Package 'silviculture' reference manual

Title:	Utility Functions for Forest Inventory and Silviculture
Description:	Perform common dendrometry operations such as inventory preparing, and inventory data analysis.
Authors:	Adrián Cidre González [aut, cre], Aitor Vázquez Veloso [aut]
Maintainer:	Adrián Cidre González <[email protected]>
License:	GPL (>= 3)
Version:	0.2.0
Built:	2026-06-20 11:23:21 UTC
Source:	https://github.com/cidree/silviculture

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
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)
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

Supported species (3):

Pinus sylvestris
Quercus petraea
"mixed" — mixed stand of Pinus sylvestris × Quercus petraea

Available components:

Aboveground group (summed automatically):

"AGB" — total aboveground biomass (sum of all components below)

Individual tree components (species availability varies):

"stem" — stem wood (all species)
"thick branches" — branches > 7 cm (all species)
"medium branches and small branches" — branches < 7 cm (all species)
"leaves" — foliage/needles (Pinus sylvestris only)

Note that no belowground biomass (BGB / roots) or total-tree equations are available in the source paper for this model.

Users can check all available species and components in the biomass_models dataset provided by the library.

Value

A ModelBiomass object containing the configured model parameters and expressions.

Examples

## Aboveground biomass for a mixed stand
eq_biomass_cudjoe_2024("mixed", "AGB")
## Aboveground biomass for a mixed stand
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
)
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

Supported species (7):

Betula alba, Eucalyptus globulus, Eucalyptus nitens, Pinus pinaster, Pinus radiata, Pinus sylvestris, Quercus robur

Available components:

Aboveground / belowground groups (summed automatically):

"AGB" — total aboveground biomass
"BGB" — total belowground biomass (roots)

Tree structural groups:

"stem" — stem fraction(s)
"branches" — all branch fractions combined
"roots" — roots (equivalent to BGB)

Individual tree components (species availability varies):

"stem and thick branches" — stem together with thickest branches
"thick branches" — branches > 7 cm
"medium branches" — branches 2–7 cm
"small branches" — branches 0.5–2 cm
"twigs" — branches < 0.5 cm
"dry branches" — dead attached branches
"leaves" — foliage (including needles)
"roots" — coarse roots

Note that total-tree equations ("tree" / "all") are not available for this model. Also, Eucalyptus globulus, Eucalyptus nitens, and Pinus pinaster lack BGB equations (requesting "BGB" or "roots" will fail).

Users can check all available species and components in the biomass_models dataset provided by the library.

Value

A ModelBiomass object containing the configured model parameters and expressions.

Examples

## Aboveground biomass for Pinus pinaster
eq_biomass_dieguez_aranda_2009("Pinus pinaster", "AGB")
## Aboveground biomass for Pinus pinaster
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
)
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

Supported species (2):

Quercus petraea
Quercus pyrenaica

Available components:

Aboveground group (summed automatically):

"AGB" — total aboveground biomass (sum of all components below)

Individual tree components:

"stem and thick branches" — stem together with branches > 7 cm
"medium branches" — branches 2–7 cm
"small branches" — branches < 2 cm

Note that no belowground biomass (BGB / roots) or total-tree equations are available in the source paper.

Users can check all available species and components in the biomass_models dataset provided by the library.

Value

A ModelBiomass object containing the configured model parameters and expressions.

Examples

## Aboveground biomass for Quercus petraea
eq_biomass_manrique_2017("Quercus petraea", "AGB")
## Aboveground biomass for Quercus petraea
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)
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

Supported species (18):

Betula sp., Fagus sylvatica, Juniperus thurifera, Pinus halepensis, Pinus nigra, Pinus pinaster, Pinus pinea, Pinus radiata, Pinus sylvestris, Quercus faginea, Quercus ilex, Quercus petraea, Quercus pyrenaica, Quercus robur, Quercus suber

Generic equations are also available for functional groups:

"Conifers" — generic equation for conifer species
"Deciduous broadleaves" — generic equation for deciduous broadleaf species
"Evergreen broadleaves" — generic equation for evergreen broadleaf species

Available components:

"AGB" — aboveground biomass (only component available; no component argument needed)

Important — non-standard input variables:

Unlike other biomass models, these equations were fitted on young plantations (< 30 years) and use different predictor variables:

Most species use rcd = root collar diameter (cm), not diameter at breast height. Pass it via the rcd argument of silv_predict_biomass().
Some species (Pinus halepensis, Pinus nigra, Quercus suber, Evergreen broadleaves) use bp = biomass packing (m³). Pass it via the bp argument of silv_predict_biomass().
Betula sp. uses only h (total height).

Note that no belowground biomass (BGB / roots) or total-tree equations are available in the source paper for this model.

Users can check all available species and components in the biomass_models dataset provided by the library.

Value

A ModelBiomass object containing the configured model parameters and expressions.

Examples

## AGB for Fagus sylvatica using root collar diameter
eq_biomass_menendez_2022("Fagus sylvatica")
## AGB for Fagus sylvatica using root collar diameter
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)
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

Supported species (35):

Abies alba, Abies pinsapo, Alnus glutinosa, Betula spp., Castanea sativa, Ceratonia siliqua, Erica arborea, Eucalyptus spp., Fagus sylvatica, Fraxinus spp., Ilex canariensis, Juniperus oxycedrus, Juniperus phoenicea, Juniperus thurifera, Laurus azorica, Myrica faya, Olea europaea var. sylvestris, Other broadleaves, Other conifers, Other laurel species, Pinus canariensis, Pinus halepensis, Pinus nigra, Pinus pinaster, Pinus pinea, Pinus radiata, Pinus sylvestris, Pinus uncinata, Populus x euramericana, Quercus canariensis, Quercus faginea, Quercus ilex, Quercus pyrenaica, Quercus robur, Quercus suber

Available components:

Aboveground / belowground groups (summed automatically):

"AGB" — total aboveground biomass
"BGB" — total belowground biomass (roots)
"all" or "tree" — total tree biomass (AGB + BGB)

Tree structural groups:

"stem" — stem fraction(s)
"branches" — all branch fractions combined
"roots" — roots (equivalent to BGB)

Individual tree components (species availability varies):

"stem" — stem wood
"stem and thick branches" — stem together with branches > 7 cm
"thick branches" — branches > 7 cm
"medium branches" — branches 2–7 cm
"small branches" — branches < 2 cm
"leaves" — foliage (including needles)
"roots" — coarse roots

Note that for this model, "tree" (or "all") is an independent regression equation fitted to total-tree data. It was not derived by summing the AGB and BGB equations. Consequently, there is a numerical discrepancy between the direct "tree" estimation and the sum of separate "AGB" and "BGB" estimations (e.g. for Pinus sylvestris at diameter = 20 cm and height = 10 m, the direct total is 89.1 kg, while AGB + BGB is 115.9 kg, a 24% difference).

Also, the following 6 species have no BGB/roots equations in this model: Abies pinsapo, Erica arborea, Eucalyptus spp., Ilex canariensis, Laurus azorica, Myrica faya (requesting "BGB" or "roots" will fail).

Users can check all available species and components in the biomass_models dataset provided by the library.

Value

A ModelBiomass object containing the configured model parameters and expressions.

Examples

## Aboveground biomass for Pinus pinaster
eq_biomass_montero_2005("Pinus pinaster", "AGB")
## Aboveground biomass for Pinus pinaster
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)
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

Supported species (10):

Abies alba, Abies pinsapo, Juniperus thurifera, Pinus canariensis, Pinus halepensis, Pinus nigra, Pinus pinaster, Pinus pinea, Pinus sylvestris, Pinus uncinata

Available components:

Aboveground / belowground groups (summed automatically):

"AGB" — total aboveground biomass
"BGB" — total belowground biomass (roots)
"tree" — total tree biomass (AGB + BGB)

Tree structural groups:

"stem" — stem wood
"branches" — all branch fractions combined
"roots" — roots (equivalent to BGB)

Individual tree components (species availability varies):

"thick branches" — branches > 7 cm
"thick and medium branches" — branches > 2 cm
"medium branches" — branches 2–7 cm
"small branches and leaves" — branches < 2 cm including leaves/needles

Note that Abies pinsapo does not have a separate BGB equation (requesting "BGB" or "roots" will fail, though "tree" still works via its pre-summed formula).

Users can check all available species and components in the biomass_models dataset provided by the library.

Value

A ModelBiomass object containing the configured model parameters and expressions.

Examples

## Aboveground biomass for Pinus pinaster
eq_biomass_ruiz_peinado_2011("Pinus pinaster", "AGB")
## Aboveground biomass for Pinus pinaster
eq_biomass_ruiz_peinado_2011("Pinus pinaster", "AGB")

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)
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

Supported species (13):

Alnus glutinosa, Castanea sativa, Ceratonia siliqua, Eucalyptus globulus, Fagus sylvatica, Fraxinus angustifolia, Olea europaea, Populus x euramericana, Quercus canariensis, Quercus faginea, Quercus ilex, Quercus pyrenaica, Quercus suber

Available components:

Aboveground / belowground groups (summed automatically):

"AGB" — total aboveground biomass
"BGB" — total belowground biomass (roots)
"tree" — total tree biomass (AGB + BGB)

Tree structural groups:

"stem" — stem wood
"branches" — all branch fractions combined
"roots" — roots (equivalent to BGB)

Individual tree components (species availability varies):

"stem and thick branches" — stem together with branches > 7 cm
"thick branches" — branches > 7 cm
"thick and medium branches" — branches > 2 cm
"medium branches" — branches 2–7 cm
"small branches" — branches 0.5–2 cm
"small branches and leaves" — branches < 2 cm including leaves
"medium branches, small branches and leaves" — branches < 7 cm including leaves

Note that Eucalyptus globulus does not have a separate BGB equation (requesting "BGB" or "roots" will fail, though "tree" still works via its pre-summed formula).

Users can check all available species and components in the biomass_models dataset provided by the library.

Value

A ModelBiomass object containing the configured model parameters and expressions.

Examples

## Aboveground biomass for Quercus suber
eq_biomass_ruiz_peinado_2012("Quercus suber", "AGB")
## Aboveground biomass for Quercus suber
eq_biomass_ruiz_peinado_2012("Quercus suber", "AGB")

Height-diameter equations from Vázquez-Veloso et al. (2025)

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"
)
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 ModelHD object containing the configured model parameters and metadata.

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

Examples

model <- eq_hd_vazquez_veloso_2025("All the species")
silv_predict_height(25, model)
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
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)
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)
# 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)
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)
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, ..., min_error = 0.01, max_error = 0.5)

## S7 method for class <silviculture::SampleSize>
plot(x, ..., min_error = 0.01, max_error = 0.5)
plot(x, ..., min_error = 0.01, max_error = 0.5)

## S7 method for class <silviculture::SampleSize>
plot(x, ..., min_error = 0.01, max_error = 0.5)

Arguments

x

Object to plot.

...

Other arguments passed to methods.

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

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

Predict carbon content from biomass

Description

silv_predict_carbon() calculates the carbon content of a tree component based on its predicted biomass. It uses species- and component-specific carbon percentages from the package's internal carbon_models database.

silv_predict_carbon_auto() is a vectorized function that automatically selects the best available carbon model for each row based on a provided priority list. If the exact species is not found, it attempts a genus-level fallback (e.g., "Pinus spp.").

Usage

silv_predict_carbon(
  biomass,
  species,
  component,
  model = "montero-2005",
  quiet = FALSE
)

silv_predict_carbon_auto(
  biomass,
  species,
  component,
  priority = c("montero-2005", "dieguez-aranda-2009"),
  quiet = FALSE
)
silv_predict_carbon(
  biomass,
  species,
  component,
  model = "montero-2005",
  quiet = FALSE
)

silv_predict_carbon_auto(
  biomass,
  species,
  component,
  priority = c("montero-2005", "dieguez-aranda-2009"),
  quiet = FALSE
)

Arguments

biomass

A numeric vector of predicted biomass (in kg).

species

A character vector of tree species (e.g., "Pinus pinaster").

component

A character string or vector specifying the tree component (e.g., "stem", "roots", "tree"). Must match the tree_component column in the carbon_models dataset. Note that some models do not provide carbon percentages for aggregate groups like "AGB".

model

A character string specifying the carbon model to use (e.g., "montero-2005").

quiet

Logical. If FALSE, informs the user about genus fallbacks or missing species.

priority

A character vector specifying the order of preference for carbon models. Default is c("montero-2005", "dieguez-aranda-2009").

Value

For silv_predict_carbon(): A numeric vector of predicted carbon (in kg). Returns NA if the species/component combination is not supported by the model.

For silv_predict_carbon_auto(): A data.frame with two columns:

carbon: The predicted carbon (in kg).
carbon_model: The model and species level used (e.g., "montero-2005" or "montero-2005 (genus fallback)").

Examples

# 1. Vector-based calculation: calculate stem carbon for Pinus pinaster
biomass_vector <- c(120.5, 230.1, 85.4)
species_vector <- c("Pinus pinaster", "Pinus pinaster", "Pinus pinaster")

carbon_vector <- silv_predict_carbon(
  biomass = biomass_vector,
  species = species_vector,
  component = "stem",
  model = "montero-2005"
)
print(carbon_vector)

# 2. Vector-based calculation with row-wise auto-selection & genus fallback
carbon_df <- silv_predict_carbon_auto(
  biomass = c(120.5, 95.0),
  species = c("Pinus pinaster", "Pinus radiata"), # Pinus radiata triggers genus fallback
  component = c("stem", "roots")
)
print(carbon_df)

# 3. Dataset-based tutorial: apply to a forest inventory data frame
inventory <- data.frame(
  tree_id   = 1:3,
  species   = c("Pinus pinaster", "Pinus sylvestris", "Quercus robur"),
  biomass_kg = c(150.0, 180.5, 220.0),
  component = c("stem", "roots", "stem")
)

# Apply row-wise auto-selection to the entire dataset
carbon_results <- silv_predict_carbon_auto(
  biomass   = inventory$biomass_kg,
  species   = inventory$species,
  component = inventory$component
)

# Combine and display results
inventory_with_carbon <- cbind(inventory, carbon_results)
print(inventory_with_carbon)
# 1. Vector-based calculation: calculate stem carbon for Pinus pinaster
biomass_vector <- c(120.5, 230.1, 85.4)
species_vector <- c("Pinus pinaster", "Pinus pinaster", "Pinus pinaster")

carbon_vector <- silv_predict_carbon(
  biomass = biomass_vector,
  species = species_vector,
  component = "stem",
  model = "montero-2005"
)
print(carbon_vector)

# 2. Vector-based calculation with row-wise auto-selection & genus fallback
carbon_df <- silv_predict_carbon_auto(
  biomass = c(120.5, 95.0),
  species = c("Pinus pinaster", "Pinus radiata"), # Pinus radiata triggers genus fallback
  component = c("stem", "roots")
)
print(carbon_df)

# 3. Dataset-based tutorial: apply to a forest inventory data frame
inventory <- data.frame(
  tree_id   = 1:3,
  species   = c("Pinus pinaster", "Pinus sylvestris", "Quercus robur"),
  biomass_kg = c(150.0, 180.5, 220.0),
  component = c("stem", "roots", "stem")
)

# Apply row-wise auto-selection to the entire dataset
carbon_results <- silv_predict_carbon_auto(
  biomass   = inventory$biomass_kg,
  species   = inventory$species,
  component = inventory$component
)

# Combine and display results
inventory_with_carbon <- cbind(inventory, carbon_results)
print(inventory_with_carbon)

Calculates Basal Area

Description

Calculates Basal Area in square meters.

Usage

silv_basal_area(diameter, ntrees = NULL, units = "cm")
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 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
)
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"
)
# 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"))
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))
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")
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"
     )
  )
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)
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)
## 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
)
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))
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")
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)
  )
## 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)
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)
  )
## 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")
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"
     )
  )
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,
  rcd = NULL,
  bp = NULL,
  quiet = FALSE
)
silv_predict_biomass(
  diameter = NULL,
  height = NULL,
  model,
  ntrees = NULL,
  rcd = NULL,
  bp = NULL,
  quiet = FALSE
)

Arguments

diameter

A numeric vector of tree diameters at breast height (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.

rcd

An optional numeric vector of root collar diameters (in cm). Required for eq_biomass_menendez_2022, which uses root collar diameter instead of diameter at breast height. Defaults to diameter if NULL.

bp

An optional numeric vector of biomass packing values (in m³). Required for a subset of species in eq_biomass_menendez_2022 (e.g. Pinus halepensis, Pinus nigra, Quercus suber, Evergreen broadleaves).

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.
eq_biomass_montero_2005(): Developed for 35 Spanish species.
eq_biomass_dieguez_aranda_2009(): Developed for 7 Galician species.
eq_biomass_manrique_2017(): Developed for Quercus petraea and Quercus pyrenaica.
eq_biomass_menendez_2022(): Developed for young plantations (< 30 years) of 18 Spanish species. Uses rcd (root collar diameter) instead of diameter; some species require bp (biomass packing) instead of rcd.
eq_biomass_cudjoe_2024(): Developed for Pinus sylvestris and Quercus petraea in Castille and León, 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 with predicted tree biomass (kg).

Examples

# 1. Vector-based calculation: predict stem/tree biomass for Pinus pinaster
model <- eq_biomass_ruiz_peinado_2011("Pinus pinaster")
predicted_biomass <- silv_predict_biomass(
  diameter = c(20, 25, 30),
  height   = c(15, 17, 18),
  model    = model
)
print(predicted_biomass)

# 2. Dataset-based tutorial: apply to a forest inventory data frame
inventory <- data.frame(
  tree_id  = 1:3,
  species  = c("Pinus pinaster", "Pinus pinaster", "Pinus pinaster"),
  dbh_cm   = c(18.5, 22.1, 29.4),
  height_m = c(14.0, 16.5, 19.0)
)

# Apply prediction and append a new column to the dataset
inventory$biomass_kg <- silv_predict_biomass(
  diameter = inventory$dbh_cm,
  height   = inventory$height_m,
  model    = model
)
print(inventory)

# 3. Young plantation example (Menendez 2022 model) using rcd and bp
# Menendez 2022 equations use root collar diameter (rcd) and/or biomass packing (bp)
model_menendez <- eq_biomass_menendez_2022("Pinus pinaster")
predicted_young_pinaster <- silv_predict_biomass(
  rcd      = c(5.2, 7.1, 9.4),   # Root collar diameter in cm
  height   = c(2.1, 3.2, 4.5),   # Height in m
  model    = model_menendez
)
print(predicted_young_pinaster)

# For Pinus halepensis, Menendez 2022 requires biomass packing (bp)
model_halepensis <- eq_biomass_menendez_2022("Pinus halepensis")
predicted_young_halepensis <- silv_predict_biomass(
  bp     = c(0.005, 0.012),     # Biomass packing in m3
  model  = model_halepensis
)
print(predicted_young_halepensis)
# 1. Vector-based calculation: predict stem/tree biomass for Pinus pinaster
model <- eq_biomass_ruiz_peinado_2011("Pinus pinaster")
predicted_biomass <- silv_predict_biomass(
  diameter = c(20, 25, 30),
  height   = c(15, 17, 18),
  model    = model
)
print(predicted_biomass)

# 2. Dataset-based tutorial: apply to a forest inventory data frame
inventory <- data.frame(
  tree_id  = 1:3,
  species  = c("Pinus pinaster", "Pinus pinaster", "Pinus pinaster"),
  dbh_cm   = c(18.5, 22.1, 29.4),
  height_m = c(14.0, 16.5, 19.0)
)

# Apply prediction and append a new column to the dataset
inventory$biomass_kg <- silv_predict_biomass(
  diameter = inventory$dbh_cm,
  height   = inventory$height_m,
  model    = model
)
print(inventory)

# 3. Young plantation example (Menendez 2022 model) using rcd and bp
# Menendez 2022 equations use root collar diameter (rcd) and/or biomass packing (bp)
model_menendez <- eq_biomass_menendez_2022("Pinus pinaster")
predicted_young_pinaster <- silv_predict_biomass(
  rcd      = c(5.2, 7.1, 9.4),   # Root collar diameter in cm
  height   = c(2.1, 3.2, 4.5),   # Height in m
  model    = model_menendez
)
print(predicted_young_pinaster)

# For Pinus halepensis, Menendez 2022 requires biomass packing (bp)
model_halepensis <- eq_biomass_menendez_2022("Pinus halepensis")
predicted_young_halepensis <- silv_predict_biomass(
  bp     = c(0.005, 0.012),     # Biomass packing in m3
  model  = model_halepensis
)
print(predicted_young_halepensis)

Automatically Predict Biomass Using the Best Available Model

Description

Evaluates vectors of tree species, diameters, and heights, and automatically selects the best available allometric model based on a specified priority. If tree height is not provided, is NA, or is 0, the function automatically falls back to the eq_biomass_montero_2005 model.

Usage

silv_predict_biomass_auto(
  species,
  diameter,
  height = NULL,
  component = "tree",
  ntrees = NULL,
  rcd = NULL,
  bp = NULL,
  priority = c("ruiz-peinado-2011", "ruiz-peinado-2012", "montero-2005",
    "dieguez-aranda-2009", "manrique-2017", "menendez-2022", "cudjoe-2024"),
  quiet = FALSE
)
silv_predict_biomass_auto(
  species,
  diameter,
  height = NULL,
  component = "tree",
  ntrees = NULL,
  rcd = NULL,
  bp = NULL,
  priority = c("ruiz-peinado-2011", "ruiz-peinado-2012", "montero-2005",
    "dieguez-aranda-2009", "manrique-2017", "menendez-2022", "cudjoe-2024"),
  quiet = FALSE
)

Arguments

species

A character vector specifying the scientific names of the tree species.

diameter

A numeric vector of tree diameters at breast height (in cm).

height

An optional numeric vector of tree heights (in m). Defaults to NULL.

component

A character string specifying the tree component for biomass calculation (e.g., "tree", "stem", "branches"). Defaults to "tree".

ntrees

An optional numeric vector indicating the number of trees in each class. Defaults to NULL (equivalent to 1 tree per entry).

rcd

An optional numeric vector of root collar diameters (in cm). Required for young plantation equations (eq_biomass_menendez_2022).

bp

An optional numeric vector of biomass packing values (in m³). Required for some species in young plantation equations.

priority

A character vector specifying the priority order for model selection. Defaults to c("ruiz-peinado-2011", "ruiz-peinado-2012", "montero-2005", "dieguez-aranda-2009", "manrique-2017", "menendez-2022", "cudjoe-2024").

quiet

A logical value. If TRUE, suppresses any informational messages and warnings.

Value

A data.frame containing two columns:

biomass: Numeric vector of predicted biomass values (in kg).
biomass_model: Character vector specifying the model ID used.

Examples

# 1. Vector-based calculation: automatic model selection for mixed species
species_vec <- c("Pinus pinaster", "Quercus petraea")
d_vec <- c(20, 25)
h_vec <- c(12, 14)
auto_results <- silv_predict_biomass_auto(species_vec, d_vec, h_vec)
print(auto_results)

# 2. Vector-based calculation: fallback to Montero 2005 when height is missing
fallback_results <- silv_predict_biomass_auto(species_vec, d_vec, height = NULL)
print(fallback_results)

# 3. Dataset-based tutorial: apply to a mixed-species forest inventory data frame
inventory <- data.frame(
  tree_id  = 1:3,
  species  = c("Pinus pinaster", "Quercus petraea", "Pinus sylvestris"),
  dbh_cm   = c(22.5, 18.0, 31.2),
  height_m = c(15.0, 11.5, 18.0)
)

# Run auto-selection and bind the results directly
biomass_data <- silv_predict_biomass_auto(
  species  = inventory$species,
  diameter = inventory$dbh_cm,
  height   = inventory$height_m
)

inventory_with_biomass <- cbind(inventory, biomass_data)
print(inventory_with_biomass)
# 1. Vector-based calculation: automatic model selection for mixed species
species_vec <- c("Pinus pinaster", "Quercus petraea")
d_vec <- c(20, 25)
h_vec <- c(12, 14)
auto_results <- silv_predict_biomass_auto(species_vec, d_vec, h_vec)
print(auto_results)

# 2. Vector-based calculation: fallback to Montero 2005 when height is missing
fallback_results <- silv_predict_biomass_auto(species_vec, d_vec, height = NULL)
print(fallback_results)

# 3. Dataset-based tutorial: apply to a mixed-species forest inventory data frame
inventory <- data.frame(
  tree_id  = 1:3,
  species  = c("Pinus pinaster", "Quercus petraea", "Pinus sylvestris"),
  dbh_cm   = c(22.5, 18.0, 31.2),
  height_m = c(15.0, 11.5, 18.0)
)

# Run auto-selection and bind the results directly
biomass_data <- silv_predict_biomass_auto(
  species  = inventory$species,
  diameter = inventory$dbh_cm,
  height   = inventory$height_m
)

inventory_with_biomass <- cbind(inventory, biomass_data)
print(inventory_with_biomass)

Predict All Individual Biomass Components for Trees

Description

Predicts all available individual biomass components (e.g., stem, bark, branches, roots) for the given trees in a single call, returning a wide data frame.

Usage

silv_predict_biomass_components(
  species,
  diameter,
  height = NULL,
  model_fn,
  ntrees = NULL,
  rcd = NULL,
  bp = NULL,
  quiet = TRUE
)
silv_predict_biomass_components(
  species,
  diameter,
  height = NULL,
  model_fn,
  ntrees = NULL,
  rcd = NULL,
  bp = NULL,
  quiet = TRUE
)

Arguments

species

A character vector specifying the scientific names of the tree species.

diameter

A numeric vector of tree diameters at breast height (in cm).

height

An optional numeric vector of tree heights (in m). Defaults to NULL.

model_fn

A function or character string. The constructer function of the model (e.g., eq_biomass_ruiz_peinado_2011) or the model ID string (e.g., "ruiz-peinado-2011").

ntrees

An optional numeric vector indicating the number of trees in each class. Defaults to NULL.

rcd

An optional numeric vector of root collar diameters (in cm).

bp

An optional numeric vector of biomass packing values (in m³).

quiet

A logical value. If TRUE, suppresses any informational messages.

Value

A data.frame with the columns species, diameter (cm), height (m, if provided), and one additional numeric column (in kg) for each individual biomass component available for the selected species and model.

Examples

# 1. Vector-based calculation: predict all components for Pinus pinaster
comp_results <- silv_predict_biomass_components(
  species = c("Pinus pinaster", "Pinus pinaster"),
  diameter = c(20, 25),
  height = c(12, 15),
  model_fn = eq_biomass_ruiz_peinado_2011
)
print(comp_results)

# 2. Dataset-based tutorial: apply to a forest inventory data frame
inventory <- data.frame(
  tree_id  = 1:3,
  species  = c("Pinus pinaster", "Pinus pinaster", "Pinus pinaster"),
  dbh_cm   = c(18.5, 22.1, 29.4),
  height_m = c(14.0, 16.5, 19.0)
)

# Predict components for the entire dataset
comp_df <- silv_predict_biomass_components(
  species  = inventory$species,
  diameter = inventory$dbh_cm,
  height   = inventory$height_m,
  model_fn = "ruiz-peinado-2011"
)

# Combine and display results (excluding repeated identifier columns)
inventory_with_components <- cbind(
  inventory, 
  comp_df[, -(1:3)]
)
print(inventory_with_components)
# 1. Vector-based calculation: predict all components for Pinus pinaster
comp_results <- silv_predict_biomass_components(
  species = c("Pinus pinaster", "Pinus pinaster"),
  diameter = c(20, 25),
  height = c(12, 15),
  model_fn = eq_biomass_ruiz_peinado_2011
)
print(comp_results)

# 2. Dataset-based tutorial: apply to a forest inventory data frame
inventory <- data.frame(
  tree_id  = 1:3,
  species  = c("Pinus pinaster", "Pinus pinaster", "Pinus pinaster"),
  dbh_cm   = c(18.5, 22.1, 29.4),
  height_m = c(14.0, 16.5, 19.0)
)

# Predict components for the entire dataset
comp_df <- silv_predict_biomass_components(
  species  = inventory$species,
  diameter = inventory$dbh_cm,
  height   = inventory$height_m,
  model_fn = "ruiz-peinado-2011"
)

# Combine and display results (excluding repeated identifier columns)
inventory_with_components <- cbind(
  inventory, 
  comp_df[, -(1:3)]
)
print(inventory_with_components)

Estimates tree height (m) from DBH (cm)

Description

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

Usage

silv_predict_height(
  diameter,
  model = eq_hd_vazquez_veloso_2025("All the species"),
  quiet = FALSE
)
silv_predict_height(
  diameter,
  model = eq_hd_vazquez_veloso_2025("All the species"),
  quiet = FALSE
)

Arguments

diameter

Numeric vector with diameters in cm (DBH).

model

A ModelHD object. An object configured via the eq_hd_*() family of functions. Defaults to eq_hd_vazquez_veloso_2025("All the species").

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 (m).

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

Examples

# 1. Predict height using the default model (Vázquez-Veloso 2025 generic model)
predicted_heights_default <- silv_predict_height(diameter = c(20, 25, 30))
print(predicted_heights_default)

# 2. Load the S7 ModelHD object for Pinus pinaster
model <- eq_hd_vazquez_veloso_2025("Pinus pinaster")

# 3. Vector-based calculation: predict tree heights from diameters
diameters <- c(20, 25, 30)
predicted_heights <- silv_predict_height(diameter = diameters, model = model)
print(predicted_heights)

# 4. Dataset-based tutorial: apply to a forest inventory data frame
inventory <- data.frame(
  tree_id = 1:3,
  species = c("Pinus pinaster", "Pinus pinaster", "Pinus pinaster"),
  dbh_cm  = c(18.5, 22.1, 29.4)
)

# Apply prediction and append a new column to the dataset
inventory$height_m <- silv_predict_height(
  diameter = inventory$dbh_cm,
  model    = model
)
print(inventory)
# 1. Predict height using the default model (Vázquez-Veloso 2025 generic model)
predicted_heights_default <- silv_predict_height(diameter = c(20, 25, 30))
print(predicted_heights_default)

# 2. Load the S7 ModelHD object for Pinus pinaster
model <- eq_hd_vazquez_veloso_2025("Pinus pinaster")

# 3. Vector-based calculation: predict tree heights from diameters
diameters <- c(20, 25, 30)
predicted_heights <- silv_predict_height(diameter = diameters, model = model)
print(predicted_heights)

# 4. Dataset-based tutorial: apply to a forest inventory data frame
inventory <- data.frame(
  tree_id = 1:3,
  species = c("Pinus pinaster", "Pinus pinaster", "Pinus pinaster"),
  dbh_cm  = c(18.5, 22.1, 29.4)
)

# Apply prediction and append a new column to the dataset
inventory$height_m <- silv_predict_height(
  diameter = inventory$dbh_cm,
  model    = model
)
print(inventory)

Predict tree volume using Spanish National Forest Inventory equations

Description

Computes four volume metrics (in dm^3) for one or more trees using the allometric equations from the 3rd and 4th Spanish National Forest Inventory (SNFI3/SNFI4), as documented by MITECO (Anexo 19).

Usage

silv_predict_snfi_volume(
  province,
  species,
  dbh = NULL,
  h = NULL,
  dnm = NULL,
  quality = "default",
  quiet = FALSE
)
silv_predict_snfi_volume(
  province,
  species,
  dbh = NULL,
  h = NULL,
  dnm = NULL,
  quality = "default",
  quiet = FALSE
)

Arguments

province

A character or numeric vector. Province name (e.g. "Cantabria") or province code (e.g. 39). See silv_snfi_provinces().

species

A character or numeric vector. Species scientific name (e.g. "Pinus radiata") or SNFI species code (e.g. 28). See silv_snfi_species().

dbh

A numeric vector of diameter at breast height in cm.

h

A numeric vector of tree heights in m. Optional for some models (e.g. SNFI3 model 14), but required by most VCC equations.

dnm

A numeric vector of mean plot diameter in cm. Required only when the IAVC equation uses model 13.

quality

A character or numeric vector. Quality/shape class (1-6). Use "default" (the default) to select the first available class.

quiet

A logical value. If TRUE, suppresses citation messages. Defaults to FALSE.

Details

Model selection

For each province, SNFI4 data are preferred. When a province is not covered by SNFI4 (11 provinces), the function falls back to SNFI3 automatically. SNFI3 also provides a supplementary file for species not present in the standard provincial tables.

Equation models

Component	Models used
VCC	1, 11
VSC	7
IAVC	8, 13, 14, 16, 17, 18, 19, 20, 21, 25
VLE	10, 12

Full model equations are available in Anexo 19 of the MITECO SIG documentation (see References).

Units

Inputs: dbh and dnm in cm (converted to mm internally), h in m. All outputs in dm^3.

Value

A data.frame with one row per input tree and five columns:

vcc: Merchantable volume with bark (dm^3).
vsc: Merchantable volume without bark (dm^3).
iavc: Annual volume increment with bark (dm^3).
vle: Coarse firewood volume (dm^3).
snfi_version: Dataset used: "SNFI4" or "SNFI3".

Any component for which parameters are missing or a required input is NA returns NA for that column, without stopping execution.

References

MITECO. 4th Spanish National Forest Inventory - SIG database codes. https://www.miteco.gob.es/content/dam/miteco/es/biodiversidad/temas/inventarios-nacionales/documentador_sig_tcm30-536622.pdf

MITECO. 3rd Spanish National Forest Inventory - SIG database codes. https://www.miteco.gob.es/content/dam/miteco/es/biodiversidad/servicios/banco-datos-naturaleza/documentador_bdcampo_ifn3_tcm30-282240.pdf

Examples

# 1. Vector-based calculation: single tree (Pinus radiata in Alava, province 1, code 28)
single_tree <- silv_predict_snfi_volume(
  province = 1,
  species  = 28,
  dbh      = 20,
  h        = 15,
  dnm      = 23
)
print(single_tree)

# 2. Vector-based calculation: multiple trees with province and species by name
multi_trees <- silv_predict_snfi_volume(
  province = c("Araba/Álava", "Cantabria"),
  species  = c("Pinus radiata", "Pinus radiata"),
  dbh      = c(20, 25),
  h        = c(15, 18),
  dnm      = c(23, 26)
)
print(multi_trees)

# 3. Dataset-based tutorial: apply to a forest inventory data frame
inventory <- data.frame(
  tree_id   = 1:3,
  province  = c("Cantabria", "Cantabria", "Araba/Álava"),
  species   = c("Pinus radiata", "Pinus radiata", "Pinus sylvestris"),
  dbh_cm    = c(20.5, 28.0, 18.2),
  height_m  = c(15.2, 18.5, 12.0)
)

# Apply the SNFI volume estimation functions row-wise across the dataset
volume_results <- silv_predict_snfi_volume(
  province = inventory$province,
  species  = inventory$species,
  dbh      = inventory$dbh_cm,
  h        = inventory$height_m
)

# Combine and display results
inventory_with_volume <- cbind(inventory, volume_results)
print(inventory_with_volume)
# 1. Vector-based calculation: single tree (Pinus radiata in Alava, province 1, code 28)
single_tree <- silv_predict_snfi_volume(
  province = 1,
  species  = 28,
  dbh      = 20,
  h        = 15,
  dnm      = 23
)
print(single_tree)

# 2. Vector-based calculation: multiple trees with province and species by name
multi_trees <- silv_predict_snfi_volume(
  province = c("Araba/Álava", "Cantabria"),
  species  = c("Pinus radiata", "Pinus radiata"),
  dbh      = c(20, 25),
  h        = c(15, 18),
  dnm      = c(23, 26)
)
print(multi_trees)

# 3. Dataset-based tutorial: apply to a forest inventory data frame
inventory <- data.frame(
  tree_id   = 1:3,
  province  = c("Cantabria", "Cantabria", "Araba/Álava"),
  species   = c("Pinus radiata", "Pinus radiata", "Pinus sylvestris"),
  dbh_cm    = c(20.5, 28.0, 18.2),
  height_m  = c(15.2, 18.5, 12.0)
)

# Apply the SNFI volume estimation functions row-wise across the dataset
volume_results <- silv_predict_snfi_volume(
  province = inventory$province,
  species  = inventory$species,
  dbh      = inventory$dbh_cm,
  h        = inventory$height_m
)

# Combine and display results
inventory_with_volume <- cbind(inventory, volume_results)
print(inventory_with_volume)

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
)
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
)
## 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
)
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
)
## 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
)
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
)
## 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
)

List provinces supported by SNFI volume equations

Description

Returns a data frame with province codes, names, and which SNFI dataset (SNFI4 or SNFI3 fallback) is used for each.

Usage

silv_snfi_provinces()
silv_snfi_provinces()

Value

A data.frame with columns province_id, province_name, and snfi_version.

Examples

silv_snfi_provinces()
silv_snfi_provinces()

List species supported by SNFI volume equations

Description

Returns a data frame of species codes and names from the SNFI4 species catalogue.

Usage

silv_snfi_species(snfi_version = "SNFI4")
silv_snfi_species(snfi_version = "SNFI4")

Arguments

snfi_version

Character. Either "SNFI4" (default) or "SNFI3".

Value

A data.frame with columns species_code and species_name.

Examples

head(silv_snfi_species())
head(silv_snfi_species("SNFI3"))
head(silv_snfi_species())
head(silv_snfi_species("SNFI3"))

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")
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))
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)
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))
## 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")
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)
  )
## 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")
)
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)
  )
## 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")
)
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

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)
  )
## 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)
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)
  )
## 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)
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))
## 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"
)
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")
 )
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
)
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

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
)
# 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")
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

Examples

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

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

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
)
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")

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")

Calculate Tree Volume

Description

Usage

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

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")

SNFI3 tree volume coefficients

Description

Allometric equation coefficients for tree volume and increment from the 3rd Spanish National Forest Inventory (SNFI3/IFN3), as documented by MITECO.

Usage

snfi3_volume_coefficients
snfi3_volume_coefficients

Format

A tibble with 37,730 rows and 16 variables:

Codigo_provincia: Integer. Province numeric code.
Nombre_provincia: Character. Province name.
Codigo_especie: Integer. Species numeric code.
Especie: Character. Species scientific name.
Parametro: Character. Volume component: VCC (merchantable volume with bark), VSC (merchantable volume without bark), IAVC (annual volume increment with bark), or VLE (coarse firewood volume).
F.c.: Integer. Tree quality class (calidad del árbol), from 1 (healthy and straight tree) to 6 (dead tree).
Par_esp: Character. Special parameter metadata.
Modelo: Integer. Equation model index.
a, b, c, d, p, q, r: Numeric. Equation coefficients (NA where not applicable).
R2: Numeric. Coefficient of determination of the model.

References

SNFI4 tree volume coefficients

Description

Allometric equation coefficients for tree volume and increment from the 4th Spanish National Forest Inventory (SNFI4/IFN4), as documented by MITECO.

Usage

snfi4_volume_coefficients
snfi4_volume_coefficients

Format

A tibble with 20,925 rows and 15 variables:

Codigo_provincia: Integer. Province numeric code.
Nombre_provincia: Character. Province name.
Codigo_especie: Integer. Species numeric code.
Especie: Character. Species scientific name.
Parametro: Character. Volume component: VCC (merchantable volume with bark), VSC (merchantable volume without bark), IAVC (annual volume increment with bark), or VLE (coarse firewood volume).
F.c.: Integer. Tree quality class (calidad del árbol), from 1 (healthy and straight tree) to 6 (dead tree).
Modelo: Integer. Equation model index.
a, b, c, d, p, q, r: Numeric. Equation coefficients (NA where not applicable).
D.n.m.: Character. Mean plot diameter metadata (diámetro normal medio), required for IAVC equation model 13.

References

MITECO. 4th Spanish National Forest Inventory - SIG database codes. https://www.miteco.gob.es/content/dam/miteco/es/biodiversidad/temas/inventarios-nacionales/documentador_sig_tcm30-536622.pdf

Package 'silviculture'

Help Index

Biomass models

Description

Usage

Format

Carbon content models

Description

Usage

Format

References

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

Description

Usage

Arguments

Details

Value

See Also

Examples

Biomass equations for Galician species

Description

Usage

Arguments

Details

Value

See Also

Examples

Biomass equations two Quercus species

Description

Usage

Arguments

Details

Value

See Also

Examples

Biomass equations for young Spanish plantations

Description

Usage

Arguments

Details

Value

See Also

Examples

Biomass equations for Spanish species

Description

Usage

Arguments

Details

Value

See Also

Examples

Biomass equations for Spanish softwood species

Description

Usage

Arguments

Details

Value

See Also

Examples

Biomass equations for Spanish hardwood species

Description

Usage

Arguments

Details

Value

See Also

Examples

Height-diameter equations from Vázquez-Veloso et al. (2025)

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Forest inventory samples

Description

Usage

Format