| Title: | Machine Learning Forest Simulator |
|---|---|
| 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>. |
| Authors: | Jernej Jevsenak |
| Maintainer: | Jernej Jevsenak <[email protected]> |
| License: | GPL-3 |
| Version: | 0.4.2 |
| Built: | 2024-11-04 03:33:06 UTC |
| Source: | https://github.com/jernejjevsenak/mlfs |
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
add_stand_variables(df)add_stand_variables(df)
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 |
a data frame with added stand variables: total stand basal area and the number of trees per hectare
data(data_v1) data_v1 <- add_stand_variables(df = data_v1)data(data_v1) data_v1 <- add_stand_variables(df = data_v1)
The Basal Area Increment BAI sub model that is run within the MLFS
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 )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 )
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 |
a list with four elements:
$predicted_BAI - a data frame with calculated basal area increments (BAI)
$eval_BAI - a data frame with predicted and observed basal area increments (BAI), or a character string indicating that BAI model was not evaluated
$rf_model_species - the output model for BAI (species level)
$rf_model_speciesGroups - the output model for BAI (species group level)
# add BA to measurement thresholds measurement_thresholds$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
library(MLFS) data(data_BAI) data(data_v6) data(measurement_thresholds)library(MLFS) data(data_BAI) data(data_v6) data(measurement_thresholds)
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
calculate_BAL(df)calculate_BAL(df)
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 |
a data frame with calculated basal area in large trees (BAL)
data(data_v1) data_v1 <- calculate_BAL(df = data_v1)data(data_v1) data_v1 <- calculate_BAL(df = data_v1)
Model for predicting crown height
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 )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 )
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? |
a list with four elements:
$predicted_crownHeight - a data frame with imputed crown heights
$eval_crownHeight - a data frame with predicted and observed crown heights, or a character string indicating that crown height model was not evaluated
$model_species - the output model for crown heights (species level)
$model_speciesGroups - the output model for crown heights (species group level)
library(MLFS) data(data_tree_heights) data(data_v3) # A) Example with linear model Crown_h_predictions <- crownHeight_prediction(df_fit = data_tree_heights, df_predict = data_v3, crownHeight_model = "lm", site_vars = c(), species_n_threshold = 100, k = 10, blocked_cv = TRUE, eval_model_crownHeight = TRUE) predicted_df <- Crown_h_predictions$predicted_crownHeight # df with imputed heights evaluation_df <- Crown_h_predictions$eval_crownHeight # df with evaluation results # B) Example with non-linear BRNN model Crown_h_predictions <- crownHeight_prediction(df_fit = data_tree_heights, df_predict = data_v3, crownHeight_model = "brnn", BRNN_neurons = 3, site_vars = c(), species_n_threshold = 100, k = 10, blocked_cv = TRUE, eval_model_crownHeight = TRUE)library(MLFS) data(data_tree_heights) data(data_v3) # A) Example with linear model Crown_h_predictions <- crownHeight_prediction(df_fit = data_tree_heights, df_predict = data_v3, crownHeight_model = "lm", site_vars = c(), species_n_threshold = 100, k = 10, blocked_cv = TRUE, eval_model_crownHeight = TRUE) predicted_df <- Crown_h_predictions$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)
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.
data_BAIdata_BAI
A data frame with 135 rows and 25 variables:
a unique identifier for plot
a unique identifier for tree
year in which plot was visited
identifier for species group
status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)
species name
tree height in meters
crown height in meters
logical, 1 if protected, otherwise 0
slope on a plot
plot elevation
plot northness, 1 is north, 0 is south
a proxy for site index, higher value represents more productive sites
basal area of individual trees in m2
upscale weight to calculate hectare values
Total stand basal area
The number of trees in a stand
Basal Area in Large trees
basal area of individual trees in m2 from previous simulation step
tree height in meters from previous simulation step
crown height in meters from previous simulation step
upscale weight to calculate hectare values from previous simulation step
basal area increment
monthly precipitation sum
monthly mean temperature
This is simulated monthly climate data, and consists of precipitation sum and mean temperature
data_climatedata_climate
A data frame with 16695 rows and 5 variables:
a unique identifier for plot
year
month
monthly mean temperature
monthly precipitation sum
Each species should have one weight that is multiplied with the probability of being harvested when final_cut is applied
data_final_cut_weightsdata_final_cut_weights
A data frame with 36 rows and 6 variables:
species name as used in data_NFI
final cut weight applied in step 1
final cut weight applied in step 2
final cut weight applied in step 3
final cut weight applied in step 4
final cut weight applied in step 5 and all subsequent steps
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
data_ingrowthdata_ingrowth
A data frame with 365 rows and 11 variables:
a unique identifier for plot
year in which plot was visited
Total stand basal area
The number of trees in a stand
Basal Area in Large trees
slope on a plot
plot elevation
a proxy for site index, higher value represents more productive sites
plot northness, 1 is north, 0 is south
the number of new trees in inner circle
the number of new trees in outer circle
This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for mortality sub model
data_mortalitydata_mortality
A data frame with 6394 rows and 25 variables:
a unique identifier for plot
a unique identifier for tree
year in which plot was visited
identifier for species group
status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)
species name
tree height in meters
crown height in meters
logical, 1 if protected, otherwise 0
slope on a plot
plot elevation
plot northness, 1 is north, 0 is south
a proxy for site index, higher value represents more productive sites
basal area of individual trees in m2
upscale weight to calculate hectare values
Total stand basal area
The number of trees in a stand
Basal Area in Large trees
basal area of individual trees in m2 from previous simulation step
tree height in meters from previous simulation step
crown height in meters from previous simulation step
upscale weight to calculate hectare values from previous simulation step
basal area increment
monthly precipitation sum
monthly mean temperature
This is simulated data that reassemble the national forest inventory
data_NFIdata_NFI
A data frame with 11984 rows and 10 variables:
a unique identifier for plot
a unique identifier for tree
year in which plot was visited
identifier for species group
status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)
diameter at breast height in cm
species name
tree height in meters
crown height in meters
logical, 1 if protected, otherwise 0
This is simulated data describing site descriptors
data_sitedata_site
A data frame with 371 rows and 5 variables:
a unique identifier for plot
slope on a plot
plot elevation
plot northness, 1 is north, 0 is south
a proxy for site index, higher value represents more productive sites
The adapted uniform French tariffs are typically used in Slovenia to determine tree volume based on tree DBH
data_tariffsdata_tariffs
A data frame with 1196 rows and 4 variables:
tariff class for a particular species on this plot
plot identifier
species name as used in data_NFI
volume of tree with DBH 45 cm
Each species should have one weight that is multiplied with the probability of being harvested when thinning is applied
data_thinning_weightsdata_thinning_weights
A data frame with 36 rows and 6 variables:
species name as used in data_NFI
thinning weight applied in step 1
thinning weight applied in step 2
thinning weight applied in step 3
thinning weight applied in step 4
thinning weight applied in step 5 and all subsequent steps
This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for some specific functions
data_tree_heightsdata_tree_heights
A data frame with 2741 rows and 8 variables:
a unique identifier for plot
a unique identifier for tree
year in which plot was visited
identifier for species group
species name
tree height in meters
crown height in meters
basal area of individual trees in m2
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
data_v1data_v1
A data frame with 11984 rows and 15 variables:
a unique identifier for plot
a unique identifier for tree
year in which plot was visited
identifier for species group
status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)
species name
tree height in meters
crown height in meters
logical, 1 if protected, otherwise 0
slope on a plot
plot elevation
plot northness, 1 is north, 0 is south
a proxy for site index, higher value represents more productive sites
basal area of individual trees in m2
upscale weight to calculate hectare values
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
data_v2data_v2
A data frame with 6948 rows and 14 variables:
a unique identifier for plot
a unique identifier for tree
year in which plot was visited
identifier for species group
status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)
species name
tree height in meters
crown height in meters
basal area of individual trees in m2
upscale weight to calculate hectare values
basal area of individual trees in m2 from previous simulation step
upscale weight to calculate hectare values from previous simulation step
tree height in meters from previous simulation step
crown height in meters from previous simulation step
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
data_v3data_v3
A data frame with 6948 rows and 14 variables:
a unique identifier for plot
a unique identifier for tree
year in which plot was visited
identifier for species group
status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)
species name
tree height in meters
crown height in meters
basal area of individual trees in m2
upscale weight to calculate hectare values
basal area of individual trees in m2 from previous simulation step
tree height in meters from previous simulation step
crown height in meters from previous simulation step
upscale weight to calculate hectare values from previous simulation step
tree volume in m3
tree volume in m3 from previous simulation step
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'
data_v4data_v4
A data frame with 6855 rows and 41 variables:
year in which plot was visited
a unique identifier for plot
a unique identifier for tree
identifier for species group
status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)
species name
slope on a plot
plot elevation
plot northness, 1 is north, 0 is south
a proxy for site index, higher value represents more productive sites
monthly precipitation sum
monthly mean temperature
basal area of individual trees in m2 in the middle of a simulation step
basal area increment in the middle of a simulation step
upscale weight to calculate hectare values in the middle of a simulation step
tree height in meters in the middle of a simulation step
crown height in meters in the middle of a simulation step
tree volume in m3 in the middle of a simulation step
Basal Area in Large trees the middle of a simulation step
Total stand basal area the middle of a simulation step
The number of trees in a stand the middle of a simulation step
This is simulated data that reassemble the national forest inventory data. We use it to show how to run examples for simulating harvesting.
data_v5data_v5
A data frame with 5949 rows and 10 variables:
species name
year in which plot was visited
a unique identifier for plot
a unique identifier for tree
identifier for species group
status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)
tree volume in m3 in the middle of a simulation step
upscale weight to calculate hectare values in the middle of a simulation step
basal area of individual trees in m2 in the middle of a simulation step
logical, 1 if protected, otherwise 0
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
data_v6data_v6
A data frame with 186 rows and 27 variables:
species name
year in which plot was visited
a unique identifier for plot
a unique identifier for tree
identifier for species group
status of a tree: 0 (normal), 1(harvested), 2(dead), 3 (ingrowth)
tree height in meters
crown height in meters
logical, 1 if protected, otherwise 0
slope on a plot
plot elevation
plot northness, 1 is north, 0 is south
a proxy for site index, higher value represents more productive sites
basal area of individual trees in m2
upscale weight to calculate hectare values
Total stand basal area
The number of trees in a stand
Basal Area in Large trees
basal area of individual trees in m2 from previous simulation step
tree height in meters from previous simulation step
crown height in meters from previous simulation step
upscale weight to calculate hectare values from previous simulation step
basal area increment
monthly precipitation sum
monthly mean temperature
tree volume in m3
tree volume in m3 from previous simulation step
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.
df_volume_parametersdf_volume_parameters
A data frame with 6 rows and 14 variables:
species name as used in data_NFI. The category REST is used for all species without specific equation
equation for selected volume function
will be multiplied with the volume
will be multiplied with tree height
will be divided with tree DBH
lower interval threshold for considered trees
upper interval threshold for considered trees
parameter a for volume equation
parameter b for volume equation
parameter c for volume equation
parameter d for volume equation
parameter e for volume equation
parameter f for volume equation
parameter g for volume equation
Form factors can be specified per species, plot or per species and plot
form_factorsform_factors
A data frame with 1199 rows and 3 variables:
a unique identifier for plot
species name as used in data_NFI
for factor used to calculate tree volume
Height model
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 )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 )
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 |
a list with four elements:
$data_height_predictions - a data frame with imputed tree heights
$data_height_eval - a data frame with predicted and observed tree heights, or a character string indicating that tree heights were not evaluated
$model_species - the output model for tree heights (species level)
$model_speciesGroups - the output model for tree heights (species group level)
library(MLFS) data(data_tree_heights) data(data_v2) # A) Example with the BRNN method h_predictions <- height_prediction(df_fit = data_tree_heights, df_predict = data_v2, species_n_threshold = 100, height_pred_level = 0, height_model = "brnn", BRNN_neurons = 3, eval_model_height = FALSE, blocked_cv = TRUE, k = 10 ) predicted_df <- h_predictions$data_height_predictions # df with imputed heights evaluation_df <- h_predictions$data_height_eval # df with evaluation resultslibrary(MLFS) data(data_tree_heights) data(data_v2) # A) Example with the BRNN method h_predictions <- height_prediction(df_fit = data_tree_heights, df_predict = data_v2, species_n_threshold = 100, height_pred_level = 0, height_model = "brnn", BRNN_neurons = 3, eval_model_height = FALSE, blocked_cv = TRUE, k = 10 ) predicted_df <- h_predictions$data_height_predictions # df with imputed heights evaluation_df <- h_predictions$data_height_eval # df with evaluation results
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
ingrowth_parameter_listingrowth_parameter_list
A list with 2 elements:
deciles of DBH distribution for ingrowth category 3
deciles of DBH distribution for ingrowth category 15
Ingrowth table is used within the ingrowth sub model to correctly simulate different ingrowth levels and associated upscale weights
ingrowth_tableingrowth_table
A data frame with 2 rows and 4 variables:
ingrowth codes
a DBH threshold for particular ingrowth category
maximum DBH for a particular ingrowth category
the upscale weight for particular measurement category
This is simulated max_size_data and used for examples in mortality sub model
max_size_datamax_size_data
A data frame with 36 rows and 2 variables:
species name
The maximum allowed basal area (BA) for each individual species
An example of measurement_thresholds table resulting from concentric plots as used in Slovenian NFI
measurement_thresholdsmeasurement_thresholds
A data frame with 2 rows and 2 variables:
a DBH threshold for particular measurement category
the upscale weight for particular measurement category
Machine Learning Forest Simulator
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 )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 )
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 |
a list of class mlfs with at least 15 elements:
$sim_results - a data frame with the simulation results
$height_eval - a data frame with predicted and observed tree heights, or a character string indicating that tree heights were not evaluated
$crownHeight_eval - a data frame with predicted and observed crown heights, or character string indicating that crown heights were not evaluated
$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
$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
$BAI_eval - a data frame with predicted and observed basal area increments (BAI), or character string indicating that BAI model was not evaluated
$height_model_species - the output model for tree heights (species level)
$height_model_speciesGroups - the output model for tree heights (species group level)
$crownHeight_model_species - the output model for crown heights (species level)
$crownHeight_model_speciesGroups - the output model for crown heights (species group level)
$mortality_model - the output model for mortality
$BAI_model_species - the output model for basal area increments (species level)
$BAI_model_speciesGroups - the output model for basal area increments (species group level)
$max_size - a data frame with maximum allowed diameter at breast height (DBH) for each species
$ingrowth_model_3 - the output model for ingrowth (level 1) – the output name depends on ingrowth codes
$ingrowth_model_15 - the output model for ingrowth (level 2) – optional and the output name depends on ingrowth codes
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 )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 )
ingrowth model for predicting new trees within the MLFS
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 )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 )
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 |
a list with four elements:
$predicted_ingrowth - a data frame with newly added trees based on the ingrowth predictions
$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
$mod_ing_3 - the output model for predicting the ingrowth of trees with code 3
$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)
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)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)
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
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 )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 )
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 |
a list with three elements:
$predicted_mortality - a data frame with updated tree status (code) based on the predicted mortality
$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
$model_output - the output model for mortality
data("data_v4") data("data_mortality") data("max_size_data") mortality_outputs <- predict_mortality( df_fit = data_mortality, df_predict = data_v4, mortality_share_type = 'volume', df_climate = data_climate, site_vars = c("slope", "elevation", "northness", "siteIndex"), sim_mortality = TRUE, mortality_model = 'naiveBayes', nb_laplace = 0, sim_crownHeight = TRUE, mortality_share = 0.02, include_climate = TRUE, select_months_climate = c(6,7,8), eval_model_mortality = TRUE, k = 10, blocked_cv = TRUE, sim_step_years = 6, df_max_size = max_size_data, ingrowth_codes = c(3,15), include_mortality_BAI = TRUE) df_predicted <- mortality_outputs$predicted_mortality df_evaluation <- mortality_outputs$eval_mortality # confusion matrix table(df_evaluation$mortality, round(df_evaluation$mortality_pred, 0))data("data_v4") data("data_mortality") data("max_size_data") mortality_outputs <- predict_mortality( df_fit = data_mortality, df_predict = data_v4, mortality_share_type = 'volume', df_climate = data_climate, site_vars = c("slope", "elevation", "northness", "siteIndex"), sim_mortality = TRUE, mortality_model = 'naiveBayes', nb_laplace = 0, sim_crownHeight = TRUE, mortality_share = 0.02, include_climate = TRUE, select_months_climate = c(6,7,8), eval_model_mortality = TRUE, k = 10, blocked_cv = TRUE, sim_step_years = 6, df_max_size = max_size_data, ingrowth_codes = c(3,15), include_mortality_BAI = TRUE) df_predicted <- mortality_outputs$predicted_mortality df_evaluation <- mortality_outputs$eval_mortality # confusion matrix table(df_evaluation$mortality, round(df_evaluation$mortality_pred, 0))
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.
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 )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 )
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 |
a data frame with updated status (code) of all individual trees based on the simulation of harvesting
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)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)
The calculation of individual tree volume using form factors, which can be defined per species, per plot, or per species and per plot
volume_form_factors( df, form_factors = NULL, form_factors_level = "species", uniform_form_factor = 0.42 )volume_form_factors( df, form_factors = NULL, form_factors_level = "species", uniform_form_factor = 0.42 )
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' |
a data frame with calculated volume for all trees
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)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)
The calculation of individual tree volume using the n-parameter volume functions for the MLFS
volume_functions(df, df_volumeF_parameters = NULL)volume_functions(df, df_volumeF_parameters = NULL)
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 |
a data frame with calculated volume for all trees
library(MLFS) data(data_v3) data(df_volume_parameters) data_v3 <- volume_functions(df = data_v3, df_volumeF_parameters = df_volume_parameters)library(MLFS) data(data_v3) data(df_volume_parameters) data_v3 <- volume_functions(df = data_v3, df_volumeF_parameters = df_volume_parameters)
One-parameter volume functions (tariffs) for the MLFS.
volume_tariffs(df, data_tariffs)volume_tariffs(df, data_tariffs)
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 |
a data frame with calculated volume for all trees
data(data_v3) data(data_tariffs) data_v3 <- volume_tariffs(df = data_v3, data_tariffs = data_tariffs)data(data_v3) data(data_tariffs) data_v3 <- volume_tariffs(df = data_v3, data_tariffs = data_tariffs)