Type: Package
Title: Machine Learning Forest Simulator
Version: 0.4.2
Author: Jernej Jevsenak
Maintainer: Jernej Jevsenak <jernej.jevsenak@gmail.com>
Description: Climate-sensitive forest simulator based on the principles of machine learning. It stimulates all key processes in the forest: radial growth, height growth, mortality, crown recession, regeneration and harvesting. The method for predicting tree heights was described by Skudnik and Jevšenak (2022) <doi:10.1016/j.foreco.2022.120017>, while the method for predicting basal area increments (BAI) was described by Jevšenak and Skudnik (2021) <doi:10.1016/j.foreco.2020.118601>.
License: GPL-3
Imports: brnn(≥ 0.6), ranger(≥ 0.13.1), reshape2 (≥ 1.4.4), pscl (≥ 1.5.5), naivebayes(≥ 0.9.7), magrittr(≥ 1.5), dplyr(≥ 0.7.0),tidyr (≥ 1.1.3), tidyselect(≥ 1.0.0)
Encoding: UTF-8
LazyData: true
Depends: R(≥ 3.4)
NeedsCompilation: no
Repository: CRAN
RoxygenNote: 7.1.2
Packaged: 2022-04-18 06:52:34 UTC; JernejJ
Date/Publication: 2022-04-20 08:22:37 UTC

BAI_prediction

Description

The Basal Area Increment BAI sub model that is run within the MLFS

Usage

BAI_prediction(
  df_fit,
  df_predict,
  species_n_threshold = 100,
  site_vars,
  include_climate,
  eval_model_BAI = TRUE,
  rf_mtry = NULL,
  k = 10,
  blocked_cv = TRUE,
  measurement_thresholds = NULL,
  area_correction = NULL
)

Arguments

df_fit

a data frame with Basal Area Increments (BAI) and all independent variables as specified with the formula

df_predict

data frame which will be used for BAI predictions

species_n_threshold

a positive integer defining the minimum number of observations required to treat a species as an independent group

site_vars

a character vector of variable names which are used as site descriptors

include_climate

logical, should climate variables be included as predictors

eval_model_BAI

logical, should the the BAI model be evaluated and returned as the output

rf_mtry

a number of variables randomly sampled as candidates at each split of a random forest model for predicting basal area increments (BAI). If NULL, default settings are applied.

k

the number of folds to be used in the k fold cross-validation

blocked_cv

logical, should the blocked cross-validation be used in the evaluation phase?

measurement_thresholds

data frame with two variables: 1) DBH_threshold and 2) weight. This information is used to assign the correct weights in BAI and increment sub-model; and to upscale plot-level data to hectares.

area_correction

an optional data frame with three variables: 1) plotID and 2) DBH_threshold and 3) the correction factor to be multiplied by weight for this particular category

Value

a list with four elements:

  1. $predicted_BAI - a data frame with calculated basal area increments (BAI)

  2. $eval_BAI - a data frame with predicted and observed basal area increments (BAI), or a character string indicating that BAI model was not evaluated

  3. $rf_model_species - the output model for BAI (species level)

  4. $rf_model_speciesGroups - the output model for BAI (species group level)

# add BA to measurement thresholds measurement_thresholds$BA_threshold <- ((measurement_thresholds$DBH_threshold/2)^2 * pi)/10000

BAI_outputs <- BAI_prediction(df_fit = data_BAI, df_predict = data_v6, site_vars = c("slope", "elevation", "northness", "siteIndex"), rf_mtry = 3, species_n_threshold = 100, include_climate = TRUE, eval_model_BAI = FALSE, k = 10, blocked_cv = TRUE, measurement_thresholds = measurement_thresholds)

# get the ranger objects BAI_outputs_model_species <- BAI_outputs$rf_model_species BAI_outputs_model_groups <- BAI_outputs$rf_model_speciesGroups

Examples

library(MLFS)
data(data_BAI)
data(data_v6)
data(measurement_thresholds)

BAI_prediction_halfPeriod

Description

BAI model for the MLFS to estimate BAI for half period

Usage

BAI_prediction_halfPeriod(
  df_fit,
  df_predict,
  species_n_threshold = 100,
  site_vars,
  include_climate,
  rf_mtry = NULL,
  measurement_thresholds = NULL,
  area_correction = NULL
)

Value

a data frame with calculated basal area increments in the middle of a simulation step

MLFS

Description

Machine Learning Forest Simulator

Usage

MLFS(
  data_NFI,
  data_site,
  data_tariffs = NULL,
  data_climate = NULL,
  df_volumeF_parameters = NULL,
  thinning_weights_species = NULL,
  final_cut_weights_species = NULL,
  thinning_weights_plot = NULL,
  final_cut_weights_plot = NULL,
  form_factors = NULL,
  form_factors_level = "species_plot",
  uniform_form_factor = 0.42,
  sim_steps,
  volume_calculation = "volume_functions",
  merchantable_whole_tree = "merchantable",
  sim_harvesting = TRUE,
  sim_mortality = TRUE,
  sim_ingrowth = TRUE,
  sim_crownHeight = TRUE,
  harvesting_sum = NULL,
  forest_area_ha = NULL,
  harvest_sum_level = NULL,
  plot_upscale_type = NULL,
  plot_upscale_factor = NULL,
  mortality_share = NA,
  mortality_share_type = "volume",
  mortality_model = "glm",
  ingrowth_model = "ZIF_poiss",
  BAI_rf_mtry = NULL,
  ingrowth_rf_mtry = NULL,
  mortality_rf_mtry = NULL,
  nb_laplace = 0,
  harvesting_type = "final_cut",
  share_thinning = 0.8,
  final_cut_weight = 10,
  thinning_small_weight = 1,
  species_n_threshold = 100,
  height_model = "brnn",
  crownHeight_model = "brnn",
  BRNN_neurons_crownHeight = 1,
  BRNN_neurons_height = 3,
  height_pred_level = 0,
  include_climate = FALSE,
  select_months_climate = c(1, 12),
  set_eval_mortality = TRUE,
  set_eval_crownHeight = TRUE,
  set_eval_height = TRUE,
  set_eval_ingrowth = TRUE,
  set_eval_BAI = TRUE,
  k = 10,
  blocked_cv = TRUE,
  max_size = NULL,
  max_size_increase_factor = 1,
  ingrowth_codes = c(3),
  ingrowth_max_DBH_percentile = 0.9,
  measurement_thresholds = NULL,
  area_correction = NULL,
  export_csv = FALSE,
  sim_export_mode = TRUE,
  include_mortality_BAI = TRUE,
  intermediate_print = FALSE
)

Arguments

data_NFI

data frame with individual tree variables

data_site

data frame with site descriptors. This data is related to data_NFI based on the 'plotID' column

data_tariffs

optional, but mandatory if volume is calculated using the one-parametric tariff functions. Data frame with plotID, species and V45. See details.

data_climate

data frame with climate data, covering the initial calibration period and all the years which will be included in the simulation

df_volumeF_parameters

optional, data frame with species-specific volume function parameters

thinning_weights_species

data frame with thinning weights for each species. The first column represents species code, each next column consists of species-specific thinning weights applied in each simulation step

final_cut_weights_species

data frame with final cut weights for each species. The first column represents species code, each next column consists of species-specific final cut weights applied in each simulation step

thinning_weights_plot

data frame with harvesting weights related to plot IDs, used for thinning

final_cut_weights_plot

data frame with harvesting weights related to plot IDs, used for final cut

form_factors

optional, data frame with species-specific form factors

form_factors_level

character, the level of specified form factors. It can be 'species', 'plot' or 'species_plot'

uniform_form_factor

numeric, uniform form factor to be used for all species and plots. Only if form_factors are not provided

sim_steps

The number of simulation steps

volume_calculation

character string defining the method for volume calculation: 'tariffs', 'volume_functions', 'form_factors' or 'slo_2p_volume_functions'

merchantable_whole_tree

character, 'merchantable' or 'whole_tree'. It indicates which type of volume functions will be used. This parameter is used only for volume calculation using the 'slo_2p_volume_functions'.

sim_harvesting

logical, should harvesting be simulated?

sim_mortality

logical, should mortality be simulated?

sim_ingrowth

logical, should ingrowth be simulated?

sim_crownHeight

logical, should crown heights be simulated? If TRUE, a crownHeight column is expected in data_NFI

harvesting_sum

a value, or a vector of values defining the harvesting sums through the simulation stage. If a single value, then it is used in all simulation steps. If a vector of values, the first value is used in the first step, the second in the second step, etc.

forest_area_ha

the total area of all forest which are subject of the simulation

harvest_sum_level

integer with value 0 or 1 defining the level of specified harvesting sum: 0 for plot level and 1 for regional level

plot_upscale_type

character defining the upscale method of plot level values. It can be 'area' or 'upscale factor'. If 'area', provide the forest area represented by all plots in hectares (forest_area_ha argument). If 'factor', provide the fixed factor to upscale the area of all plots. Please note: forest_area_ha/plot_upscale_factor = number of unique plots. This argument is important when harvesting sum is defined on regional level.

plot_upscale_factor

numeric value to be used to upscale area of each plot

mortality_share

a value, or a vector of values defining the proportion of the volume which is to be the subject of mortality. If a single value, then it is used in all simulation steps. If a vector of values, the first value is used in the first step, the second in the second step, and so on.

mortality_share_type

character, it can be 'volume' or 'n_trees'. If 'volume' then the mortality share relates to total standing volume, if 'n_trees' then mortality share relates to the total number of standing trees

mortality_model

model to be used for mortality prediction: 'glm' for generalized linear models; 'rf' for random forest algorithm; 'naiveBayes' for Naive Bayes algorithm

ingrowth_model

model to be used for ingrowth predictions. 'glm' for generalized linear models (Poisson regression), 'ZIF_poiss' for zero inflated Poisson regression and 'rf' for random forest

BAI_rf_mtry

a number of variables randomly sampled as candidates at each split of a random forest model for predicting basal area increments (BAI). If NULL, default settings are applied.

ingrowth_rf_mtry

a number of variables randomly sampled as candidates at each split of a random forest model for predicting ingrowth. If NULL, default settings are applied

mortality_rf_mtry

a number of variables randomly sampled as candidates at each split of a random forest model for predicting mortality. If NULL, default settings are applied

nb_laplace

value used for Laplace smoothing (additive smoothing) in naive Bayes algorithm. Defaults to 0 (no Laplace smoothing)

harvesting_type

character, it could be 'random', 'final_cut', 'thinning' or 'combined'. The latter combines 'final_cut' and 'thinning' options, where the share of each is specified with the argument 'share_thinning'

share_thinning

numeric, a number or a vector of numbers between 0 and 1 that specifies the share of thinning in comparison to final_cut. Only used if harvesting_type is 'combined'

final_cut_weight

numeric value affecting the probability distribution of harvested trees. Greater value increases the share of harvested trees having larger DBH. Default is 10.

thinning_small_weight

numeric value affecting the probability distribution of harvested trees. Greater value increases the share of harvested trees having smaller DBH. Default is 1.

species_n_threshold

a positive integer defining the minimum number of observations required to treat a species as an independent group

height_model

character string defining the model to be used for height prediction. If brnn, then ANN method with Bayesian Regularization is applied.

crownHeight_model

character string defining the model to be used for crown heights. Available are ANN with Bayesian regularization (brnn) or linear regression (lm)

BRNN_neurons_crownHeight

a positive integer defining the number of neurons to be used in the brnn method for predicting crown heights

BRNN_neurons_height

a positive integer defining the number of neurons to be used in the brnn method for predicting tree heights

height_pred_level

integer with value 0 or 1 defining the level of prediction for height-diameter (H-D) models. The value 1 defines a plot-level prediction, while the value 0 defines regional-level predictions. Default is 0. If using 1, make sure to have representative plot-level data for each species.

include_climate

logical, should climate variables be included as predictors

select_months_climate

vector of subset months to be considered. Default is c(1,12), which uses all months.

set_eval_mortality

logical, should the mortality model be evaluated and returned as the output

set_eval_crownHeight

logical, should the crownHeight model be evaluated and returned as the output

set_eval_height

logical, should the height model be evaluated and returned as the output

set_eval_ingrowth

logical, should the the ingrowth model be evaluated and returned as the output

set_eval_BAI

logical, should the the BAI model be evaluated and returned as the output

k

the number of folds to be used in the k fold cross-validation

blocked_cv

logical, should the blocked cross-validation be used in the evaluation phase?

max_size

a data frame with the maximum values of DBH for each species. If a tree exceeds this value, it dies. If not provided, the maximum is estimated from the input data. Two columns must be present, i.e. 'species' and 'DBH_max'

max_size_increase_factor

numeric value, which will be used to increase the max DBH for each species, when the maximum is estimated from the input data. If the argument 'max_size' is provided, the 'max_size_increase_factor' is ignored. Default is 1. To increase maximum for 10 percent, use 1.1.

ingrowth_codes

numeric value or a vector of codes which refer to ingrowth trees

ingrowth_max_DBH_percentile

which percentile should be used to estimate the maximum simulated value of ingrowth trees?

measurement_thresholds

data frame with two variables: 1) DBH_threshold and 2) weight. This information is used to assign the correct weights in BAI and increment sub-model; and to upscale plot-level data to hectares.

area_correction

optional data frame with three variables: 1) plotID and 2) DBH_threshold and 3) the correction factor to be multiplied by weight for this particular category.

export_csv

logical, if TRUE, at each simulation step, the results are saved in the current working directory as csv file

sim_export_mode

logical, if FALSE, the results of the individual simulation steps are not merged into the final export table. Therefore, output element 1 ($sim_results) will be empty. This was introduced to allow simulations when using larger data sets and long term simulations that might exceed the available RAM. In such cases, we recommend setting the argument export_csv = TRUE, which will export each simulation step to the current working directory.

include_mortality_BAI

logical, should basal area increments (BAI) be used as independent variable for predicting individual tree morality?

intermediate_print

logical, if TRUE intermediate steps will be printed while MLFS is running

Value

a list of class mlfs with at least 15 elements:

  1. $sim_results - a data frame with the simulation results

  2. $height_eval - a data frame with predicted and observed tree heights, or a character string indicating that tree heights were not evaluated

  3. $crownHeight_eval - a data frame with predicted and observed crown heights, or character string indicating that crown heights were not evaluated

  4. $mortality_eval - a data frame with predicted and observed probabilities of dying for all individual trees, or character string indicating that mortality sub-model was not evaluated

  5. $ingrowth_eval - a data frame with predicted and observed number of new ingrowth trees, separately for each ingrowth level, or character string indicating that ingrowth model was not evaluated

  6. $BAI_eval - a data frame with predicted and observed basal area increments (BAI), or character string indicating that BAI model was not evaluated

  7. $height_model_species - the output model for tree heights (species level)

  8. $height_model_speciesGroups - the output model for tree heights (species group level)

  9. $crownHeight_model_species - the output model for crown heights (species level)

  10. $crownHeight_model_speciesGroups - the output model for crown heights (species group level)

  11. $mortality_model - the output model for mortality

  12. $BAI_model_species - the output model for basal area increments (species level)

  13. $BAI_model_speciesGroups - the output model for basal area increments (species group level)

  14. $max_size - a data frame with maximum allowed diameter at breast height (DBH) for each species

  15. $ingrowth_model_3 - the output model for ingrowth (level 1) – the output name depends on ingrowth codes

  16. $ingrowth_model_15 - the output model for ingrowth (level 2) – optional and the output name depends on ingrowth codes

Examples


library(MLFS)

# open example data
data(data_NFI)
data(data_site)
data(data_climate)
data(df_volume_parameters)
data(measurement_thresholds)

test_simulation <- MLFS(data_NFI = data_NFI,
 data_site = data_site,
 data_climate = data_climate,
 df_volumeF_parameters = df_volume_parameters,
 form_factors = volume_functions,
 sim_steps = 2,
 sim_harvesting = TRUE,
 harvesting_sum = 100000,
 harvest_sum_level = 1,
 plot_upscale_type = "factor",
 plot_upscale_factor = 1600,
 measurement_thresholds = measurement_thresholds,
 ingrowth_codes = c(3,15),
 volume_calculation = "volume_functions",
 select_months_climate = seq(6,8),
 intermediate_print = FALSE
 )

add_stand_variables

Description

This function adds two variables to existing data frame of individual tree measurements: 1) stand basal area and 2) the number of trees per hectare

Usage

add_stand_variables(df)

Arguments

df

a data frame with individual tree measurements that include basal area and the upscale factors. All trees should also be described with plotID and year variables

Value

a data frame with added stand variables: total stand basal area and the number of trees per hectare

Examples

data(data_v1)
data_v1 <- add_stand_variables(df = data_v1)

add_stand_variables_halfPeriod

Description

Function for the calculation of stand variables: stand basal area and the number of trees

Usage

add_stand_variables_halfPeriod(df)

Value

a data frame with added stand variables: total stand basal area and the number of trees per hectare in the middle of a simulation step

calculate_BAL

Description

This function calculates the competition index BAL (Basal Area in Large trees) and adds it to the table of individual tree measurements that include basal area and the upscale factors. All trees should also be described with plotID and year variables

Usage

calculate_BAL(df)

Arguments

df

a data frame with individual tree measurements that include basal area and the upscale factors. All trees should also be described with plotID and year variables

Value

a data frame with calculated basal area in large trees (BAL)

Examples

data(data_v1)
data_v1 <- calculate_BAL(df = data_v1)

calculate_BAL_halfPeriod

Description

Function for the calculation of competition index BAL (Basal area in larger trees)

Usage

calculate_BAL_halfPeriod(df)

Value

a data frame with calculated basal area in large trees (BAL) in the middle of a simulation step

crownHeight_prediction

Description

Model for predicting crown height

Usage

crownHeight_prediction(
  df_fit,
  df_predict,
  site_vars = site_vars,
  species_n_threshold = 100,
  k = 10,
  eval_model_crownHeight = TRUE,
  crownHeight_model = "lm",
  BRNN_neurons = 3,
  blocked_cv = TRUE
)

Arguments

df_fit

data frame with tree heights and basal areas for individual trees

df_predict

data frame which will be used for predictions

site_vars

optional, character vector with names of site variables

species_n_threshold

a positive integer defining the minimum number of observations required to treat a species as an independent group

k

the number of folds to be used in the k fold cross-validation

eval_model_crownHeight

logical, should the crown height model be evaluated and returned as the output

crownHeight_model

character string defining the model to be used for crown heights. Available are ANN with Bayesian regularization (brnn) or linear regression (lm)

BRNN_neurons

positive integer defining the number of neurons to be used in the brnn method.

blocked_cv

logical, should the blocked cross-validation be used in the evaluation phase?

Value

a list with four elements:

  1. $predicted_crownHeight - a data frame with imputed crown heights

  2. $eval_crownHeight - a data frame with predicted and observed crown heights, or a character string indicating that crown height model was not evaluated

  3. $model_species - the output model for crown heights (species level)

  4. $model_speciesGroups - the output model for crown heights (species group level)

Examples

library(MLFS)
data(data_tree_heights)
data(data_v3)

# A) Example with linear model
Crown_h_predictions <- crownHeight_prediction(df_fit = data_tree_heights,
    df_predict = data_v3,
    crownHeight_model = "lm",
    site_vars = c(),
    species_n_threshold = 100,
    k = 10, blocked_cv = TRUE,
    eval_model_crownHeight = TRUE)

predicted_df <- Crown_h_predictions$predicted_crownHeight # df with imputed heights
evaluation_df <- Crown_h_predictions$eval_crownHeight # df with evaluation results

# B) Example with non-linear BRNN model
Crown_h_predictions <- crownHeight_prediction(df_fit = data_tree_heights,
    df_predict = data_v3,
    crownHeight_model = "brnn",
    BRNN_neurons = 3,
    site_vars = c(),
    species_n_threshold = 100,
    k = 10, blocked_cv = TRUE,
    eval_model_crownHeight = TRUE)

crownHeight_prediction_halfPeriod

Description

Models to predict crown height (half period)

Usage

crownHeight_prediction_halfPeriod(
  df_fit,
  df_predict,
  site_vars = site_vars,
  species_n_threshold = 100,
  crownHeight_model = "lm",
  BRNN_neurons = 3
)

Value

a data frame with imputed crown heights in the middle of a simulation step

An example of joined national forest inventory data, site descriptors, and climate data that is used as a fitting data frame for BAI sub model

Description

This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for BAI sub model. To make examples running more quickly, we keep only one tree species: PINI.

Usage

data_BAI

Format

A data frame with 135 rows and 25 variables:

plotID

a unique identifier for plot

treeID

a unique identifier for tree

year

year in which plot was visited

speciesGroup

identifier for species group

code

status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)

species

species name

height

tree height in meters

crownHeight

crown height in meters

protected

logical, 1 if protected, otherwise 0

slope

slope on a plot

elevation

plot elevation

northness

plot northness, 1 is north, 0 is south

siteIndex

a proxy for site index, higher value represents more productive sites

BA

basal area of individual trees in m2

weight

upscale weight to calculate hectare values

stand_BA

Total stand basal area

stand_n

The number of trees in a stand

BAL

Basal Area in Large trees

p_BA

basal area of individual trees in m2 from previous simulation step

p_height

tree height in meters from previous simulation step

p_crownHeight

crown height in meters from previous simulation step

p_weight

upscale weight to calculate hectare values from previous simulation step

BAI

basal area increment

p_sum

monthly precipitation sum

t_avg

monthly mean temperature

An example of national forest inventory data

Description

This is simulated data that reassemble the national forest inventory

Usage

data_NFI

Format

A data frame with 11984 rows and 10 variables:

plotID

a unique identifier for plot

treeID

a unique identifier for tree

year

year in which plot was visited

speciesGroup

identifier for species group

code

status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)

DBH

diameter at breast height in cm

species

species name

height

tree height in meters

crownHeight

crown height in meters

protected

logical, 1 if protected, otherwise 0

An example of climate data

Description

This is simulated monthly climate data, and consists of precipitation sum and mean temperature

Usage

data_climate

Format

A data frame with 16695 rows and 5 variables:

plotID

a unique identifier for plot

year

year

month

month

t_avg

monthly mean temperature

p_sum

monthly precipitation sum

An example of data_final_cut_weights

Description

Each species should have one weight that is multiplied with the probability of being harvested when final_cut is applied

Usage

data_final_cut_weights

Format

A data frame with 36 rows and 6 variables:

species

species name as used in data_NFI

step_1

final cut weight applied in step 1

step_2

final cut weight applied in step 2

step_3

final cut weight applied in step 3

step_4

final cut weight applied in step 4

step_5

final cut weight applied in step 5 and all subsequent steps

An example of data_ingrowth suitable for the MLFS

Description

An example of plot-level data with plotID, stand variables and site descriptors, and the two target variables describing the number of ingrowth trees for inner (ingrowth_3) and outer (ingrowth_15) circles

Usage

data_ingrowth

Format

A data frame with 365 rows and 11 variables:

plotID

a unique identifier for plot

year

year in which plot was visited

stand_BA

Total stand basal area

stand_n

The number of trees in a stand

BAL

Basal Area in Large trees

slope

slope on a plot

elevation

plot elevation

siteIndex

a proxy for site index, higher value represents more productive sites

northness

plot northness, 1 is north, 0 is south

ingrowth_3

the number of new trees in inner circle

ingrowth_15

the number of new trees in outer circle

An example of joined national forest inventory data, site descriptors, and climate data that is used as a fitting data frame for mortality sub model

Description

This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for mortality sub model

Usage

data_mortality

Format

A data frame with 6394 rows and 25 variables:

plotID

a unique identifier for plot

treeID

a unique identifier for tree

year

year in which plot was visited

speciesGroup

identifier for species group

code

status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)

species

species name

height

tree height in meters

crownHeight

crown height in meters

protected

logical, 1 if protected, otherwise 0

slope

slope on a plot

elevation

plot elevation

northness

plot northness, 1 is north, 0 is south

siteIndex

a proxy for site index, higher value represents more productive sites

BA

basal area of individual trees in m2

weight

upscale weight to calculate hectare values

stand_BA

Total stand basal area

stand_n

The number of trees in a stand

BAL

Basal Area in Large trees

p_BA

basal area of individual trees in m2 from previous simulation step

p_height

tree height in meters from previous simulation step

p_crownHeight

crown height in meters from previous simulation step

p_weight

upscale weight to calculate hectare values from previous simulation step

BAI

basal area increment

p_sum

monthly precipitation sum

t_avg

monthly mean temperature

An example of site descriptors

Description

This is simulated data describing site descriptors

Usage

data_site

Format

A data frame with 371 rows and 5 variables:

plotID

a unique identifier for plot

slope

slope on a plot

elevation

plot elevation

northness

plot northness, 1 is north, 0 is south

siteIndex

a proxy for site index, higher value represents more productive sites

An example of table with one-parametric volume functions (adapted uniform French tariffs)

Description

The adapted uniform French tariffs are typically used in Slovenia to determine tree volume based on tree DBH

Usage

data_tariffs

Format

A data frame with 1196 rows and 4 variables:

tarifa_class

tariff class for a particular species on this plot

plotID

plot identifier

species

species name as used in data_NFI

v45

volume of tree with DBH 45 cm

An example of data_thinning_weights

Description

Each species should have one weight that is multiplied with the probability of being harvested when thinning is applied

Usage

data_thinning_weights

Format

A data frame with 36 rows and 6 variables:

species

species name as used in data_NFI

step_1

thinning weight applied in step 1

step_2

thinning weight applied in step 2

step_3

thinning weight applied in step 3

step_4

thinning weight applied in step 4

step_5

thinning weight applied in step 5 and all subsequent steps

An example of data with individual tree and crown heights that can be used as a fitting data frame for predicting tree and crown heights in MLFS

Description

This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for some specific functions

Usage

data_tree_heights

Format

A data frame with 2741 rows and 8 variables:

plotID

a unique identifier for plot

treeID

a unique identifier for tree

year

year in which plot was visited

speciesGroup

identifier for species group

species

species name

height

tree height in meters

crownHeight

crown height in meters

BA

basal area of individual trees in m2

An example of joined national forest inventory and site data that is used within the MLFS

Description

This is simulated data that reassemble the national forest inventory and simulated data. We use it to show how to run examples for some specific functions

Usage

data_v1

Format

A data frame with 11984 rows and 15 variables:

plotID

a unique identifier for plot

treeID

a unique identifier for tree

year

year in which plot was visited

speciesGroup

identifier for species group

code

status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)

species

species name

height

tree height in meters

crownHeight

crown height in meters

protected

logical, 1 if protected, otherwise 0

slope

slope on a plot

elevation

plot elevation

northness

plot northness, 1 is north, 0 is south

siteIndex

a proxy for site index, higher value represents more productive sites

BA

basal area of individual trees in m2

weight

upscale weight to calculate hectare values

An example of joined national forest inventory and site data that is used within the MLFS

Description

This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for tree and crown height predictions

Usage

data_v2

Format

A data frame with 6948 rows and 14 variables:

plotID

a unique identifier for plot

treeID

a unique identifier for tree

year

year in which plot was visited

speciesGroup

identifier for species group

code

status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)

species

species name

height

tree height in meters

crownHeight

crown height in meters

BA

basal area of individual trees in m2

weight

upscale weight to calculate hectare values

p_BA

basal area of individual trees in m2 from previous simulation step

p_weight

upscale weight to calculate hectare values from previous simulation step

p_height

tree height in meters from previous simulation step

p_crownHeight

crown height in meters from previous simulation step

An example of joined national forest inventory and site data that is used within the MLFS

Description

This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for tree and crown height predictions. The difference between data_v2 and data_v3 is that in data_v3, tree heights are already predicted

Usage

data_v3

Format

A data frame with 6948 rows and 14 variables:

plotID

a unique identifier for plot

treeID

a unique identifier for tree

year

year in which plot was visited

speciesGroup

identifier for species group

code

status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)

species

species name

height

tree height in meters

crownHeight

crown height in meters

BA

basal area of individual trees in m2

weight

upscale weight to calculate hectare values

p_BA

basal area of individual trees in m2 from previous simulation step

p_height

tree height in meters from previous simulation step

p_crownHeight

crown height in meters from previous simulation step

p_weight

upscale weight to calculate hectare values from previous simulation step

volume

tree volume in m3

p_volume

tree volume in m3 from previous simulation step

An example of joined national forest inventory and site data that is used within the MLFS

Description

This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for predicting tree mortality. Mortality occurs in the middle of a simulation step, so all variables have the preposition 'mid'

Usage

data_v4

Format

A data frame with 6855 rows and 41 variables:

year

year in which plot was visited

plotID

a unique identifier for plot

treeID

a unique identifier for tree

speciesGroup

identifier for species group

code

status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)

species

species name

slope

slope on a plot

elevation

plot elevation

northness

plot northness, 1 is north, 0 is south

siteIndex

a proxy for site index, higher value represents more productive sites

p_sum

monthly precipitation sum

t_avg

monthly mean temperature

BA_mid

basal area of individual trees in m2 in the middle of a simulation step

BAI_mid

basal area increment in the middle of a simulation step

weight_mid

upscale weight to calculate hectare values in the middle of a simulation step

height_mid

tree height in meters in the middle of a simulation step

crownHeight_mid

crown height in meters in the middle of a simulation step

volume_mid

tree volume in m3 in the middle of a simulation step

BAL_mid

Basal Area in Large trees the middle of a simulation step

stand_BA_mid

Total stand basal area the middle of a simulation step

stand_n_mid

The number of trees in a stand the middle of a simulation step

An example of joined national forest inventory and site data that is used within the MLFS

Description

This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for simulating harvesting.

Usage

data_v5

Format

A data frame with 5949 rows and 10 variables:

species

species name

year

year in which plot was visited

plotID

a unique identifier for plot

treeID

a unique identifier for tree

speciesGroup

identifier for species group

code

status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)

volume_mid

tree volume in m3 in the middle of a simulation step

weight_mid

upscale weight to calculate hectare values in the middle of a simulation step

BA_mid

basal area of individual trees in m2 in the middle of a simulation step

protected

logical, 1 if protected, otherwise 0

An example of joined national forest inventory and site data that is used within the MLFS

Description

This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for simulating Basal Area Increments (BAI) and the ingrowth of new trees. To make examples running more quickly, we keep only one tree species: PINI

Usage

data_v6

Format

A data frame with 186 rows and 27 variables:

species

species name

year

year in which plot was visited

plotID

a unique identifier for plot

treeID

a unique identifier for tree

speciesGroup

identifier for species group

code

status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)

height

tree height in meters

crownHeight

crown height in meters

protected

logical, 1 if protected, otherwise 0

slope

slope on a plot

elevation

plot elevation

northness

plot northness, 1 is north, 0 is south

siteIndex

a proxy for site index, higher value represents more productive sites

BA

basal area of individual trees in m2

weight

upscale weight to calculate hectare values

stand_BA

Total stand basal area

stand_n

The number of trees in a stand

BAL

Basal Area in Large trees

p_BA

basal area of individual trees in m2 from previous simulation step

p_height

tree height in meters from previous simulation step

p_crownHeight

crown height in meters from previous simulation step

p_weight

upscale weight to calculate hectare values from previous simulation step

BAI

basal area increment

p_sum

monthly precipitation sum

t_avg

monthly mean temperature

volume

tree volume in m3

p_volume

tree volume in m3 from previous simulation step

An example table with parameters and equations for n-parametric volume functions

Description

Volume functions can be specified for each species and plot separately, also limited to specific DBH interval. The factor variables (vol_factor, h_factor and DBH_factor) are used to control the input and output units.

Usage

df_volume_parameters

Format

A data frame with 6 rows and 14 variables:

species

species name as used in data_NFI. The category REST is used for all species without specific equation

equation

equation for selected volume function

vol_factor

will be multiplied with the volume

h_factor

will be multiplied with tree height

d_factor

will be divided with tree DBH

DBH_min

lower interval threshold for considered trees

DBH_max

upper interval threshold for considered trees

a

parameter a for volume equation

b

parameter b for volume equation

c

parameter c for volume equation

d

parameter d for volume equation

e

parameter e for volume equation

f

parameter f for volume equation

g

parameter g for volume equation

An example table with form factors used to calculate tree volume

Description

Form factors can be specified per species, plot or per species and plot

Usage

form_factors

Format

A data frame with 1199 rows and 3 variables:

plotID

a unique identifier for plot

species

species name as used in data_NFI

form

for factor used to calculate tree volume

height_prediction

Description

Height model

Usage

height_prediction(
  df_fit,
  df_predict,
  species_n_threshold = 100,
  height_model = "naslund",
  BRNN_neurons = 3,
  height_pred_level = 0,
  eval_model_height = TRUE,
  blocked_cv = TRUE,
  k = 10
)

Arguments

df_fit

data frame with tree heights and basal areas for individual trees

df_predict

data frame which will be used for predictions

species_n_threshold

a positive integer defining the minimum number of observations required to treat a species as an independent group

height_model

character string defining the model to be used for height prediction. If 'brnn', then ANN method with Bayesian Regularization is applied. In addition, all 2- and 3- parametric H-D models from lmfor R package are available.

BRNN_neurons

positive integer defining the number of neurons to be used in the brnn method.

height_pred_level

integer with value 0 or 1 defining the level of prediction for height-diameter (H-D) models. The value 1 defines a plot-level prediction, while the value 0 defines regional-level predictions. Default is 0. If using 1, make sure to have representative plot-level data for each species.

eval_model_height

logical, should the height model be evaluated and returned as the output

blocked_cv

logical, should the blocked cross-validation be used in the evaluation phase?

k

the number of folds to be used in the k fold cross-validation

Value

a list with four elements:

  1. $data_height_predictions - a data frame with imputed tree heights

  2. $data_height_eval - a data frame with predicted and observed tree heights, or a character string indicating that tree heights were not evaluated

  3. $model_species - the output model for tree heights (species level)

  4. $model_speciesGroups - the output model for tree heights (species group level)

Examples

library(MLFS)
data(data_tree_heights)
data(data_v2)

# A) Example with the BRNN method
h_predictions <- height_prediction(df_fit = data_tree_heights,
                                   df_predict = data_v2,
                                   species_n_threshold = 100,
                                   height_pred_level = 0,
                                   height_model = "brnn",
                                   BRNN_neurons = 3,
                                   eval_model_height = FALSE,
                                   blocked_cv = TRUE, k = 10
                                   )

predicted_df <- h_predictions$data_height_predictions # df with imputed heights
evaluation_df <- h_predictions$data_height_eval # df with evaluation results

height_prediction_halfPeriod

Description

Height model

Usage

height_prediction_halfPeriod(
  df_fit,
  df_predict,
  species_n_threshold = 100,
  height_model = "naslund",
  BRNN_neurons = 3,
  height_pred_level = 0
)

Arguments

df_fit

data frame with data being used for model fit

Value

a data frame with imputed tree heights in the middle of a simulation step

An example data of ingrowth_parameter_list

Description

This is a list with two ingrowth levels: 3 (inner circle) and 15 (outer circle). In each list there are deciles of DBH distributions that are used to simulate DBH for new trees, separately for each ingrowth category

Usage

ingrowth_parameter_list

Format

A list with 2 elements:

3

deciles of DBH distribution for ingrowth category 3

15

deciles of DBH distribution for ingrowth category 15

An example data of ingrowth_table

Description

Ingrowth table is used within the ingrowth sub model to correctly simulate different ingrowth levels and associated upscale weights

Usage

ingrowth_table

Format

A data frame with 2 rows and 4 variables:

code

ingrowth codes

DBH_threshold

a DBH threshold for particular ingrowth category

DBH_max

maximum DBH for a particular ingrowth category

weight

the upscale weight for particular measurement category

An example of data with maximum allowed BA that is used in the mortality sub model

Description

This is simulated max_size_data and used for examples in mortality sub model

Usage

max_size_data

Format

A data frame with 36 rows and 2 variables:

species

species name

BA_max

The maximum allowed basal area (BA) for each individual species

An example of measurement_thresholds table

Description

An example of measurement_thresholds table resulting from concentric plots as used in Slovenian NFI

Usage

measurement_thresholds

Format

A data frame with 2 rows and 2 variables:

DBH_threshold

a DBH threshold for particular measurement category

weight

the upscale weight for particular measurement category

predict_ingrowth

Description

ingrowth model for predicting new trees within the MLFS

Usage

predict_ingrowth(
  df_fit,
  df_predict,
  site_vars = site_vars,
  include_climate = include_climate,
  eval_model_ingrowth = TRUE,
  k = 10,
  blocked_cv = TRUE,
  ingrowth_model = "glm",
  rf_mtry = NULL,
  ingrowth_table = NULL,
  DBH_distribution_parameters = NULL
)

Arguments

df_fit

a plot-level data with plotID, stand variables and site descriptors, and the two target variables describing the number of ingrowth trees for inner (ingrowth_3) and outer (ingrowth_15) circles

df_predict

data frame which will be used for ingrowth predictions

site_vars

a character vector of variable names which are used as site descriptors

include_climate

logical, should climate variables be included as predictors

eval_model_ingrowth

logical, should the the ingrowth model be evaluated and returned as the output

k

the number of folds to be used in the k fold cross-validation

blocked_cv

logical, should the blocked cross-validation be used in the evaluation phase?

ingrowth_model

model to be used for ingrowth predictions. 'glm' for generalized linear models (Poisson regression), 'ZIF_poiss' for zero inflated Poisson regression and 'rf' for random forest

rf_mtry

a number of variables randomly sampled as candidates at each split of a random forest model for predicting ingrowth. If NULL, default settings are applied.

ingrowth_table

a data frame with 4 variables: (ingrowth) code, DBH_threshold, DBH_max and weight. Ingrowth table is used within the ingrowth sub model to correctly simulate different ingrowth levels and associated upscale weights

DBH_distribution_parameters

A list with deciles of DBH distributions that are used to simulate DBH for new trees, separately for each ingrowth category

Value

a list with four elements:

  1. $predicted_ingrowth - a data frame with newly added trees based on the ingrowth predictions

  2. $eval_ingrowth - a data frame with predicted and observed number of new trees, separately for each ingrowth level, or character string indicating that ingrowth model was not evaluated

  3. $mod_ing_3 - the output model for predicting the ingrowth of trees with code 3

  4. $mod_ing_15 - the output model for predicting the ingrowth of trees with code 15 (the output name depends on the code used for this particular ingrowth level)

Examples


library(MLFS)

data(data_v6)
data(data_ingrowth)
data(ingrowth_table)
data(ingrowth_parameter_list)

ingrowth_outputs <- predict_ingrowth(
   df_fit = data_ingrowth,
   df_predict = data_v6,
   site_vars = c("slope", "elevation", "northness", "siteIndex"),
   include_climate = TRUE,
   eval_model_ingrowth = FALSE,
   rf_mtry = 3,
   k = 10, blocked_cv = TRUE,
   ingrowth_model = 'rf',
   ingrowth_table = ingrowth_table,
   DBH_distribution_parameters = ingrowth_parameter_list)

predict_mortality

Description

This sub model first fits a binary model to derive the effects of individual tree, site and climate variables on mortality; and afterwards predict the probability of dying for each tree from df_predict

Usage

predict_mortality(
  df_fit,
  df_predict,
  df_climate,
  mortality_share = NA,
  mortality_share_type = "volume",
  include_climate,
  site_vars,
  select_months_climate = c(6, 8),
  mortality_model = "rf",
  nb_laplace = 0,
  sim_crownHeight = FALSE,
  k = 10,
  eval_model_mortality = TRUE,
  blocked_cv = TRUE,
  sim_mortality = TRUE,
  sim_step_years = 5,
  rf_mtry = NULL,
  df_max_size = NULL,
  ingrowth_codes = 3,
  include_mortality_BAI = TRUE,
  intermediate_print = FALSE
)

Arguments

df_fit

a data frame with individual tree data and site descriptors where code is used to specify a status of each tree

df_predict

data frame which will be used for mortality predictions

df_climate

data frame with monthly climate data

mortality_share

a value defining the proportion of the volume which is to be the subject of mortality

mortality_share_type

character, it can be 'volume' or 'n_trees'. If 'volume' then the mortality share relates to total standing volume, if 'n_trees' then mortality share relates to the total number of standing trees

include_climate

logical, should climate variables be included as predictors

site_vars

a character vector of variable names which are used as site descriptors

select_months_climate

vector of subset months to be considered. Default is c(1,12), which uses all months.

mortality_model

logical, should the mortality model be evaluated and returned as the output

nb_laplace

value used for Laplace smoothing (additive smoothing) in naive Bayes algorithm. Defaults to 0 (no Laplace smoothing).

sim_crownHeight

logical, should crown heights be considered as a predictor variable? If TRUE, a crownHeight column is expected in data_NFI

k

the number of folds to be used in the k fold cross-validation

eval_model_mortality

logical, should the mortality model be evaluated and returned as the output

blocked_cv

logical, should the blocked cross-validation be used in the evaluation phase?

sim_mortality

logical, should mortality be simulated?

sim_step_years

the simulation step in years

rf_mtry

number of variables randomly sampled as candidates at each split of a random forest model. If NULL, default settings are applied.

df_max_size

a data frame with the maximum BA values for each species. If a tree exceeds this value, it dies.

ingrowth_codes

numeric value or a vector of codes which refer to ingrowth trees

include_mortality_BAI

logical, should basal area increments (BAI) be used as independent variable for predicting individual tree morality?

intermediate_print

logical, if TRUE intermediate steps will be printed while the mortality sub model is running

Value

a list with three elements:

  1. $predicted_mortality - a data frame with updated tree status (code) based on the predicted mortality

  2. $eval_mortality - a data frame with predicted and observed probabilities of dying for all individual trees, or character string indicating that mortality sub-model was not evaluated

  3. $model_output - the output model for mortality

Examples

data("data_v4")
data("data_mortality")
data("max_size_data")

mortality_outputs <- predict_mortality(
 df_fit = data_mortality,
 df_predict = data_v4,
 mortality_share_type = 'volume',
 df_climate = data_climate,
 site_vars = c("slope", "elevation", "northness", "siteIndex"),
 sim_mortality = TRUE,
 mortality_model = 'naiveBayes',
 nb_laplace = 0,
 sim_crownHeight = TRUE,
 mortality_share = 0.02,
 include_climate = TRUE,
 select_months_climate = c(6,7,8),
 eval_model_mortality = TRUE,
 k = 10, blocked_cv = TRUE,
 sim_step_years = 6,
 df_max_size = max_size_data,
 ingrowth_codes = c(3,15),
 include_mortality_BAI = TRUE)

 df_predicted <- mortality_outputs$predicted_mortality
 df_evaluation <- mortality_outputs$eval_mortality

 # confusion matrix
 table(df_evaluation$mortality, round(df_evaluation$mortality_pred, 0))

A sub model to simulate harvesting within the MLFS

Description

Harvesting is based on probability sampling, which depends on the selected parameters and the seize of a tree. Bigger trees have higher probability of being harvested when final cut is applied, while smaller trees have higher probability of being sampled in the case of thinning.

Usage

simulate_harvesting(
  df,
  harvesting_sum,
  df_thinning_weights_species = NULL,
  df_final_cut_weights_species = NULL,
  df_thinning_weights_plot = NULL,
  df_final_cut_weights_plot = NULL,
  harvesting_type = "random",
  share_thinning = 0.8,
  final_cut_weight = 1e+07,
  thinning_small_weight = 1e+05,
  harvest_sum_level = 1,
  plot_upscale_type,
  plot_upscale_factor,
  forest_area_ha
)

Arguments

df

a data frame with individual tree data, which include basal areas in the middle of a simulation step, species name and code

harvesting_sum

a value, or a vector of values defining the harvesting sums through the simulation stage. If a single value, then it is used in all simulation steps. If a vector of values, the first value is used in the first step, the second in the second step, etc.

df_thinning_weights_species

data frame with thinning weights for each species. The first column represents species code, each next column consists of species-specific thinning weights

df_final_cut_weights_species

data frame with final cut weights for each species. The first column represents species code, each next column consists of species-specific final cut weights

df_thinning_weights_plot

data frame with harvesting weights related to plot IDs, used for thinning

df_final_cut_weights_plot

data frame with harvesting weights related to plot IDs, used for final cut

harvesting_type

character, it could be 'random', 'final_cut', 'thinning' or 'combined'. The latter combines 'final_cut' and 'thinning' options, where the share of each is specified with the argument 'share_thinning'

share_thinning

numeric, a number between 0 and 1 that specifies the share of thinning in comparison to final_cut. Only used if harvesting_type is 'combined'

final_cut_weight

numeric value affecting the probability distribution of harvested trees. Greater value increases the share of harvested trees having larger DBH. Default is 10.

thinning_small_weight

numeric value affecting the probability distribution of harvested trees. Greater value increases the share of harvested trees having smaller DBH. Default is 1.

harvest_sum_level

integer with value 0 or 1 defining the level of specified harvesting sum: 0 for plot level and 1 for regional level

plot_upscale_type

character defining the upscale method of plot level values. It can be 'area' or 'upscale factor'. If 'area', provide the forest area represented by all plots in hectares (forest_area_ha argument). If 'factor', provide the fixed factor to upscale the area of all plots. Please note: forest_area_ha/plot_upscale_factor = number of unique plots. This argument is important when harvesting sum is defined on regional level.

plot_upscale_factor

numeric value to be used to upscale area of each plot

forest_area_ha

the total area of all forest which are subject of the simulation

Value

a data frame with updated status (code) of all individual trees based on the simulation of harvesting

Examples


library(MLFS)
data(data_v5)

data_v5 <- simulate_harvesting(df = data_v5,
            harvesting_sum = 5500000,
            harvesting_type = "combined",
            share_thinning = 0.50,
            harvest_sum_level = 1,
            plot_upscale_type = "factor",
            plot_upscale_factor = 1600,
            final_cut_weight = 5,
            thinning_small_weight = 1)

transform_data

Description

Initial data transformation

Usage

transform_data(df, include_climate, df_climate, select_months_climate)

Value

a data frame with transformed data, which is used in the next steps of the MLFS

volume_form_factors

Description

The calculation of individual tree volume using form factors, which can be defined per species, per plot, or per species and per plot

Usage

volume_form_factors(
  df,
  form_factors = NULL,
  form_factors_level = "species",
  uniform_form_factor = 0.42
)

Arguments

df

data frame with tree heights and basal areas for individual trees

form_factors

data frame with for factors for species, plot or both

form_factors_level

character, the level of specified form factors. It can be 'species', 'plot' or 'species_plot'

uniform_form_factor

a uniform form factor to be applied to all trees. If specified, it overwrites the argument 'form_factors'

Value

a data frame with calculated volume for all trees

Examples

library(MLFS)
data(data_v3)
data(form_factors)

data_v3 <- volume_form_factors(df = data_v3, form_factors = form_factors,
  form_factors_level = "species_plot")

summary(data_v3)

volume_form_factors_halfPeriod

Description

The calculation of individual tree volume using form factors, which can be defined per species, per plot, or per species and per plot

Usage

volume_form_factors_halfPeriod(
  df,
  form_factors = NULL,
  form_factors_level = "species",
  uniform_form_factor = 0.42
)

Value

a data frame with calculated volume for all trees in the middle of a simulation step

volume_functions

Description

The calculation of individual tree volume using the n-parameter volume functions for the MLFS

Usage

volume_functions(df, df_volumeF_parameters = NULL)

Arguments

df

data frame with tree heights and basal areas for individual trees

df_volumeF_parameters

data frame with equations and parameters for n-parametric volume functions

Value

a data frame with calculated volume for all trees

Examples

library(MLFS)
data(data_v3)
data(df_volume_parameters)

data_v3 <- volume_functions(df = data_v3,
  df_volumeF_parameters = df_volume_parameters)

volume_functions_halfPeriod

Description

n-parameter volume functions for the MLFS (half period)

Usage

volume_functions_halfPeriod(df, df_volumeF_parameters = NULL)

Value

a data frame with calculated volume for all trees in the middle of a simulation step

volume_merchantable

Description

two-parameter volume functions for the MLFS

Usage

volume_merchantable(df)

volume_merchantable_halfPeriod

Description

two-parameter volume functions for the MLFS

Usage

volume_merchantable_halfPeriod(df)

Value

a data frame with calculated volume for all trees in the middle of a simulation step

volume_tariffs

Description

One-parameter volume functions (tariffs) for the MLFS.

Usage

volume_tariffs(df, data_tariffs)

Arguments

df

data frame with tree heights and basal areas for individual trees

data_tariffs

data frame with plot- and species-specific parameters for the calculations of tree volume

Value

a data frame with calculated volume for all trees

Examples

data(data_v3)
data(data_tariffs)
data_v3 <- volume_tariffs(df = data_v3, data_tariffs = data_tariffs)

volume_tariffs_halfPeriod

Description

One-parameter volume functions (tariffs) for the MLFS (half period)

Usage

volume_tariffs_halfPeriod(df, data_tariffs)

Value

a data frame with calculated volume for all trees in the middle of a simulation step

volume_whole_tree

Description

two-parameter volume functions for the MLFS

Usage

volume_whole_tree(df)

Value

a data frame with calculated volume for all trees

volume_whole_tree_halfPeriod

Description

two-parameter volume functions for the MLFS

Usage

volume_whole_tree_halfPeriod(df)

Value

a data frame with calculated volume for all trees in the middle of a simulation step