylvestris

• stem: for all species

• medium branches and small brances: for all species

• thick branches: for all species

• AGB: total biomass, results of summing the previous components

Value

A S7 list of parameters

See Also

silv_predict_biomass(), biomass_models, eq_biomass_montero_2005(), eq_biomass_dieguez_aranda_2009() eq_biomass_ruiz_peinado_2011(), eq_biomass_ruiz_peinado_2012(), eq_biomass_manrique_2017(), eq_biomass_menendez_2022()

Examples

## get model parameters for silv_predict_biomass
eq_biomass_cudjoe_2024("mixed", "AGB")

---

Biomass equations for Galician species

Description

Allometric equations adjusted for Galician (Spain) species

Usage

eq_biomass_dieguez_aranda_2009(
  species,
  component = "stem",
  return_r2 = FALSE,
  return_rmse = FALSE
)

Arguments

species	A character string specifying the scientific name of the tree species. It can be a column name if all the species are included in this model. See Details for available species.
component	A character string specifying the tree component for biomass calculation (e.g., "tree", "stem", "branches"). See Details.
return_r2	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.
return_rmse	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.

Details

There are seven species included in this model: Pinus pinaster, Pinaster radiata, Pinus sylvestris, Eucalyptus globulus, Eucalyptus nitens, Quercus robur, and Betula alba

The tree components are divided into groups, and any of them can be introduced in the component argument:

• AGB: all aboveground biomass components

• BGB: all belowground biomass compoponents

• tree: total tree biomass includying AGB and BGB

Then we have the second group of components, which are related to tree groups:

• stem: includes the stem and bark

• branches: includes all branches

• roots: includes the roots (same as BGB)

Finally, we have the last level, which includes tree components (not all of them are available for all species): stem, bark, thick branches (>7cm), medium branches (2-7cm), thin branches (0.5-2cm), twigs (<0.5cm), dry branches, leaves, roots. In some species, there's "stem and thick branches", instead of two groups.

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

Value

A S7 list of parameters

See Also

silv_predict_biomass(), biomass_models, eq_biomass_montero_2005(), eq_biomass_ruiz_peinado_2011(), eq_biomass_ruiz_peinado_2012(), eq_biomass_manrique_2017(), eq_biomass_menendez_2022(), eq_biomass_cudjoe_2024()

Examples

## get model parameters for silv_predict_biomass
eq_biomass_dieguez_aranda_2009("Pinus pinaster", "AGB")

---

Biomass equations two Quercus species

Description

Allometric equations adjusted for Quercus petraea and Quercus pyrenaica in Palencia, Spain

Usage

eq_biomass_manrique_2017(
  species,
  component = "AGB",
  return_r2 = FALSE,
  return_rmse = FALSE
)

Arguments

species	A character string specifying the scientific name of the tree species. It can be a column name if all the species are included in this model. See Details for available species.
component	A character string specifying the tree component for biomass calculation (e.g., "stem", "branches"). See Details.
return_r2	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.
return_rmse	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.

Details

There are two species in this model: Quercus petraea and Quercus pyrenaica

The tree components include:

• stem: includes stem and the thickest branches

• medium branches

• thin branches

• AGB: total biomass, results of summing the previous three components

Value

A S7 list of parameters

See Also

silv_predict_biomass(), biomass_models, eq_biomass_montero_2005(), eq_biomass_dieguez_aranda_2009() eq_biomass_ruiz_peinado_2011(), eq_biomass_ruiz_peinado_2012(), eq_biomass_menendez_2022(), eq_biomass_cudjoe_2024()

Examples

## get model parameters for silv_predict_biomass
eq_biomass_manrique_2017("Quercus petraea", "AGB")

---

Biomass equations for young Spanish plantations

Description

Allometric equations for young (<30) plantations of 18 Spanish species including broadleaf and conifer species. Only aboveground biomass.

Usage

eq_biomass_menendez_2022(species, return_r2 = FALSE, return_rmse = FALSE)

Arguments

species	A character string specifying the scientific name of the tree species. It can be a column name if all the species are included in this model. See Details for available species.
return_r2	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.
return_rmse	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.

Details

There are 15 species in this model, including generic equations for Conifers, Deciduous broadleaves, and Evergreen broadleaves.

All the models measure only aboveground biomass.

Value

A S7 list of parameters

See Also

silv_predict_biomass(), biomass_models, eq_biomass_montero_2005(), eq_biomass_dieguez_aranda_2009() eq_biomass_ruiz_peinado_2011(), eq_biomass_ruiz_peinado_2012(), eq_biomass_manrique_2017(), eq_biomass_cudjoe_2024()

Examples

## get model parameters for silv_predict_biomass
eq_biomass_menendez_2022("Fagus sylvatica")

---

Biomass equations for Spanish species

Description

Allometric equations adjusted for Spanish species

Usage

eq_biomass_montero_2005(species, component = "stem", return_r2 = FALSE)

Arguments

species	A character string specifying the scientific name of the tree species. It can be a column name if all the species are included in this model. See Details for available species.
component	A character string specifying the tree component for biomass calculation (e.g., "tree", "stem", "branches"). See Details.
return_r2	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.

Details

There are 35 species included in the model.

The tree components are divided into groups, and any of them can be introduced in the component argument:

• AGB: all aboveground biomass components

• BGB: all belowground biomass compoponents

• tree or all: total tree biomass includying AGB and BGB

Then we have the second group of components, which are related to tree groups:

• stem: includes the stem and bark

• branches: includes all branches

• roots: includes the roots (same as BGB)

Finally, we have the last level, which includes tree components (not all of them are available for all species): stem, bark, thick branches (>7cm), medium branches (2-7cm), thin branches (0.5-2cm), leaves (include needles), roots. In some species, there's "stem and thick branches", instead of two groups.

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

Value

A S7 list of parameters

See Also

silv_predict_biomass(), biomass_models, eq_biomass_dieguez_aranda_2009() eq_biomass_ruiz_peinado_2011(), eq_biomass_ruiz_peinado_2012(), eq_biomass_manrique_2017(), eq_biomass_menendez_2022(), eq_biomass_cudjoe_2024()

Examples

## get model parameters for silv_predict_biomass
eq_biomass_montero_2005("Pinus pinaster", "AGB")

---

Biomass equations for Spanish softwood species

Description

Allometric equations adjusted for Spanish softwood species

Usage

eq_biomass_ruiz_peinado_2011(species, component = "stem", return_rmse = FALSE)

Arguments

species	A character string specifying the scientific name of the tree species. It can be a column name if all the species are included in this model. See Details for available species.
component	A character string specifying the tree component for biomass calculation (e.g., "tree", "stem", "branches"). See Details.
return_rmse	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.

Details

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

Value

A S7 list of parameters

See Also

silv_predict_biomass(), biomass_models, eq_biomass_montero_2005(), eq_biomass_dieguez_aranda_2009(), eq_biomass_ruiz_peinado_2012(), eq_biomass_manrique_2017(), eq_biomass_menendez_2022(), eq_biomass_cudjoe_2024()

Examples

## get model parameters for silv_predict_biomass
eq_biomass_ruiz_peinado_2011("Pinus pinaster")

---

Biomass equations for Spanish hardwood species

Description

Allometric equations adjusted for Spanish hardwood species

Usage

eq_biomass_ruiz_peinado_2012(species, component = "stem", return_rmse = FALSE)

Arguments

species	A character string specifying the scientific name of the tree species. It can be a column name if all the species are included in this model. See Details for available species.
component	A character string specifying the tree component for biomass calculation (e.g., "tree", "stem", "branches"). See Details.
return_rmse	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.

Details

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

Value

A S7 list of parameters

See Also

silv_predict_biomass(), biomass_models, eq_biomass_montero_2005(), eq_biomass_dieguez_aranda_2009() eq_biomass_ruiz_peinado_2011(), eq_biomass_manrique_2017(), eq_biomass_menendez_2022(), eq_biomass_cudjoe_2024()

Examples

## get model parameters for silv_predict_biomass
eq_biomass_ruiz_peinado_2012("Quercus suber")

---

Estimates tree height from DBH

Description

This function is intended to be used in silv_predict_height(). It implements the h-d equations developed in Vázquez-Veloso et al. (2025). These equations have been developed using the Spanish National Forest Inventory, and therefore, they should only be applied within Spain. The model includes parameters for 91 tree species.

Usage

eq_hd_vazquez_veloso_2025(
  species,
  bioregion = "mediterranean",
  origin = "natural",
  mixture = "pure"
)

Arguments

species	A character string specifying the scientific name of the tree species. It can be a column name if all the species are included in this model. See Details for available species. If not specified, it takes the value "All the species", which corresponds to a generic model applicable to all species.
bioregion	The biogeopgrahic region of the species. Available options are: mediterranean, atlantic, alpine, and macaronesian. If not specified, it takes the value mediterranean, which is the most common region in Spain. You can check the distribution of regions here: https://ars.els-cdn.com/content/image/1-s2.0-S037811272500489X-gr1.jpg
origin	The origin of the stand. Available options are: natural and plantation. If not specified, it takes the value natural, which is the most common origin in Spain.
mixture	The species available in the stand. Available options are: pure and mix. Consider the characteristics of the plot you are evaluating and not the entire forest, as the conditions of each stand are different. In this study, it was considered a stand to be mixed when the combined proportion of at least two species exceeds 90% of the plot's basal area, and the proportion of both species is greater than 15% of the total. It does not matter which species is accompanying or the proportion of mixing. If not specified, it takes the value pure, which is the most common condition in Spain.

Details

The model adjusts the species-specific coefficients using the selected bioregion, stand origin, and mixture type before returning the model object.

Value

A numeric vector with predicted height

References

Vázquez-Veloso, A., Yang, S.-I., Bullock, B.P., Bravo, F., 2025. One model to rule them all: A nationwide height–diameter model for 91 Spanish forest species. Forest Ecology and Management 595, 122981. https://doi.org/10.1016/j.foreco.2025.122981

See Also

silv_predict_height()

Examples

model <- eq_hd_vazquez_veloso_2025("All the species")
silv_predict_height(25, model)

---

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

z	A numeric vector representing the heights of LiDAR returns
rn	An integer vector indicating the return number for each LiDAR point. First returns are identified by a value of 1
th	a numeric vector of length one specifying the height threshold

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

z	coordinate Z (height) of the point
interval	height of the intervals to calculate the metric

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

z <- c(0.5, 1.2, 1.8, 2.4, 3.1)
lid_lhdi(z)

---

Plot an object

Description

Generic for plotting objects.

Usage

plot(x, ...)

Arguments

x	Object to plot.
...	Other arguments passed to methods.

Value

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

---

Plot Sample Size vs Error

Description

This method generates a plot showing how the required sample size varies with the maximum allowed relative error.

Arguments

x	An object of class SimpleSampleSize containing sampling options and results.
min_error	A numeric value specifying the minimum relative error to consider (default is 0.01).
max_error	A numeric value specifying the maximum relative error to consider (default is 0.5).

Value

A ggplot object representing the relationship between error and sample size.

---

Calculates Basal Area

Description

Calculates Basal Area in square meters.

Usage

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

Arguments

diameter	Numeric vector of diameters or diameter classes
ntrees	Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree. Therefore, basal area will be calculated for each individual tree
units	The units of the diameter (one of cm, mm, or m)

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

diameter	A numeric vector of tree diameters (in cm).
height	A numeric vector of tree heights (in m).
ntrees	An optional numeric value indicating the number of trees in this diameter-height class. Defaults to 1 if NULL.
species	A character string specifying the scientific name of the tree species. See Details for available species.
component	A character string specifying the tree component for biomass calculation (e.g., "tree", "stem", "branches"). See Details.
model	A character string indicating the ID of the publication in which the model was developed. Currently supported models: "ruiz-peinado-2012" (hardwood species in Spain) and "ruiz-peinado-2011" (softwood species in Spain). See Details.
return_rmse	A logical value. If TRUE, the function returns the root mean squared error (RMSE) of the selected model instead of the biomass value.
quiet	A logical value. If TRUE, suppresses any informational messages.

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"
)

---

Hart or Hart-Becking spacing index

Description

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

Usage

silv_density_hart(h0, ntrees, which = c("hart", "hart-becking"))

Arguments

h0	Numeric vector with dominant height
ntrees	Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree
which	A character with the name of the index (either hart or hart-brecking). See details

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_stand_dominant_height(diameter, height),
    ntrees = n(),
    .by    = plot_id
  ) |>
  ## calculate number of trees per hectare
  mutate(ntrees_ha = silv_density_ntrees_ha(ntrees, plot_size = 14.1)) |>
  mutate(spacing = silv_density_hart(h0, ntrees_ha))

---

Calculates number of trees per hectare

Description

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

Usage

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

Arguments

ntrees	A numeric vector representing the number of trees in a sampling plot
plot_size	A numeric vector of length one for circular radius in meters; or a numeric vector of length two for each side of a rectangular plot shape
plot_shape	The shape of the sampling plot. Either circular or rectangular

Value

A numeric vector

Examples

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

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

---

Calculates the Stand Density Index

Description

The Stand Density Index (SDI) is relationship between the average tree size and density of trees per hectare.

Usage

silv_density_sdi(ntrees, dg, classify = FALSE, max_sdi = NULL)

Arguments

ntrees	Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree
dg	Numeric vector of quadratic mean diameters
classify	whether to classify the values using USDA thresholds
max_sdi	used when classify = TRUE. The maximum SDi, which depends on the species, stand type, and site

Details

The SDI has different interpretation depending on the species, location, and also the management type (even-aged, uneven-aged...). The value of maximum SDI must be determined from the literature and used carefully. The option classify = TRUE will use this value to classify the SDI in low density (<24%), moderate density (24-35%), high density (34-55%), and extremely high density (>55%).

Value

A numeric vector

Examples

## calculate SDI for a Pinus sulvestris stand (max 990)
silv_density_sdi(ntrees = 800, dg = 23.4, max_sdi = 990)

## check base classification (other can be used)
silv_density_sdi(ntrees = 800, dg = 23.4, classify = TRUE, max_sdi = 990)

---

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

diameter	A numeric vector of diameters
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
return_intervals	If FALSE, it returns the intervals. Otherwise (the default), it returns the class center

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

diameter	Numeric vector with diameter classes
height	Numeric vector with averaged heights by diameter class
ntrees	Optional. Numeric vector with number of trees per hectare. Use this argument when you have aggregated data by diametric classes (see details).
which	The method to calculate the dominant height (see details)

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 tree. 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

#' @description

Usage

silv_lorey_height(height, g, ntrees = NULL)

Arguments

height	Numeric vector of heights
g	Numeric vector of basal areas
ntrees	Optional. Numeric vector of number of trees per hectare. Use this argument when you have aggregated data by diametric classes (see details).

Details

Tree's mean height weighted by basal area

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

ntrees	A numeric vector representing the number of trees in a sampling plot
plot_size	A numeric vector of length one for circular radius in meters; or a numeric vector of length two for each side of a rectangular plot shape
plot_shape	The shape of the sampling plot. Either circular or rectangular

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"
     )
  )

---

Calculate Tree Biomass

Description

Computes the biomass of a tree species using species-specific allometric equations (in kg). Currently, only equations for Spain are available.

Usage

silv_predict_biomass(
  diameter = NULL,
  height = NULL,
  model,
  ntrees = NULL,
  quiet = FALSE
)

Arguments

diameter	A numeric vector of tree diameters (in cm).
height	A numeric vector of tree heights (in m).
model	A function. A function with the structure eq_biomass_*() with additional arguments depending on the model used.
ntrees	An optional numeric value indicating the number of trees in this diameter-height class. Defaults to 1 if NULL.
quiet	A logical value. If TRUE, suppresses any informational messages.

Details

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

• eq_biomass_ruiz_peinado_2011(): Developed for softwood species in Spain.

• eq_biomass_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

See Also

biomass_models, eq_biomass_montero_2005(), eq_biomass_dieguez_aranda_2009(), eq_biomass_ruiz_peinado_2011(), eq_biomass_ruiz_peinado_2012(), eq_biomass_manrique_2017(), eq_biomass_menendez_2022(), eq_biomass_cudjoe_2024()

Examples

# Calculate biomass for a single tree
silv_predict_biomass(
  diameter = 45,
  height   = 22,
  model    = eq_biomass_ruiz_peinado_2011("Pinus pinaster")
)

---

Estimates tree height from DBH

Description

Estimates total tree height using height-diameter (h-d) equations. Currently, only models developed for Spain are available.

Usage

silv_predict_height(diameter, model, quiet = FALSE)

Arguments

diameter	Numeric vector with diameters in cm
model	A function. A function with the structure eq_hd_*() with additional arguments depending on the specific model. Currently only eq_hd_vazquez_veloso_2025() is available.
quiet	A logical value. If TRUE, suppresses any informational messages.

Details

The function estimates total tree height (in meters) using diameter at breast height (in centimeters), and may require additional information depending on the specific model. See each model’s documentation for details.

Value

A numeric vector with predicted heights

References

References for the models available:

• eq_hd_vazquez_veloso_2025(): Vázquez-Veloso, A., Yang, S.-I., Bullock, B.P., Bravo, F., 2025. One model to rule them all: A nationwide height–diameter model for 91 Spanish forest species. Forest Ecology and Management 595, 122981. https://doi.org/10.1016/j.foreco.2025.122981

See Also

eq_hd_vazquez_veloso_2025()

Examples

1 + 1 #TODO

---

Calculates sample size for a random sampling inventory

Description

Usage

silv_sample_size(
  x,
  plot_size,
  total_area,
  method = "random",
  max_error = 0.05,
  conf_level = 0.95,
  max_iter = 1000,
  quiet = FALSE
)

Arguments

x	vector of field survey
plot_size	a numeric vector of length one with plot size in squared meters
total_area	total area of the study area in squared meters
method	sampling method. Available options are random
max_error	maximum allowed error
conf_level	confidence level
max_iter	maximum number of iteration to find the plot size
quiet	if TRUE, messages will be supressed

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
)

---

Calculates sample size for a simple random sampling (SRS)

Description

Calculates the sample size needed for a SRS inventory, estimated from pilot inventory data.

Usage

silv_sample_size_simple(
  x,
  plot_size,
  total_area,
  max_error = 0.05,
  conf_level = 0.95,
  max_iter = 1000,
  quiet = FALSE
)

Arguments

x	vector of the variable measured in the pilot inventory (e.g. basal area, volume)
plot_size	a numeric vector of length one with plot size in squared meters
total_area	total area of the study area in squared meters
max_error	maximum allowed relative error
conf_level	confidence level
max_iter	maximum number of iteration to find the plot size
quiet	if TRUE, messages will be supressed

Details

Sample size is very important to be optimized, since a small sample size will entail a higher error, while a huge sample size will entail higher costs. The SRS is typically used for random sampling, although it might be used also for regular sampling. The number of samples is calculated using the expression:

$n \ge \frac{t^2 \cdot CV^2}{\epsilon^2 + \frac{t^2 \cdot CV^2}{N}}$

Where:

• t: the value of student's t for given sample size of the pilot inventory

• CV: the coefficient of variation of x

• $\epsilon$: the relative error (max_error)

• N: the size of the pilot inventory

x is a variable measured in a pilot inventory. Let's say we measure forest variables in 10 pilot plots, aiming at basal area measurement so we have to measure only the DBH. After some calculations, we will have the basal area per hectare in each of the 10 plots. The sample size is then calculated from the variation of these values and the error that we will allow.

Value

SimpleSampleSize 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_simple(
  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
)

---

Calculates sample size for a stratified sampling

Description

Calculates the sample size needed for a stratified inventory, estimated from pilot inventory data.

Usage

silv_sample_size_stratified(
  data,
  x,
  strata,
  total_area,
  plot_size,
  method = "optimal",
  cost = NA,
  max_error = 0.05,
  conf_level = 0.95,
  max_iter = 1000,
  currency = "EUR",
  quiet = FALSE
)

Arguments

data	a data.frame of pilot inventory data
x	name of the variable in data that was measured (e.g. basal area, volume)
strata	name of the variable in data with the name of the stratum
total_area	name of the variable in data with the area of the stratum
plot_size	a numeric vector of length one with plot size in squared meters
method	a charater vector of length one with the id of the method. Available options are optimal, cost, and prop. See details
cost	name of the variable in data with the average cost of measuring one plot of the stratum. Used with method = 'cost' for sample size, and for message output in other methods
max_error	maximum allowed relative error
conf_level	confidence level
max_iter	maximum number of iteration to find the plot size
currency	currency to be shown in console output when using method = 'cost'
quiet	if TRUE, messages will be supressed

Details

Stratified Sampling calculates the number of plots to be inventored in different strata. For instance, you might have Pinus sylvestris and Pinus pinaster plots in the same forest, and you might want to get the optimal number of plots for field inventory of each stratum, for a given maximum relative error (e.g. 5%), and with a certain level of confidence (e.g 95%). Of course, the area of P. sylvestris will be different than the area occupied by P. pinaster. For instance, the total area of P. sylvestris could be 100 ha, while the area of P. pinaster could be 200 ha. Therefore, you need to create a pilot inventory and measure a variable such as basal area maybe in 5 pilot plots of P. sylvestris and 7 pilot plots of P. pinaster. With that data collected, you can use three stratified sample size methods:

• Optimal Allocation with Constant Cost: using method = 'optimal'. The sampling units are distributed within the different strata taking into account the size (e.g. 100 ha vs 200 ha) and the heterogeinity (e.g. differences in basal area). It minimizes the number of sampling units.

$n = \frac{t^2_{n - m} \cdot (\sum^{j = m}_{j = 1} P_j \cdot s_j)^2 }{\epsilon^2 + \frac{t^2_{n - m} \cdot \sum^{j = m}_{j = 1} P_j \cdot s_j^2}{N}}$

• Optimal Allocation with Variable Cost: using method = 'cost'. This method needs to know the cost of a sampling unit in each strata. It will minimize the cost of the inventory, taking into account the size, the heterogeinity, and the cost of the sampling unit of the strata.

$n = \frac{t^2_{n-m} \cdot (\sum^{j = m}_{j = 1} \cdot P_j \cdot s_j \cdot \sqrt{c_j}) \cdot (\sum^{j = m}_{j = 1} \cdot \frac{P_j \cdot s_j}{\sqrt{c_j}})}{\epsilon^2 + \frac{t^2_{n - m} \cdot \sum^{j = m}_{j = 1} P_j \cdot s_j^2}{N}}$

• Proportional Allocation: using method = 'prop'. The sampling units are distributed proportional to the size of the strata. In the example, 33% of the estimated sampling units will be allocated to P. sylvestris and 66% to P. pinaster.

$n = \frac{t^2_{n - m} \cdot \sum^{j = m}_{j = 1} P_j \cdot s_j^2 }{\epsilon^2 + \frac{t^2_{n - m} \cdot \sum^{j = m}_{j = 1} P_j \cdot s_j^2}{N}}$

Where:

• n: estimated sample size

• t: the value of student's t

• $P_j$: proportion of pilot plots of $j^{th}$ strata

• $s_j$: standard deviation of x

• $s_j^2$: variance of x

• N: population size (number of plots of plot_size that fit in total_area)

• $\epsilon$: maximum allowed absolute error. Calculated from x and max_error

• N: the size of the pilot inventory

Value

S7 StratifiedSampleSize object with:

• results: data.frame with the main results by stratum

• strata_error: data.frame with maximum absolute error $\mp$ C.I (max_abs_error, x_min, x_max), and the esimator of the typical error $\mp$ C.I (sampling error, x_ci_lo, x_ci_hi)

• sampling_error: data.frame with the maximum absolute error $\mp$ C.I (max_abs_error, x_min, x_max), and the typical sampling error of the weighted mean $\mp$ C.I (sampling error, x_ci_lo, x_ci_hi)

• sampling_opts: list with function options

Examples

## read pilot inventory ficticious data
data_path <- system.file("extdata/pilot_inventory.csv", package = "silviculture")
inventory_tbl <- read.csv(data_path)

## calculate sample size
sample_size_list <- silv_sample_size_stratified(
  data  = inventory_tbl,
  x     = basal_area,
  strata = stratum,
  total_area = area,
  method = "optimal",
  cost = cost,
  plot_size = 100,
  conf_level = .95,
  max_error = .05
)

---

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

h0	Numeric vector with dominant height
ntrees	Numeric vector with number of trees of the dominant height per hectare
which	A character with the name of the index (either hart or hart-brecking). See details

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 quadratic mean diameter (QMD)

Description

Usage

silv_sqrmean_diameter(diameter, ntrees = NULL)

Arguments

diameter	Numeric vector of diameters or diameter classes
ntrees	Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree. Therefore, the QMD will be calculated for each individual tree

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 Basal Area

Description

Calculates Basal Area in square meters.

Usage

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

Arguments

diameter	Numeric vector of diameters or diameter classes
ntrees	Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree
units	The units of the diameter (one of mm, cm, dm, or m)

Details

The function uses the next formula:

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

where G is the basal area in $m^2$, and D is the diameter in cm. If ntrees in the number of trees per hectare, then the result will be $m^2/ha$. It is recommended to use the squared mean diameter calculated with silv_stand_qmean_diameter().

Note that if ntrees = NULL, the output of the function will be exactly the same as in silv_tree_basal_area().

Value

A numeric vector

Examples

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

---

Calculates the dominant diameter

Description

Calculates the dominant diameter using Assman and Friedrich method, or Weise method

Usage

silv_stand_dominant_diameter(
  diameter,
  ntrees = NULL,
  which = c("assman", "weise")
)

Arguments

diameter	Numeric vector of diameters or diameter classes
ntrees	Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree
which	The method to calculate the dominant diameter (see details)

Details

The dominant diameter $D_0$ is the mean diameter of the 100 thickest trees per hectare. Therefore, diameter and ntrees should be vectors of the same length.

• Assman: calculates the $D_0$ as the mean diameter of the 100 thickest trees per hectare

• Weise: calculates the $D_0$ as the quadratic mean diameter of the 20% thickest trees per hectare

Value

A numeric vector

Examples

## calculate d0 for inventory data grouped by plot_id and species
library(dplyr)
inventory_samples |>
mutate(dclass = silv_tree_dclass(diameter)) |>
  summarise(
    height = mean(height, na.rm = TRUE),
    ntrees = n(),
    .by    = c(plot_id, species, dclass)
  ) |>
  mutate(
    ntrees_ha = silv_density_ntrees_ha(ntrees, plot_size = 10),
    d0        = silv_stand_dominant_diameter(dclass, ntrees_ha),
    .by       = c(plot_id, species)
  )

---

Calculates the dominant height

Description

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

Usage

silv_stand_dominant_height(
  diameter,
  height,
  ntrees = NULL,
  which = c("assman", "hart")
)

Arguments

diameter	Numeric vector of diameters or diameter classes
height	Numeric vector of tree heights
ntrees	Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree
which	The method to calculate the dominant height (see details)

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 tree. 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_tree_dclass(diameter)) |>
  summarise(
    height = mean(height, na.rm = TRUE),
    ntrees = n(),
    .by    = c(plot_id, species, dclass)
  ) |>
  mutate(
    ntrees_ha = silv_density_ntrees_ha(ntrees, plot_size = 10),
    h0        = silv_stand_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_stand_lorey_height(height, g, ntrees = NULL)

Arguments

height	Numeric vector of tree heights
g	Numeric vector of basal areas
ntrees	Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree

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_tree_basal_area(diameter)) |>
  summarise(
    lh  = silv_stand_lorey_height(height, g),
    .by = c(plot_id, species)
  )

---

Calculates the quadratic mean diameter (QMD)

Description

Calculates the quadratic mean diameter (QMD)

Usage

silv_stand_qmean_diameter(diameter, ntrees = NULL)

Arguments

diameter	Numeric vector of diameters or diameter classes
ntrees	Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree

Value

A numeric vector

Examples

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

## calculate dg for a vector of diameters
silv_stand_qmean_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

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()

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 Forestry Thinning Schemes

Description

Calculates thinning schemes for forest management by selecting trees to extract based on specified criteria. Supports both thinning from below (removing smaller trees) and thinning from above (removing larger trees) approaches.

Usage

silv_treatment_thinning(
  data,
  var,
  diameter,
  ntrees,
  thinning = c("below", "above"),
  perc = 0.3,
  .groups = NULL
)

Arguments

data	A data frame, or silviculture::Inventory object. See details.
var	A variable used for calculating the thinning. Typically used variables basal area, number of trees, or volume
diameter	Numeric vector of diameters or diameter classes
ntrees	Numeric vector with number of trees of the diameter class per hectare. If ntrees = NULL, the function will assume that each diameter corresponds to only one tree
thinning	Charater string specifying the thinning type. Available options are below and above
perc	Numeric value between 0 and 1 specifying the percentage of var to extract
.groups	A character vector with variables to group by (e.g. plot id, tree species, etc). Ignored when using a silviculture::Inventory object

Details

This function implements common silvicultural thinning practices:

Thinning from below: Removes trees with the lowest values of the specified variable. This approach typically removes suppressed, damaged, or poor-quality trees, mimicking natural mortality processes.

Thinning from above: Removes trees with the highest values of the specified variable. This approach harvests the most valuable trees while leaving smaller trees to continue growing.

The function calculates which trees to extract based on the ranking of the specified variable and the desired thinning percentage. When grouping variables are provided, thinning is calculated separately for each group.

Using a silviculture::Inventory object The result of silv_summary() can be used as the data argument. If so, the .groups will be taken from this object, and it will keep the previous data in a new S7 object.

Value

A silviculture::Thinning object with three items:

• data: the input data with two new columns

• group_metrics: it will include the data from the silviculture::Inventory object

• thinning_opts: options used for S7 methods

See Also

silv_summary()

Examples

# Get summary of inventory data
inventory <- inventory_samples |>
 silv_summary(
   diameter  = diameter,
   height    = height,
   plot_size = 25,
   .groups   = c('plot_id', 'species')
 )

## Thinning from below removing 30% of trees based on basal area
silv_treatment_thinning(
  data     = inventory,
  var      = g_ha,
  diameter = dclass,
  ntrees   = ntrees_ha,
  thinning = "below",
  perc     = 0.3
)

## Thinning from above removing 20% of trees based on basal area
silv_treatment_thinning(
  data     = inventory,
  var      = g_ha,
  diameter = dclass,
  ntrees   = ntrees_ha,
  thinning = "above",
  perc     = 0.2
)

---

Calculates Basal Area

Description

Calculates Basal Area in square meters.

Usage

silv_tree_basal_area(diameter, units = "cm")

Arguments

diameter	Numeric vector of diameters or diameter classes
units	The units of the diameter (one of mm, cm, dm, or m)

Details

The function uses the next formula:

$g = \frac{\pi}{40000} \cdot D^2$

where g is the basal area in $m^2$ of one tree, and D is the diameter in cm.

If you want to calculate the basal area for a group of trees (e.g. per hectares), please use silv_stand_basal_area()

Value

A numeric vector

See Also

silv_stand_basal_area()

Examples

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

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Classify diameters in classes

Description

Classifies the measured diameters into classes of a specified length

Usage

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

Arguments

diameter	Numeric vector of diameters or diameter classes
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
return_intervals	If FALSE, it returns the intervals. Otherwise (the default), it returns the class center

Value

A numeric vector

Examples

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

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

Arguments

diameter_base	A numeric vector. The diameter at the base of the tree (required for Pressler, Smalian, and Newton formulas).
diameter_top	A numeric vector. The diameter at the top of the tree (required for Smalian and Newton formulas).
diameter_center	A numeric vector. The diameter at the center of the tree (required for Huber and Newton formulas).
diameter	A numeric vector. The diameter at breast height (used in Pressler formula if provided instead of diameter_base).
height	A numeric vector. The tree or log height (required for all formulas).
formula	Character. The volume formula to use. Options: "pressler", "huber", "smalian", "newton". Default is "pressler".
ntrees	A numeric vector with number of trees of the same dimensions. Default is 1.

Value

A numeric value representing the tree volume.

Examples

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

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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

diameter_base	A numeric vector. The diameter at the base of the tree (required for Pressler, Smalian, and Newton formulas).
diameter_top	A numeric vector. The diameter at the top of the tree (required for Smalian and Newton formulas).
diameter_center	A numeric vector. The diameter at the center of the tree (required for Huber and Newton formulas).
diameter	A numeric vector. The diameter at breast height (used in Pressler formula if provided instead of diameter_base).
height	A numeric vector. The tree or log height (required for all formulas).
formula	Character. The volume formula to use. Options: "pressler", "huber", "smalian", "newton". Default is "pressler".
ntrees	A numeric vector with number of trees of the same dimensions. Default is 1.

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")

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