Package 'ForestElementsR'

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
  )

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
  )

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")

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")

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")

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")

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")

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")

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)

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)

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

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
  )

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)

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

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.

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()

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)

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)
)

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)
)

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)
)

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)

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")

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")
)

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)
)

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))

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()

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()

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")
    )

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

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

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

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

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

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

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()

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

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
  )

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)

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

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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

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()

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()

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()

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))
)

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)
)

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))
)

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")
)

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))
)

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))
)

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()

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()

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)
  )

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)