Title:	Data Structures and Functions for Working with Forest Data
Version:	2.1.0
Date:	2025-02-07
Description:	Provides generic data structures and algorithms for use with forest mensuration data in a consistent framework. The functions and objects included are a collection of broadly applicable tools. More specialized applications should be implemented in separate packages that build on this foundation. Documentation about 'ForestElementsR' is provided by three vignettes included in this package. For an introduction to the field of forest mensuration, refer to the textbooks by Kershaw et al. (2017) <doi:10.1002/9781118902028>, and van Laar and Akca (2007) <doi:10.1007/978-1-4020-5991-9>.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	magrittr, ggplot2, tibble, dplyr, purrr, rlang, Rdpack, tidyr, vctrs, stringr, tidyselect (≥ 1.2.0), doBy
RdMacros:	Rdpack
Depends:	R (≥ 4.3), sf
LazyData:	true
Suggests:	knitr, rmarkdown, testthat (≥ 3.2.0)
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-02-07 12:43:19 UTC; casto
Author:	Peter Biber [aut, cre, cph], Astor Toraño Caicoya [aut], Torben Hilmers [ctb]
Maintainer:	Peter Biber <p.biber@tum.de>
Repository:	CRAN
Date/Publication:	2025-02-07 13:00:07 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Internal vctrs methods

Description

Internal vctrs methods

Inverse Stem Diameter Growth Model of the 3rd German National Forest Inventory (2012)

Description

Inverse tree diameter growth model of the third German National Forest Inventory of 2012 (Riedel et al. 2017). Allows to estimate a tree's age at any dbh if its dbh is known at a given age.

Usage

age_d_gnfi3(species_id, dbh_cm, dbh_cm_known, age_yr_known)

Arguments

Details

Originally, the function was parameterized for species and species groups corresponding to the national forest inventory's species coding (fe_species_ger_nfi_2012). We have attributed in addition these the original parameters also to the species codings fe_species_tum_wwk_short, and fe_species_bavrn_state_short. When called with a given species coding, the function will try to use the "nearest" of these three alternatives. Fallback option is the attempt to use fe_species_tum_wwk_short.

Value

A single age value or vector of age values corresponding to dbh_cm

References

Riedel T, Hennig P, Kroiher F, Polley H, Schmitz F, F. S (2017). Die dritte Bundeswaldinventur (BWI 2012). Inventur- und Auswertungsmethoden. Thuenen Institut fuer Waldoekosysteme.

See Also

Other growth functions: age_h_gnfi3(), d_age_gnfi3(), h_age_gnfi3()

Examples

  # A Norway spruce has a diameter of 17.5 cm at age 45. Estimate
  # its age at dbh 20.6 cm
  age_d_gnfi3(10, 20.6, 17.5, 45) # 10 is ger_nfi_2012 code for Norway spruce

  # Do the same backward in time, age at dbh 12.4 cm
  age_d_gnfi3(10, 12.4, 17.5, 45)

  # Apply for more than one tree, different species, same age
  d_known   <- c(23.1, 16.2, 35.2, 19.3, 21.8)
  d_age     <- c(27.0, 19.0, 40.8, 22.9, 25.8)
  species   <- as_fe_species_tum_wwk_short(c(3, 3, 3, 6, 6))
  age_d_gnfi3(
    species, dbh_cm = d_age, dbh_cm_known = d_known, age_yr_known = 40
  )

Core Function For the Inverse Stem Diameter Growth Model of the 3rd German National Forest Inventory (2012)

Description

See documentation of age_d_gnfi3 for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

age_d_gnfi3_core(species_id, dbh_cm, dbh_cm_known, age_yr_known, params)

Arguments

Details

The function age_d_gnfi3 is fully vectorized, inputs are recyled according to the tibble rules

Value

A vector with estimates of the trees' expected dbh (see age_d_gnfi3)

Inverse Tree Height Growth Model of the 3rd German National Forest Inventory (2012)

Description

Inverse tree height growth model of the third German National Forest Inventory of 2012 (Riedel et al. 2017). Allows to estimate a tree's age at any height if its height is known at a given age.

Usage

age_h_gnfi3(species_id, h_m, h_m_known, age_yr_known)

Arguments

Details

Originally, the function was parameterized for species and species groups corresponding to the national forest inventory's species coding (fe_species_ger_nfi_2012). We have attributed in addition these the original parameters also to the species codings fe_species_tum_wwk_short, and fe_species_bavrn_state_short. When called with a given species coding, the function will try to use the "nearest" of these three alternatives. Fallback option is the attempt to use fe_species_tum_wwk_short.

Value

A single age value or vector of age values corresponding to h_m

References

Riedel T, Hennig P, Kroiher F, Polley H, Schmitz F, F. S (2017). Die dritte Bundeswaldinventur (BWI 2012). Inventur- und Auswertungsmethoden. Thuenen Institut fuer Waldoekosysteme.

See Also

Other growth functions: age_d_gnfi3(), d_age_gnfi3(), h_age_gnfi3()

Examples

  # A European beech has a height of 25.2 m at age 75. Estimate
  # its age at height 26.8 m
  age_h_gnfi3(100, 26.8, 25.2, 75) # 100 is ger_nfi_2012 code for E. beech

  # Do the same backward in time, age at height 21.7 m
  age_h_gnfi3(100, 21.7, 25.2, 75)

  # Apply for more than one tree, different species, same age
  h_known   <- c(23.1, 16.2, 35.2, 19.3, 21.8)
  h_age     <- c(25.5, 18.2, 38.0, 21.6, 24.2)
  species   <- as_fe_species_tum_wwk_short(c(3, 3, 3, 6, 6))
  age_h_gnfi3(
    species, h_m = h_age, h_m_known = h_known, age_yr_known = 60
  )

Core Function For the Inverse Tree Height Growth Model of the 3rd German National Forest Inventory (2012)

Description

See documentation of age_h_gnfi3 for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

age_h_gnfi3_core(species_id, h_m, h_m_known, age_yr_known, params)

Arguments

Details

The function age_h_gnfi3 is fully vectorized, inputs are recyled according to the tibble rules

Value

A vector with estimates of the trees' ages (see age_h_gnfi3)

Check Species Codes for Compliance with a Given Coding

Description

Check Species Codes for Compliance with a Given Coding

Usage

all_codes_allowed(x, coding)

Arguments

Value

A list with two elements: 1) rqmt_ok which is TRUE if the requirement is fulfilled and FALSE otherwise; 2) err_message, the body of an error message to be used by the calling function (mentions all problematic elements of x)

Cast Appropriate Objects Into a fe_stand_bavrn_state Species Class Object

Description

If the cast is forward ambiguous, the function terminates with an error. "Forward ambiguous" means that one code in the original object corresponds to more than one codes in the goal coding. If the cast loses information, a warning is raised, but the cast is performed. "Information loss" in this context means that several codes from the orginal coding correspond to only one code in the goal coding.

Usage

as_fe_species_bavrn_state(x)

Arguments

Details

Note that a cast where only one species id from the original coding translates in a goal coding which represents a group of species is NOT considered losing information (i.e. backward ambiguous), because of the 1:1 match in the constellation of the specific cast.

Value

If a meaningful cast is possible, an fe_species_bavrn_state object is returned

Examples

as_fe_species_bavrn_state(c(10L, 40L, 40L, 20L)) # integer
as_fe_species_bavrn_state(c(10, 40, 40, 20)) # double
as_fe_species_bavrn_state(c("10", "40", "40", "20")) # character

# cast other fe_species classes
as_fe_species_bavrn_state(
  fe_species_tum_wwk_short(as.character(c(1, 1, 1, 3, 3, 5)))
)
as_fe_species_bavrn_state(
  fe_species_ger_nfi_2012(as.character(c(20, 20, 10, 30, 30, 100)))
)

# display the casting result in terms of scientific species names
as_fe_species_bavrn_state(c(10L, 40L, 40L, 20L)) |> format("sci")

Cast Appropriate Objects Into a fe_stand_bavrn_state_short Species Class Object

Description

If the cast is forward ambiguous, the function terminates with an error. "Forward ambiguous" means that one code in the original object corresponds to more than one codes in the goal coding. If the cast loses information, a warning is raised, but the cast is performed. "Information loss" in this context means that several codes from the orginal coding correspond to only one code in the goal coding.

Usage

as_fe_species_bavrn_state_short(x)

Arguments

Details

Note that a cast where only one species id from the original coding translates in a goal coding which represents a group of species is NOT considered losing information (i.e. backward ambiguous), because of the 1:1 match in the constellation of the specific cast.

Value

If a meaningful cast is possible, an fe_species_bavrn_state_short object is returned

Examples

as_fe_species_bavrn_state_short(c(1L, 4L, 4L, 2L)) # integer
as_fe_species_bavrn_state_short(c(1, 4, 4, 2)) # double
as_fe_species_bavrn_state_short(c("1", "4", "4", "2")) # character

# cast other fe_species classes
as_fe_species_bavrn_state_short(
  fe_species_tum_wwk_short(as.character(c(1, 1, 1, 3, 3, 5)))
)
as_fe_species_bavrn_state_short(
  fe_species_ger_nfi_2012(as.character(c(20, 20, 10, 30, 30, 100)))
)

# display the casting result in terms of scientific species names
as_fe_species_bavrn_state_short(c(1L, 4L, 4L, 2L)) |> format("sci")

Cast Appropriate Objects Into a fe_stand_ger_nfi_2012 Species Class Object

Description

If the cast is forward ambiguous, the function terminates with an error. "Forward ambiguous" means that one code in the original object corresponds to more than one codes in the goal coding. If the cast loses information, a warning is raised, but the cast is performed. "Information loss" in this context means that several codes from the orginal coding correspond to only one code in the goal coding.

Usage

as_fe_species_ger_nfi_2012(x)

Arguments

Details

Note that a cast where only one species id from the original coding translates in a goal coding which represents a group of species is NOT considered losing information (i.e. backward ambiguous), because of the 1:1 match in the constellation of the specific cast.

Value

If a meaningful cast is possible, an fe_species_ger_nfi_2012 object is returned

Examples

as_fe_species_ger_nfi_2012(c(10L, 40L, 40L, 20L)) # integer
as_fe_species_ger_nfi_2012(c(10, 40, 40, 20)) # double
as_fe_species_ger_nfi_2012(c("10", "40", "40", "20")) # character

# cast other fe_species classes
as_fe_species_ger_nfi_2012(
  fe_species_tum_wwk_short(as.character(c(1, 1, 1, 3, 3, 5)))
)
as_fe_species_ger_nfi_2012(
  fe_species_bavrn_state(as.character(c(20, 20, 10, 30, 30, 60)))
)
as_fe_species_ger_nfi_2012(
  fe_species_tum_wwk_long(as.character(c(83, 83, 10, 31, 40, 61)))
)

# display the casting result in terms of scientific species names
as_fe_species_ger_nfi_2012(c(10L, 40L, 40L, 20L)) |> format("sci")

Cast Appropriate Objects Into a fe_stand_master Species Class Object

Description

If the cast is forward ambiguous, the function terminates with an error. "Forward ambiguous" means that one code in the original object corresponds to more than one codes in the goal coding. If the cast loses information, a warning is raised, but the cast is performed. "Information loss" in this context means that several codes from the orginal coding correspond to only one code in the goal coding.

Usage

as_fe_species_master(x)

Arguments

Details

Note that a cast where only one species id from the original coding translates in a goal coding which represents a group of species is NOT considered losing information (i.e. backward ambiguous), because of the 1:1 match in the constellation of the specific cast.

Value

If a meaningful cast is possible, an fe_species_master object is returned

Examples

as_fe_species_master(c("abies_001", "fagus_001")) # character

# cast other fe_species classes
as_fe_species_master(
  fe_species_tum_wwk_short(as.character(c(1, 1, 1, 3, 3, 5)))
)
as_fe_species_master(
  fe_species_ger_nfi_2012(as.character(c(20, 20, 10, 30, 30, 100)))
)
as_fe_species_master(
  fe_species_bavrn_state(as.character(c(10L, 40L, 40L, 20L)))
)
as_fe_species_master(
  fe_species_tum_wwk_long(as.character(c(10L, 30L, 88L, 87L)))
)

# display the casting result in terms of scientific species names
as_fe_species_master(c("abies_001", "fagus_001")) |> format("sci")

Cast Appropriate Objects Into a fe_species_tum_wwk_long Species Class Object

Description

If the cast is forward ambiguous, the function terminates with an error. "Forward ambiguous" means that one code in the original object corresponds to more than one codes in the goal coding. If the cast loses information, a warning is raised, but the cast is performed. "Information loss" in this context means that several codes from the orginal coding correspond to only one code in the goal coding.

Usage

as_fe_species_tum_wwk_long(x)

Arguments

Details

Note that a cast where only one species id from the original coding translates in a goal coding which represents a group of species is NOT considered losing information (i.e. backward ambiguous), because of the 1:1 match in the constellation of the specific cast.

Value

If a meaningful cast is possible, an fe_species_ger_nfi_2012 object is returned

Examples

as_fe_species_tum_wwk_long(c(10L, 41L, 41L, 31L)) # integer
as_fe_species_tum_wwk_long(c(10, 41, 41, 31)) # double
as_fe_species_tum_wwk_long(c("10", "41", "41", "31")) # character

# cast other fe_species classes
as_fe_species_tum_wwk_long(
  fe_species_tum_wwk_short(as.character(c(1, 1, 1, 3, 3, 5)))
)
as_fe_species_tum_wwk_long(
  fe_species_bavrn_state(as.character(c(20, 20, 10, 30, 30, 60)))
)

# display the casting result in terms of scientific species names
as_fe_species_tum_wwk_long(c(10L, 41L, 41L, 31L)) |> format("sci")

Cast Appropriate Objects Into a fe_stand_tum_wwk_short Species Class Object

Description

If the cast is forward ambiguous, the function terminates with an error. "Forward ambiguous" means that one code in the original object corresponds to more than one codes in the goal coding. If the cast loses information, a warning is raised, but the cast is performed. "Information loss" in this context means that several codes from the orginal coding correspond to only one code in the goal coding.

Usage

as_fe_species_tum_wwk_short(x)

Arguments

Details

Note that a cast where only one species id from the original coding translates in a goal coding which represents a group of species is NOT considered losing information (i.e. backward ambiguous), because of the 1:1 match in the constellation of the specific cast.

Value

If a meaningful cast is possible, an fe_species_tum_wwk_short object is returned

Examples

as_fe_species_tum_wwk_short(c(1L, 4L, 4L, 2L, 6L, 7L)) # integer
as_fe_species_tum_wwk_short(c(1, 4, 4, 2, 6, 7)) # double
as_fe_species_tum_wwk_short(c("1", "4", "4", "2", "6")) # character

# cast other fe_species classes
as_fe_species_tum_wwk_short(
  fe_species_ger_nfi_2012(as.character(c(20, 20, 10, 30, 30, 100)))
)
as_fe_species_tum_wwk_short(
  fe_species_bavrn_state(as.character(c(20, 20, 10, 30, 30, 60)))
)
as_fe_species_tum_wwk_short(
  fe_species_tum_wwk_long(as.character(c(10, 20, 10, 31, 31, 812, 87)))
)

# display the casting result in terms of scientific species names
as_fe_species_tum_wwk_short(c(1, 4, 4, 2, 6, 7, 5, 5)) |> format("sci")

Attribute Tree Heights to Layers After Ernst Assmann

Description

Tree heights are attributed to three layers as proposed by Assmann (1961). The layers are called Top, T, Middle, M, and Bottom, B, and correspond to >80%, >50%, and >0% of a reference height (usually the height of the highest tree in the stand of interest).

Usage

assmann_layers(heights, reference_height = NULL)

Arguments

Value

An ordered factor of T, M, B values, corresponding to heights in the order as heights was provided.

References

Assmann E (1961). Waldertragskunde. Organische Produktion, Struktur, Zuwachs und Ertrag von Waldbestaenden. BLV Verlagsgesellschaft, Muenchen, Bonn, Wien.

See Also

Other structure and diversity: shannon_index(), species_profile()

Examples

  # Monospecific stand
  trees <- norway_spruce_1_fe_stand$trees
  assmann_layers(trees$height_m)

  # Selection forest
  trees <- selection_forest_1_fe_stand$trees
  assmann_layers(trees$height_m)

Check a Number's Order of Magnitude Against a Requirement

Description

Check a Number's Order of Magnitude Against a Requirement

Usage

check_order_of_magnitude(x, min_ok, max_ok)

Arguments

Value

TRUE if a number x's integer power of 10 is inside the range given by min_ok and max_ok (min_ok and max_ok included)

Check the Orders of Magnitude of the Variables Given in a 'trees' data.frame as Required in an fe_stand object

Description

Check the Orders of Magnitude of the Variables Given in a 'trees' data.frame as Required in an fe_stand object

Usage

check_trees_orders_of_magnitude(trees)

Arguments

Value

The function issues a warning if it suspects that one of the variables in trees does not have the right units. This is the side effect it is actually called for. It always returns TRUE invisibly.

Estimate a tree's crown diameter

Description

This function can be used for estimating a tree's crown diameter, given its species, its stem diameter at breast height, and its total height. This is the crown diameter function which is implemented in the forest growth simulator SILVA (Pretzsch et al. 2002). The crown diameter in this context is defined as the average diameter of the crown at its greatest lateral extension. The crown diameter equations are available for exactly the species (groups) defined in the coding tum_wwk_short. If they are called with another species coding supported by the package ForestElementsR, crown_diameter_silva will attempt to convert them accordingly.

Usage

crown_diameter_silva(species_id, dbh_cm, height_m)

Arguments

Value

An estimate of the tree's diameter of the crown at its greatest lateral extension in m.

References

Pretzsch H, Biber P, Dursky J (2002). “The single tree-based stand simulator SILVA: construction, application and evaluation.” Forest Ecology and Management, 162(1), 3–21. ISSN 0378-1127, doi:10.1016/S0378-1127(02)00047-6.

Examples

# Estimate the crown diameter of a Scots pine with a stem diameter
# at breast height of 45.2 cm and a total height of 29.2 m:
crown_diameter_silva(
  species_id = "3",    # will be internally converted to tum_wwk_short
  dbh_cm = 45.2,
  height_m = 29.2
) # 6.1 m (rounded)

# Crown diameter estimate for a European beech with
# the same height and diameter:
crown_diameter_silva(
  species_id = "5",   # will be internally converted to tum_wwk_short
  dbh_cm = 45.2,
  height_m = 29.2
) # 9.6 m (rounded)

# Run vectorized
spec <- mm_forest_1_fe_stand_spatial$trees$species_id
d <- mm_forest_1_fe_stand_spatial$trees$dbh_cm
h <- mm_forest_1_fe_stand_spatial$trees$height_m
crown_diameter_silva(spec, d, h)

Core function for the silva tree crown diameter calculation

Description

See documentation of crown_diameter_silva for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

crown_diameter_silva_core(species_id, dbh_cm, height_m)

Arguments

Details

The function crown_diameter_silva is fully vectorized, inputs are recycled according to the tibble rules

Value

Tree crown diameter (see crown_diameter_silva)

Dominant Diameter d100

Description

The dominant diameter d100 was conceptuated by Ernst Assmann and Friedrich Franz in order obtain a mean diameter value for those trees which usually dominate a stand throughout its whole life.

Usage

d_100(d, n_rep_ha)

Arguments

Details

The d100 is defined as the quadratic mean diameter of the hundred thickest trees per ha. If there are only 100 trees or less on one ha, the d100 is the same as the quadratic mean diameter d_q. While the d100 is well defined and useful in monospecific stands, it is less so in mixed stands.

Value

The dominant diameter d100 value resulting from the input data

See Also

Other stand diameters: d_dom_weise(), d_q()

Examples

# A sample of trees from an angle count sample, where each
# tree represents a basal area of 4 m²/ha
d_cm <- c(12, 13, 25, 27, 28, 26, 26.1, 32, 35, 31, 42)
n_rep_ha <- 4 / ((d_cm / 100)^2 * pi / 4) # representation number of each tree
d_100(d_cm, n_rep_ha)
d_q(d_cm, n_rep_ha) # quadratic mean diameter for comparison


# Typical application to a set of single tree data grouped by survey
# time and species
# (note that everyone is applying d_100 mixed stands, but you should do it
# only, if you know exactly what you are doing)
library(dplyr)
oldopt <- options(fe_spec_lang = "eng") # display colloquial species names
# for d_100 in mixed stands, we require species shares
spec_shares <- species_shares(
  mm_forest_1_fe_stand_spatial,
  tree_filter = !removal, # include remaining trees only
  method = "ba_wd"
)
# extract the tree data to allow insights into the mechanics
trees <- mm_forest_1_fe_stand_spatial$trees |> filter(!removal)
# join with the shares
trees |>
  left_join(spec_shares) |>
  group_by(species_id, time_yr) |>
  summarise(
    n_ha     = round(sum(n_rep_ha)),
    d_q      = d_q(dbh_cm, n_rep_ha), # For comparison
    d_100_cm = d_100(dbh_cm, n_rep_ha / species_share)
  ) |>
  print(n = Inf)
options(oldopt) # set species name display to previous value

Estimate Stem Diameter Growth With the 3rd German National Forest Inventory Growth Model (2012)

Description

Tree diameter growth model of the third German National Forest Inventory of 2012 (Riedel et al. 2017). Allows to estimate a tree's dbh at any age if its dbh is known at a given age.

Usage

d_age_gnfi3(species_id, age_yr, dbh_cm_known, age_yr_known)

Arguments

Details

Originally, the function was parameterized for species and species groups corresponding to the national forest inventory's species coding (fe_species_ger_nfi_2012). We have attributed in addition these the original parameters also to the species codings fe_species_tum_wwk_short, and fe_species_bavrn_state_short. When called with a given species coding, the function will try to use the "nearest" of these three alternatives. Fallback option is the attempt to use fe_species_tum_wwk_short.

Value

A single diameter value or vector of diameter values corresponding to age_yr

References

Riedel T, Hennig P, Kroiher F, Polley H, Schmitz F, F. S (2017). Die dritte Bundeswaldinventur (BWI 2012). Inventur- und Auswertungsmethoden. Thuenen Institut fuer Waldoekosysteme.

See Also

Other growth functions: age_d_gnfi3(), age_h_gnfi3(), h_age_gnfi3()

Examples

  # A Norway spruce has a diameter of 17.5 cm at age 45. Estimate
  # its diameter at age 55
  d_age_gnfi3(10, 55, 17.5, 45) # 10 is ger_nfi_2012 code for Norway spruce

  # Do the same but 10 years backward in time (dbh at age 35)
  d_age_gnfi3(10, 35, 17.5, 45)

  # Apply for more than one tree, different species, same age
  d_known   <- c(23.1, 16.2, 35.2, 19.3, 21.8)
  species   <- as_fe_species_tum_wwk_short(c(3, 3, 3, 6, 6))
  d_age_gnfi3(
    species, age_yr = 50, dbh_cm_known = d_known, age_yr_known = 40
  )

Core Function for Estimate Stem Diameter Growth With the 3rd German National Forest Inventory Growth Model (2012)

Description

See documentation of d_age_gnfi3 for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

d_age_gnfi3_core(species_id, age_yr, dbh_cm_known, age_yr_known, params)

Arguments

Details

The function d_age_gnfi3 is fully vectorized, inputs are recyled according to the tibble rules

Value

A vector with estimates of the trees' expected dbh (see d_age_gnfi3)

Helper function for cascaded species conversion when applying the function d_age_gnfi3(). Try to convert given species_id into ger_nfi_2012. If this causes an error convert to tum_wwk_short instead

Description

Helper function for cascaded species conversion when applying the function d_age_gnfi3(). Try to convert given species_id into ger_nfi_2012. If this causes an error convert to tum_wwk_short instead

Usage

d_age_gnfi3_spec_convert(species_id)

Weise's Dominant Diameter

Description

The dominant diameter after Weise is the quadratic mean diameter of the 20% biggest trees in a stand. In contrast to the dominant diameter d_100 it is well defined not only in monospecific stands, but also in mixed stands.

Usage

d_dom_weise(d, n_rep = 1)

Arguments

Value

The value of Weise's dominant diameter resulting from the input data

See Also

Other stand diameters: d_100(), d_q()

Examples

# A sample of trees from an angle count sample, where each
# tree represents a basal area of 4 m²/ha
d_cm <- c(12, 13, 25, 27, 28, 26, 26.1, 32, 35, 31, 42)
n_rep_ha <- 4 / ((d_cm / 100)^2 * pi / 4) # representation number of each tree
d_dom_weise(d_cm, n_rep_ha)
d_100(d_cm, n_rep_ha) # dominant diameter d100 for comparison
d_q(d_cm, n_rep_ha) # quadratic mean diameter for comparison

# if 20% of the trees are 100 stems/ha, Weise's dominant diameter and
# d100 are equal
d_cm <- rnorm(n = 500, mean = 35, sd = 7)
d_dom_weise(d_cm, 1)
d_100(d_cm, 1)

# Weise's dominant diameter is greater than d100, if 20% of the trees
# represent less than 100 trees/ha
d_cm <- rnorm(n = 200, mean = 35, sd = 7)
d_dom_weise(d_cm, 1)
d_100(d_cm, 1)

# Weise's dominant diameter is smaller than d100, if 20% of the trees
# represent more than 100 trees/ha
d_cm <- rnorm(n = 800, mean = 35, sd = 7)
d_dom_weise(d_cm, 1)
d_100(d_cm, 1)

Quadratic Mean Diameter

Description

Function for calculating the quadratic mean of a vector. The typical application in forestry is to calculate the quadratic mean diameter.

Usage

d_q(d, n_rep = 1)

Arguments

Value

the quadratic mean of d

See Also

Other stand diameters: d_100(), d_dom_weise()

Examples

# Evaluate a sample of equally weighted tree diameters
d_cm <- c(12, 13, 25, 27, 28, 26, 26.1, 32, 35, 31, 42)
d_q(d_cm) # quadratic mean diameter
mean(d_cm) # the arithmetic mean is not the same!

# Assume, the same sample comes from an angle count sample, where each
# tree represents a basal area of 4 m²/ha
n_rep_ha <- 4 / ((d_cm / 100)^2 * pi / 4) # representation number of each tree
d_q(d_cm, n_rep_ha)


# Typical application to a set of single tree data grouped by survey
# time and species
library(dplyr)
oldopt <- options(fe_spec_lang = "eng") # display colloquial species names
# extract the tree data to allow insights into the mechanics
trees <- mm_forest_1_fe_stand_spatial$trees |> filter(!removal)
trees |>
  group_by(species_id, time_yr) |>
  summarise(
    n_ha   = round(sum(n_rep_ha)),
    d_q_cm = d_q(dbh_cm, n_rep_ha)
  ) |>
  print(n = Inf)
options(oldopt) # set species name display to previous value

Example Stands

Description

ForestelementsR comes with five example stands that exist each in a 'raw' format that could have been taken from a user data base, and one of the package's specific stand object classes (fe_stand, fe_stand_spatial, ,fe_ccircle_spatial). Four of the example stands correspond to the fe_stand class, one to the fe_stand_spatial and one to fe_ccircle_spatialclass. The fe_stand, fe_stand_spatial and fe_ccircle_spatial objects have been constructed from the raw data using the functions fe_stand, fe_stand_spatial and fe_ccircle_spatial (see the examples section of the functions' documentation).

fe_stand compatible examples:

The first stand is a 53 years-old Norway spruce stand, the second one a 79 year old European beech stand, the third an even-aged mixed stand of Norway spruce and European beech (75 and 88 years, respectively). The fourth stand is a selection forest comprising the species Norway spruce, silver fir, European beech, and sycamore. All these stands have an area of 0.49 ha. The raw data (see name suffix) are data.frames (tibbles) comprising the following columns

stand

Name of the stand

species

Integer numbers following the TUM_WWK_short convention

no

Tree id

age

Tree age (i.e. stand age, in these examples) in years; note that the selection forest data do not provide age information, as this is usually not available in practice and mostly not meaningful for this forest type

d

Breast height (1.3 m) stem diameter in cm

h

Tree height in m

hcb

Crown base height in m

crad

Crown radius in m

The fe_stand objects (see name suffix) resulted from converting the raw data with fe_stand

fe_stand_spatial compatible example:

mm_forest_1_raw represents raw data from a research plot in a mixed mountain forest that has been surveyed several times. mm_forest_1_fe_stand_spatial is the representation of this plot as an object of class fe_stand_spatial

fe_ccircle_spatial compatible examples

spruce_pine_ccircle_raw represents raw data that could come from a typical inventory plot of the Bavarian State Forest, while spruce_pine_ccircle_spatial is an object of class fe_ccircle_spatial.

Usage

norway_spruce_1_raw

norway_spruce_1_fe_stand

european_beech_1_raw

european_beech_1_fe_stand

spruce_beech_1_raw

spruce_beech_1_fe_stand

selection_forest_1_raw

selection_forest_1_fe_stand

mm_forest_1_raw

mm_forest_1_fe_stand_spatial

spruce_pine_ccircle_raw

spruce_pine_ccircle_spatial

spruce_pine_ccircle_spatial_notrees

Format

An object of class tbl_df (inherits from tbl, data.frame) with 498 rows and 8 columns.

An object of class fe_stand of length 4.

An object of class tbl_df (inherits from tbl, data.frame) with 268 rows and 8 columns.

An object of class fe_stand of length 4.

An object of class tbl_df (inherits from tbl, data.frame) with 225 rows and 8 columns.

An object of class fe_stand of length 4.

An object of class tbl_df (inherits from tbl, data.frame) with 283 rows and 7 columns.

An object of class fe_stand of length 4.

An object of class list of length 5.

An object of class fe_stand_spatial (inherits from fe_stand) of length 6.

An object of class list of length 8.

An object of class fe_ccircle_spatial (inherits from fe_stand_spatial, fe_stand) of length 6.

An object of class fe_ccircle_spatial_notrees (inherits from fe_ccircle_spatial, fe_stand_spatial, fe_stand) of length 6.

User Friendly Construction of an fe_ccircle_spatial Object from a List of Data Frames

Description

fe_ccircle_spatial() provides a user-friendly interface for the constructor new_fe_ccircle_spatial. While the constructor does not prevent users from creating malformed fe_ccircle_spatial objects, fe_ccircle_spatial does everything to achieve a well-defined object mostly based on an initial list of data.frames that might be, e.g. drawn out of a user's own data base.

Usage

fe_ccircle_spatial(
  x,
  method = c("strict", "flexible"),
  tree_frame_name = "trees",
  tree_pos_frame_name = "tree_positions",
  circle_frame_name = "circle_definition",
  center_coord = "center_coordinate",
  small_trees_name = "small_trees",
  time_yr_name = "time_yr",
  tree_id_col,
  species_id_col,
  time_yr_col,
  dbh_cm_col,
  radius_col,
  angle_col,
  stand_id = "my_fe_ccircle_spatial",
  layer_key_col = NA,
  age_yr_col = NA,
  height_m_col = NA,
  crown_base_height_m_col = NA,
  crown_radius_m_col = NA,
  removal_col = NA,
  ingrowth_col = NA,
  n_rep_ha_col = NA,
  verbose = TRUE
)

Arguments

Details

An object of class fe_ccircle_spatial is a child object of fe_stand_spatial which, however, contains information about the horizontal positions of the trees in a concentric circle representation scheme. All spatial information and its processing is based on the R-package sf.

The input object x to fe_ccircle_spatial must be a list that comprises two and an optional third data frame(s):

• a data frame containing the single tree information (same requirements as for fe_stand). This data frame must contain a a minimum set of columns (tree id, species id, time variable, diameter at breast height). These columns must not contain missing values. Other columns (containing tree height, height to crown base, crown radius, tree age) are optional for the user to provide. If provided, they may contain missing values. If not provided, these columns will only contain missing values in the fe_ccircle_spatial object. The columns about the trees' removal and ingrowth status are also optional, but if provided, they must not contain missing values. If not provided, both columns will be filled with FALSE in the resulting fe_ccircle_spatial object. fe_ccircle_spatial will automatically add a column n_rep_ha which contains for each tree the number of trees it represents per ha. This may seem redundant if looking at fe_ccircle objects alone, but it allows a broad range of evaluation functions to be applied to different objects containing trees. In addition to the input object of fe_ccircle_spatial, there is one additional list element needed which defines a single (calendar) year or a vector of years in order to give the object a time relation. If small tree information is present (i.e. the small tree data frame is not empty), time_yr must absolutely match with the time_yr column of this data frame.

• a data frame that contains information about the tree positions. This is not part of the first data frame, because the latter could contain several observations (at different times) of the same tree, which would lead to redundant coordinate representation. This data frame must contain a column with tree id's, and the x and y coordinates of the stem center points. NA values are not allowed in this data frame.

Value

If the user input allows to construct a well-defined fe_ccircle_spatial object, this object will be returned. If not, the function will terminate with an error.

Examples

# Transform the example data collection mm_forest_1_raw (could e.g. have
# been drawn out of a user's data base) into an fe_ccircle_spatial object

spruce_pine_ccircle_sp <- spruce_pine_ccircle_raw |>
  fe_ccircle_spatial(
    method = "flexible",
    tree_id_col = "tree_id",
    species_id_col = "species_id",
    time_yr_col = "time_yr",
    dbh_cm_col = "dbh_cm",
    radius_col = "R",
    angle_col = "angle",
    stand_id = mm_forest_1_raw$stand_id,
    height_m_col = "height_m",
    removal_col = "removal",
    time_yr_name = "time_yr"
  )
# Show a little summary, display scientific species names
options(fe_spec_lang = "sci")
spruce_pine_ccircle_sp |> summary()

User Friendly Construction of an fe_ccircle_spatial_notrees Object from a List of Data Frames

Description

fe_ccircle_spatial_notrees() provides a user-friendly interface for the constructor new_fe_ccircle_spatial_notrees. While the constructor does not prevent users from creating malformed new_fe_ccircle_spatial_notrees objects, new_fe_ccircle_spatial_notrees does everything to achieve a well-defined object mostly based on an initial list of data.frames that might be, e.g. drawn out of a user's own data base.

Usage

fe_ccircle_spatial_notrees(
  x,
  time_yr_name = "time_yr",
  circle_frame_name = "circle_definition",
  center_coord = "center_coordinate",
  small_trees_name = "small_trees",
  stand_id = "my_fe_ccircle_spatial_notrees"
)

Arguments

Details

An object of class fe_ccircle_spatial_notrees is a child object of fe_ccircle_spatial which, however does not contain trees which are big enough to have a dbh. So, in contrast to fe_ccircle_spatial objects, where the elements trees, and tree_positions must be data frames, both are NULL in this class. The class fe_ccircle_spatial_notrees has been designed for covering a comparably rare case, i.e. an inventory point where no regular trees (trees that are big enough to have a dbh), but possibly small trees are present. The input object x to fe_ccircle_spatial must be a list that follows the same conventions as the input object of fe_ccircle_spatial, except that no tree data frame is required. If it does exist, it will be ignored. In addition to the input object of fe_ccircle_spatial, there is one additional list element needed which defines a single (calendar) year or a vector of years in order to give the object a time relation. If small tree information is present (i.e. the small tree data frame is not empty), time_yr must absolutely match with the time_yr column of this data frame.

Value

If the user input allows to construct a well-defined fe_ccircle_spatial_notrees object, this object will be returned. If not, the function will terminate with an error.

Examples

# Transform the example data collection mm_forest_1_raw (could e.g. have
# been drawn out of a user's data base) into an fe_ccircle_spatial_notrees
# object. This means, that in the resulting data frame the trees and their
# positions are ignored.
notrees_example <- spruce_pine_ccircle_raw |>
  fe_ccircle_spatial_notrees()

plot(notrees_example)

Construct a fe_species_bavrn_state Species Code Vector

Description

User interface for constructing a vector of species codes follwing the fe_species_bavrn_state convention

Usage

fe_species_bavrn_state(x = character())

Arguments

Details

The bavrn_state species coding is the species coding used by the Bavarian State Forest Service. See the example section for how to look up the coding.

Value

If the user input allows to construct a well-defined fe_species_bavrn_state object, this object will be returned. If not, the function will terminate with an error.

Examples

# Libraries required for the following two examples
library(dplyr)
library(purrr)

# Look up the bavrn_state species codes for all supported species
# the column species_id contains the bavrn_state codes
species_codings |>
  filter(species_coding == "bavrn_state") |>
  pluck(2, 1) |>
  arrange(as.numeric(species_id)) |> # just for the look of it
  print(n = Inf)

# Display a summary table which shows the number of single species behind
# each bavrn_state species code
species_codings |>
  filter(species_coding == "bavrn_state") |>
  pluck(2, 1) |>
  group_by(name_eng, species_id) |> # display english names
  summarise(n = n()) |>
  arrange(as.numeric(species_id)) |> # just for the look of it
  print(n = Inf)


# Make an fe_species_bavrn_state vector from a vector of integer codes
spec_ids <- fe_species_bavrn_state(
  c(10, 10, 10, 60, 60, 60, 60, 30, 30, 80, 86, 80)
)

Construct a fe_species_bavrn_state_short Species Code Vector

Description

User interface for constructing a vector of species codes follwing the fe_species_bavrn_state_short convention

Usage

fe_species_bavrn_state_short(x = character())

Arguments

Details

The bavrn_state_short species coding is the species coding used by the Bavarian State Forest Service for aggregated data evaluations. It is actually a grouped version of the detailed bavrn_state coding. See the example section for how to look up the coding.

Value

If the user input allows to construct a well-defined fe_species_bavrn_state_short object, this object will be returned. If not, the function will terminate with an error.

Examples

# Libraries required for the following two examples
library(dplyr)
library(purrr)

# Look up the bavrn_state_short species codes for all supported species
# the column species_id contains the bavrn_state_short codes
species_codings |>
  filter(species_coding == "bavrn_state_short") |>
  pluck(2, 1) |>
  arrange(as.numeric(species_id)) |> # just for the look of it
  print(n = Inf)

# Or, use an even easier access with
fe_species_get_coding_table("bavrn_state_short")

# Display a summary table which shows the number of single species behind
# each bavrn_state_short species code
fe_species_get_coding_table("bavrn_state_short") |>
  group_by(name_eng, species_id) |> # display english names
  summarise(n = n()) |>
  arrange(as.numeric(species_id)) |> # just for the look of it
  print(n = Inf)


# Make an fe_species_bavrn_state_short vector from a vector of integer codes
spec_ids <- fe_species_bavrn_state_short(
  c(1, 1, 1, 6, 6, 6, 6, 3, 3, 8, 8, 8)
)

Construct a fe_species_ger_nfi_2012 Species Code Vector

Description

User interface for constructing a vector of species codes follwing the fe_species_ger_nfi_2012 convention

Usage

fe_species_ger_nfi_2012(x = character())

Arguments

Details

The ger_nfi_2012 species coding is the species coding used by the German National Forest Inventory of 2012. See the example section for how to look up the coding.

Value

If the user input allows to construct a well-defined fe_species_ger_nfi_2012 object, this object will be returned. If not, the function will terminate with an error.

Examples

# Libraries required for the following two examples
library(dplyr)
library(purrr)

# Look up the ger_nfi_2012 species codes for all supported species
# the column species_id contains the ger_nfi_2012 codes
species_codings |>
  filter(species_coding == "ger_nfi_2012") |>
  pluck(2, 1) |>
  arrange(as.numeric(species_id)) |> # just for the look of it
  print(n = Inf)

# Display a summary table which shows the number of single species behind
# each ger_nfi_2012 species code
species_codings |>
  filter(species_coding == "ger_nfi_2012") |>
  pluck(2, 1) |>
  group_by(name_eng, species_id) |> # display english names
  summarise(n = n()) |>
  arrange(as.numeric(species_id)) |> # just for the look of it
  print(n = Inf)


# Make an fe_species_ger_nfi_2012 vector from a vector of integer codes
spec_ids <- fe_species_ger_nfi_2012(
  c(10, 10, 10, 100, 100, 100, 100, 20, 20, 190, 290, 190)
)

Get Name of the Coding Belonging to an fe_species Object

Description

Get Name of the Coding Belonging to an fe_species Object

Usage

fe_species_get_coding(x)

Arguments

Value

The requested coding name (character)

Examples

spec_ids <- fe_species_ger_nfi_2012(c("10", "10", "30"))
fe_species_get_coding(spec_ids)

Get the Coding Table of a Supported fe_species Coding

Description

Get the Coding Table of a Supported fe_species Coding

Usage

fe_species_get_coding_table(coding)

Arguments

Value

A data.frame (tibble) representing the requested coding

Examples

fe_species_get_coding_table("ger_nfi_2012")
fe_species_get_coding_table("bavrn_state")
fe_species_get_coding_table("tum_wwk_short")

Construct a fe_species_master Species Code Vector

Description

User interface for constructing a vector of species codes follwing the fe_species_master convention

Usage

fe_species_master(x = character())

Arguments

Details

The master species coding is the original species coding used by the package ForestElementsR. It contains each species from the species_master_table and no species groups. See the example section for how to look up the coding.

Value

If the user input allows to construct a well-defined fe_species_master object, this object will be returned. If not, the function will terminate with an error.

Examples

# Libraries required for the following two examples
library(dplyr)
library(purrr)

# Look up the master species codes for all supported species
# the column species_id contains the master codes
fe_species_get_coding_table("master") |>
  print(n = Inf)

# Display a summary table which shows the number of single species behind
# each master species code (must be 1 with no exception)
fe_species_get_coding_table("master") |>
  group_by(name_eng, species_id) |> # display english names
  summarise(n = n()) |>
  arrange(as.numeric(species_id)) |> # just for the look of it
  print(n = Inf)

# Make an fe_species_master vector from a character vector of appropriate
# codes
spec_ids <- fe_species_master(
  c("pinus_002", "sorbus_002", "sorbus_002", "quercus_002", "prunus_001")
)

Construct a fe_species_tum_wwk_long Species Code Vector

Description

User interface for constructing a vector of species codes follwing the fe_species_tum_wwk_long convention

Usage

fe_species_tum_wwk_long(x = character())

Arguments

Details

The tum_wwk_long species coding is one of two codings in use at the Chair of Forest Growth and Yield Science (see species_codings for more information). See the example section for how to look up the coding.

Value

If the user input allows to construct a well-defined fe_species_ger_nfi_2012 object, this object will be returned. If not, the function will terminate with an error.

Examples

# Libraries required for the following two examples
library(dplyr)
library(purrr)

# Look up the tum_wwk_long species codes for all supported species
# the column species_id contains the tum_wwk_long codes
species_codings |>
  filter(species_coding == "tum_wwk_long") |>
  pluck(2, 1) |>
  arrange(as.numeric(species_id)) |> # just for the look of it
  print(n = Inf)

# Display a summary table which shows the number of single species behind
# each tum_wwk_long species code
species_codings |>
  filter(species_coding == "tum_wwk_long") |>
  pluck(2, 1) |>
  group_by(name_eng, species_id) |> # display english names
  summarise(n = n()) |>
  arrange(as.numeric(species_id)) |> # just for the look of it
  print(n = Inf)


# Make an fe_species_tum_wwk_long vector from a vector of integer codes
spec_ids <- fe_species_tum_wwk_long(
  c(10, 10, 10, 20, 20, 20, 50, 50, 811, 811, 811, 891)
)

Construct a fe_species_tum_wwk_short Species Code Vector

Description

User interface for constructing a vector of species codes follwing the fe_species_tum_wwk_short convention

Usage

fe_species_tum_wwk_short(x = character())

Arguments

Details

The tum_wwk_short species coding is one of two codings in use at the Chair of Forest Growth and Yield Science. It defines only a small set of single species explicitly (the most important ones in Central Europe), while all other species are attributed to three large container groups. See the example section for how to look up the coding.

Value

If the user input allows to construct a well-defined fe_species_tum_wwk_short object, this object will be returned. If not, the function will terminate with an error.

Examples

# Libraries required for the following two examples
library(dplyr)
library(purrr)

# Look up the tum_wwk_short species codes for all supported species
# the column species_id contains the tum_wwk_short codes
species_codings |>
  filter(species_coding == "tum_wwk_short") |>
  pluck(2, 1) |>
  print(n = Inf)

# Display a summary table which shows the number of single species behind
# each tum_wwk_short species code
species_codings |>
  filter(species_coding == "tum_wwk_short") |>
  pluck(2, 1) |>
  group_by(name_eng, species_id) |> # display english names
  summarise(n = n()) |>
  arrange(as.numeric(species_id)) |> # just for the look of it
  print(n = Inf)


# Make an fe_species_tum_wwk_short vector from a vector of integer codes
spec_ids <- fe_species_tum_wwk_short(c(1, 1, 1, 5, 5, 5, 5, 3, 3, 8, 9, 8))

User Friendly Construction of an fe_stand Object from a Data Frame

Description

fe_stand() provides a user-friendly interface for the constructor new_fe_stand. While the constructor does not prevent users from creating malformed fe_stand objects, fe_stand does everything to achieve a well-defined object mostly based on an initial data.frame that might be, e.g. drawn out of a user's own data base.

Usage

fe_stand(
  x,
  tree_id_col,
  species_id_col,
  time_yr_col,
  dbh_cm_col,
  area_ha,
  stand_id = "my_fe_stand",
  layer_key_col = NA,
  age_yr_col = NA,
  height_m_col = NA,
  crown_base_height_m_col = NA,
  crown_radius_m_col = NA,
  removal_col = NA,
  ingrowth_col = NA,
  n_rep_ha_col = NA,
  small_trees = data.frame(),
  verbose = TRUE
)

Arguments

Details

The initial data.frame (or even nicer, tibble) provided by the user must contain a a minimum set of columns (tree id, species id, time variable, diameter at breast height). These columns must not contain missing values. Other columns (containing tree height, height to crown base, crown radius, tree age) are optional for the user to provide. If provided, they may contain missing values. If not provided these columns will only contain missing values in the fe_stand object. The columns about the trees' removal and ingrowth status are also optional, but if provided, they must not contain missing values. If not provided, both columns will be filled with FALSE in the resulting fe_stand object.

The columns from the user's data.frame that correspond to the columns defined in fe_stand objects will turn up in the object under standard names. All other columns that might be in the data.frame will be transferred to the fe_stand object with their orignal names. It is the user's responsibility to take care of them.

fe_stand will automatically add a column n_rep_ha which contains for each tree the number of trees it represents per ha. This may seem redundant if looking at fe_stand objects alone, but it allows a broad range of evaluation functions to be applied to different objects containing trees.

Value

If the user input allows to construct a well-defined fe_stand object, this object will be returned. If not, the function will terminate with an error.

Examples

# Constructing an fe_stand object based on the minimum required information
# - make data.frame (or, nicer, a tibble) with stand information from
#   scratch
candidate_stand <- tibble::tibble(
  tree_no    = as.character(c(1:100)),
  species_id = as_fe_species_tum_wwk_short(c(rep("1", 30), rep("5", 70))),
  time_yr    = 2022,
  dbh        = c(rnorm(30, 45, 12), rnorm(70, 38, 9))
)

# - call fe_stand
goal_fe_stand_object <- fe_stand(
  x = candidate_stand,
  tree_id_col = "tree_no",
  species_id_col = "species_id",
  time_yr_col = "time_yr",
  dbh_cm_col = "dbh",
  area_ha = 0.33
)


# Using raw data that could come out of a user's data bases; here one
# example stands (spruce_beech_1_raw) provided with the ForestElementsR
# package
spruce_beech_1_raw$year <- 2022 # No time information in the data frame
spruce_beech_1_raw$species <- as_fe_species_tum_wwk_short(
  spruce_beech_1_raw$species
)

spruce_beech_stand <- fe_stand(
  spruce_beech_1_raw,
  tree_id_col = "no",
  species_id_col = "species",
  time_yr_col = "year",
  dbh_cm_col = "d",
  area_ha = 0.49,
  stand_id = spruce_beech_1_raw[1, ]$stand,
  age_yr_col = "age",
  height_m_col = "h",
  crown_base_height_m_col = "hcb",
  crown_radius_m_col = "crad"
)

# Little summary
spruce_beech_stand |> summary()

User Friendly Construction of an fe_stand_spatial Object from a List of Data Frames

Description

fe_stand_spatial() provides a user-friendly interface for the constructor new_fe_stand_spatial. While the constructor does not prevent users from creating malformed fe_stand_spatial objects, fe_stand_spatial does everything to achieve a well-defined object mostly based on an initial list of data.frames that might be, e.g. drawn out of a user's own data base.

Usage

fe_stand_spatial(
  x,
  tree_frame_name = "trees",
  tree_pos_frame_name = "tree_positions",
  outline_frame_name = "outline",
  orientation = NA,
  tree_id_col,
  species_id_col,
  time_yr_col,
  dbh_cm_col,
  x_m_col,
  y_m_col,
  stand_id = "my_fe_stand_spatial",
  layer_key_col = NA,
  age_yr_col = NA,
  height_m_col = NA,
  crown_base_height_m_col = NA,
  crown_radius_m_col = NA,
  removal_col = NA,
  ingrowth_col = NA,
  n_rep_ha_col = NA,
  small_trees = data.frame(),
  verbose = TRUE
)

Arguments

Details

An object of class fe_stand_spatial is a child object of fe_stand which, however, contains information about the horizontal positions of the trees and the horizontal outline of the stand. All spatial information and its processing is based on the R-package sf.

The input object x to fe_stand_spatial must be a list that comprises two and an optional third data frame(s):

• a data frame containing the single tree information (same requirements as for fe_stand). This data frame must contain a a minimum set of columns (tree id, species id, time variable, diameter at breast height). These columns must not contain missing values. Other columns (containing tree height, height to crown base, crown radius, tree age) are optional for the user to provide. If provided, they may contain missing values. If not provided, these columns will only contain missing values in the fe_stand_spatial object. The columns about the trees' removal and ingrowth status are also optional, but if provided, they must not contain missing values. If not provided, both columns will be filled with FALSE in the resulting fe_stand_spatial object. fe_stand_spatial will automatically add a column n_rep_ha which contains for each tree the number of trees it represents per ha. This may seem redundant if looking at fe_stand objects alone, but it allows a broad range of evaluation functions to be applied to different objects containing trees. Note that in a fe_stand_spatial object trees are allowed which are outside the actual stand outine (e.g. "buffer zone trees"). Such trees will be automatically identified by their coordinates and not taken into account for calculating the stand's growth and yield characteristics. However, they can be included in visual displays.

• a data frame that contains information about the tree positions. This is not part of the first data frame, because the latter could contain several observations (at different times) of the same tree, which would lead to redundant coordinate representation. This data frame must contain a column with tree id's, and the x and y coordinates of the stem center points according to the same coordinate system as the stand outline (see below). NA values are not allowed in this data frame.

• The optional third data frame must comprise the corner points of a polygon that describes the stand's outline. The polygon must not be self-intersection, therefore, the points must be given in correct sequence. The first point does not need to be repeated - fe_stand_spatial will take care for closing the polygon. This data frame needs to contain only the x and y coordinates of the corner points (in m), no NA values allowed.

Value

If the user input allows to construct a well-defined fe_stand_spatial object, this object will be returned. If not, the function will terminate with an error.

Examples

# Transform the example data collection mm_forest_1_raw (could e.g. have
# been drawn out of a user's data base) into an fe_stand_spatial object

mm_forest_spatial <- mm_forest_1_raw |>
  fe_stand_spatial(
    orientation = mm_forest_1_raw$north_dev,
    tree_id_col = "tree_id",
    species_id_col = "species_id",
    time_yr_col = "time_yr",
    dbh_cm_col = "dbh_cm",
    x_m_col = "x",
    y_m_col = "y",
    stand_id = mm_forest_1_raw$stand_id,
    height_m_col = "height_m",
    crown_base_height_m_col = "crown_base_height_m",
    crown_radius_m_col = "crown_radius_m",
    removal_col = "removal"
  )

# Show a little summary, display scientific species names
options(fe_spec_lang = "sci")
mm_forest_spatial |> summary()

User Friendly Construction of an fe_tally_list Object from a List of Data Frames

Description

fe_tally_list() provides a user-friendly interface for the constructor new_fe_tally_list. While the constructor does not prevent users from creating malformed fe_tally_list objects, fe_tally_list does everything to achieve a well-defined object mostly based on an initial list of data.frames that might be, e.g. drawn out of a user's own data base.

Usage

fe_tally_list(
  x,
  stand_id = "my_tally_list",
  tree_count_name = "tree_count",
  class_def_name = "class_definition",
  area_ha_name = "area_ha",
  size_class_col,
  tree_nr_col,
  species_id_col,
  count_col,
  class_type_col,
  class_min_col,
  class_max_col
)

Arguments

Details

The input object x to fe_tally_list must be a list that comprises two data frame(s):

• a data frame containing the tree counts per class. This data frame must contain a a minimum set of columns (Nr, size_class, N, species.

• a data frame that contains information about the class definition,

Value

If the user input allows to construct a well-defined fe_tally_list object, this object will be returned. If not, the function will terminate with an error.

User Friendly Construction of an fe_yield_table Object from a Data Frame

Description

User Friendly Construction of an fe_yield_table Object from a Data Frame

Usage

fe_yield_table(
  x,
  name_orig,
  name_int,
  si_variable = "h_q_m",
  mai_variable = NA
)

Arguments

Value

An object of class fe_yield_table, if the input allows to build a valid one. If this is not the case, the function will terminate with an error.

See Also

Other yield table functions: plot.fe_yield_table(), si_to_mai_age(), si_to_mai_max(), site_index(), stocking_level(), yield_tables_for_species, ytable_age_slice(), ytable_lookup(), ytable_max_slice()

Examples

  # Make fe_yield_table object from a very original-table-like data frame
  ytable_pine_wiedemann_moderate_1943_raw |>
    fe_yield_table(
      name_orig = "Kiefer Wiedemann 1943 Maessige Durchforstung",
      name_int  = "Scots Pine Wiedemann 1943 Moderate Thinning",
      si_variable = "h_q_m",
      mai_variable = c("mai_m3_ha_yr", "red_mai_m3_ha_yr")
    )

Formatted Output of an fe_species_bavrn_state Vector

Description

Usually, this function is not required to be called explicitly. It Will always be used automatically, when an object of type fe_species_bavrn_state is printed, be it alone, be it as part of another object (e.g. a tibble)

Usage

## S3 method for class 'fe_species_bavrn_state'
format(x, spec_lang = options("fe_spec_lang")$fe_spec_lang, ...)

Arguments

Value

A character vector either displaying the original species codes provided in x, or the species (group) names in the desired language

Examples

# Create an fe_species_bavrn_state object
spec_ids <- fe_species_bavrn_state(
  as.character(c(10, 30, 20, 40, 50, 60, 87, 63, 73, 72, 86, 80))
)

# Display in default style, scientific names, English, and German names
format(spec_ids)
format(spec_ids, spec_lang = "sci")
format(spec_ids, spec_lang = "eng")
format(spec_ids, spec_lang = "ger")

# Usual application: Set option for species code output
# Any print of an fe_species object will use the last setting of the option
options(fe_spec_lang = "sci")
spec_ids

Formatted Output of an fe_species_bavrn_state_short Vector

Description

Usually, this function is not required to be called explicitly. It Will always be used automatically, when an object of type fe_species_bavrn_state_short is printed, be it alone, be it as part of another object (e.g. a tibble)

Usage

## S3 method for class 'fe_species_bavrn_state_short'
format(x, spec_lang = options("fe_spec_lang")$fe_spec_lang, ...)

Arguments

Value

A character vector either displaying the original species codes provided in x, or the species (group) names in the desired language

Examples

# Create an fe_species_bavrn_state_short object
spec_ids <- fe_species_bavrn_state_short(
  as.character(1:9)
)

# Display in default style, scientific names, English, and German names
format(spec_ids)
format(spec_ids, spec_lang = "sci")
format(spec_ids, spec_lang = "eng")
format(spec_ids, spec_lang = "ger")

# Usual application: Set option for species code output
# Any print of an fe_species object will use the last setting of the option
options(fe_spec_lang = "sci")
spec_ids

Formatted Output of an fe_species_ger_nfi_2012 Vector

Description

Usually, this function is not required to be called explicitly. It Will always be used automatically, when an object of type fe_species_ger_nfi_2012 is printed, be it alone, be it as part of another object (e.g. a tibble)

Usage

## S3 method for class 'fe_species_ger_nfi_2012'
format(x, spec_lang = options("fe_spec_lang")$fe_spec_lang, ...)

Arguments

Value

A character vector either displaying the original species codes provided in x, or the species (group) names in the desired language

Examples

# Create an fe_species_ger_nfi_2012 object
spec_ids <- fe_species_ger_nfi_2012(
  as.character(c(10, 20, 30, 40, 50, 100, 110, 120, 130, 140, 150, 170))
)

# Display in default style, scientific names, English, and German names
format(spec_ids)
format(spec_ids, spec_lang = "sci")
format(spec_ids, spec_lang = "eng")
format(spec_ids, spec_lang = "ger")

# Usual application: Set option for species code output
# Any print of an fe_species object will use the last setting of the option
options(fe_spec_lang = "eng")
spec_ids

Formatted Output of an fe_species_master Vector

Description

Usually, this function is not required to be called explicitly. It Will always be used automatically, when an object of type fe_species_master is printed, be it alone, be it as part of another object (e.g. a tibble)

Usage

## S3 method for class 'fe_species_master'
format(x, spec_lang = options("fe_spec_lang")$fe_spec_lang, ...)

Arguments

Value

A character vector either displaying the original species codes provided in x, or the species (group) names in the desired language

Examples

# Create an fe_species_master object
spec_ids <- fe_species_master(
  c("picea_001", "fagus_001", "quercus_002", "quercus_001")
)

# Display in default style, scientific names, English, and German names
format(spec_ids)
format(spec_ids, spec_lang = "sci")
format(spec_ids, spec_lang = "eng")
format(spec_ids, spec_lang = "ger")

# Usual application: Set option for species code output
# Any print of an fe_species object will use the last setting of the option
options(fe_spec_lang = "eng")
spec_ids

Formatted Output of an fe_species_tum_wwk_long Vector

Description

Usually, this function is not required to be called explicitly. It Will always be used automatically, when an object of type fe_species_tum_wwk_long is printed, be it alone, be it as part of another object (e.g. a tibble)

Usage

## S3 method for class 'fe_species_tum_wwk_long'
format(x, spec_lang = options("fe_spec_lang")$fe_spec_lang, ...)

Arguments

Value

A character vector either displaying the original species codes provided in x, or the species (group) names in the desired language

Examples

# Create an fe_species_tum_wwk_long object
spec_ids <- fe_species_tum_wwk_long(
  as.character(c(70, 61, 88, 88, 10, 971, 32))
)

# Display in default style, scientific names, English, and German names
format(spec_ids)
format(spec_ids, spec_lang = "sci")
format(spec_ids, spec_lang = "eng")
format(spec_ids, spec_lang = "ger")

# Usual application: Set option for species code output
# Any print of an fe_species object will use the last setting of the option
options(fe_spec_lang = "sci")
spec_ids

Formatted Output of an fe_species_tum_wwk_short Vector

Description

Usually, this function is not required to be called explicitly. It Will always be used automatically, when an object of type fe_species_tum_wwk_short is printed, be it alone, be it as part of another object (e.g. a tibble)

Usage

## S3 method for class 'fe_species_tum_wwk_short'
format(x, spec_lang = options("fe_spec_lang")$fe_spec_lang, ...)

Arguments

Value

A character vector either displaying the original species codes provided in x, or the species (group) names in the desired language

Examples

# Create an fe_species_tum_wwk_short object
spec_ids <- fe_species_tum_wwk_short(as.character(1:10))

# Display in default style, scientific names, English, and German names
format(spec_ids)
format(spec_ids, spec_lang = "sci")
format(spec_ids, spec_lang = "eng")
format(spec_ids, spec_lang = "ger")

# Usual application: Set option for species code output
# Any print of an fe_species object will use the last setting of the option
options(fe_spec_lang = "sci")
spec_ids

Generic Formatted Output for fe_species Objects

Description

Generic Formatted Output for fe_species Objects

Usage

format_fe_species(x, spec_lang = c("code", "eng", "ger", "sci"))

Arguments

Value

A character vector either displaying the original species codes provided in x, or the species (group) names in the desired language

Get the Area in ha of a Compatible Object

Description

The function get_area_ha will return the correct area for all appropriate objects provided by ForestElementsR. While the fe_stand class contains this information directly, this is not the case for the fe_stand_spatial class. However, get_area_ha will work for both (and other objects to be implemented) in the same way. In case the object x does not contain sufficient information, the function returns NULL.

Usage

get_area_ha(x)

Arguments

Value

The object's area in ha. In case the object x does not contain sufficient information, the function returns NULL.

Examples

# Example for an fe_stand object
selection_forest_1_fe_stand |> get_area_ha()

# Example for an fe_stand_spatial object
mm_forest_1_fe_stand_spatial |> get_area_ha()

Dominant Height h100

Description

The dominant height h100 was conceptuated by Ernst Assmann and Friedrich Franz in order obtain a mean height value for those trees which usually dominate a stand throughout its whole life.

Usage

h_100(h, d, n_rep_ha)

Arguments

Details

The h100 is defined as the quadratic mean height of the hundred thickest trees per ha. If there are only 100 trees or less on one ha, the h100 is the same as the quadratic mean height d_q. While the h100 is well defined and useful in monospecific stands, it is less so in mixed stands.

Value

the dominant height value h100 resulting from the input data

See Also

Other stand heights: h_dom_weise(), h_q(), h_q_from_d_q()

Examples

# A sample of trees from an angle count sample , where each
# tree represents a basal area of 4 m²/ha
d_cm <- c(12, 13, 25, 27, 28, 26, 26.1, 32, 35, 31, 42)
n_rep_ha <- 4 / ((d_cm / 100)^2 * pi / 4) # representation number of each tree
dq_cm <- d_q(d_cm, n_rep_ha)
h_m <- 0.9 * dq_cm * (d_cm / dq_cm)^0.8 # quick plausible height estim.
h_100(h_m, d_cm, n_rep_ha)
h_q(h_m, d_cm) # quadratic mean height for comparison


# Typical application to a set of single tree data grouped by survey
# time and species
# (note that everyone is applying h_100 mixed stands, but you should do it
# only, if you know exactly what you are doing)
library(dplyr)
oldopt <- options(fe_spec_lang = "eng") # display colloquial species names
# for d_100 in mixed stands, we require species shares
spec_shares <- species_shares(
  mm_forest_1_fe_stand_spatial,
  tree_filter = !removal, # remaining trees only
  method = "ba_wd"
)
# extract the tree data to allow insights into the mechanics
trees <- mm_forest_1_fe_stand_spatial$trees |> filter(!removal)
# join with the shares
trees |>
  left_join(spec_shares) |>
  group_by(species_id, time_yr) |>
  summarise(
    n_ha     = round(sum(n_rep_ha)),
    d_q      = d_q(dbh_cm, n_rep_ha),
    d_100_cm = d_100(dbh_cm, n_rep_ha / species_share),
    h_q      = h_q(height_m, dbh_cm, n_rep_ha),
    h_100_m  = h_100(height_m, dbh_cm, n_rep_ha / species_share)
  ) |>
  print(n = Inf)
options(oldopt) # set species name display to previous value

Estimate Tree Height Growth With the 3rd German National Forest Inventory Growth Model (2012)

Description

Tree height growth model of the third German National Forest Inventory of 2012 (Riedel et al. 2017). Allows to estimate a tree's height at any age if its height is known at a given age.

Usage

h_age_gnfi3(species_id, age_yr, h_m_known, age_yr_known)

Arguments

Details

Originally, the function was parameterized for species and species groups corresponding to the national forest inventory's species coding (fe_species_ger_nfi_2012). We have attributed in addition these the original parameters also to the species codings fe_species_tum_wwk_short, and fe_species_bavrn_state_short. When called with a given species coding, the function will try to use the "nearest" of these three alternatives. Fallback option is the attempt to use fe_species_tum_wwk_short.

Value

A single height value or vector of height values corresponding to age_yr

References

Riedel T, Hennig P, Kroiher F, Polley H, Schmitz F, F. S (2017). Die dritte Bundeswaldinventur (BWI 2012). Inventur- und Auswertungsmethoden. Thuenen Institut fuer Waldoekosysteme.

See Also

Other growth functions: age_d_gnfi3(), age_h_gnfi3(), d_age_gnfi3()

Examples

  # A European beech tree has a height of 25.2 m at age 75. Estimate
  # its height at age 83
  h_age_gnfi3(100, 83, 25.2, 75) # 100 is ger_nfi_2012 code for E. beech

  # Do the same backward in time, height at age 67
  h_age_gnfi3(100, 67, 25.2, 75)

  # Apply for more than one tree, different species, same age
  h_known   <- c(23.1, 16.2, 35.2, 19.3, 21.8)
  species   <- as_fe_species_tum_wwk_short(c(3, 3, 3, 6, 6))
  h_age_gnfi3(
    species, age_yr = 70, h_m_known = h_known, age_yr_known = 60
  )

Core Function for Estimate Tree Height Growth With the 3rd German National Forest Inventory Growth Model (2012)

Description

See documentation of h_age_gnfi3 for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

h_age_gnfi3_core(species_id, age_yr, h_m_known, age_yr_known, params)

Arguments

Details

The function h_age_gnfi3 is fully vectorized, inputs are recyled according to the tibble rules

Value

A vector with estimates of the trees' expected heights (see h_age_gnfi3)

Helper function for cascaded species conversion when applying the function h_age_gnfi3(). Try to convert given species_id into ger_nfi_2012. If this causes an error convert to tum_wwk_short instead

Description

Helper function for cascaded species conversion when applying the function h_age_gnfi3(). Try to convert given species_id into ger_nfi_2012. If this causes an error convert to tum_wwk_short instead

Usage

h_age_gnfi3_spec_convert(species_id)

Weise's Dominant Height

Description

The dominant height after Weise is the quadratic mean height of the 20% biggest trees in a stand. In contrast to the dominant height h_100 it is well defined not only in monospecific stands, but also in mixed stands.

Usage

h_dom_weise(h, d, n_rep = 1)

Arguments

Value

The value of Weise's dominant diameter resulting from the input data

See Also

Other stand heights: h_100(), h_q(), h_q_from_d_q()

Examples

# A sample of trees from an angle count sample , where each
# tree represents a basal area of 4 m²/ha
d_cm <- c(12, 13, 25, 27, 28, 26, 26.1, 32, 35, 31, 42)
n_rep_ha <- 4 / ((d_cm / 100)^2 * pi / 4) # representation number of each tree
dq_cm <- d_q(d_cm, n_rep_ha)
h_m <- 0.9 * dq_cm * (d_cm / dq_cm)^0.8 # quick plausible height estim.
h_dom_weise(h_m, d_cm, n_rep_ha)
h_100(h_m, d_cm, n_rep_ha) # dominant height h100 for comparison
h_q(h_m, d_cm) # quadratic mean height for comparison

# if 20% of the trees are 100 stems/ha, Weise's dominant diameter and
# d100 are equal
d_cm <- rnorm(n = 500, mean = 35, sd = 7)
dq_cm <- d_q(d_cm)
h_m <- 0.8 * dq_cm * (d_cm / dq_cm)^0.8 # quick plausible height estim.
h_dom_weise(h_m, d_cm, 1)
h_100(h_m, d_cm, 1)

# Weise's dominant diameter is greater than d100, if 20% of the trees
# represent less than 100 trees/ha
d_cm <- rnorm(n = 200, mean = 35, sd = 7)
dq_cm <- d_q(d_cm)
h_m <- 0.8 * dq_cm * (d_cm / dq_cm)^0.8 # quick plausible height estim.
h_dom_weise(h_m, d_cm, 1)
h_100(h_m, d_cm, 1)

# Weise's dominant diameter is smaller than d100, if 20% of the trees
# represent more than 100 trees/ha
d_cm <- rnorm(n = 800, mean = 35, sd = 7)
dq_cm <- d_q(d_cm)
h_m <- 0.8 * dq_cm * (d_cm / dq_cm)^0.8 # quick plausible height estim.
h_dom_weise(h_m, d_cm, 1)
h_100(h_m, d_cm, 1)

Quadratic Mean Height

Description

The quadratic mean height is the stand height corresponding to the quadratic mean diameter d_q. It is actually the basal area weighted average height of a tree in the stand of interest. Insofar, its name is somewhat misleading.

Usage

h_q(h, d, n_rep = 1, na_h.rm = FALSE)

Arguments

Value

the quadratic mean height value resulting from the input data

See Also

Other stand heights: h_100(), h_dom_weise(), h_q_from_d_q()

Examples

# Evaluate a sample of equally weighted trees
d_cm <- c(12, 13, 25, 27, 28, 26, 26.1, 32, 35, 31, 42)
dq_cm <- d_q(d_cm)
h_m <- 0.9 * dq_cm * (d_cm / dq_cm)^0.8 # quick plausible height estim.
h_q(h_m, d_cm) # quadratic mean height
mean(h_m) # the arithmetic mean height is not the same!

# Assume, the same sample are trees from an angle count sample, where each
# tree represents a basal area of 4 m²/ha
n_rep_ha <- 4 / ((d_cm / 100)^2 * pi / 4) # representation number of each tree
h_q(h_m, d_cm, n_rep_ha)
# Interestingly, the h_q obtained here is the same as the unweighted
# arithmetic mean height above. The reason: the n_rep_ha used here act as
# inverse basal area weights, which is exactly compensated by the basal
# area weights coming from d_cm.


# Typical application to a set of single tree data grouped by survey
# time and species
library(dplyr)
oldopt <- options(fe_spec_lang = "eng") # display colloquial species names
# extract the tree data to allow insights into the mechanics
trees <- mm_forest_1_fe_stand_spatial$trees |> filter(!removal)
trees |>
  group_by(species_id, time_yr) |>
  summarise(
    n_ha   = round(sum(n_rep_ha)),
    d_q_cm = d_q(dbh_cm, n_rep_ha),
    h_q_m  = h_q(height_m, dbh_cm, n_rep_ha)
  ) |>
  print(n = Inf)
options(oldopt) # set species name display to previous value

Estimate Quadratic Mean Height from Quadratic Mean Diameter

Description

Fallback function to be used in the unfortunate case when a mean stand (cohort) height value is required but nothing useful is contained in the available data. The function has originally been developed for and used in the forest growth simulator SILVA (Pretzsch et al. 2002). The version here is slightly simplified for the species Norway spruce and soft deciduous woods (codes 1 and 9 in the species coding fe_species_tum_wwk_short), because the original version requires non-standard information which is not available outside the simulator.

Usage

h_q_from_d_q(species_id, d_q_cm)

Arguments

Details

A typical application of this function would be to calculate the quadratic mean diameter, d_q of the tree cohort of interest. If height information is totally lacking, a reasonable fallback value of the corresponding quadratic mean height, h_q, can be obtained from this function. Both values could then be used as the entry point of a standard height curve system (like h_standard_gnfi3 and h_standard_bv) in order to get plausible height estimates for the single trees in the cohort. Be aware that, though the height estimates obtained from this function are plausible, they should be interpreted with care, because the variation of mean stand height at a given mean diameter is very high in reality.

Value

vector of estimated quadratic mean heights corresponding to the given values of d_q_cm

References

Pretzsch H, Biber P, Dursky J (2002). “The single tree-based stand simulator SILVA: construction, application and evaluation.” Forest Ecology and Management, 162(1), 3–21. ISSN 0378-1127, doi:10.1016/S0378-1127(02)00047-6.

See Also

Other stand heights: h_100(), h_dom_weise(), h_q()

Examples

 # Species codes handed to h_q_from_d_q will be attempted to convert
 # into tum_wwk_short
 h_q_from_d_q(5, 25.4) # Apply to a common beech stand width d_q = 25.4 cm
 h_q_from_d_q(fe_species_ger_nfi_2012(100), 25.4)

 # Works also vectorized, i.e. when vectors of d_q values and species codes
 # are given
 species <- fe_species_tum_wwk_short(c(1, 3, 5))
 d_qmean <- c(23.2, 47.2, 12.7)
 h_q_from_d_q(species, d_qmean)

# Typical application: From diameter values to single tree height estimates
# when no height information is available
# - single tree diameters in a stand/cohort
dbh <- c(10.2, 43.3, 37.5, 28.8, 12.4, 19.2, 25.4, 27.3, 32.0)
# - quadratic mean diameter
d_qmean <- d_q(dbh)
species <- fe_species_tum_wwk_short(3) # we assume it's a Scots pine stand
# - quadrativ mean height fallback value
h_qest <- h_q_from_d_q(species, d_qmean)
# - single tree height estimates with the German National Inventory height
#   curve system
h_standard_gnfi3(species, dbh, d_qmean, h_qest)

Core Function for Estimating Quadratic Mean Height from Quadratic Mean Diameter

Description

See documentation of h_q_from_d_q for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

h_q_from_d_q_core(species_id, d_q_cm)

Arguments

Value

see h_q_from_d_q

Calculate Tree Heights with the Bavarian Standard Height Curve System

Description

Implementation of the standard height curve system used by the Bavarian State Forest Service (Kennel 1972). The structure of the approach was developed by R. Kennel together with a parameterisation for European beech (Fagus sylvatica). Later, anonymous scientists have extended the parameters for all species (groups) covered by the tum_wwk_short species coding. The standard height curve system allows to estimate a tree's height when its dbh is given together with the quadratic mean diameter, the corresponding quadratic mean height, and the age of the stand it belongs to.

Usage

h_standard_bv(species_id, dbh_cm, age_yr, d_q_cm, h_q_m)

Arguments

Details

In order to provide maximum flexibility in applying the function h_standard_bv, the stand values (age, mean height, mean diameter) can be provided with each tree diameter individually. This allows estimating heights for trees from different stands at the same time. In the same way, the provided species codes are not required to be the same for each tree.

Value

A vector of the estimated heights

References

Kennel R (1972). Die Buchendurchforstungsversuche in Bayern von 1870 bis 1970. Mit dem Modell einer Strukturertragstafel für die Buche, volume 7 of Forstliche Forschungsberichte München. Forstwissenschaftliche Fakultät der Universität München und Bayerische Forstliche Versuchs- und Forschungsanstalt.

See Also

Other standard height curve systems: h_standard_gnfi3()

Examples

  species_id <- fe_species_tum_wwk_short(rep(3, 7))  # Seven Scots pines
  dbh <- c(10.1, 27.4, 31.4, 35.5, 39.8, 45.2, 47.2) # and their diameters
  # Estimate the heights of these trees assuming they are from a 100 year old
  # stand with a mean diameter of 35.5 cm, and a corresponding mean height
  # of 28 m.
  h_standard_bv(species_id, dbh, age_yr = 100, d_q_cm = 35.5, h_q_m = 28.0)

  # Compare with sister function h_standard_gnfi3 which does not require
  # stand age
  h_standard_gnfi3(species_id, dbh, d_q_cm = 35.5, h_q_m = 28.0)

Core Function for calculating Individual Tree Heights with the Bavarian Standard Height Curve System

Description

See documentation of h_standard_bv for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

h_standard_bv_core(species_id, dbh_cm, age_yr, d_q_cm, h_q_m, params)

Arguments

Details

The function h_standard_bv is fully vectorized, inputs are recyled according to the tibble rules

Value

A vector of the estimated heights

Calculate Tree Heights with the Bavarian Standard Height Curve System of the 3rd German National Forest Inventory (2012)

Description

Implementation of the standard height curve developed during the the 3nd German National Forest Inventory (Riedel et al. 2017). Structurally, this is a height curve system after Sloboda (Sloboda et al. 1993) which allows to estimate a tree's height when its species, diameter, and the quadratic mean diameter and height of (the species in) the stand is given.

Usage

h_standard_gnfi3(species_id, dbh_cm, d_q_cm, h_q_m)

Arguments

Details

Originally, the height curve system was parameterized for species and species groups corresponding to the national forest inventory's species coding (fe_species_ger_nfi_2012). We have attributed in addition these the original parameters also to the species codings fe_species_tum_wwk_short, and fe_species_bavrn_state_short. When called with a given species coding, the function will try to use the "nearest" of these three alternatives. Fallback option is the attempt to use fe_species_tum_wwk_short.

In order to provide maximum flexibility in applying the function h_standard_gnfi3, the stand values (mean height, mean diameter) can be provided with each tree diameter individually. This allows estimating heights for trees from different stands at the same time. In the same way, the provided species codes are not required to be the same for each tree.

Value

A vector of the estimated heights

References

Riedel T, Hennig P, Kroiher F, Polley H, Schmitz F, F. S (2017). Die dritte Bundeswaldinventur (BWI 2012). Inventur- und Auswertungsmethoden. Thuenen Institut fuer Waldoekosysteme.

Sloboda B, Gaffrey D, Matsamura N (1993). “Regionale und lokale Systeme von Hoehenkurven gleichartiger Waldbestaende.” Allgmeine Forst- und Jagdzeitung, 164(12), 225–228.

See Also

Other standard height curve systems: h_standard_bv()

Examples

  # Three examples for single tree applications with species codes given
  # as integers (but following the ger_nfi_2012 coding)

  # European beech, dbh_cm < dq_cm
  h_standard_gnfi3(species_id = 100, dbh_cm = 14.8, d_q_cm = 25, h_q_m = 22)

  # Scots pine, dbh_cm == dq_cm
  h_standard_gnfi3(species_id = 20, dbh_cm = 25, d_q_cm = 25, h_q_m = 22)

  # Douglas fir, dbh_cm > dq_cm
  h_standard_gnfi3(species_id = 40, dbh_cm = 45, d_q_cm = 25, h_q_m = 22)

  # Same Douglas fir but species_id = 7 (i.e. tum_wwk_short),
  # note the message, because numeric 7 is not convertible into ger_nfi_2012
  h_standard_gnfi3(species_id = 7, dbh_cm = 45, d_q_cm = 25, h_q_m = 22)

  # But no message, when species_id = 7 is made a tum_wwk_short object first,
  # because this can be unambiguously converted into ger_nfi_2012
  h_standard_gnfi3(
    fe_species_tum_wwk_short(7), dbh_cm = 45, d_q_cm = 25, h_q_m = 22
  )

  # Usually, applications will be vectorized
  species_id <- fe_species_ger_nfi_2012(rep(20, 7))  # Seven Scots pines
  dbh <- c(10.1, 27.4, 31.4, 35.5, 39.8, 45.2, 47.2) # and their diameters
  # Estimate the heights of these trees, assuming they are from a
  # stand with a mean diameter of 35.5 cm, and a corresponding mean height
  # of 28 m.
  h_standard_gnfi3(species_id, dbh, d_q_cm = 35.5, h_q_m = 28.0)

  # Compare with sister function h_standard_bv, assuming a stand age of
  # 100 years
  h_standard_bv(species_id, dbh, age_yr = 100, d_q_cm = 35.5, h_q_m = 28.0)

Core Function for the Estimation of Tree Heights Using the Standard Height Curve System of the 3rd German National Forest Inventory

Description

See documentation of h_standard_gnfi3 for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

h_standard_gnfi3_core(species_id, dbh_cm, d_q_cm, h_q_m, params)

Arguments

Details

The function h_standard_gnfi3 is fully vectorized, inputs are recyled according to the tibble rules

Value

A vector with estimates of the trees' heights (m) (see h_standard_gnfi3)

Check if Selected Columns in a Data Frame Contain Missing Values

Description

Check if Selected Columns in a Data Frame Contain Missing Values

Usage

has_no_missing_values(x, names)

Arguments

Value

A list with two elements: 1) rqmt_ok which is TRUE if the requirement is fulfilled and FALSE otherwise; 2) err_message, the body of an error message to be used by the calling function (mentions all problematic elements)

Check Required Length for a List of Objects

Description

Check Required Length for a List of Objects

Usage

has_required_lengths(x, names_and_rqmts)

Arguments

Value

A list with two elements: 1) rqmt_ok which is TRUE if the requirement is fulfilled and FALSE otherwise; 2) err_message, the body of an error message to be used by the calling function (mentions all problematic elements)

Check for Required Elements Existing in an Object

Description

Check for Required Elements Existing in an Object

Usage

has_required_names(x, required_names)

Arguments

Value

A list with two elements: 1) rqmt_ok which is TRUE if the requirement is fulfilled and FALSE otherwise; 2) err_message, the body of an error message to be used by the calling function (mentions all problematic elements)

Check if an Object has a Required Type or Class

Description

Not a complete coverage of all base types and classes, but tailored to the needs of validate_fe_stand

Usage

has_required_type_or_class(x, rqmt)

Arguments

Value

TRUE or FALSE, indicating if the requirement is met or not

Check Required Type or Class for a List of Objects

Description

Check Required Type or Class for a List of Objects

Usage

has_required_types_or_classes(x, names_and_rqmts)

Arguments

Value

A list with two elements: 1) rqmt_ok which is TRUE if the requirement is met and FALSE otherwise; 2) err_message, the body of an error message to be used by the calling function (mentions all problematic elements)

Estimate a tree's height to crown base

Description

This function can be used to estimate a tree's height to crown base, given its stem diameter at breast height, its height and its species. This is the height to crown base function implemented in the forest growth simulator SILVA (Pretzsch et al. 2002). Height to crown base in this context is defined as the height where the lowest living branch of the tree's primary crown branches of the stem.

Usage

height_crown_base_silva(species_id, dbh_cm, height_m)

Arguments

Value

An estimate of the tree's height to crown base in m.

References

Pretzsch H, Biber P, Dursky J (2002). “The single tree-based stand simulator SILVA: construction, application and evaluation.” Forest Ecology and Management, 162(1), 3–21. ISSN 0378-1127, doi:10.1016/S0378-1127(02)00047-6.

Examples

# Estimate the height to crown base of a Scots pine with a stem diameter
# at breast height of 45.2 cm and a total height of 29.2 m:
height_crown_base_silva(
  species_id = "3",     # will be internally converted to tum_wwk_short
  dbh_cm = 45.2,
  height_m = 29.2
) # 20.9 m (rounded)

# Height to crown base estimate for a European beech with
# the same height and diameter:
height_crown_base_silva(
  species_id = "5",     # will be internally converted to tum_wwk_short
  dbh_cm = 45.2,
  height_m = 29.2
) # 15.0 m (rounded)

# Run on vectors
spec <- mm_forest_1_fe_stand_spatial$trees$species_id
h <- mm_forest_1_fe_stand_spatial$trees$height_m
d <- mm_forest_1_fe_stand_spatial$trees$dbh_cm
height_crown_base_silva(spec, d, h)

Core function for the silva tree height to crown base calculation

Description

See documentation of height_crown_base_silva for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

height_crown_base_silva_core(species_id, dbh_cm, height_m)

Arguments

Details

The function height_crown_base_silva is fully vectorized, inputs are recycled according to the tibble rules

Value

Tree height to crown base (see height_crown_base_silva)

Helper function for cascaded species conversion when applying the German National forest inventory 2012 standard height curve system. Try to convert given species_id into ger_nfi_2012. If this causes an error convert to tum_wwk_short instead

Description

Helper function for cascaded species conversion when applying the German National forest inventory 2012 standard height curve system. Try to convert given species_id into ger_nfi_2012. If this causes an error convert to tum_wwk_short instead

Usage

hstd_gnfi3_spec_convert(species_id)

Check if Given Variable Combinations in a Data Frame are Distinct

Description

Check if Given Variable Combinations in a Data Frame are Distinct

Usage

is_distinct(x, combinations)

Arguments

Value

A list with two elements: 1) rqmt_ok which is TRUE if the requirement is fulfilled and FALSE otherwise; 2) err_message, the body of an error message to be used by the calling function (mentions all problematic elements)

Check if an Object is an fe_ccircle_spatial

Description

Check if an Object is an fe_ccircle_spatial

Usage

is_fe_ccircle_spatial(x)

Arguments

Value

TRUE if the object inherits from the fe_ccircle_spatial class

Examples

strange <- "xyzabc"
is_fe_ccircle_spatial(strange)

Check if an Object is an fe_ccircle_spatial_notrees object

Description

Check if an Object is an fe_ccircle_spatial_notrees object

Usage

is_fe_ccircle_spatial_notrees(x)

Arguments

Value

TRUE if the object inherits from the fe_ccircle_spatial_notrees class

Examples

  strange <- "xyzabc"
  is_fe_ccircle_spatial_notrees(strange)

Check if an Object is a fe_species_bavrn_state species code vector

Description

Check if an Object is a fe_species_bavrn_state species code vector

Usage

is_fe_species_bavrn_state(x)

Arguments

Value

TRUE if the object inherits from the fe_species_bavrn_state class

Examples

spec_ids <- new_fe_species_bavrn_state(
  as.character(c(40, 40, 30, 30, 60, 60, 60))
)
is_fe_species_bavrn_state(spec_ids)

Check if an Object is a fe_species_bavrn_state_short species code vector

Description

Check if an Object is a fe_species_bavrn_state_short species code vector

Usage

is_fe_species_bavrn_state_short(x)

Arguments

Value

TRUE if the object inherits from the fe_species_bavrn_state_short class

Examples

spec_ids <- new_fe_species_bavrn_state_short(
  as.character(1:9)
)
is_fe_species_bavrn_state_short(spec_ids)

Check if an Object is a fe_species_ger_nfi_2012 species code vector

Description

Check if an Object is a fe_species_ger_nfi_2012 species code vector

Usage

is_fe_species_ger_nfi_2012(x)

Arguments

Value

TRUE if the object inherits from the fe_species_ger_nfi_2012 class

Examples

spec_ids <- new_fe_species_ger_nfi_2012(
  as.character(c(50, 50, 20, 20, 100, 100, 100))
)
is_fe_species_ger_nfi_2012(spec_ids)

Check if an Object is a fe_species_master species code vector

Description

Check if an Object is a fe_species_master species code vector

Usage

is_fe_species_master(x)

Arguments

Value

TRUE if the object inherits from the fe_species_master class

Examples

spec_ids <- new_fe_species_master(
  c("picea_001", "fagus_001", "quercus_002", "quercus_001")
)
is_fe_species_master(spec_ids)

Check if an Object is a fe_species_tum_wwk_long species code vector

Description

Check if an Object is a fe_species_tum_wwk_long species code vector

Usage

is_fe_species_tum_wwk_long(x)

Arguments

Value

TRUE if the object inherits from the fe_species_tum_wwk_long class

Examples

spec_ids <- new_fe_species_tum_wwk_long(
  as.character(c(70, 61, 88, 88, 10, 971, 32))
)
is_fe_species_tum_wwk_long(spec_ids)

Check if an Object is a fe_species_tum_wwk_short species code vector

Description

Check if an Object is a fe_species_tum_wwk_short species code vector

Usage

is_fe_species_tum_wwk_short(x)

Arguments

Value

TRUE if the object inherits from the fe_species_tum_wwk_short class

Examples

spec_ids <- new_fe_species_tum_wwk_short(
  as.character(c(4, 4, 3, 3, 5, 5, 5))
)
is_fe_species_tum_wwk_short(spec_ids)

Check if an Object is an fe_stand

Description

Check if an Object is an fe_stand

Usage

is_fe_stand(x)

Arguments

Value

TRUE if the object inherits from the fe_stand class

Examples

strange <- "xyzabc"
is_fe_stand(strange)

is_fe_stand(norway_spruce_1_fe_stand)

Check if an Object is an fe_stand_spatial

Description

Check if an Object is an fe_stand_spatial

Usage

is_fe_stand_spatial(x)

Arguments

Value

TRUE if the object inherits from the fe_stand_spatial class

Examples

strange <- "xyzabc"
is_fe_stand_spatial(strange)

Check if an Object is an fe_tally_list

Description

Check if an Object is an fe_tally_list

Usage

is_fe_tally_list(x)

Arguments

Value

TRUE if the object inherits from the fe_tally_list class

Check if an Object is an fe_yield_table

Description

Check if an Object is an fe_yield_table

Usage

is_fe_yield_table(x)

Arguments

Value

TRUE if the object inherits from the fe_yield_table class

Examples

  x <- "I want to be a yield table!"  # strange object
  is_fe_yield_table(x)                # sorry

  is_fe_yield_table(ytable_pine_wiedemann_moderate_1943_raw) # Nope
  is_fe_yield_table(fe_ytable_pine_wiedemann_moderate_1943)  # That's better

Build a 'trees' data.frame (tibble) as Required for fe_stand and fe_stand_spatial Objects

Description

Build a 'trees' data.frame (tibble) as Required for fe_stand and fe_stand_spatial Objects

Usage

make_trees_dataframe(
  x,
  tree_id_col,
  species_id_col,
  time_yr_col,
  dbh_cm_col,
  layer_key_col,
  age_yr_col,
  height_m_col,
  crown_base_height_m_col,
  crown_radius_m_col,
  removal_col,
  ingrowth_col,
  n_rep_ha_col
)

Arguments

Value

A data.frame (tibble) that can be used as the list element 'trees' in an fe_stand or fe_stand_spatial object

Calculate or Return the Representation Number per ha for the Trees Contained in a Compatible Object

Description

The idea behind creating this function was to allow for using the very same evaluation algorithms for ha-based values for a broad range of different objects, e.g. stands/research plots, inventory plots, etc.

Usage

n_rep_ha(x)

Arguments

Details

If the object x contains information about its area, the representation numbers for each tree will be calculated in the following way:

While n_rep_ha will return a vector of equal numbers for fe_stand objects, this is less trivial for the class fe_stand_spatial. The latter might contain 'buffer zone trees' beyond the actual stand outline. Such trees will obtain a zero representation number in contrast to the trees inside the outline. If x does not contain sufficient information about its area, the function will simply hand back the n_rep_ha column of the x$trees data frame.

Value

A vector of representation numbers corresponding to the order of the trees data frame in x.

Examples

# example for an fe_stand object
spruce_beech_1_fe_stand |> n_rep_ha()

# example for an fe_stand_spatial object
mm_forest_1_fe_stand_spatial |> n_rep_ha()

Constructor for the fe_ccircle_spatial Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_stand for creating an object of that class.

Usage

new_fe_ccircle_spatial(x = list(), ..., class = character())

Arguments

Value

An object of class fe_ccircle_spatial

Examples

#' # Constructing a minimal fe_ccircle_spatial object from scratch
# Use fe_ccircle_spatial() if you are not absolutely sure

trees <- data.frame(
  tree_id = as.character(c(1:10)),
  species_id = as_fe_species_tum_wwk_short(rep("5", 10)),
  layer_key = 1,
  time_yr = 2024,
  dbh_cm = rnorm(10, 50, 8),
  age_yr = NA_real_,
  height_m = NA_real_,
  crown_base_height_m = NA_real_,
  crown_radius_m = NA_real_,
  removal = FALSE,
  ingrowth = FALSE,
  n_rep_ha = 1 / 0.75
)

# define a circle definition with three concentric circles
circle_def <- data.frame(
  dbh_lower = c(0,12,30),
  dbh_upper = c(11.9,29.9,999.0),
  c_area = c(0.0025, 0.0060, 0.0500)
)

# generate tree positions in polar coordinates for the 10 trees

tree_positions <- data.frame(
 tree_id = as.character(c(1:10)),
 R = runif(10, 1, 12),
 angle = runif(10, 0, 360)) |>
 dplyr::mutate(
 #convert the polar coordinates to cartesian and into an sf object
 x_pos = R*sin(angle*pi / 180),
 y_pos = R*cos(angle*pi / 180)
 ) |> sf::st_as_sf(coords = c("x_pos", "y_pos"))

# generate a NA dummy for small_trees

  small_trees <- data.frame(
  tree_id = NA_character_,
  species_id = NA_character_,
  layer_key = NA_real_,
  time_yr = NA_real_,
  dbh_cm = NA_real_,
  age_yr = NA_real_,
  height_m = NA_real_
  )

fe_ccircle_spatial_candidate <- list(
  stand_id = "my_interesting_stand",
  small_trees = small_trees,
  trees = trees,
  circle_definition = circle_def,
  tree_positions = tree_positions,
  time_yr = 2024
)

fe_ccircle_object <- new_fe_ccircle_spatial(fe_ccircle_spatial_candidate)

# Better validate it
fe_ccircle_object |> validate_fe_ccircle_spatial()

Constructor for the fe_ccircle_spatial_notrees Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_stand for creating an object of that class.

Usage

new_fe_ccircle_spatial_notrees(x = list(), ..., class = character())

Arguments

Details

The class fe_ccircle_spatial_notrees has been designed for covering a comparably rare case, i.e. an inventory point where no regular trees (trees that are big enough to have a dbh), but possibly small trees are present.

Value

An object of class fe_ccircle_spatial_notrees

Examples

#' # Constructing a minimal fe_ccircle_spatial object from scratch
# Use fe_ccircle_spatial() if you are not absolutely sure

trees <- NULL
tree_positions <- NULL

# define a circle definition with three concentric circles
circle_def <- data.frame(
  dbh_lower = c(0,12,30),
  dbh_upper = c(11.9,29.9,999.0),
  c_area = c(0.0025, 0.0060, 0.0500)
)


# generate a NA dummy for small_trees

  small_trees <- data.frame(
  tree_id = NA_character_,
  species_id = NA_character_,
  layer_key = NA_real_,
  time_yr = NA_real_,
  dbh_cm = NA_real_,
  age_yr = NA_real_,
  height_m = NA_real_
  )

fe_ccircle_spatial_notrees_candidate <- list(
  stand_id = "my_interesting_stand",
  small_trees = small_trees,
  trees = trees,
  circle_definition = circle_def,
  tree_positions = tree_positions,
  time_yr = 2024
)

fe_ccircle_notrees_object <-
new_fe_ccircle_spatial_notrees(fe_ccircle_spatial_notrees_candidate)

# Better validate it
fe_ccircle_notrees_object |> validate_fe_ccircle_spatial_notrees()

Generic Constructor for fe_species Objects

Description

Generic Constructor for fe_species Objects

Usage

new_fe_species(x = character(), class_name)

Arguments

Value

An object of class class_name if x is character and class_name is among the supported names. Otherwise, the function terminates with an error.

Constructor for the fe_species_bavrn_state Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_species_bavrn_state for creating an object of that class.

Usage

new_fe_species_bavrn_state(x = character())

Arguments

Value

An object of class fe_species_bavrn_state

Examples

# Constructing a fe_species_bavrn_state object from scratch
# Use fe_species_bavrn_state() if you are not absolutely sure
spec_ids <- new_fe_species_bavrn_state(
  as.character(c(40, 40, 30, 30, 60, 60, 60))
)

Constructor for the fe_species_bavrn_state_short Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_species_bavrn_state_short for creating an object of that class.

Usage

new_fe_species_bavrn_state_short(x = character())

Arguments

Value

An object of class fe_species_bavrn_state_short

Examples

# Constructing a fe_species_bavrn_state_short object from scratch
# Use fe_species_bavrn_state_short() if you are not absolutely sure
spec_ids <- new_fe_species_bavrn_state_short(
  as.character(1:9)
)

Constructor for the fe_species_ger_nfi_2012 Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_species_ger_nfi_2012 for creating an object of that class.

Usage

new_fe_species_ger_nfi_2012(x = character())

Arguments

Value

An object of class fe_species_ger_nfi_2012

Examples

# Constructing a fe_species_ger_nfi_2012 object from scratch
# Use fe_species_ger_nfi_2012() if you are not absolutely sure
spec_ids <- new_fe_species_ger_nfi_2012(
  as.character(c(50, 50, 20, 20, 100, 100, 100))
)

Constructor for the fe_species_master Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_species_master for creating an object of that class.

Usage

new_fe_species_master(x = character())

Arguments

Value

An object of class fe_species_master

Examples

# Constructing a fe_species_master object from scratch
# Use fe_species_master() if you are not absolutely sure
spec_ids <- new_fe_species_master(
  c("picea_001", "fagus_001", "quercus_002", "quercus_001")
)

Constructor for the fe_species_tum_wwk_long Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_species_tum_wwk_long for creating an object of that class.

Usage

new_fe_species_tum_wwk_long(x = character())

Arguments

Value

An object of class fe_species_tum_wwk_long

Examples

# Constructing a fe_species_tum_wwk_long object from scratch
# Use fe_species_tum_wwk_long() if you are not absolutely sure
spec_ids <- new_fe_species_tum_wwk_long(
  as.character(c(70, 61, 88, 88, 10, 971, 32))
)

Constructor for the fe_species_tum_wwk_short Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_species_tum_wwk_short for creating an object of that class.

Usage

new_fe_species_tum_wwk_short(x = character())

Arguments

Value

An object of class fe_species_tum_wwk_short

Examples

# Constructing a fe_species_tum_wwk_short object from scratch
# Use fe_species_tum_wwk_short() if you are not absolutely sure
spec_ids <- new_fe_species_tum_wwk_short(
  as.character(c(4, 4, 3, 3, 5, 5, 5))
)

Constructor for the fe_stand Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_stand for creating an object of that class.

Usage

new_fe_stand(x = list(), ..., class = character())

Arguments

Value

An object of class fe_stand

Examples

# Constructing a minimal fe_stand object from scratch
# Use fe_stand() if you are not absolutely sure

trees <- data.frame(
  tree_id = as.character(c(1:100)),
  species_id = as_fe_species_tum_wwk_short(rep("5", 100)),
  layer_key = 1,
  time_yr = 2022,
  dbh_cm = rnorm(100, 50, 8),
  age_yr = NA_real_,
  height_m = NA_real_,
  crown_base_height_m = NA_real_,
  crown_radius_m = NA_real_,
  removal = FALSE,
  ingrowth = FALSE,
  n_rep_ha = 1 / 0.75
)

fe_stand_candidate <- list(
  stand_id = "my_interesting_stand",
  area_ha = 0.75,
  small_trees = data.frame(),
  trees = trees
)

fe_stand_object <- new_fe_stand(fe_stand_candidate)

# Better validate it
fe_stand_object |> validate_fe_stand()

Constructor for the fe_stand_spatial Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_stand for creating an object of that class.

Usage

new_fe_stand_spatial(x = list(), ..., class = character())

Arguments

Value

An object of class fe_stand_spatial

Examples

#' # Constructing a minimal fe_stand_spatial object from scratch
# Use fe_stand_spatial() if you are not absolutely sure

trees <- data.frame(
  tree_id = as.character(c(1:50)),
  species_id = as_fe_species_tum_wwk_short(rep("5", 50)),
  layer_key = 1,
  time_yr = 2024,
  dbh_cm = rnorm(50, 50, 8),
  age_yr = NA_real_,
  height_m = NA_real_,
  crown_base_height_m = NA_real_,
  crown_radius_m = NA_real_,
  removal = FALSE,
  ingrowth = FALSE,
  n_rep_ha = 1 / 0.75
)

# generate tree positions

tree_positions <- data.frame(
 tree_id = as.character(c(1:50)),
 x_pos = runif(50, 1, 49),
 y_pos = runif(50, 1, 49)
 ) |> sf::st_as_sf(coords = c("x_pos", "y_pos"))


fe_stand_spatial_candidate <- list(
  stand_id = "my_interesting_stand",
  outline = NULL,
  orientation = 0,
  small_trees = data.frame(),
  trees = trees,
  tree_positions = tree_positions
)

fe_stand_object <- new_fe_stand_spatial(fe_stand_spatial_candidate)

# Better validate it
fe_stand_object |> validate_fe_stand_spatial()

Constructor for the fe_tally_list Class

Description

Should be used by expert users only who know exactly what they are doing. Other users please take the function fe_stand for creating an object of that class.

Usage

new_fe_tally_list(x = list(), ..., class = character())

Arguments

Value

An object of class fe_tally_list

Constructor for the fe_yield_table Class

Description

Provided for expert users only who know exactly what they are doing. Other users please take the function fe_yield_table for creating an object of that class.

Usage

new_fe_yield_table(x = list())

Arguments

Value

An object of class fe_yield_table

Examples

  # Some highly motivated object, even a list
  x <- list(my_dream = "I want to be a yield table!")
  x <- new_fe_yield_table(x) # let's help the guy
  is_fe_yield_table(x)       # nice - it worked?!

  # But here's the error. That's why you should not use the bare constructor
  # if you don't know what you are doing.
  try(
    validate_fe_yield_table(x)
  )

Plot an fe_ccircle_spatial Object

Description

Plot an fe_ccircle_spatial Object

Usage

## S3 method for class 'fe_ccircle_spatial'
plot(x, tree_filter = TRUE, dbh_scale = 1, show_labels = FALSE, ...)

Arguments

Value

A map (ggplot2) of the plot layout including the trees with coordinates

Examples

 opt_old <- getOption("fe_spec_lang") # store user's current setting
 options(fe_spec_lang = "eng")      # choose Englisch species name display
 spruce_pine_ccircle_spatial |> plot()
 spruce_pine_ccircle_spatial |> plot(dbh_scale = 4)
 spruce_pine_ccircle_spatial |>
   plot(
     dbh_scale = 4,
     tree_filter = species_id == fe_species_tum_wwk_long(30) & dbh_cm > 35
   )
 options(fe_spec_lang = opt_old)

Plot an fe_ccircle_spatial_notrees Object

Description

For an object of classfe_ccircle_spatial_notrees only the concentric circles are plotted at their correct positions.

Usage

## S3 method for class 'fe_ccircle_spatial_notrees'
plot(x, ...)

Arguments

Value

A map (ggplot2) of the plot layout including the trees with coordinates

Examples

  # Abuse input data, that would actually allow for a full fe_ccircle_spatial
  # object to construct a fe_ccircle_spatial_notrees object.
  x <- spruce_pine_ccircle_raw
  x <- x |> fe_ccircle_spatial_notrees()

  # Plot it
  plot(x)

Plot an fe_stand Object

Description

Diameter distributions in number of trees per ha, one diagrame by year of survey, tree cohort to be displayed can be filtered.

Usage

## S3 method for class 'fe_stand'
plot(x, tree_filter = TRUE, ...)

Arguments

Details

The diagram(s) are made with ggplot2::geom_bar, the colours for the species, the number and width of the diameter bins correspond to the default settings in ggplot.

Value

A plot (ggplot2) of the diameter distribution

Examples

# display scientific species names in all examples
old_opt <- getOption("fe_spec_lang") # store current user sertting
options(fe_spec_lang = "sci") # display scientific names

# mixed mountain forest - all trees
mm_forest_1_fe_stand_spatial |> plot()
# ... remaining trees only
mm_forest_1_fe_stand_spatial |> plot(tree_filter = !removal)
# ... removal only
mm_forest_1_fe_stand_spatial |> plot(tree_filter = removal)
# ... all trees with dbh > 30 cm
mm_forest_1_fe_stand_spatial |> plot(tree_filter = dbh_cm > 20)

# other example stands
selection_forest_1_fe_stand |> plot()
norway_spruce_1_fe_stand |> plot()
spruce_beech_1_fe_stand |> plot()

# reset to previous species name settings
options(fe_spec_lang = old_opt)

Plot an fe_yield_table Object

Description

Plot an fe_yield_table Object

Usage

## S3 method for class 'fe_yield_table'
plot(x, variable = NA, ...)

Arguments

Value

An object of class ggplot

See Also

Other yield table functions: fe_yield_table(), si_to_mai_age(), si_to_mai_max(), site_index(), stocking_level(), yield_tables_for_species, ytable_age_slice(), ytable_lookup(), ytable_max_slice()

Examples

  fe_ytable_pine_wiedemann_moderate_1943 |> plot()
  fe_ytable_pine_wiedemann_moderate_1943 |> plot(variable = "ba_m2_ha")
  fe_ytable_pine_wiedemann_moderate_1943 |> plot(variable = "mai_m3_ha_yr")

  # Modify plot post hoc ...
  fe_ytable_pine_wiedemann_moderate_1943 |> plot(variable = "n_ha")
  # ... better to read on log scale
  fe_ytable_pine_wiedemann_moderate_1943 |> plot(variable = "n_ha") +
    ggplot2::scale_y_log10()

Convenient Information About the Precision of a Random Sample

Description

Given an arbitrary random sample, t-statistics are calculated in order to obtain information about the precision of the sample mean or sum. A list of useful statistics is returned, most importantly the standard error and the confidence boundaries according to the confidence level provided by the user.

Usage

se_tests(
  x,
  mu = 0,
  ref = c("mean", "sum"),
  alternative = c("two.sided", "greater", "less"),
  conf.level = 0.95
)

Arguments

Value

A list containing various statistics such as standard error, t-statistic, degrees of freedom, p-value, and confidence interval and margin of error as a percentage of the mean or sum, respectively.

Examples

set.seed(123)
data <- rnorm(100, mean = 5, sd = 2)

# Test for sum
se_tests(
  data, mu = 0, ref = "sum", alternative = "two.sided", conf.level = 0.95
)
# Test for mean
se_tests(
  data, mu = 0, ref = "mean", alternative = "two.sided", conf.level = 0.95
)

Shannon Diversity Index for Tree Species

Description

Species diversity index after Shannon and Weaver (1948). Note that this function calculates comparable output only when the same species coding is used for the input parameter species_id.

Usage

shannon_index(species_id, weights = 1, n_rep = 1)

Arguments

Value

The Shannon Index value resulting from the input data

References

Shannon CE, Weaver W (1948). The Mathematical Theory of Communication. University of Illinois Press.

See Also

Other structure and diversity: assmann_layers(), species_profile()

Examples

  # Monospecific stand
  trees <- norway_spruce_1_fe_stand$trees
  shannon_index(trees$species_id)

  # Two-species mixed stand
  trees <- spruce_beech_1_fe_stand$trees
  shannon_index(trees$species_id)

  # Selection forest
  trees <- selection_forest_1_fe_stand$trees
  shannon_index(trees$species_id)

  # weigh with basal area (i.e. dbh^2)
  shannon_index(trees$species_id, weights = trees$dbh_cm^2)

  # weigh with inverse basal area (i.e. 1 / dbh^2)
  shannon_index(trees$species_id, weights = 1 / trees$dbh_cm^2)

Convert a Standard Site Index Into an MAI(age) Site Index

Description

A useful way of site indexing is to give the site index of a stand in terms of a mean annual increment (mai) at a given age, typically 100 years. This function converts a standard site index into such an mai site index. See si_to_mai_max for an alternative mai based site indexing method.

Usage

si_to_mai_age(si, mai_variable, age, ytable)

Arguments

Value

The requested mai value corresponding to the given standard site index at the given age

See Also

Other yield table functions: fe_yield_table(), plot.fe_yield_table(), si_to_mai_max(), site_index(), stocking_level(), yield_tables_for_species, ytable_age_slice(), ytable_lookup(), ytable_max_slice()

Examples

 age <- 100
 mai_var <- "mai_m3_ha_yr" # mai in volume over bark before harvest

 si_to_mai_age(2.3, mai_var, age, fe_ytable_larch_schober_moderate_1946)
 si_to_mai_age(0.7, mai_var, age, fe_ytable_larch_schober_moderate_1946)
 si_to_mai_age(2.3, mai_var, age, fe_ytable_beech_wiedemann_moderate_1931)
 si_to_mai_age(0.7, mai_var, age, fe_ytable_beech_wiedemann_moderate_1931)

 mai_var <- "red_mai_m3_ha_yr" # mai in vol. under bark minus harvest losses
 si_to_mai_age(2.3, mai_var, age, fe_ytable_larch_schober_moderate_1946)
 si_to_mai_age(0.7, mai_var, age, fe_ytable_larch_schober_moderate_1946)
 si_to_mai_age(2.3, mai_var, age, fe_ytable_beech_wiedemann_moderate_1931)
 si_to_mai_age(0.7, mai_var, age, fe_ytable_beech_wiedemann_moderate_1931)

Convert a Standard Site Index Into an maximum MAI Site Index

Description

A less common, but sometimes useful way of site indexing is to give the site function converts a standard site index into such an maximum mai site index. Typically, the stand age where the maximum mai is obtained increases from better to lesser site index classes. See si_to_mai_age for an alternative mai based site indexing method.

Usage

si_to_mai_max(si, mai_variable, ytable)

Arguments

Value

The requested maximum mai value corresponding to the given standard site index

See Also

Other yield table functions: fe_yield_table(), plot.fe_yield_table(), si_to_mai_age(), site_index(), stocking_level(), yield_tables_for_species, ytable_age_slice(), ytable_lookup(), ytable_max_slice()

Examples

 mai_var <- "mai_m3_ha_yr" # mai in volume over bark before harvest

 si_to_mai_max(2.3, mai_var, fe_ytable_larch_schober_moderate_1946)
 si_to_mai_max(0.7, mai_var, fe_ytable_larch_schober_moderate_1946)
 si_to_mai_max(2.3, mai_var, fe_ytable_beech_wiedemann_moderate_1931)
 si_to_mai_max(0.7, mai_var, fe_ytable_beech_wiedemann_moderate_1931)

 mai_var <- "red_mai_m3_ha_yr" # mai in vol. under bark minus harvest losses
 si_to_mai_max(2.3, mai_var, fe_ytable_larch_schober_moderate_1946)
 si_to_mai_max(0.7, mai_var, fe_ytable_larch_schober_moderate_1946)
 si_to_mai_max(2.3, mai_var, fe_ytable_beech_wiedemann_moderate_1931)
 si_to_mai_max(0.7, mai_var, fe_ytable_beech_wiedemann_moderate_1931)

Find Site Indexes With a Yield Table

Description

Find Site Indexes With a Yield Table

Usage

site_index(age, size, ytable, si_variable)

Arguments

Value

The site index resulting from age and height

See Also

Other yield table functions: fe_yield_table(), plot.fe_yield_table(), si_to_mai_age(), si_to_mai_max(), stocking_level(), yield_tables_for_species, ytable_age_slice(), ytable_lookup(), ytable_max_slice()

Examples

  site_index(72, 19.7, fe_ytable_pine_wiedemann_moderate_1943, "h_q_m")

Supported Species Codings in the Package ForestElementsR

Description

Data of all supported species codings in the package ForestElementsR. Currently supported codings are

master

The master species coding is the original species coding used by the package ForestElementsR. It contains each species from the species_master_table and no species groups. This coding corresponds directly to the species_master_table. Its species_id's (see below) are the master table's columns genus and species combined into one character string, separated by an underscore.

tum_wwk_short

The tum_wwk_short species coding is one of two codings in use at the Chair of Forest Growth and Yield Science. It defines only a small set of single species explicitly (the most important ones in Central Europe), while all other species are attributed to a few large container groups.

tum_wwk_long

The tum_wwk_long species coding is one of two codings in use at the Chair of Forest Growth and Yield Science. It defines a larger set of single species than the tum_wwk_short coding. In its original version, this coding contains several species groups, but most of these groups are ambiguous as they include species which also have a single coding. These ambiguous groups were not included in this package.

bavrn_state

The bavrn_state species coding is the species coding used by the Bavarian State Forest Service.

bavrn_state_short

The bavrn_state_short is a coding that combines the species of bavrn_state into groups. These groups are typically used by the Bavarian State Forest Service in aggregated evaluations.

ger_nfi_2012

The ger_nfi_2012 species coding is the species coding used by the German National Forest Inventory of 2012 (Riedel et al. 2017)

Usage

species_codings

Format

A tibble containing the supported species codings together with the coding tables (which are tibbles themselves). Its columns are:

species_coding

name of the coding

code_table

tibble describing the species coding with the columns

species_id

the species code (character)

species_name_sci

the scientific species name (for species groups english terms are used)

species_name_eng

English species names

species_name_ger

German species names

References

Riedel T, Hennig P, Kroiher F, Polley H, Schmitz F, F. S (2017). Die dritte Bundeswaldinventur (BWI 2012). Inventur- und Auswertungsmethoden. Thuenen Institut fuer Waldoekosysteme.

Examples

# Get specific coding tables out of the data 'species_codings'
fe_species_get_coding_table("master")
fe_species_get_coding_table("tum_wwk_short")
fe_species_get_coding_table("tum_wwk_long")
fe_species_get_coding_table("bavrn_state")
fe_species_get_coding_table("bavrn_state_short")
fe_species_get_coding_table("ger_nfi_2012")

# Check number of species behind each code in a given coding
fe_species_get_coding_table("tum_wwk_short") |>
  dplyr::group_by(species_id) |>
  dplyr::summarise(n = dplyr::n()) |>
  dplyr::arrange(as.numeric(species_id)) # just for the look of it

fe_species_get_coding_table("bavrn_state_short") |>
  dplyr::group_by(species_id) |>
  dplyr::summarise(n = dplyr::n()) |>
  dplyr::arrange(as.numeric(species_id)) # just for the look of it

List of Supported Species in the Package ForestElementsR

Description

A data.frame (tibble) where each row represents a single species which is - in principle - currently supported by the package ForestElementsR. Each species which is coded (as a single species or a group of species) in any of the supported species_codings, must also be represented in this data frame. No specific species coding, however, must necessarily use all of the species listed here.

The internal universal species coding used in this package is defined in the two columns genus and species_no (see below).

Usage

species_master_table

Format

A tibble with the following columns are:

deciduous_conifer

Character column with allowed values conif, and decid

for confifer and deciduous species, respectively

genus

Character, the genus of the species in plaintext, but always lowercase

species_no

Character, a three digit number, always with leading zeroes, indicating the species inside the genus. A number was chosen instead of plaintext, because spelling and naming must be considered somewhat instable on that level. The order of the numbers reflects (very) roughly the importance of the species inside a given genus in Central Europe.

name_sci

Scientific species names (character)

name_eng

Colloquial English species names (character)

name_ger

Colloquial German species names (character)

Species Profile Index After Pretzsch

Description

As an extension of the Shannon Index (shannon_index), the species profile index by Pretzsch (2009) takes into account the vertical structure of a forest stand. For doing so, the function assmann_layers is called in the background.

Usage

species_profile(
  species_id,
  heights,
  weights = 1,
  n_rep = 1,
  reference_height = NULL
)

Arguments

Details

Note that this function calculates comparable output only when the same species coding is used for the input parameter species_id.

Value

The Species Profile Index value resulting from the input data

References

Pretzsch H (2009). Forest Dynamics, Growth and Yield: From Measurement to Model, 2010 edition. Springer, Berlin. ISBN 978-3-540-88306-7.

See Also

Other structure and diversity: assmann_layers(), shannon_index()

Examples

  # Monospecific stand
  trees <- norway_spruce_1_fe_stand$trees
  species_profile(trees$species_id, trees$height_m)

  # Two-species mixed stand
  trees <- spruce_beech_1_fe_stand$trees
  species_profile(trees$species_id, trees$height_m)

  # Selection forest
  trees <- selection_forest_1_fe_stand$trees
  species_profile(trees$species_id, trees$height_m)

  # weigh with basal area (i.e. dbh^2)
  species_profile(trees$species_id, trees$height_m, weights = trees$dbh_cm^2)

  # weigh with inverse basal area (i.e. 1 / dbh^2)
  species_profile(
    trees$species_id, trees$height_m, weights = 1 / trees$dbh_cm^2
  )

species_shares

Description

Calculate tree species shares for a fe_stand object. Different methods and scopes are available.

Usage

species_shares(
  x,
  tree_filter = TRUE,
  method = c("ba_wd", "ba", "n"),
  include_ingrowth = TRUE
)

Arguments

Details

The calculation uses the trees data frame of the input fe_stand object. The small tree cohort is not taken into account. Three different methods are available to choose from (parameter method:

Basal area shares, weighted with wood density (method "ba_wd"):

The species shares are based on basal areas which are weighted with the species specific wood densities raised to the power of 2/3. The exponent of 2/3 takes into account that wood density is a three-dimensional quantity, while basal area is two-dimensional. This is the default method. It works, however, only with species codings that can be converted into the fe_species_tum_wwk_short coding (default), or with fe_species_bavrn_state_short. The latter is used if this species coding is directly provided or if the species coding is fe_species_bavrn_state. The reason for that restriction is that wood densities are currently only provided for the two species codings fe_species_tum_wwk_short, and fe_species_bavrn_state_short. The resulting shares, however, will always relate to the original coding.

Unweighted basal area shares (method "ba"):

Species shares are calculated as shares of the unweighted basal areas.

Stem number shares (method "n"):

Species shares are calculated as stem number shares, i.e. tree size does not matter for that calculation.

Value

A data frame (tibble) with the three columns species_id, time_yr, and species_share. If no tree passes the user-defined tree_filter, the tibble will have no lines.

Examples

species_shares(selection_forest_1_fe_stand) # default method ("ba_wd")
species_shares(selection_forest_1_fe_stand, method = "ba")
species_shares(selection_forest_1_fe_stand, method = "n")

# Same stand, different cohorts
mm_forest_1_fe_stand_spatial |> species_shares() # all trees
mm_forest_1_fe_stand_spatial |> species_shares(!removal) # remaining only
mm_forest_1_fe_stand_spatial |> species_shares(removal) # removal only

stand_level_increment

Description

Calculate periodic annual stand level increments from time-ordered vectors of appropriate stand sum variables. Typically the variable of interest is a stand's wood volume per unit area, but it works equally for stand basal area and biomass.

Usage

stand_level_increment(time, x_remain, x_remove)

Arguments

Details

The input vector x_remove is to be understood as the amount removed up to (or at) the corresponding points in time and after the preceding points in time in the time input vector. The resulting increments are to be understood in a similar way: The entries in the increment vector relate to the period between the entry at the same position in the time input vector and the time entry at the position before. Therefore, the first element of the resulting increment vector is always NA.

Value

A vector of the annual increments for the stand level variable of interest corresponding to the input vector time. The increments always relate to the period from (and including) the corresponding point in time back to (and excluding) the previous point in time. Thus, the first element of the output vector is always NA. If the input vectors are of length 1 only, the function consequently returns NA.

Examples

 # Stand age, remaining and removal volume (m³/ha)
 age        <- seq(20, 70, 5)
 vol_remain <- c(65, 118, 175, 233, 293, 355, 416, 476, 534, 589, 642)
 vol_remove <- c(16,  29,  35,  39,  39,  39,  38,  37,  36,  35,  34)

 stand_level_increment(age, vol_remain, vol_remove) # m³/ha/yr

 # Works also with basal area (m²/ha)
 age        <- seq(20, 60, 5)
 ba_remain  <- c(26.0, 30.1, 32.5, 34.2, 35.5, 37.1, 38.7, 40.3, 41.9)
 ba_remove  <- c( 0.0,  5.0,  5.9,  5.4,  5.1,  4.2,  3.3,  3.0,  2.7)

 stand_level_increment(age, ba_remain, ba_remove) # m²/ha/yr

stand_sums_dynamic

Description

Calculate periodic annual volume and basal area increments for fe_stand objects with repeated surveys

Usage

stand_sums_dynamic(x, tree_filter = TRUE)

Arguments

Details

For the sake of robustness, stand_sums_dynamic does not perform a plausibility check on single tree level before calculating increments. Internally, the function stand_sums_static is called separately for the remaining and the removal stand. Both are required for calculating meaningful increments. Basal area increments are always calculated, because valid fe_stand objects always contain the required information (i.e. all trees' dbh); volume increments are calculated if also height values (either measured or estimated) are available for all trees.

Value

A data frame (tibble) that contains the periodic annual basal area and volume growth (the latter if enough information is available, see Details) per ha for each species and each period. The year where the increment entry is, means the end point of the period of interest. Therefore, the first increment value will always be NA. If the input fe_stand object x does only comprise one survey, the increment values will be NA, because it takes at least two subsequent surveys to calculate meaningful increments. In case an object of class fe_ccircle_spatial_notrees (which is a special child of fe_stand) is provided as input x, the function returns an empty data frame.

Examples

  oo <- options(fe_spec_lang = "eng") # Display species names in English

  # Mixed mountain forest with several surveys
  stand_inc <- stand_sums_dynamic(mm_forest_1_fe_stand_spatial)
  stand_inc

  # Combine to species overarching increments. Zero in the first year results
  # as there cannot be increments available at the first survey
  stand_inc |>
    dplyr::group_by(time_yr) |>
      dplyr::summarise(
        iba_m2_ha_yr = sum(iba_m2_ha_yr, na.rm = TRUE),
        iv_m3_ha_yr  = sum(iv_m3_ha_yr,  na.rm = TRUE)
      )

  # When there is only one single survey, all increments must be NA
  stand_sums_dynamic(spruce_beech_1_fe_stand)

  options(oo) # Set options to previous values

Static Stand Sum and Mean Values for an fe_stand Object

Description

Calculate ha-wise static stand sum and mean values for a fe_stand object. The term 'static' means that no growth and increment variables are calculated, only descriptive variables for each point in time.

Usage

stand_sums_static(x, tree_filter = TRUE, hd_dom_method = c("Weise", "Assmann"))

Arguments

Details

Default setting for the dominant heights and diameters is the method by Weise, i.e. quadratic mean diameter and height for the 20% biggest trees. Alternatively, the d100, h100 method by Assmann can be selected. This should be, however, done with care, because d100 and h100 are only well defined in monospecific stands. Note, that this function does not take into account species shares in mixed stands when calculating d100 and h100. If the tree heights in the input object contain missing values, the output variables requiring height information will be NA.

Value

A data frame (tibble) with the ha-related static sum values stem number, basal area, volume, quadratic mean diameter, dominant diameter, quadratic mean height, dominant height. If no tree in x$trees passes tree_filter, as defined above, an empty data frame is returned. In case an object of class fe_ccircle_spatial_notrees (which is a special child of fe_stand) is provided as input x, the function also returns an empty data frame. In case the tree heights in the input object contain missing values, the output variables requiring height information will be NA.

Examples

# Evaluation for all trees
mm_forest_1_fe_stand_spatial |> stand_sums_static()

# Exclude removal trees
mm_forest_1_fe_stand_spatial |> stand_sums_static(!removal)

# Exclude removal trees and include only trees with dbh > 30 cm
mm_forest_1_fe_stand_spatial |> stand_sums_static(!removal & dbh_cm > 30)

# Exclude removal trees, use Assmann's d100 h100 for dominant height
# and diameter
mm_forest_1_fe_stand_spatial |>
  stand_sums_static(!removal, hd_dom_method = "Assmann")

# Include all trees, use Assmann's d100 h100 for dominant height
# and diameter
mm_forest_1_fe_stand_spatial |>
  stand_sums_static(hd_dom_method = "Assmann")

# Incomplete height information leads to missing values in all variables
# that require height as an input
demo_stand <- spruce_beech_1_fe_stand     # Copy an existing fe_stand object
index <- seq(2:nrow(demo_stand$trees))    # remove every 2nd height
demo_stand$trees[index, ]$height_m <- NA
demo_stand |> stand_sums_static()

Estimate the Standing Area of Single Trees

Description

An implementation of the standing area estimation of the third German National Forest Inventory (Riedel et al. 2017). Its main intended use is the calculation of virtual species areas in mixed stands. According to (Riedel et al. 2017), it is recommended only to include the main stand in such calculations, neither understorey, nor any layers above the main stand.

Usage

standing_area_gnfi3(species_id, dbh_cm)

Arguments

Details

Originally, the function was parameterized for species and species groups corresponding to the national forest inventory's species coding (fe_species_ger_nfi_2012). We have attributed in addition these the original parameters also to the species codings fe_species_tum_wwk_short, and fe_species_bavrn_state_short. When called with a given species coding, the function will try to use the "nearest" of these three alternatives. Fallback option is the attempt to use fe_species_tum_wwk_short.

Value

A vector of the estimated standing areas in m²

Examples

  # Three spruces, two pines, two beech
  species_id <- fe_species_ger_nfi_2012(c(10, 10, 10, 20, 20, 100, 100))
  dbh_cm     <- c(10.1, 27.4, 31.4, 35.5, 39.8, 45.2, 47.2)

  standing_area_gnfi3(species_id, dbh_cm)

Core Function for the Estimation of Tree Standing Areas According to the 3rd German National Forest Inventory

Description

See documentation of standing_area_gnfi3 for most information, this function is the workhorse behind, and should never be called directly by a user. The function standing_area_gnfi3 is fully vectorized, inputs are recyled according to the tibble rules

Usage

standing_area_gnfi3_core(species_id, dbh_cm, params)

Arguments

Value

A vector with estimates of the trees' standing areas in m²

Helper function for cascaded species conversion when applying the standing area estimation of the German National forest inventory 2012. Try to convert given species_id into ger_nfi_2012. If this causes an error convert to tum_wwk_short instead (to be used in the default version of the function).

Description

Helper function for cascaded species conversion when applying the standing area estimation of the German National forest inventory 2012. Try to convert given species_id into ger_nfi_2012. If this causes an error convert to tum_wwk_short instead (to be used in the default version of the function).

Usage

stndg_area_gnfi3_spec_convert(species_id)

Calculate the Stocking Level ("Bestockungsgrad") of a Stand

Description

The stocking level (German "Bestockungsgrad") is an important measure for stand density in practice. It is the ratio of a stand's actual basal area and its expected basal area due to a yield table.

Usage

stocking_level(ba, age, si, ytable)

Arguments

Value

The stocking level of the stand based on the yield table of interest

See Also

Other yield table functions: fe_yield_table(), plot.fe_yield_table(), si_to_mai_age(), si_to_mai_max(), site_index(), yield_tables_for_species, ytable_age_slice(), ytable_lookup(), ytable_max_slice()

Examples

  # Scots pine stand, 72 years old, site index 1.2, basal area 41.3 m²/ha

  # 1. Reference: Yield table for pine by Wiedemann
  stocking_level(
    ba = 41.3, age = 72, si = 1.2,
    ytable = fe_ytable_pine_wiedemann_moderate_1943
  )

  # 2. Reference: Yield table for pine by Wiedemann
  stocking_level(
    ba = 41.3, age = 72, si = 1.2,
    ytable = fe_ytable_pine_gehrhardt_moderate_1921
  )

  # Norway spruce stand, 72 years old, site index 38, basal area 41.3 m²/ha
  # 1. Reference Yield Table by Assmann-Franz
  stocking_level(
    ba = 41.3, age = 72, si = 38,
    ytable = fe_ytable_spruce_assmann_franz_mean_yield_level_1963
  )

  # 2. Reference Yield Table for spruce by Wiedemann, moderate thinning,
  # site index 1.0
  stocking_level(
    ba = 41.3, age = 72, si = 1.0,
    ytable = fe_ytable_spruce_wiedemann_moderate_1936_42
  )

summary For a fe_ccircle_spatial_notrees object

Description

For the time being, this is function serves only to obtain a controled output when summary is called for an object of class fe_ccircle_spatial_notrees. This output is just an empty data data frame.

Usage

## S3 method for class 'fe_ccircle_spatial_notrees'
summary(object, ...)

Arguments

Value

An empty data frame

Examples

  # Abuse input data, that would actually allow for a full fe_ccircle_spatial
  # object to construct a fe_ccircle_spatial_notrees object.
  x <- spruce_pine_ccircle_raw
  x <- x |> fe_ccircle_spatial_notrees()

  # Make the dummy summary
  summary(x)

Summary of an fe_species_bavrn_state Vector

Description

Produces a summary for a fe_species_bavrn_state object in the same style as R does for factors. Actually, after some conversions summary.factor is called by this function. The species naming in the summary depends on the parameter spec_lang.

Usage

## S3 method for class 'fe_species_bavrn_state'
summary(
  object,
  spec_lang = options("fe_spec_lang")$fe_spec_lang,
  maxsum = 100L,
  ...
)

Arguments

Value

A named vector in the same style as returned by summary.factor

Examples

# Construct some species id vector
spec_ids <- c(
  rep(fe_species_bavrn_state(c("10", "20", "21", "89")),
    times = c(15, 31, 70, 12)
  ),
  NA, NA
)

summary(spec_ids)
spec_ids |> summary()
spec_ids |> summary(spec_lang = "eng")

# Usual application: Set option for species code output
# Any summary of an fe_species object will use the last setting of the
# option
options(fe_spec_lang = "sci")
spec_ids |> summary()

Summary of an fe_species_bavrn_state_short Vector

Description

Produces a summary for a fe_species_bavrn_state_short object in the same style as R does for factors. Actually, after some conversions summary.factor is called by this function. The species naming in the summary depends on the parameter spec_lang.

Usage

## S3 method for class 'fe_species_bavrn_state_short'
summary(
  object,
  spec_lang = options("fe_spec_lang")$fe_spec_lang,
  maxsum = 100L,
  ...
)

Arguments

Value

A named vector in the same style as returned by summary.factor

Examples

# Construct some species id vector
spec_ids <- c(
  rep(fe_species_bavrn_state_short(c("1", "2", "6", "9")),
    times = c(15, 31, 70, 12)
  ),
  NA, NA
)

summary(spec_ids)
spec_ids |> summary()
spec_ids |> summary(spec_lang = "eng")

# Usual application: Set option for species code output
# Any summary of an fe_species object will use the last setting of the
# option
options(fe_spec_lang = "sci")
spec_ids |> summary()

Summary of an fe_species_ger_nfi_2012 Vector

Description

Produces a summary for a fe_species_ger_nfi_2012 object in the same style as R does for factors. Actually, after some conversions summary.factor is called by this function. The species naming in the summary depends on the parameter spec_lang.

Usage

## S3 method for class 'fe_species_ger_nfi_2012'
summary(
  object,
  spec_lang = options("fe_spec_lang")$fe_spec_lang,
  maxsum = 100L,
  ...
)

Arguments

Value

A named vector in the same style as returned by summary.factor

Examples

# Construct some species id vector
spec_ids <- c(
  rep(fe_species_ger_nfi_2012(c("170", "140", "120", "150")),
    times = c(151, 231, 70, 122)
  ),
  NA, NA, NA, NA
)

summary(spec_ids)
spec_ids |> summary()
spec_ids |> summary(spec_lang = "eng")

# Usual application: Set option for species code output
# Any summary of an fe_species object will use the last setting of the
# option
options(fe_spec_lang = "sci")
spec_ids |> summary()

Summary of an fe_species_master Vector

Description

Produces a summary for a fe_species_master object in the same style as R does for factors. Actually, after some conversions summary.factor is called by this function. The species naming in the summary depends on the parameter spec_lang.

Usage

## S3 method for class 'fe_species_master'
summary(
  object,
  spec_lang = options("fe_spec_lang")$fe_spec_lang,
  maxsum = 100L,
  ...
)

Arguments

Value

A named vector in the same style as returned by summary.factor

Examples

# Construct some species id vector
spec_ids <- c(
  rep(
    fe_species_master(c(
      "pinus_001", "quercus_003", "tilia_002", "carpinus_001", "sorbus_002"
    )),
    times = c(12, 7, 24, 16, 32)
  ),
  NA, NA, NA, NA
)

summary(spec_ids)
spec_ids |> summary()
spec_ids |> summary(spec_lang = "eng")

# Usual application: Set option for species code output
# Any summary of an fe_species object will use the last setting of the
# option
options(fe_spec_lang = "sci")
spec_ids |> summary()

Summary of an fe_species_tum_wwk_long Vector

Description

Produces a summary for a fe_species_tum_wwk_long object in the same style as R does for factors. Actually, after some conversions summary.factor is called by this function. The species naming in the summary depends on the parameter spec_lang.

Usage

## S3 method for class 'fe_species_tum_wwk_long'
summary(
  object,
  spec_lang = options("fe_spec_lang")$fe_spec_lang,
  maxsum = 100L,
  ...
)

Arguments

Value

A named vector in the same style as returned by summary.factor

Examples

# Construct some species id vector
spec_ids <- c(
  rep(fe_species_tum_wwk_long(c("10", "60", "87", "811")), each = 15),
  NA, NA
)

summary(spec_ids)
spec_ids |> summary()
spec_ids |> summary(spec_lang = "eng")

# Usual application: Set option for species code output
# Any summary of an fe_species object will use the last setting of the
# option
options(fe_spec_lang = "sci")
spec_ids |> summary()

Summary of an fe_species_tum_wwk_short Vector

Description

Produces a summary for a fe_species_tum_wwk_short object in the same style as R does for factors. Actually, after some conversions summary.factor is called by this function. The species naming in the summary depends on the parameter spec_lang.

Usage

## S3 method for class 'fe_species_tum_wwk_short'
summary(
  object,
  spec_lang = options("fe_spec_lang")$fe_spec_lang,
  maxsum = 100L,
  ...
)

Arguments

Value

A named vector in the same style as returned by summary.factor

Examples

# Construct some species id vector
spec_ids <- c(
  rep(fe_species_tum_wwk_short(as.character(1:10)), each = 5), NA
)

summary(spec_ids)
spec_ids |> summary()
spec_ids |> summary(spec_lang = "eng")

# Usual application: Set option for species code output
# Any summary of an fe_species object will use the last setting of the
# option
options(fe_spec_lang = "sci")
spec_ids |> summary()

# The summary is also used in the summary of a data frame which contains
# an fe_species object, but displayed differently
options(fe_spec_lang = "eng")
selection_forest_1_fe_stand$trees |> summary()

Summary for an fe_stand Object

Description

Compute an overview of basic stand level variables for an fe_stand object

Usage

## S3 method for class 'fe_stand'
summary(object, ...)

Arguments

Details

The summary calls the function stand_sums_static with its default settings. The result is a data.frame (tibble) with species and year wise stand sum values per ha and mean values. Stem numbers, basal areas, quadratic mean diameters and dominant diameters (d 100) are always calculated. Quadratic mean heights, dominant heights (h 100), and wood volumes are only calculated if the heights of all trees are given in object (i.e. no NA). The summary contains a column species_id. Depending on the setting of options("fe_spec_lang"), the species ids will be printed as the species code (settings NULL or "ger"), scientific, English, or German species names (settings "sci", "eng", or "ger", respectively).

Value

data.frame (tibble) resulting from applying the function stand_sums_static to object

See Also

stand_sums_static

Examples

# Make a stand data.frame (or nicer, a tibble) that meets the minimum
# requirements for setting up a fe_Stand object
some_stand <- tibble::tibble(
  tree_id = as.character(c(1:100)),
  species_id = as_fe_species_tum_wwk_short(
    as.character(c(rep(1, 40), rep(5, 60)))
  ),
  time_yr = rep(2022, 100),
  dbh_cm = c(rnorm(40, 40.1, 7.3), rnorm(60, 32.8, 8.4)),
)

# Make the object
some_fe_stand <- fe_stand(
  some_stand,
  tree_id_col = "tree_id",
  species_id_col = "species_id",
  time_yr_col = "time_yr",
  dbh_cm_col = "dbh_cm",
  area_ha = 0.25
)

# The summary with different language choices
options(fe_spec_lang = "code")
summary(some_fe_stand)
options(fe_spec_lang = "sci")
summary(some_fe_stand)
options(fe_spec_lang = "eng")
summary(some_fe_stand)
options(fe_spec_lang = "ger")
summary(some_fe_stand)


# Use example stands
options(fe_spec_lang = "eng")
norway_spruce_1_fe_stand |> summary()
summary(european_beech_1_fe_stand)
options(fe_spec_lang = "sci")
summary(selection_forest_1_fe_stand)
spruce_beech_1_fe_stand |> summary()
options(fe_spec_lang = "code")
summary(selection_forest_1_fe_stand)
spruce_beech_1_fe_stand |> summary()

Generic Summary for fe_species Objects

Description

Produces a summary for a fe_species object in the same style as R does factors. Actually, after some conversions summary.factor is called by this function. The species naming in the summary depends on the parameter spec_lang.

Usage

summary_fe_species(
  x,
  spec_lang = c("code", "eng", "ger", "sci"),
  maxsum = 100L
)

Arguments

Value

A named vector in the same style as returned by summary.factor

Generate an Overview of the Surveys of an fe_stand Object

Description

The tree data frame of an fe_stand object is evaluated in order to get survey and species specific meta information about these data.

Usage

survey_overview(x, tree_filter = TRUE)

Arguments

Details

This function provides meta information that is useful for evaluations that can be complex on the detail level, e.g. increment calculations from subsequent surveys. In such contexts, the column n_species_occurence of the output data frame can be of special interest. If a species is present for a number of consecutive surveys, all these surveys get the same integer number in this column. If the same species vanishes, but occurs again later, the next block of surveys gets the subsequent number, and so on. So, all consecutive blocks of a species' occurrence are numbered as 1, 2, 3, etc. At surveys where the species is not present n_species_occurrence has the value 1.

Value

A data frame (tibble) that gives an overview of the surveys represented in the input object x. It is basically an evaluation of the data frame x$trees. It provides information about how many trees were present in each survey, how many dbh and heights were measured, and if the dbh and height measurements cover all trees, For dbh this must be always true, because this is a requirement for a valid fe_stand object. In addition, we are informed whether a species that has been documented in the object is represented in a specific survey or not. As a basis for advanced evaluations, species occurences are numbered in the columen n_species_occurrence (see Details). In case an object of class fe_ccircle_spatial_notrees (which is a special child of fe_stand) is provided as input x, the function returns an empty data frame.

Examples

  # Example data: Mixed mountain forest plot with several surveys
  mm_forest_1_fe_stand_spatial |> survey_overview()

Translate Species Codes into Names

Description

Translate Species Codes into Names

Usage

translate_spec_codes_into_names(x, spec_lang = c("eng", "ger", "sci"), coding)

Arguments

Value

A character vector in the same order as x, containing the species (group names) in the desired language

Calculate Tree Volumes With the GRI Volume Equations (Franz et al. 1973)

Description

Merchantable standing tree volumes over bark calculated with the GRI volume equations developed by Friedrich Franz in 1971. These volume equations are standard in the German Federal State of Bavaria

Usage

v_gri(species_id, dbh_cm, height_m)

Arguments

Details

The abbreviation GRI stands for the German word "Großrauminventur" (large area inventory). This forest inventory was conducted in 1971 by Friedrich Franz and his team of the Chair for Forest Growth and Yield Science at the Munich Ludwig-Maximilians-University (Franz et al. 1973). The inventory covered the whole federal state of Bavaria (~ 70,600 km²). The volume equations implemented in this function were calibrated with the data of several ten thousands of trees which were felled for that purpose during the inventory. The volume equations are available for exactly the species (groups) defined in the coding tum_wwk_short. If they are called with another species coding supported by the package ForestElementsR, v_gri will attempt to convert them accordingly.

Value

A vector of merchantable standing tree wood volumes over bark in m³. "Merchantable" means only wood with a minimum diameter of 7 cm over bark is taken into account. Therefore, for small trees without any merchandable wood, the function will return 0 m³.

References

Franz F, Bachler B, Deckelmann E, Kennel R, Schmidt A, Wotschikowski U (1973). Bayerische Waldinventur 1970/71, Inventurabschnitt I: Großrauminventur Aufnahme- und Auswertungsverfahren, volume 11 of Forstliche Forschungsberichte München. Forstwissenschaftliche Fakultät der Universität München und Bayerische Forstliche Versuchs- und Forschungsanstalt.

Examples

# Find out the species codes that work with v_gri
fe_species_get_coding_table("tum_wwk_short") |>
  dplyr::select(-genus, -species_no) |>
  dplyr::distinct()

# Merchantable volume of a European beech with dbh = 30 cm,
# and height = 29 m
v_gri("5", 30, 29)
v_gri(5, 30, 29)
v_gri(as_fe_species_tum_wwk_short(5), 30, 29)

# Several trees (three species, three sizes)
species_id <- fe_species_tum_wwk_short(c(1, 1, 1, 3, 3, 3, 5, 5, 5))
dbh_cm <- c(12, 30, 55, 12, 30, 55, 12, 30, 55)
height_m <- c(14, 33, 39, 14, 33, 39, 14, 33, 39)
v_gri(species_id, dbh_cm, height_m)

# The same, but the species id's are now originally defined in the
# coding of the 2012 German national forest inventory
species_id <- fe_species_ger_nfi_2012(
  c(10, 10, 10, 20, 20, 20, 100, 100, 100)
)
v_gri(species_id, dbh_cm, height_m)

Core Function For the GRI Tree Volume Calculation

Description

See documentation of v_gri for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

v_gri_core(species_id, dbh_cm, height_m, v_param)

Arguments

Details

The function v_gri is fully vectorized, inputs are recyled according to the tibble rules

Value

Tree wood volume (see v_gri)

Reduce a Given standing Volume Over Bark to Harvested Volume Under Bark

Description

Many tree volume functions (like v_gri) calculate wood volumes defined as standing and over bark. Practictioners often prefer to work with volumes where the harvest losses and the bark volume have been substracted. Given an over bark standing volume, this function uses species specific reduction factors in order to obtain harvested volume under bark. The reduction factors are taken from (BayMinELF 1990); they relate to the species coding fe_species_tum_wwk_short or, alternatively fe_species_bavrn_state_short.

Usage

v_red_harvest_ubark(species_id, v_orig_m3)

Arguments

Value

A vector of the reduced volumes defined as harveest

References

BayMinELF (1990). Hilfstafeln für die Forsteinrichtung. Zusammengestellt für den Gebrauch in der Bayerischen Staatsforstverwaltung. Bayerisches Staatsministerium für Ernährung Landwirtschaft und Forsten.

Examples

  # Take all species groups of tum_wwk_short and a standing volume of 1 m³
  # over bark
  species_id <- fe_species_tum_wwk_short(1:10)
  v_red_harvest_ubark(species_id, 1)

Core Function For the Volume Reduction With v_red_harvest_ubark

Description

See documentation of v_red_harvest_ubark for most information, this function is the workhorse behind, and should never be called directly by a user.

Usage

v_red_harvest_ubark_core(species_id, v_orig_m3, params)

Arguments

Details

The function v_red_harvest_ubark is fully vectorized, inputs are recyled according to the tibble rules

Value

A vector of wood volumes defined as harvested under bark (see v_red_harvest_ubark)

Validate an fe_ccircle_spatial Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor new_fe_ccircle_spatial. Regular users, please construct fe_ccircle_spatial objects with fe_ccircle_spatial.

Usage

validate_fe_ccircle_spatial(x, method = c("strict", "flexible"))

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_ccircle_spatial object specifications. In case of such violations, the function will terminate with an error.

Examples

  # Validate the example fe_ccircle_spatial object
  spruce_pine_ccircle_spatial |>
    validate_fe_ccircle_spatial(method = "flexible")

Validate an fe_ccircle_spatial_notrees Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor new_fe_ccircle_spatial_notrees. Regular users, please construct fe_ccircle_spatial_notrees objects with fe_ccircle_spatial_notrees.

Usage

validate_fe_ccircle_spatial_notrees(x)

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_ccircle_spatial_notrees object specifications. In case of such violations, the function will terminate with an error.

Examples

  # Validate the example fe_ccircle_spatial object
  spruce_pine_ccircle_spatial_notrees |>
    validate_fe_ccircle_spatial_notrees()

Generic Validator for fe_species Objects

Description

Generic Validator for fe_species Objects

Usage

validate_fe_species(x)

Arguments

Value

Returns x if it passes the validation, terminates with an error otherwise

Validate an fe_species_bavrn_state Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor fe_species_bavrn_state. Regular users, please construct fe_species_bavrn_state objects with fe_species_bavrn_state.

Usage

validate_fe_species_bavrn_state(x = character())

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_species_bavrn_state object specifications. In case of such violations, the function will terminate with an error.

Examples

# Passes validation
spec_ids <- as.character(c(30, 30, 30, 10, 10, 10, 10, 20, 20, 80))
spec_ids <- new_fe_species_bavrn_state(spec_ids)
validate_fe_species_bavrn_state(spec_ids)

# Validating the following spec_ids throws an error due to
# non-supported species codes
spec_ids <- as.character(c(30, 30, 8712, 10, 10, 10, 349, 20, 20, 80))
spec_ids <- new_fe_species_bavrn_state(spec_ids)
try(
  validate_fe_species_bavrn_state(spec_ids)
)

Validate an fe_species_bavrn_state_short Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor fe_species_bavrn_state_short. Regular users, please construct fe_species_bavrn_state_short objects with fe_species_bavrn_state_short.

Usage

validate_fe_species_bavrn_state_short(x = character())

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_species_bavrn_state_short object specifications. In case of such violations, the function will terminate with an error.

Examples

# Passes validation
spec_ids <- as.character(c(3, 3, 3, 1, 1, 1, 1, 2, 2, 8))
spec_ids <- new_fe_species_bavrn_state_short(spec_ids)
validate_fe_species_bavrn_state_short(spec_ids)

# Validating the following spec_ids throws an error due to
# non-supported species codes
spec_ids <- as.character(c(3, 3, 8712, 1, 1, 1, 349, 2, 2, 8))
spec_ids <- new_fe_species_bavrn_state_short(spec_ids)
try(
  validate_fe_species_bavrn_state_short(spec_ids)
)

Validate an fe_species_ger_nfi_2012 Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor new_fe_species_ger_nfi_2012. Regular users, please construct fe_species_ger_nfi_2012 objects with fe_species_ger_nfi_2012.

Usage

validate_fe_species_ger_nfi_2012(x = character())

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_species_ger_nfi_2012 object specifications. In case of such violations, the function will terminate with an error.

Examples

# Passes validation
spec_ids <- as.character(c(30, 30, 30, 10, 10, 10, 10, 20, 20, 290))
spec_ids <- new_fe_species_ger_nfi_2012(spec_ids)
validate_fe_species_ger_nfi_2012(spec_ids)

# Validating the following spec_ids throws an error due to
# non-supported species codes
spec_ids <- as.character(c(30, 30, 542, 10, 10, 10, 1234, 20, 20, 290))
spec_ids <- new_fe_species_ger_nfi_2012(spec_ids)
try(
  validate_fe_species_ger_nfi_2012(spec_ids)
)

Validate an fe_species_master Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor new_fe_species_master. Regular users, please construct fe_species_master objects with fe_species_master.

Usage

validate_fe_species_master(x = character())

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_species_master object specifications. In case of such violations, the function will terminate with an error.

Examples

# Passes validation
spec_ids <- c("pinus_001", "quercus_002", "pinus_001", "fagus_001")
spec_ids <- new_fe_species_master(spec_ids)
validate_fe_species_master(spec_ids)

# Validating the following spec_ids throws an error due to
# non-supported species codes
spec_ids <- c("pinus_001", "my_awesome_species_003", "wonder_tree_3012")
spec_ids <- new_fe_species_master(spec_ids)
try(
  validate_fe_species_master(spec_ids)
)

Validate an fe_species_tum_wwk_long Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor new_fe_species_tum_wwk_long. Regular users, please construct fe_species_tum_wwk_long objects with fe_species_tum_wwk_long.

Usage

validate_fe_species_tum_wwk_long(x = character())

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_species_tum_wwk_long object specifications. In case of such violations, the function will terminate with an error.

Examples

# Passes validation
spec_ids <- as.character(c(70, 61, 88, 88, 10, 971, 32))
spec_ids <- new_fe_species_tum_wwk_long(spec_ids)
validate_fe_species_tum_wwk_long(spec_ids)

# Validating the following spec_ids throws an error due to
# non-supported species codes
spec_ids <- as.character(c(70, 61, 1221, 88, 88, 10, 971, 32, 4031))
spec_ids <- new_fe_species_tum_wwk_long(spec_ids)
try(
  validate_fe_species_tum_wwk_long(spec_ids)
)

Validate an fe_species_tum_wwk_short Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor new_fe_species_tum_wwk_short. Regular users, please construct fe_species_tum_wwk_short objects with fe_species_tum_wwk_short.

Usage

validate_fe_species_tum_wwk_short(x = character())

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_species_tum_wwk_short object specifications. In case of such violations, the function will terminate with an error.

Examples

# Passes validation
spec_ids <- as.character(c(2, 2, 2, 1, 1, 1, 1, 3, 3, 9))
spec_ids <- new_fe_species_tum_wwk_short(spec_ids)
validate_fe_species_tum_wwk_short(spec_ids)

# Validating the following spec_ids throws an error due to
# non-supported species codes
spec_ids <- as.character(c(2, 2, 52, 1, 1, 1, 123, 3, 3, 9))
spec_ids <- new_fe_species_tum_wwk_short(spec_ids)
try(
  validate_fe_species_tum_wwk_short(spec_ids)
)

Validate an fe_stand Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor new_fe_stand. Regular users, please construct fe_stand objects with fe_stand.

Usage

validate_fe_stand(x)

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_stand object specifications. In case of such violations, the function will terminate with an error.

Examples

# Validate the example stands
validate_fe_stand(norway_spruce_1_fe_stand)
validate_fe_stand(european_beech_1_fe_stand)
validate_fe_stand(spruce_beech_1_fe_stand)
selection_forest_1_fe_stand |> validate_fe_stand()

Validate an fe_stand_spatial Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor new_fe_stand_spatial. Regular users, please construct fe_stand_spatial objects with fe_stand_spatial.

Usage

validate_fe_stand_spatial(x)

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_stand_spatial object specifications. In case of such violations, the function will terminate with an error.

Examples

  # Validate the example fe_stand_spatial object
  mm_forest_1_fe_stand_spatial |> validate_fe_stand_spatial()

Validate an fe_tally_list Object

Description

Regular users will not require this function. Expert users will want to use it in combination with the constructor new_fe_tally_list. Regular users, please construct fe_tally_list objects with fe_tally_list.

Usage

validate_fe_tally_list(x)

Arguments

Value

Returns x, but this function is mainly called for its side effect which is pointing out any violations of the fe_tally_list object specifications. In case of such violations, the function will terminate with an error.

Validate a Candidate For an fe_yield_table Object

Description

Validate a Candidate For an fe_yield_table Object

Usage

validate_fe_yield_table(x)

Arguments

Value

If x is not a valid fe_yield_table object, the function will terminate with an error. Otherwise, x will be returned.

Examples

 validate_fe_yield_table(fe_ytable_beech_wiedemann_moderate_1931)
 validate_fe_yield_table(fe_ytable_larch_schober_moderate_1946)
 validate_fe_yield_table(fe_ytable_pine_wiedemann_moderate_1943)
 validate_fe_yield_table(fe_ytable_spruce_gehrhardt_moderate_1921)
 validate_fe_yield_table(fe_ytable_douglas_schober_moderate_1956)
 validate_fe_yield_table(fe_ytable_spruce_wiedemann_moderate_1936_42)
 validate_fe_yield_table(fe_ytable_silver_fir_hausser_moderate_1956)

Abbreviation for the fe_species_bavrn_state Type

Description

Provide an abbreviated name for the class fe_species_bavrn_state to be displayed in tibbles and str()

Usage

## S3 method for class 'fe_species_bavrn_state'
vec_ptype_abbr(x, ...)

Arguments

Value

The abbreviation to be displayed for the species coding (character) in tibbles and in str()

Examples

spec_ids <- fe_species_bavrn_state(as.character(c(10, 30, 60)))
vctrs::vec_ptype_abbr(spec_ids)
str(spec_ids)

Abbreviation for the fe_species_bavrn_state_short Type

Description

Provide an abbreviated name for the class fe_species_bavrn_state_short to be displayed in tibbles and str()

Usage

## S3 method for class 'fe_species_bavrn_state_short'
vec_ptype_abbr(x, ...)

Arguments

Value

The abbreviation to be displayed for the species coding (character) in tibbles and in str()

Examples

spec_ids <- fe_species_bavrn_state_short(as.character(c(2, 4, 6)))
vctrs::vec_ptype_abbr(spec_ids)
str(spec_ids)

Abbreviation for the fe_species_ger_nfi_2012 Type

Description

Provide an abbreviated name for the class fe_species_ger_nfi_2012 to be displayed in tibbles and str()

Usage

## S3 method for class 'fe_species_ger_nfi_2012'
vec_ptype_abbr(x, ...)

Arguments

Value

The abbreviation to be displayed for the species coding (character) in tibbles and in str()

Examples

spec_ids <- fe_species_ger_nfi_2012(as.character(c(10, 20, 50)))
vctrs::vec_ptype_abbr(spec_ids)
str(spec_ids)

Abbreviation for the fe_species_master Type

Description

Provide an abbreviated name for the class fe_species_master to be displayed in tibbles and str()

Usage

## S3 method for class 'fe_species_master'
vec_ptype_abbr(x, ...)

Arguments

Value

The abbreviation to be displayed for the species coding (character) in tibbles and in str()

Examples

spec_ids <- fe_species_master(c("pinus_001", "quercus_002", "pinus_001"))
vctrs::vec_ptype_abbr(spec_ids)
str(spec_ids)

Abbreviation for the fe_species_tum_wwk_long Type

Description

Provide an abbreviated name for the class fe_species_tum_wwk_long to be displayed in tibbles and str()

Usage

## S3 method for class 'fe_species_tum_wwk_long'
vec_ptype_abbr(x, ...)

Arguments

Value

The abbreviation to be displayed for the species coding (character) in tibbles and in str()

Examples

spec_ids <- fe_species_tum_wwk_long(as.character(c(10, 50, 87, 813)))
vctrs::vec_ptype_abbr(spec_ids)
str(spec_ids)

Abbreviation for the fe_species_tum_wwk_short Type

Description

Provide an abbreviated name for the class fe_species_tum_wwk_short to be displayed in tibbles and str()

Usage

## S3 method for class 'fe_species_tum_wwk_short'
vec_ptype_abbr(x, ...)

Arguments

Value

The abbreviation to be displayed for the species coding (character) in tibbles and in str()

Examples

spec_ids <- fe_species_tum_wwk_short(as.character(c(1, 3, 4)))
vctrs::vec_ptype_abbr(spec_ids)
str(spec_ids)

Yield Tables

Description

The yield table system of ForestElementsR allows easy use of yield tables that are available as an fe_yield_table object. Here, we list all yield tables that come with the current version of the package. For test purposes and as an example, the Scots Pine table by Wiedemann (1943) is provided as a raw data frame (prefix "ytable_") and an fe_yield_table object (prefix "fe_ytable_"). The raw data frame can be transformed into a fe_yield_table object with the function fe_yield_table (see the example there). When we refer to Schober's yield table collection below, we mean Schober (1975). Many of the yield tables listed were implemented in the version published in what we refer to as the Hilfstafeln edited by the Bavarian Forest Administration. Hereby, we mean, more precisely, BayMinELF (1990), and BayMinELF (2018). The yield table collection in both editions is almost identical, and represented in ForestElementsR (including error corrections beyond the 2018 edition). Only the yield table by Kenk and Hradetzky for Douglas fir that is new in the 2018 hasn't been imported here, yet.

The following yield tables are currently implemented (alphabetically ordered by author names):

• Assmann-Franz 1963, Norway Spruce, Mean Yield Level: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. The total volume production (tvp), mean annual increment (mai), and the periodic annual increment were given as reduced values (under bark, harvest losses substracted) only. Thus, they were converted into standing m³ over bark by dividing them by 0.81 (standard factor for Norway spruce in Bavaria). The yield table allows site indexing based on the dominant height h100, and the quadratic mean height hq, whereby the former is the way Ernst Assmann and Friedrich Franz had in mind. The site index of this table is, unusual for German yield tables, given as a stand's expected dominant height at an age of 100 years. Unlike in most yield table, the number of removal trees (n_rmv_ha), the volume of the removal stand (v_rmv_ha), and the periodic annual increment (pai_m3_ha_yr, red_m3_ha_yr) relate to the subsequent, not the previous time span. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 19 % which takes into account both, harvest losses (mainly stump) and bark volume.

• Bauer, 1955, Red Oak: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. In the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights were taken from the version published in Schober's (1975) yield table collection for the whole site indexes (I.0, II.0, etc.). The values for the site indexes I.5 and II.5 were generated by linear inter- and extrapolation. The total volume production was not contained in the Bavarian edition of the table; so, it was added from the version published in Schober's (1975) collection. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 21 % which takes into account both, harvest losses (mainly stump) and bark volume.

• Blume, 1949, Poplar: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. Originally, this table was designed for site indexing by quadratic mean diameter (d_q_cm), not by height. Therefore, this diameter was given in the table as lower threshold, here listed as "d_q_cm_si_plus_025". As site indexing by height is always preferable, options for site indexing by both, diameter and height, were included in the fe_yield_table representation of this table. The actual quadratic mean diameter (d_q_cm) which is used for site indexing if the user decides so was calculated from dividing the given basal area by the stem number, multiplying it with 4/pi, and taking the square root of the result. The total volume production (tvp) was not contained in the Bavarian edition of the table. Therefore, it was re-calculated by multiplying the table's mean annual increment (mai) with the stand age. Despite the lower diameter threshold for site indexing, the table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 19 % which takes into account both, harvest losses (mainly stump) and bark volume. Overall, this table seems not really well constructed, the time-curves of its variables are often not intuitively plausible. Some inconsistencies (e.g. decreasing tvp resulting from a to low mean annual increment value) were corrected.

• Gehrhardt, 1908, European Beech, Moderate Thinning:Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights, as required for automated use were generated by inter- and extrapolation. The total volume production (tvp) was not contained in the Bavarian edition of the table. Therefore, it was re-calculated by multiplying the table's mean annual increment (mai) with the stand age. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 15.4 % which takes into account both, harvest losses (mainly stump) and bark volume. The quadratic mean diameter (d_q_cm) for site index 2.0 was obviously wrong in the source data. It was corrected by interpolation.

• Gehrhardt 1921, Norway Spruce, Moderate Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights, as required for automated use were generated by inter- and extrapolation. The total volume production (tvp) was not contained in the Bavarian edition of the table. Therefore, it was re-calculated by multiplying the table's mean annual increment (mai) with the stand age. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 19 % which takes into account both, harvest losses (mainly stump) and bark volume.

• Gehrhardt, 1921, Scots Pine, Moderate Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights, as required for automated use were generated by inter- and extrapolation. The total volume production (tvp) was not contained in the Bavarian edition of the table. Therefore, it was re-calculated by multiplying the table's mean annual increment (mai) with the stand age. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 21 % which takes into account both, harvest losses (mainly stump) and bark volume.

• Guttenberg, 1915, Norway Spruce, High Mountains: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights, as required for automated use were generated by inter- and extrapolation. The total volume production (tvp) was not contained in the Bavarian edition of the table. Therefore, it was re-calculated by multiplying the table's mean annual increment (mai) with the stand age. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 19 % which takes into account both, harvest losses (mainly stump) and bark volume. The interpolated site indexes contained many mistakes (highlighted in the source data), mostly in the mai which could, however, be easily corrected by interpolation between the adjacent site index tables.

• Hausser 1956, Silver Fir, Moderate Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. In the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights were taken from the version published in Schober's (1975) yield table collection for the whole site indexes (I.0, II.0, etc.). The values for the site indexes I.5, II.5, etc. were generated by linear inter- and extrapolation. The total volume production was not contained in the Bavarian edition of the table; so, it was added from the version published in Schober's (1975) collection. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 19 % which takes into account both, harvest losses (mainly stump) and bark volume.

• Juettner 1955, Oak, Moderate Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. In the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights were taken from the version published in Schober's (1975) yield table collection for the whole site indexes (I.0, II.0, etc.). The values for the site indexes I.5, II.5, etc. were generated by linear inter- and extrapolation. The total volume production was not contained in the Bavarian edition of the table; so, it was added from the version published in Schober's (1975) collection.

• Mitscherlich, 1945, Black Alder: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights, as required for automated use were generated by inter- and extrapolation. The total volume production (tvp) was not contained in the Bavarian edition of the table. Therefore, it was re-calculated by multiplying the table's mean annual increment (mai) with the stand age. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 19 % which takes into account both, harvest losses (mainly stump) and bark volume.

• Schober 1946, European Larch, Moderate Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights were taken from the version published in Schober's (1975) yield table collection for the whole site indexes (I.0, II.0, etc.). The values for the site indexes I.5, II.5, etc. were generated by linear inter- and extrapolation. The total volume production was not contained in the Bavarian edition of the table; so, it was added from the version published in Schober's (1975) collection. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 28 % which takes into account both, harvest losses (mainly stump) and bark volume. There were some mistakes in the mean diameters and basal areas of the interpolated site index tables I.5 (most) and II.5 (less). These have been corrected by interpolating between the original values of the integer site index tables (I.0, II.0, III.0).

• Schober, 1953, Japanese Larch, Moderate Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. In the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights were taken from the version published in Schober's (1975) yield table collection for the whole site indexes (I.0, II.0, etc.). The values for the site index I.5 were generated by linear inter- and extrapolation. The total volume production was not contained in the Bavarian edition of the table; so, it was added from the version published in Schober's (1975) collection. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 28 % which takes into account both, harvest losses (mainly stump) and bark volume.

• Schober 1956, Douglas Fir, Moderate Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights, as required for automated use were generated by inter- and extrapolation. The total volume production (tvp) was not contained in the Bavarian edition of the table. Therefore, it was re-calculated by multiplying the table's mean annual increment (mai) with the stand age. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 21 % which takes into account both, harvest losses (mainly stump) and bark volume.

• Schwappach, 1903/29, Birch: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. In the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights were taken from the version published in Schober's (1975) yield table collection for the whole site indexes (I.0, II.0, etc.). The values for the site indexes I.5 and II.5 were generated by linear inter- and extrapolation. The total volume production was not contained in the Bavarian edition of the table; so, it was added from the version published in Schober's (1975) collection. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 19 % which takes into account both, harvest losses (mainly stump) and bark volume. An obvious interpoliation error was corrected (site index 1.5, basal area at age = 40 years).

• Vanselow, 1951, Norway Spruce, Southern Bavaria: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights, as required for automated use were generated by inter- and extrapolation. The total volume production (tvp) was not contained in the Bavarian edition of the table. Therefore, it was re-calculated by multiplying the table's mean annual increment (mai) with the stand age. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 19 % which takes into account both, harvest losses (mainly stump) and bark volume. Two values, basal area at age 20 and standing volume at age 40 for site index I.5 were obviously wrong, probably due to interpolation errors. Both values were replaced by those obtained from correct interpolation between site indexes I and II.

• Wiedemann 1931, European Beech, Moderate Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights, as required for automated use were generated by inter- and extrapolation. The total volume production (tvp) was not contained in the Bavarian edition of the table. Therefore, it was re-calculated by multiplying the table's mean annual increment (mai) with the stand age. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 15.4 % which takes into account both, harvest losses (mainly stump) and bark volume. Site index I.5, age 130: Mistake in n_ha, value 465 was wrong. Linearly interpolated from the neighboring values to 238.5.

• Wiedemann 1936/42, Norway Spruce, Moderate Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights were taken from the version published in Schober's (1975) yield table collection for the whole site indexes (I.0, II.0, etc.). The values for the site indexes I.5, II.5, etc. were generated by linear inter- and extrapolation. The total volume production was not contained in the Bavarian edition of the table; so, it was added from the version published in Schober's (1975) collection. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 19 % which takes into account both, harvest losses (mainly stump) and bark volume. Several mistakes were found in the source files, especially consistently wrong stem numbers and mean diameters in site indexes 4 and 5.

• Wiedemann 1943, Scots Pine, Moderate Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. in the source files, the quadratic mean height of the site index classes was only available as lower threshold (i.e 1/4 site index lower). So the original heights were taken from the version published in Schober's (1975) yield table collection for the whole site indexes (I.0, II.0, etc.). The values for the site indexes I.5, II.5, etc. were generated by linear inter- and extrapolation. The total volume production was not contained in the Bavarian edition of the table; so, it was added from the version published in Schober's (1975) collection. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume means is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 21 % which takes into account both, harvest losses (mainly stump) and bark volume. The Bavarian original values had a mistake in N/ha, site index I.0, age 95: 343 (wrong) instead of 373 (right), correct value was taken from the version in Schober's (1975) yield table collection.

• Wimmenauer-Schwappach, 1919/29, Ash, Weak Thinning: Imported from the "Hilfstafeln" edited by the Bavarian Forest Administration. The table follows the standard structure of all tables that are part of the Bavarian "Hilfstafeln": Stem number, mean diameter, standing volume, and basal area describe the remaining stand. Wood volume is defined as standing coarse wood over bark. Variables whose names begin with "red_" relate to harvested volume under bark; i.e. reduced by 19 % which takes into account both, harvest losses (mainly stump) and bark volume.

Usage

fe_ytable_spruce_assmann_franz_mean_yield_level_1963

fe_ytable_poplar_blume_1949

fe_ytable_redoak_bauer_1955

fe_ytable_beech_gehrhardt_moderate_1908

fe_ytable_spruce_gehrhardt_moderate_1921

fe_ytable_pine_gehrhardt_moderate_1921

fe_ytable_spruce_guttenberg_1915

fe_ytable_silver_fir_hausser_moderate_1956

fe_ytable_oak_juettner_moderate_1955

fe_ytable_blackalder_mitscherlich_heavy_1945

fe_ytable_larch_schober_moderate_1946

fe_ytable_japanlarch_schober_moderate_1953

fe_ytable_douglas_schober_moderate_1956

fe_ytable_birch_schwappach_1903_29

fe_ytable_beech_wiedemann_moderate_1931

fe_ytable_spruce_wiedemann_moderate_1936_42

fe_ytable_pine_wiedemann_moderate_1943

ytable_pine_wiedemann_moderate_1943_raw

fe_ytable_ash_wimmenauer_1919_29

fe_ytable_spruce_vanselow_1951

Format

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

An object of class tbl_df (inherits from tbl, data.frame) with 237 rows and 16 columns.

An object of class fe_yield_table of length 7.

An object of class fe_yield_table of length 7.

References

BayMinELF (1990). Hilfstafeln für die Forsteinrichtung. Zusammengestellt für den Gebrauch in der Bayerischen Staatsforstverwaltung. Bayerisches Staatsministerium für Ernährung Landwirtschaft und Forsten.

BayMinELF (2018). Hilfstafeln für die Forsteinrichtung. Bayerisches Staatsministerium für Ernährung Landwirtschaft und Forsten.

Schober R (1975). Ertragstafeln wichtiger Baumarten bei verschiedener Durchforstung. J. D. Sauerländer's Verlag, Frankfurt a. M.

Yield Tables To Species Assignments

Description

In order to facilitate the application of yield tables, we provide data frames that link the names of implemented fe_yield_table objects to species codings. Currently, there are three such data frames: fe_species_tum_wwk_short, fe_species_bavrn_state_short, fe_species_bavrn_state Note, that different yield table assignemnts for the same coding can be defined and coexist. In future, such tables will be added also for less aggregated species codings.

Usage

ytables_bavrn_state_short_var_1

ytables_tum_wwk_short_var_1

ytables_bavrn_state_var_1

Format

An object of class tbl_df (inherits from tbl, data.frame) with 9 rows and 2 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 9 rows and 2 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 45 rows and 2 columns.

See Also

Other yield table functions: fe_yield_table(), plot.fe_yield_table(), si_to_mai_age(), si_to_mai_max(), site_index(), stocking_level(), ytable_age_slice(), ytable_lookup(), ytable_max_slice()

Take an Age Slice out of an fe_yield_table_object

Description

Age slices out of yield tables are typically required for finding out the site index for a given age-height pair, or for extracting a yield table value when age and site index are given.

Usage

ytable_age_slice(age, variable, ytable)

Arguments

Details

If the age provided by the user is not directly contained in the table, linear interpolation and also extrapolation is used for obtaining the age slice. Currently, this is only done inside the general age span covered by the table (slot $age_coverage of the yield table object). For ages outside this range, the slice is given for the nearest covered age extreme, and a warning is issued.

Value

A (named) vector representing the vertical slice of the desired yield table variable. The names are the site indexes as defined in the yield table's element $site_index (in the same order) with the prefix "si_".

See Also

Other yield table functions: fe_yield_table(), plot.fe_yield_table(), si_to_mai_age(), si_to_mai_max(), site_index(), stocking_level(), yield_tables_for_species, ytable_lookup(), ytable_max_slice()

Examples

  # Get the yield table heights of the Wiedemann 1943 Scots pine table at age
  # 73
  ytable_age_slice(
    fe_ytable_pine_wiedemann_moderate_1943,
    age = 73,
    variable = "h_q_m"
  )

Look Up Values From Yield Tables

Description

Provide yield table values for a given age and site index. If necessary, values are linearly inter- and extrapolated.

Usage

ytable_lookup(age, si, variable, ytable)

Arguments

Value

The requested yield table value

See Also

Other yield table functions: fe_yield_table(), plot.fe_yield_table(), si_to_mai_age(), si_to_mai_max(), site_index(), stocking_level(), yield_tables_for_species, ytable_age_slice(), ytable_max_slice()

Examples

  age <- 72
  si  <- 3.2

  ytable_lookup(
    age, si, "h_q_m", fe_ytable_spruce_gehrhardt_moderate_1921
  )

  ytable_lookup(
    age, si, "v_m3_ha", fe_ytable_spruce_gehrhardt_moderate_1921
  )

  ytable_lookup(
    age, si, "mai_m3_ha_yr", fe_ytable_spruce_gehrhardt_moderate_1921
  )

Take a Max Slice out of an fe_yield_table_object

Description

Max slices out of yield tables are typically required for finding out the mai max site index for a given height based standard site index.

Usage

ytable_max_slice(variable, ytable)

Arguments

Details

A max slice in the sense of this function means a vector that, for each of the yield table's standard site indexes, contains the table's max value of the variable of interest.

Value

A (named) vector representing the max slice of the desired yield table variable. The names are the site indexes as defined in the yield table's element $site_index (in the same order) with the prefix "si_".

See Also

Other yield table functions: fe_yield_table(), plot.fe_yield_table(), si_to_mai_age(), si_to_mai_max(), site_index(), stocking_level(), yield_tables_for_species, ytable_age_slice(), ytable_lookup()

Examples

  ytable_max_slice("mai_m3_ha_yr", fe_ytable_beech_wiedemann_moderate_1931)