Help for package forestat

Type:

Package

Title:

Forest Carbon Sequestration and Potential Productivity Calculation

Version:

1.2.0

Description:

Include assessing site classes based on the stand height growth and establishing a nonlinear mixed-effect biomass model under different site classes based on the whole stand model to achieve more accurate estimation of carbon sequestration. In particular, a carbon sequestration potential productivity calculation method based on the potential mean annual increment is proposed. This package is applicable to both natural forests and plantations. It can quantitatively assess stand’s potential productivity, realized productivity, and possible improvement under certain site, and can be used in many aspects such as site quality assessment, tree species suitability evaluation, and forest degradation evaluation. Reference: Lei X, Fu L, Li H, et al (2018) <doi:10.11707/j.1001-7488.20181213>. Fu L, Sharma R P, Zhu G, et al (2017) <doi:10.3390/f8040119>.

License:

GPL (≥ 3)

Maintainer:

Yuanyuan Han <jackhanyuan@foxmail.com>

URL:

https://github.com/caf-ifrit/forestat

BugReports:

https://github.com/caf-ifrit/forestat/issues

Encoding:

UTF-8

Language:

en-US

LazyData:

true

Depends:

R (≥ 3.5.0)

RoxygenNote:

7.3.2

Imports:

dplyr, ggplot2, graphics, nlme, stats, rlang

Suggests:

knitr, rmarkdown

VignetteBuilder:

knitr

NeedsCompilation:

Author:

Liyong Fu [aut], Shirong Liu [aut], Shouzheng Tang [aut], Guangshuang Duan [aut], Zhong Wang [aut], Linyan Feng [aut], Dongbo Xie [aut], Yuanyuan Han [aut, cre]

Packaged:

2026-09-29 07:46:23 UTC; CodexSandboxOffline

Repository:

CRAN

Date/Publication:

2026-10-09 04:00:02 UTC

Calculate Fitting Evaluation Indices

Description

This function calculates several fitting evaluation indices based on estimated and observed values.

Usage

FittingEvaluationIndex(EstiH, ObsH)

Arguments

EstiH

A numeric vector of estimated values.

ObsH

A numeric vector of observed values.

Details

EstiH and ObsH must have the same length. Non-finite pairs are removed, and at least two finite pairs are required. R2 is NA when all observed values are equal, and TRE is NA when all estimated values are zero.

Value

A named numeric vector with evaluation indices:

pe: Mean error
RMSE: Root Mean Squared Error
R2: Coefficient of determination
Var: Variance of the error
TRE: Total Relative Error (percentage)

Examples

FittingEvaluationIndex(c(1, 2, 4), c(1, 3, 5))

Birch Dataset

Description

A dataset containing information about birch trees in the Winter Olympics core area.

Usage

birch

Format

A data frame with 2603 rows and 17 variables:

PLOT: Numeric sample plot identifier.
TREE: Tree species code; always "birch" in this dataset.
D: Diameter at breast height (DBH) of the tree, in centimeters.
H: Height of the tree, in meters.
CBH: Crown base height (branch-free height), in meters.
LIFE: Tree condition code: "alive", "broken_head", "dead", "healthy", "standing_dead", "unknown", or "withered_tip".
CW_E: Horizontal crown extent from the stem toward the east, in meters.
CW_S: Horizontal crown extent from the stem toward the south, in meters.
CW_W: Horizontal crown extent from the stem toward the west, in meters.
CW_N: Horizontal crown extent from the stem toward the north, in meters.
X: Within-plot X-coordinate of the tree, in meters; missing coordinates are NA.
Y: Within-plot Y-coordinate of the tree, in meters; missing coordinates are NA.
SD: Stand density, in trees per hectare.
CLR: Crown length ratio, calculated as crown length divided by tree height.
CW: Mean crown diameter, calculated as the mean of CW_EW and CW_SN, in meters.
CW_EW: East-west crown diameter, calculated as CW_E + CW_W, in meters.
CW_SN: South-north crown diameter, calculated as CW_S + CW_N, in meters.

Details

Paired zero coordinates used as missing-value placeholders in the source records are represented as NA.

Calculating degraded forest grade

Description

Calculation of degraded forest grade.

Usage

calc_degraded_forest_grade(plot_data)

Arguments

plot_data

Preprocessed plot_data

Details

Calculation of degraded forest grade, icluding p1, p2,p3, p4, p5, p1m, p2m, p3m, p4m, Z1, Z2, Z3, Z4, Z5, Z, Z_weights, Z_grade, Z_weights_grade etc.

Value

res_data with degraded forest grade

Examples


# Load forest survey data
data(tree_1)
data(tree_2)
data(tree_3)
data(plot_1)
data(plot_2)
data(plot_3)

# Preprocess the degraded forest data
plot_data <- degraded_forest_preprocess(tree_1,tree_2,tree_3,plot_1,plot_2,plot_3)

# Calculation of degraded forest grade
res_data <- calc_degraded_forest_grade(plot_data)

Calculate the site classes based on stand height growth

Description

class.plot adds new variables: the original height classes and the adjusted height classes. And the existing variables are retained.

Usage

class.plot(
  data,
  model = "Richards",
  interval = 5,
  number = 5,
  maxiter = 1000,
  H_start = c(a = 20, b = 0.05, c = 1),
  BA_start = c(a = 80, b = 1e-04, c = 8, d = 0.1),
  Bio_start = c(a = 450, b = 1e-04, c = 12, d = 0.1)
)

Arguments

data

A data.frame data in which at least four columns are required as input: ID, code, AGE, H.

model

Type of model used for building the H-model (stand height model), options are 'Logistic', 'Richards', 'Korf', 'Gompertz', 'Weibull', or 'Schumacher'.

interval

The initial stand age interval for height classes.

number

The maximum number of initial height classes.

maxiter

The maximum number of iterations to fit the H-model.

H_start

The initial parameters for fitting the H-model, the default value is c(a=20,b=0.05,c=1.0).

BA_start

The initial parameters for fitting the BA-model, the default value is c(a = 80, b = 0.0001, c = 8, d = 0.1).

Bio_start

The initial parameters for fitting the Bio-model, the default value is c(a=450, b=0.0001, c=12, d=0.1).

Details

Input takes a data.frame with three variables ID, AGE, H and returns height classes of every sample (rows in the data.frame).

Value

A data of forestData class with output values, models and model parameters.

Examples

if (interactive()) {
# Load sample data
data("forestData")

# Build a model based on the forestData and return a forestData class object
forestData <- class.plot(forestData,model="Richards",
                         interval=5,number=5,maxiter=1000,
                         H_start=c(a=20,b=0.05,c=1.0))
}

Preprocess the degraded forest data

Description

Preprocess the degraded forest data and return the plot_data.

Usage

degraded_forest_preprocess(tree_1, tree_2, tree_3, plot_1, plot_2, plot_3)

Arguments

tree_1

Tree data for the 1st period

tree_2

Tree data for the 2nd period

tree_3

Tree data for the 3rd period

plot_1

Sample plot data for the 1st period

plot_2

Sample plot data for the 2nd period

plot_3

Sample plot data for the 3rd period

Details

tree_1, tree_2, tree_3 are required to include the fields "plot_id", "inspection_type", and "tree_species_code". plot_1, plot_2, and plot_3 are required to include the fields "plot_id", "standing_stock", "forest_cutting_stock", "crown_density", "disaster_level", "origin", "dominant_tree_species", "age_group", "naturalness", and "land_type".

Value

Preprocessed plot_data

Examples


# Load forest survey data
data(tree_1)
data(tree_2)
data(tree_3)
data(plot_1)
data(plot_2)
data(plot_3)

# Preprocess the degraded forest data
plot_data <- degraded_forest_preprocess(tree_1,tree_2,tree_3,plot_1,plot_2,plot_3)

Mixed birch-broadleaf forest data

Description

Mixed birch-broadleaf forest data

Usage

forestData

Format

'forestData' A data frame with 320 rows and 16 columns:

ID: Plot ID
AGE: The average age of the stand
H: Stand height
BA: Stand basal area
Bio: Stand biomass
S: Stand density index
code: Forest type code of plot

...

Larch Dataset

Description

A dataset containing information about larch trees in the Winter Olympics core area.

Usage

larch

Format

A data frame with 4538 rows and 18 variables:

PLOT: Numeric sample plot identifier.
TREE: Tree species code; always "larch" in this dataset.
D: Diameter at breast height (DBH) of the tree, in centimeters.
H: Height of the tree, in meters.
CBH: Crown base height (branch-free height), in meters.
LIFE: Tree condition code: "alive", "broken_head", "broken_tip", "healthy", or "unknown".
CW_E: Horizontal crown extent from the stem toward the east, in meters.
CW_S: Horizontal crown extent from the stem toward the south, in meters.
CW_W: Horizontal crown extent from the stem toward the west, in meters.
CW_N: Horizontal crown extent from the stem toward the north, in meters.
X: Within-plot X-coordinate of the tree, in meters; missing coordinates are NA.
Y: Within-plot Y-coordinate of the tree, in meters; missing coordinates are NA.
SD: Stand density, in trees per hectare.
CLR: Crown length ratio, calculated as crown length divided by tree height.
CW: Mean crown diameter, calculated as the mean of CW_EW and CW_SN, in meters.
CW_EW: East-west crown diameter, calculated as CW_E + CW_W, in meters.
CW_SN: South-north crown diameter, calculated as CW_S + CW_N, in meters.
AGE.GROUP: Age-group label stored in Chinese, with categories corresponding to young, middle-aged, near-mature, mature, and over-mature forest.

Details

Paired zero coordinates used as missing-value placeholders in the source records are represented as NA; an isolated zero is retained as a valid plot-boundary coordinate.

Qinghai Spruce Dataset

Description

A dataset containing information about Qinghai spruce trees at the Xishui forest farm of Su'nan Yuguzu Autonomous County, China, in Gansu Qilian Mountain National Nature Reserve.

Usage

picea

Format

A data frame with 402 rows and 24 variables:

LIDAR-X: Projected X-coordinate of the LIDAR-detected tree, in meters.
LIDAR-Y: Projected Y-coordinate of the LIDAR-detected tree, in meters.
LH: Tree height derived from LIDAR data, in meters.
LHCB: Crown base height derived from LIDAR data, in meters.
LCW1: East-west crown diameter derived from LIDAR data, in meters.
LCW2: South-north crown diameter derived from LIDAR data, in meters.
LCW: Mean LIDAR-derived crown diameter, calculated as the mean of LCW1 and LCW2, in meters.
CPA: Crown projection area, in square meters.
X: Projected X-coordinate of the ground-measured tree, in meters.
Y: Projected Y-coordinate of the ground-measured tree, in meters.
Z: Elevation of the tree base, in meters above sea level.
PLOT: Character sample plot name, from "yd01" to "yd16".
PLOT1: Numeric sample plot identifier, from 1 to 16.
OBS: Tree observation identifier.
D0: Ground-measured diameter at breast height (DBH), in centimeters.
H0: Ground-measured height of the tree, in meters.
HCB0: Crown base height (branch-free height) measured on the ground, in meters.
CW01: Ground-measured east-west crown diameter, in meters.
CW02: Ground-measured south-north crown diameter, in meters.
CW: Mean ground-measured crown diameter, calculated as the mean of CW01 and CW02, in meters.
STEM: Estimated biomass of the tree stem, in kilograms.
BRANCH: Estimated biomass of the tree branches, in kilograms.
FOLIAGE: Estimated biomass of the tree foliage, in kilograms.
FRUIT: Estimated biomass of the tree fruit, in kilograms.

ForestData Plot

Description

Plot graphs about the forestData.

Usage

## S3 method for class 'forestData'
plot(
  x,
  model.type = "H",
  plot.type = "Curve",
  xlab = NA,
  ylab = NA,
  legend.lab = "Site class",
  title = "Mixed birch-broadleaf forest",
  ...
)

Arguments

x

A data of forestData class.

model.type

Type of model used for fitting, options are 'H' (stand height growth model), 'BA' (stand basal area model), or 'Bio' (stand biomass model).

plot.type

Type of plot, options are 'Curve' (curve plot), 'Scatter_Curve' (scatter plot with curve), 'Residual' (residual plot), or 'Scatter' (scatter plot).

xlab

The title for the x axis.

ylab

The title for the y axis.

legend.lab

The title for the legends.

title

The text for the Plot title.

...

Additional arguments affecting the figure plotted.

Value

A trellis plot object

Examples

if (interactive()) {
# Load sample data
data("forestData")

# Build a model based on the forestData and return a forestData class object
forestData <- class.plot(forestData,model="Richards",
                         interval=5,number=5,maxiter=1000,
                         H_start=c(a=20,b=0.05,c=1.0))

# Plot the curve of the height classes
plot(forestData, model.type="H",
     plot.type="Curve",
     xlab="Stand age (year)",ylab="Height (m)",legend.lab="Site class",
     title="The H-model curve of the mixed birch-broadleaf forest")
}

1st period sample plot survey data

Description

The 1st period sample plot survey data (e.g. 2005)

Usage

plot_1

Format

'plot_1' A data frame with 62 rows and 23 columns:

plot_id: Plot ID
standing_stock: Standing stock
forest_cutting_stock: Forest cutting stock
crown_density: Crown density
disaster_level: Disaster level
origin: origin
dominant_tree_species: Dominant tree species
age_group: Age group
naturalness: Naturalness
land_type: Land type

...

2nd period sample plot survey data

Description

The 2nd period sample plot survey data (e.g. 2010)

Usage

plot_2

Format

'plot_2' A data frame with 100 rows and 5 columns:

plot_id: Plot ID
standing_stock: Standing stock
forest_cutting_stock: Forest cutting stock
crown_density: Crown density
disaster_level: Disaster level
origin: origin
dominant_tree_species: Dominant tree species
age_group: Age group
naturalness: Naturalness
land_type: Land type

...

3rd period sample plot survey data

Description

The 3rd period sample plot survey data (e.g. 2015)

Usage

plot_3

Format

'plot_3' A data frame with 100 rows and 5 columns:

plot_id: Plot ID
standing_stock: Standing stock
forest_cutting_stock: Forest cutting stock
crown_density: Crown density
disaster_level: Disaster level
origin: origin
dominant_tree_species: Dominant tree species
age_group: Age group
naturalness: Naturalness
land_type: Land type

...

Calculate the potential productivity.

Description

potential.productivity calculate the potential productivity of stand based on model parameters(obtained from the parameterOutput function).

Usage

potential.productivity(
  forestData,
  code = 1,
  age.min = 5,
  age.max = 150,
  left = 0.05,
  right = 100,
  e = 1e-05,
  maxiter = 50
)

Arguments

forestData

A forestData class data

code

Codes for forest types.

age.min

The minimum age of the stand.

age.max

The maximum age of the stand.

left

Solving for the left boundary of the potential productivity.

right

Solving for the right boundary of the potential productivity.

e

Accuracy parameters for solving the stand density index according to Newton's iterative method.

maxiter

Maximum number of iterations parameter for solving the stand density index according to Newton's iteration method.

Details

potential.productivity takes data_BA,data_V parameters as required inputs.

Value

A forestData class in which a data.frame with potential productivity parameters is added.

Examples

if (interactive()) {
# Load sample data
data("forestData")

# Build a model based on the forestData and return a forestData class object
forestData <- class.plot(forestData,model="Richards",
                         interval=5,number=5,maxiter=1000,
                         H_start=c(a=20,b=0.05,c=1.0))

# Calculate the potential productivity of the forestData object
forestData <- potential.productivity(forestData,code=1,
                                     age.min=5,age.max=150,
                                     left=0.05,right=100,
                                     e=1e-05,maxiter=50)
}

Calculate the realized productivity.

Description

realized.productivity calculate the realized productivity of each stand based on model parameters (obtained from the parameterOutput function).

Usage

realized.productivity(forestData, left = 0.05, right = 100)

Arguments

forestData

A forestData class data

left

Solving for the left boundary of the realized productivity.

right

Solving for the right boundary of the realized productivity.

Details

realized.productivity takes data,data_BA,data_V parameters as required inputs.

Value

A forestData class in which a data.frame with realized productivity parameters is added.

Examples

if (interactive()) {
# Load sample data
data("forestData")

# Build a model based on the forestData and return a forestData class object
forestData <- class.plot(forestData,model="Richards",
                         interval=5,number=5,maxiter=1000,
                         H_start=c(a=20,b=0.05,c=1.0))

# Calculate the realized productivity of the forestData object
forestData <- realized.productivity(forestData,left=0.05,right=100)
}

Summary of forestData

Description

Generates summary statistics for forestData objects.

Usage

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

Arguments

object

A forestData object (after class.plot).

...

Additional arguments affecting the summary produced.

Details

The summary includes the summary of raw data, the model, the model parameters, potential productivity and real productivity in forestData(if available)

Value

A summary object of class "summary.forestData"

Examples

if (interactive()) {
# Load sample data
data("forestData")

# Build a model based on the forestData and return a forestData class object
forestData <- class.plot(forestData,model="Richards",
                         interval=5,number=5,maxiter=1000,
                         H_start=c(a=20,b=0.05,c=1.0))

# Get the summary data of the forestData object
summary(forestData)
}

1st period trees survey data

Description

The 1st period trees survey data (e.g. 2005)

Usage

tree_1

Format

'tree_1' A data frame with 1634 rows and 5 columns:

plot_id: Plot ID
inspection_type: Inspection type
tree_species_code: Tree species code

...

2nd period trees survey data

Description

The 2nd period trees survey data (e.g. 2010)

Usage

tree_2

Format

'tree_2' A data frame with 4778 rows and 5 columns:

plot_id: Plot ID
inspection_type: Inspection type
tree_species_code: Tree species code

...

3rd period trees survey data

Description

The 3rd period trees survey data (e.g. 2015)

Usage

tree_3

Format

'tree_3' A data frame with 4528 rows and 5 columns:

plot_id: Plot ID
inspection_type: Inspection type
tree_species_code: Tree species code

...

Package {forestat}

Calculate Fitting Evaluation Indices

Description

Usage

Arguments

Details

Value

Examples

Birch Dataset

Description

Usage

Format

Details

Calculating degraded forest grade

Description

Usage

Arguments

Details

Value

Examples

Calculate the site classes based on stand height growth

Description

Usage

Arguments

Details

Value

Examples

Preprocess the degraded forest data

Description

Usage

Arguments

Details

Value

Examples

Mixed birch-broadleaf forest data

Description

Usage

Format

Larch Dataset

Description

Usage

Format

Details

Qinghai Spruce Dataset

Description

Usage

Format

ForestData Plot

Description

Usage

Arguments

Value

Examples

1st period sample plot survey data

Description

Usage

Format

2nd period sample plot survey data

Description

Usage

Format

3rd period sample plot survey data

Description

Usage

Format

Calculate the potential productivity.

Description

Usage

Arguments

Details

Value

Examples

Calculate the realized productivity.

Description

Usage

Arguments

Details

Value

Examples

Summary of forestData