In paul-carteron/SimCop: SimCop
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.align = 'center',
fig.dim = c(5.5,5.5),
message=FALSE,
warning=FALSE
)
This vignette explains how the different functions of this package can be used to analyze SimCop outputs. This package has been created primarily for the study of DGD, but it can be used for other types of analysis such as thinning
Output from SimCop
The classic SimCop output contains the following files :
- branch_biomass_data.csv
- crownmap_data.csv
- forenerchips_input.csv
- forestry_scenario.csv
- generated_inventory.csv
- generated_scenario.csv
- generated_thinnings.csv
- stand_biomass_data.csv
- stand_data.csv
- stem_profiles_data.csv
- tree_biomass_data.csv
- trees_unique_data.csv
- vigor_distribution_data.csv
Functions of SimCop only relies on :
- stand_data.csv : variables characterizing the forest stand
- generated_scenario.csv : parameters used to initialize the simulation(s)
- forestry_scenario.csv : parameters characterizing thinnings
When using SimCop, you can produce two type of data :
- with capsis interface you can save one simulation at a time
- with script mode, you can generate multiple simulation stored in the same file. In this case,
generated_scenario.csv is replace by parameter_scenarii.csv.
All functions of the package are designed to take both types of data as input
Installation
Run devtools::install_github("paul-carteron/SimCop") to install development version from GitHub.
Import data
To import data, use the import_stand_data function with the filepath of the folder containing all ".csv" file describe above. This function will also format the dataset.
Already import dataset are available in the package for example (see ?stand_data_example or ? stand_data_ecl_example).
stand_data = import_stand_data(filepath = "Your filepath")
SimCop, an additive function package
Function from SimCop are design to be used like ggplot2 package. To add new object to the graph you need to use + and if you read well, you understood that you need a starting graph; To get it, you have to used plot_traj function.
Beginning plot
library(SimCop)
plot_traj()
This produces a graph representing the mortality trajectory(s) of the stand on log-log scale. These are based on the work of Ningre et al. "Trajectories of self-thinning of Douglas-fir in France". The trajectories are initialized by the density_init and coeff_traj arguments.
Other arguments are used to change aesthetic of graph (scale, theme, aes).
coeff = c(a = 13.532, b = -1.461, a0 = 14.21, b0 = -1.79) #/!\ it must be a named vector
new_scale = c(xmin = 25, xmax = 700, ymin = 10, ymax = 1500) #/!\ it must be a named vector
plot_traj(density_init = c(100,500, 1000),
coeff_traj = coeff,
scale = new_scale,
linetype = "dotted",
color = "mediumvioletred",
size = 1)
RDI lines
The first object you wanna plot in stand density management diagram is RDI line. Relative Density Index compare the number of stems to the maximum number of stems:
$$ RDI = \frac{Nobserved}{Nmax} $$
The RDI max corresponds to the size-density line such that RDI = 1. The RDI is between 0 and 1
coeff = c(a = 13.532, b = -1.461, a0 = 14.21, b0 = -1.79) #/!\ it must be a named vector
SDMD = plot_traj()+
add_RDI_line(RDI = 1,
coeff_traj = coeff,
color = "firebrick")+
add_RDI_line(RDI = 0.55,
coeff_traj = coeff,
color = "steelblue",
linetype = "dashed")+
add_RDI_line(RDI = 0.15,
coeff_traj = coeff,
color = "purple",
linetype = "dashed")
plot(SDMD)
Diameter zone
In forest management, it is common to determine a diameter of exploitability. The add_diameter_zone function allows to highlight several zones defined by the diameter.
SDMD +
add_diameter_zone(20,50,
fill = "orange",
alpha = 0.2)
Following recommendation of the National Development and Management Directive for Douglas :
- The objective is the production of large quality wood with a diameter of 55-65 cm
- For stands of exceptional quality, of exceptional quality, the exploitable diameter can be increased to 70-75 cm
- Stands of poor quality quality or on poor or inadequate sites should be renewed at a diameter of 50-55 cm
library(purrr) #allow to do optimize loop
SDMD +
purrr::pmap(.l = list(min = c(50,55,70),
max = c(55,65,75),
fill_color = c("red","orange","green")),
.f = ~ add_diameter_zone(..1, ..2, fill = ..3,
alpha = 0.2))
Non-simulated data
For now, plot_traj, add_RDI_line and add_diameter_zone only rely on the coefficient of the trajectory to work. The following functions will need simulated data, we will use the example stand_data_example dataset (more information with ?stand_data_example)
The add_non_simulated_zone highlights areas where there is no simulated data. Often, a simulation stops at 70 years which explains the existence of these areas.
data("stand_data_example")
coeff = c(a = 13.532, b = -1.461, a0 = 14.21, b0 = -1.79) #/!\ it must be a named vector
SDMD = plot_traj(linetype = "dotted")+
add_RDI_line(RDI = 1,
coeff_traj = coeff)+
add_non_simulated_zone(stand_data = stand_data_example,
coeff_traj = coeff)
plot(SDMD)
Isolines
Isolines are lines of equal value. SDMDs always represent dominant height isolines because they add a temporal dimension to the diagram if used in combination with fertility curves. These lines are calculted from the stand_data_example. Every variables from the dataset can be used to create isolignes which mean variable argument can be :
- "Nha","Gha","Vha","Hdom", "Hg", "Cdom", "Cg", "Vha_dead", "Vha_mean", "Vha_Tot","Dg", "Ddom", "Nha_thinned", "Vha_thinned", "Out_of_cover_crown_cover_ha"
SDMD +
add_isolines(stand_data = stand_data_example,
variable = "Hdom",
iso_values = c(15,25,35),
label_position = "bottom",
color = "red",
show_point = TRUE) # Point used for the regression
SDMD +
add_isolines(stand_data = stand_data_example,
variable = "Vha",
iso_values = c(100,300,500),
label_position = "bottom",
color = "steelblue",
show_point = TRUE)
Zone of growth
One of the primary objectives in silviculture is to maximize increment to reduce the rotation cycle of a stand. Often, we want to maximize the increment in volume. The function add_growth_zone automatically detects the maximum, annual or average, growth of a chosen variable from the data in stand_data_example (see ?calculate growth). Then, the zone is drawn from the reduction_rate argument which delimits the width of the zone. For example, reduction_rate = 0.05 will delimit a zone where the growth is never less than 5% of the maximum.
coeff = c(a = 13.532, b = -1.461, a0 = 14.21, b0 = -1.79) #/!\ it must be a named vector
data("stand_data_example")
plot_traj(linetype = "dotted")+
add_RDI_line(RDI = 1)+
add_isolines(stand_data_example,
variable = Hdom,
iso_value = c(15,20,35),
label_position = "bottom",
color = "firebrick")+
add_growth_zone(stand_data_example,
variable = Vha,
growth_type = "annual",
reduction_rate = 0.02)
As we can see, the zone in purple is not completely filled. You can use grab_force argument to fix it.This reduces or increases the force of attraction between the points; use show_point = TRUE to see them
plot_traj(linetype = "dotted")+
add_RDI_line(RDI = 1)+
add_isolines(stand_data_example,
variable = Hdom,
iso_value = c(15,20,35),
label_position = "bottom",
color = "firebrick")+
add_growth_zone(stand_data_example,
variable = Vha,
growth_type = "annual",
reduction_rate = 0.02,
show_point = TRUE,
grab_force = 3)
Another example :
plot_traj(linetype = "dotted")+
add_RDI_line(RDI = 1)+
add_isolines(stand_data_example,
variable = Hdom,
iso_value = c(15,20,35),
label_position = "bottom",
color = "firebrick")+
add_growth_zone(stand_data_example,
variable = Cdom,
growth_type = average,
reduction_rate = 0.02,
grab_force = 4,
zone_color = "midnightblue")
paul-carteron/SimCop documentation built on Dec. 22, 2021, 6:42 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.align = 'center', fig.dim = c(5.5,5.5), message=FALSE, warning=FALSE )
This vignette explains how the different functions of this package can be used to analyze SimCop outputs. This package has been created primarily for the study of DGD, but it can be used for other types of analysis such as thinning
Output from SimCop
The classic SimCop output contains the following files :
- branch_biomass_data.csv
- crownmap_data.csv
- forenerchips_input.csv
- forestry_scenario.csv
- generated_inventory.csv
- generated_scenario.csv
- generated_thinnings.csv
- stand_biomass_data.csv
- stand_data.csv
- stem_profiles_data.csv
- tree_biomass_data.csv
- trees_unique_data.csv
- vigor_distribution_data.csv
Functions of SimCop only relies on :
- stand_data.csv : variables characterizing the forest stand
- generated_scenario.csv : parameters used to initialize the simulation(s)
- forestry_scenario.csv : parameters characterizing thinnings
When using SimCop, you can produce two type of data :
- with capsis interface you can save one simulation at a time
- with script mode, you can generate multiple simulation stored in the same file. In this case, generated_scenario.csv is replace by parameter_scenarii.csv.
All functions of the package are designed to take both types of data as input
Installation
Run devtools::install_github("paul-carteron/SimCop") to install development version from GitHub.
Import data
To import data, use the import_stand_data function with the filepath of the folder containing all ".csv" file describe above. This function will also format the dataset.
Already import dataset are available in the package for example (see ?stand_data_example or ? stand_data_ecl_example).
stand_data = import_stand_data(filepath = "Your filepath")
SimCop, an additive function package
Function from SimCop are design to be used like ggplot2 package. To add new object to the graph you need to use + and if you read well, you understood that you need a starting graph; To get it, you have to used plot_traj function.
Beginning plot
library(SimCop) plot_traj()
This produces a graph representing the mortality trajectory(s) of the stand on log-log scale. These are based on the work of Ningre et al. "Trajectories of self-thinning of Douglas-fir in France". The trajectories are initialized by the density_init and coeff_traj arguments.
Other arguments are used to change aesthetic of graph (scale, theme, aes).
coeff = c(a = 13.532, b = -1.461, a0 = 14.21, b0 = -1.79) #/!\ it must be a named vector new_scale = c(xmin = 25, xmax = 700, ymin = 10, ymax = 1500) #/!\ it must be a named vector plot_traj(density_init = c(100,500, 1000), coeff_traj = coeff, scale = new_scale, linetype = "dotted", color = "mediumvioletred", size = 1)
RDI lines
The first object you wanna plot in stand density management diagram is RDI line. Relative Density Index compare the number of stems to the maximum number of stems:
$$ RDI = \frac{Nobserved}{Nmax} $$ The RDI max corresponds to the size-density line such that RDI = 1. The RDI is between 0 and 1
coeff = c(a = 13.532, b = -1.461, a0 = 14.21, b0 = -1.79) #/!\ it must be a named vector SDMD = plot_traj()+ add_RDI_line(RDI = 1, coeff_traj = coeff, color = "firebrick")+ add_RDI_line(RDI = 0.55, coeff_traj = coeff, color = "steelblue", linetype = "dashed")+ add_RDI_line(RDI = 0.15, coeff_traj = coeff, color = "purple", linetype = "dashed") plot(SDMD)
Diameter zone
In forest management, it is common to determine a diameter of exploitability. The add_diameter_zone function allows to highlight several zones defined by the diameter.
SDMD + add_diameter_zone(20,50, fill = "orange", alpha = 0.2)
Following recommendation of the National Development and Management Directive for Douglas :
- The objective is the production of large quality wood with a diameter of 55-65 cm
- For stands of exceptional quality, of exceptional quality, the exploitable diameter can be increased to 70-75 cm
- Stands of poor quality quality or on poor or inadequate sites should be renewed at a diameter of 50-55 cm
library(purrr) #allow to do optimize loop SDMD + purrr::pmap(.l = list(min = c(50,55,70), max = c(55,65,75), fill_color = c("red","orange","green")), .f = ~ add_diameter_zone(..1, ..2, fill = ..3, alpha = 0.2))
Non-simulated data
For now, plot_traj, add_RDI_line and add_diameter_zone only rely on the coefficient of the trajectory to work. The following functions will need simulated data, we will use the example stand_data_example dataset (more information with ?stand_data_example)
The add_non_simulated_zone highlights areas where there is no simulated data. Often, a simulation stops at 70 years which explains the existence of these areas.
data("stand_data_example") coeff = c(a = 13.532, b = -1.461, a0 = 14.21, b0 = -1.79) #/!\ it must be a named vector SDMD = plot_traj(linetype = "dotted")+ add_RDI_line(RDI = 1, coeff_traj = coeff)+ add_non_simulated_zone(stand_data = stand_data_example, coeff_traj = coeff) plot(SDMD)
Isolines
Isolines are lines of equal value. SDMDs always represent dominant height isolines because they add a temporal dimension to the diagram if used in combination with fertility curves. These lines are calculted from the stand_data_example. Every variables from the dataset can be used to create isolignes which mean variable argument can be :
- "Nha","Gha","Vha","Hdom", "Hg", "Cdom", "Cg", "Vha_dead", "Vha_mean", "Vha_Tot","Dg", "Ddom", "Nha_thinned", "Vha_thinned", "Out_of_cover_crown_cover_ha"
SDMD + add_isolines(stand_data = stand_data_example, variable = "Hdom", iso_values = c(15,25,35), label_position = "bottom", color = "red", show_point = TRUE) # Point used for the regression
SDMD + add_isolines(stand_data = stand_data_example, variable = "Vha", iso_values = c(100,300,500), label_position = "bottom", color = "steelblue", show_point = TRUE)
Zone of growth
One of the primary objectives in silviculture is to maximize increment to reduce the rotation cycle of a stand. Often, we want to maximize the increment in volume. The function add_growth_zone automatically detects the maximum, annual or average, growth of a chosen variable from the data in stand_data_example (see ?calculate growth). Then, the zone is drawn from the reduction_rate argument which delimits the width of the zone. For example, reduction_rate = 0.05 will delimit a zone where the growth is never less than 5% of the maximum.
coeff = c(a = 13.532, b = -1.461, a0 = 14.21, b0 = -1.79) #/!\ it must be a named vector data("stand_data_example") plot_traj(linetype = "dotted")+ add_RDI_line(RDI = 1)+ add_isolines(stand_data_example, variable = Hdom, iso_value = c(15,20,35), label_position = "bottom", color = "firebrick")+ add_growth_zone(stand_data_example, variable = Vha, growth_type = "annual", reduction_rate = 0.02)
As we can see, the zone in purple is not completely filled. You can use grab_force argument to fix it.This reduces or increases the force of attraction between the points; use show_point = TRUE to see them
plot_traj(linetype = "dotted")+ add_RDI_line(RDI = 1)+ add_isolines(stand_data_example, variable = Hdom, iso_value = c(15,20,35), label_position = "bottom", color = "firebrick")+ add_growth_zone(stand_data_example, variable = Vha, growth_type = "annual", reduction_rate = 0.02, show_point = TRUE, grab_force = 3)
Another example :
plot_traj(linetype = "dotted")+ add_RDI_line(RDI = 1)+ add_isolines(stand_data_example, variable = Hdom, iso_value = c(15,20,35), label_position = "bottom", color = "firebrick")+ add_growth_zone(stand_data_example, variable = Cdom, growth_type = average, reduction_rate = 0.02, grab_force = 4, zone_color = "midnightblue")
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