Package 'silviculture'

Description

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

Usage

biomass_models

Format

A tibble

Description

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

Usage

carbon_models

Format

A tibble with 264 rows and 13 variables:

article_id

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

title

Character. Full title of the source article.

doi_url

Character. DOI URL of the source article.

country

Character. Country where the study was conducted.

region

Character. Region within the country.

species

Character. Scientific name of the tree species.

biomass_group

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

tree_group

Character. Sub-group within the biomass group.

tree_component

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

carbon_percentage

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

r2

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

rmse

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

obs

Character. Additional observations from the source article.

References

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

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

Description

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

Usage

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

Arguments

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.

See Also

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

Examples

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

Description

Allometric equations adjusted for Galician (Spain) species

Usage

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

Arguments

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.

See Also

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

Examples

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

Description

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

Usage

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

Arguments

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.

See Also

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

Examples

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

Description

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

Usage

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

Arguments

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.

See Also

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

Examples

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

Description

Allometric equations adjusted for Spanish species

Usage

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

Arguments

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.

See Also

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

Examples

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

Description

Allometric equations adjusted for Spanish softwood species

Usage

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

Arguments

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.

See Also

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

Examples

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

Description

Allometric equations adjusted for Spanish hardwood species

Usage

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

Arguments

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.

See Also

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

Examples

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

Description

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

Usage

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

Arguments

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

See Also

silv_predict_height()

Examples

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

Description

Inventory data from Spanish National Forest Inventory

Usage

inventory_samples

Format

A tibble

Description

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

Usage

lid_fcov(z, rn, th = 5)

Arguments

Value

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

Examples

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

# Calculate forest fraction cover
lid_fcov(z, rn)

Description

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

Usage

lid_lhdi(z, interval = 0.5)

Arguments

Value

numeric

References

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

Examples

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

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)

Arguments

Value

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

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
)

Arguments

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)

Description

Specific beta coefficients for Reineke's Stand Density Index (SDI) per species, country, and region.

Usage

sdi_coefficients

Format

A tibble

article_id

Character. Short identifier of the source article.

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.

beta

Numeric. Beta coefficient for SDI calculation.

Description

Coefficients for calculating maximum stand density index (SDImax) from Rodríguez de Prado (2020).

Usage

sdimax_models

Format

A tibble with 88 rows and 13 variables:

article_id

Character. Identifier of the article.

title

Character. Title of the article.

doi_url

Character. DOI URL of the article.

country

Character. Country where the study was conducted.

species

Character. Tree species scientific name.

model_name

Character. Name of the model/equation variant (e.g. "basic", "P1", "MXT3").

a0

Numeric. Coeffient a0.

a1

Numeric. Coeffient a1 (0 if not used/applicable).

b0

Numeric. Coeffient b0.

b1

Numeric. Coeffient b1 (0 if not used/applicable).

aic

Numeric. Akaike Information Criterion.

pseudo_r2

Numeric. Pseudo R-squared value.

q_index

Numeric. Q index value.

References

Rodríguez-de-Prado, M., et al. (2020). Potential climatic influence on maximum stand carrying capacity for 15 Mediterranean coniferous and broadleaf species. Forest Ecology and Management, 458, 117824.

Description

Calculates Basal Area in square meters.

Usage

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

Arguments

Details

The function uses the next formula:

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

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

Value

A numeric vector

Examples

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

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

Description

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

Usage

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

Arguments

Details

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

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

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

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

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

Value

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

Examples

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

Description

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

Usage

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

Arguments

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))

Description

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

Usage

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

Arguments

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"
    )
  )

Description

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

Usage

silv_density_sdi(ntrees, dg, beta = 1.605)

Arguments

Details

The SDI has different interpretations 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 beta exponent allows adjustments for different species or mixed stands.

Value

A numeric vector representing the absolute SDI.

References

Reineke, L. H. (1933). Perfecting a stand-density index for even-aged forests. Journal of Agricultural Research, 46(7), 627-638. URL: https://research.fs.usda.gov/download/treesearch/60134.pdf

Examples

## calculate SDI for a Pinus sylvestris stand (beta = 1.605)
silv_density_sdi(ntrees = 800, dg = 23.4)

## calculate SDI with custom beta
silv_density_sdi(ntrees = 800, dg = 23.4, beta = 1.7)

Usage

silv_density_sdi_auto(
  ntrees,
  dg,
  species,
  country = NULL,
  region = NULL,
  classify = FALSE,
  climatic_model = NULL,
  clim_value = NULL,
  quiet = FALSE
)

Arguments

Value

A data.frame with the columns:

• sdi: The computed absolute Stand Density Index.

• beta: The beta exponent used for the calculation.

• sdi_model: The model used for beta exponent.

• sdimax: (If classify = TRUE) The maximum SDI for the species.

• sdi_class: (If classify = TRUE) The density classification.

• quietLogical. If FALSE, informs the user about fallbacks to genus or default models.

A data.frame with three columns:

• sdi: The computed absolute Stand Density Index.

• beta: The beta exponent used for the calculation.

• sdi_model: The model used (e.g., "del-rio-2006 (Spain, Castilla y León)", "reineke-1933 (-1.605)", etc.).

silv_density_sdi_auto() is a vectorized function that automatically selects the best available Stand Density Index exponent (beta) for each row based on a provided species, country, and region from the internal sdi_coefficients database.

If an exact species, country, and region match is not found, the function falls back to a country-wide species model (region = "all"), then searches other countries, then falls back to a genus-level fallback (e.g., "Pinus spp."), and finally to the default SDI exponent (beta = -1.605 from Reineke 1933).

# Calculate SDI with automatic selection silv_density_sdi_auto( ntrees = 800, dg = 23.4, species = "Pinus sylvestris", region = "Castilla y León" )

# With automatic classification silv_density_sdi_auto( ntrees = 800, dg = 23.4, species = "Pinus sylvestris", classify = TRUE # Fallback to default silv_density_sdi_auto( ntrees = 800, dg = 23.4, species = "Unknown species" )

Description

Classifies the Stand Density Index (SDI) into density classes or calculates the relative SDI percentage based on USDA thresholds.

Usage

silv_density_sdi_class(sdi, max_sdi, classify = TRUE)

Arguments

Details

The option classify = TRUE will use the max_sdi value to classify the SDI into four competitive and growth conditions: low density (<24%), moderate density (24-35%), high density (34-55%), and extremely high density (>55%).

Value

A character vector with the density classes if classify = TRUE, or a numeric vector with the relative SDI percentage if classify = FALSE.

References

USDA Forest Service. (n.d.). Stand Density Index. https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb5270993.pdf

Examples

## calculate SDI for a Pinus sylvestris stand (max 990)
sdi_val <- silv_density_sdi(ntrees = 800, dg = 23.4)

## check base classification
silv_density_sdi_class(sdi = sdi_val, max_sdi = 990)

## get relative SDI percentage
silv_density_sdi_class(sdi = sdi_val, max_sdi = 990, classify = FALSE)

Description

The Maximum Stand Density Index (SDImax) represents the maximum stand carrying capacity, calculated using coefficients from Rodríguez de Prado (2020) by default.

Usage

silv_density_sdimax(
  species,
  model = "rodriguez-prado-2020",
  climatic_model = NULL,
  clim_value = NULL
)

Arguments

Details

If clim_value is NULL, the function computes the reference SDImax (SDImaxREF) based on the "basic" model parameters:

$SDImaxREF = exp(a0 + b0 * log(25.4))$

If clim_value is provided, a climate-dependent model must be specified in climatic_model, and the climate-dependent SDImax is calculated as:

$SDImax(Clim) = exp((a0 + a1 * log(clim_value)) + (b0 + b1 * clim_value) * log(25.4))$

Value

A numeric vector representing the SDImax for each species.

References

Rodríguez-de-Prado, M., et al. (2020). Potential climatic influence on maximum stand carrying capacity for 15 Mediterranean coniferous and broadleaf species. Forest Ecology and Management, 458, 117824.

Examples

## Calculate reference SDImax for Pinus sylvestris
silv_density_sdimax("Pinus sylvestris")

## Calculate climate-dependent SDImax for Pinus canariensis using model P1
silv_density_sdimax("Pinus canariensis", climatic_model = "P1", clim_value = 400)

Description

Classifies the measured diameters into classes of a specified length

Usage

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

Arguments

Value

A numeric vector

Examples

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

Description

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

Usage

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

Arguments

Details

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

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

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

When ntrees = NULL, the function will assume that each diameter and height belongs to only one 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)
  )

Description

#' @description

Usage

silv_lorey_height(height, g, ntrees = NULL)

Arguments

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)
  )

Description

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

Usage

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

Arguments

Value

A numeric vector

Examples

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

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

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
)

Arguments

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).

See Also

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

Examples

# 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)

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
)

Arguments

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)

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
)

Arguments

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)

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
)

Arguments

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

See Also

eq_hd_vazquez_veloso_2025()

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)

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
)

Arguments

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

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

See Also

silv_snfi_provinces(), silv_snfi_species(), silv_tree_volume()

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)

Description

Usage

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

Arguments

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
)

Description

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

Usage

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

Arguments

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
)

Description

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

Usage

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

Arguments

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
)

Description

The site factor is a variable used in growth equations based on the dominant height and dominant dbh.

Usage

silv_site_factor(species, d0, h0)

Arguments

Value

A numeric vector representing the Site Factor. NA for unsupported species.

References

Aguirre, A., et al. (2022).

Examples

silv_site_factor(species = "Pinus sylvestris", d0 = 25, h0 = 15)

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()

Value

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

Examples

silv_snfi_provinces()

Description

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

Usage

silv_snfi_species(snfi_version = "SNFI4")

Arguments

Value

A data.frame with columns species_code and species_name.

Examples

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

Description

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

Usage

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

Arguments

Details

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

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

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

Value

A numeric vector

References

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

Examples

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

Description

Usage

silv_sqrmean_diameter(diameter, ntrees = NULL)

Arguments

Value

A numeric vector

Examples

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

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

Description

Calculates Basal Area in square meters.

Usage

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

Arguments

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)
  )

Description

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

Usage

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

Arguments

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)
  )

Description

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

Usage

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

Arguments

Details

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

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

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

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

Value

A numeric vector

References

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

Examples

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

Description

Tree's mean height weighted by basal area

Usage

silv_stand_lorey_height(height, g, ntrees = NULL)

Arguments

Details

The function calculates Lorey's mean height according to:

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

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

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

Value

A numeric vector

Examples

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

Description

Calculates the quadratic mean diameter (QMD)

Usage

silv_stand_qmean_diameter(diameter, ntrees = NULL)

Arguments

Value

A numeric vector

Examples

## calculate dg for inventory data grouped by plot_id and species
library(dplyr)
inventory_samples |>
mutate(dclass = silv_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))

Description

The Stand Slenderness Index is defined as the ratio of height to diameter, multiplied by 100. It can be applied to mean height and quadratic mean diameter, or dominant height and dominant diameter.

Usage

silv_stand_slenderness(height, diameter)

Arguments

Details

The formula used is:

$Slenderness = \frac{H}{D} \times 100$

where H is the height in meters and D is the diameter in centimeters.

Value

A numeric vector representing the slenderness index.

Examples

## Calculate slenderness using mean height and quadratic mean diameter
silv_stand_slenderness(height = 18, diameter = 25)

Description

Summarize forest inventory data calculating most typical variables

Usage

silv_summary(
  data,
  diameter,
  height,
  plot_size = NULL,
  .groups = NULL,
  plot_shape = c("circular", "rectangular", "snfi"),
  dmin = 7.5,
  dmax = NULL,
  class_length = 5,
  include_lowest = TRUE,
  which_h0 = "assman",
  which_spacing = "hart",
  volume = NULL,
  volume_units = "dm3",
  biomass = NULL,
  carbon = NULL,
  predict_volume = FALSE,
  province = NULL,
  species = NULL,
  predict_biomass = FALSE,
  biomass_component = "tree",
  predict_carbon = FALSE
)

Arguments

Details

The function calculates many inventory parameters and returns two tibbles:

• dclass_metrics: metrics summarized by .groups and diametric classes

• group_metrics: metrics summarized by .groups

Volume is reported at stand level in $m^3/\text{ha}$ (v_ha). Biomass and carbon are reported at stand level in $t/\text{ha}$ (w_ha, c_ha).

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")
 )

Description

Calculates the number of trees, basal area, and optional volume, biomass, and carbon per hectare for live and dead trees in a plot.

Usage

silv_summary_mortality(
  data,
  plot_id,
  dead,
  expan = NULL,
  g = NULL,
  diameter = NULL,
  volume = NULL,
  volume_units = "dm3",
  biomass = NULL,
  carbon = NULL
)

Arguments

Value

A tibble with plot-level mortality summaries.

Examples

library(dplyr)
inventory_samples |>
  mutate(expan = silv_density_ntrees_ha(1, 10)) |>
  mutate(is_dead = sample(c(TRUE, FALSE), n(), replace = TRUE, prob = c(0.1, 0.9))) |>
  silv_summary_mortality(plot_id, is_dead, expan, diameter = diameter)

Description

Calculates the number of trees, basal area, and optional volume, biomass, and carbon per hectare for each species in a plot, and optionally provides the top species formatted into columns.

Usage

silv_summary_species(
  data,
  plot_id,
  species,
  expan = NULL,
  g = NULL,
  diameter = NULL,
  volume = NULL,
  volume_units = "dm3",
  biomass = NULL,
  carbon = NULL,
  top_n = 3
)

Arguments

Value

A tibble with species-level summaries.

Examples

library(dplyr)
inventory_samples |>
  mutate(expan = silv_density_ntrees_ha(1, 10)) |>
  silv_summary_species(plot_id, species, expan, diameter = diameter, top_n = 3)

Description

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

Usage

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

Arguments

Details

This function implements common silvicultural thinning practices:

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

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

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

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

Value

A silviculture::Thinning object with three items:

• data: the input data with two new columns

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

• thinning_opts: options used for S7 methods

See Also

silv_summary()

Examples

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

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

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

Description

Calculates the Basal Area Larger (BAL) for each tree in each plot.

Usage

silv_tree_bal(
  data,
  plot_id,
  tree_id,
  diameter = NULL,
  expansion_factor = NULL,
  basal_area_ha = NULL,
  units = "cm"
)

Arguments

Value

A numeric vector with the BAL for each tree.

Examples

## Not run: 
inventory_samples |>
  dplyr::mutate(bal = silv_tree_bal(
    inventory_samples, plot_id, tree_id, diameter, exp_factor))

## End(Not run)

Description

Calculates the Basal Area Smaller (BAS) for each tree in each plot.

Usage

silv_tree_bas(
  data,
  plot_id,
  tree_id,
  diameter = NULL,
  expansion_factor = NULL,
  basal_area_ha = NULL,
  units = "cm"
)

Arguments

Value

A numeric vector with the BAS for each tree.

Description

Calculates Basal Area in square meters.

Usage

silv_tree_basal_area(diameter, units = "cm")

Arguments

Details

The function uses the next formula:

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

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

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

Value

A numeric vector

See Also

silv_stand_basal_area()

Examples

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

Description

Calculates the tree basal area expanded to hectare.

Usage

silv_tree_basal_area_ha(
  diameter = NULL,
  basal_area = NULL,
  expansion_factor,
  units = "cm"
)

Arguments

Value

A numeric vector with the basal area per hectare (m2/ha).

Examples

silv_tree_basal_area_ha(diameter = 20, expansion_factor = 127)

Description

Calculate Tree Circumference

Usage

silv_tree_circumference(diameter, units = "cm")

Arguments

Value

A numeric vector with the circumference in the same units.

Examples

silv_tree_circumference(20)

Description

Calculate Relative and Absolute Tree Coordinates

Usage

silv_tree_coordinates(
  distance,
  bearing,
  x_center = NULL,
  y_center = NULL,
  dist_units = "m",
  bearing_units = "grad"
)

Arguments

Value

A data frame with relative coordinates (x_rel, y_rel) and, if plot centers are provided, absolute coordinates (x_abs, y_abs).

Examples

silv_tree_coordinates(distance = 10, bearing = 100)

Description

Calculate Tree Crown Ratio

Usage

silv_tree_crown_ratio(height, height_base_crown)

Arguments

Value

A numeric vector with the crown ratio.

Examples

silv_tree_crown_ratio(height = 20, height_base_crown = 8)

Description

Classifies the measured diameters into classes of a specified length

Usage

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

Arguments

Value

A numeric vector

Examples

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

Description

Calculates the tree expansion factor according to the plot type (fixed area or SNFI concentric plots).

Usage

silv_tree_expansion_factor(
  type = c("fixed_area", "snfi"),
  plot_area = NULL,
  diameter = NULL,
  d_units = "cm",
  a_units = "m2"
)

Arguments

Value

A numeric vector with the expansion factor.

Examples

silv_tree_expansion_factor(type = "fixed_area", plot_area = 500)
silv_tree_expansion_factor(type = "snfi", diameter = 15)

Description

Calculates the mean diameter at breast height (DBH) from two perpendicular measurements or derives it from the perimeter.

Usage

silv_tree_mean_dbh(dbh1 = NULL, dbh2 = NULL, perimeter = NULL, units = "cm")

Arguments

Value

A numeric vector with the mean DBH.

Examples

silv_tree_mean_dbh(dbh1 = 20, dbh2 = 22)
silv_tree_mean_dbh(perimeter = 65)

Description

Calculate Tree Slenderness

Usage

silv_tree_slenderness(diameter, height, d_units = "cm", h_units = "m")

Arguments

Value

A numeric vector with the tree slenderness.

Examples

silv_tree_slenderness(diameter = 20, height = 15)

Description

Computes individual tree-level dendrometric metrics, diametric classes, expansion factors, competition indices, and optional volume, biomass, and carbon predictions.

Usage

silv_tree_summary(
  data,
  diameter,
  height = NULL,
  plot_id = NULL,
  species = NULL,
  expan = NULL,
  plot_size = NULL,
  plot_shape = c("circular", "rectangular", "snfi"),
  dmin = 7.5,
  dmax = NULL,
  class_length = 5,
  include_lowest = TRUE,
  compute_bal = FALSE,
  predict_volume = FALSE,
  province = NULL,
  predict_biomass = FALSE,
  biomass_component = "tree",
  predict_carbon = FALSE
)

Arguments

Value

The original data frame enriched with computed tree-level metrics.

Examples

library(dplyr)
silv_tree_summary(
  data       = inventory_samples,
  diameter   = diameter,
  height     = height,
  plot_id    = plot_id,
  species    = species,
  plot_size  = 10,
  compute_bal = TRUE
)

Description

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

Usage

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

Arguments

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")

Description

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

Usage

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

Arguments