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Forest Carbon Sequestration and Potential Productivity Calculation

Forestat version: 1.1.0

Date: 10/10/2023


Forestat is an R package based on Methodology and Applications of Site Quality Assessment Based on Potential Mean Annual Increment [1] and A basal area increment-based approach of site productivity evaluation for multi-aged and mixed forests [2] proposed by the Institute of Forest Resource Information Techniques, Chinese Academy of Forestry. This package can be used to classify site classes based on the stand height growth and establish 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.

1 Overview

Forestat can be used to implement the calculation of carbon sequestration potential productivity and the assessment of degraded forests. The calculation of carbon sequestration potential productivity includes the assessment of site classes based on stand height growth, establishment of the growth models of height (H-model), basal area at breast-height (BA-model), and biomass (Bio-model), as well as calculation of stand’s realized site productivity and potential productivity. The H-model can be constructed using Richard, Logistic, Korf, Gompertz, Weibull, and Schumacher model, while the BA-model and Bio-model can only be constructed using Richard model. The calculation of carbon sequestration potential productivity relies on data from several plots for a given forest type (tree species). The assessment of degraded forests relies on data from several trees and sample plots. Some sample datas are provided in the Forestat package.

1.1 forestat Flowchart

Figure 1.1 Flowchart of the carbon sequestration potential productivity calculation


Figure 1.2 Flowchart of degraded forest assessment

1.2 R Packages Required by forestat

Package Download Link
dplyr https://CRAN.R-project.org/package=dplyr
ggplot2 https://CRAN.R-project.org/package=ggplot2
nlme https://CRAN.R-project.org/package=nlme

2 Installation

2.1 Install from CRAN or GitHub

To install forestat from CRAN in R, use the following command:

# Install forestat
install.packages("forestat")

Alternatively, you can install forestat from GitHub in R using the following command:

# Install devtools
install.packages("devtools")

# Install forestat
devtools::install_github("caf-ifrit/forestat/forestat")

2.2 Load forestat

library(forestat)

3 Quick Start

This part demonstrates the complete steps to perform the calculation of stand’s site classes, realized site productivity and potential productivity quickly using the sample dataset called forestData included in the package.

# Load the forestData sample data included in the package
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, H_start=c(a=20,b=0.05,c=1.0))

# Plot the scatter plot of the H-model
plot(forestData,model.type="H",plot.type="Scatter",
     title="The H-model scatter plot of the mixed birch-broadleaf forest")

# Calculate the potential productivity of the forestData object
forestData <- potential.productivity(forestData)

# Calculate the realized productivity of the forestData object
forestData <- realized.productivity(forestData)

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

This part demonstrates the complete steps to perform the assessment of degraded forests using the sample data: tree_1, tree_2, tree_3, plot_1, plot_2, and plot_3 included in the package.

# Load the sample data tree_1, tree_2, tree_3, plot_1, plot_2, and plot_3 included in the package
data(tree_1)
data(tree_2)
data(tree_3)
data(plot_1)
data(plot_2)
data(plot_3)

# Preprocessing 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
res_data <- calc_degraded_forest_grade(plot_data)

# View calculation results
res_data

4 Carbon Sequestration Potential Productivity Calculation

4.1 Build Model
4.1.1 Custom Data

To build an accurate model, high quality data is essential. The forestat package includes a cleaned sample dataset that can be loaded and viewed using the following command:

# Load the forestData sample data included in the package
data("forestData")

# Select the ID, code, AGE, H, S, BA, and Bio fields from the forestData sample data 
# and view the first 6 rows of data
head(dplyr::select(forestData, ID, code, AGE, H, S, BA, Bio))

# Output
  ID code AGE   H         S       BA       Bio
1  1    1  13 2.0 152.67461 4.899382 32.671551
2  2    1  15 3.5  68.23825 1.387268  5.698105
3  3    1  20 4.2 128.32683 3.388492 22.631467
4  4    1  19 4.2 204.93928 4.375324 18.913886
5  5    1  13 4.2  95.69713 1.904063  6.511951
6  6    1  25 4.7 153.69393 4.129810 28.024739

Of course, you can also choose to load custom data:

# Load custom data
forestData <- read.csv("/path/to/your/folder/your_file.csv")

The data from customers is required to have the csv or excel xlsx format. The following columns or fields including ID (plot ID), code (forest type code of plot), AGE (the average age of stand), and H (the average height of stand) are required to build the H-Model and make the relevant example graphs.

The S (stand density index), BA (stand basal area), and Bio (stand biomass) are optional fields to build the BA-model and Bio-model.

In the subsequent calculation of potential productivity and realized productivity, the BA-model and Bio-model are required. That is, if the customized data lacks the S, BA, and Bio fields, potential productivity and realized productivity cannot be calculated.

Figure 2. Custom data format requirements


4.1.2 Build Stand Growth Model

After the data is loaded, forestat will use the class.plot() function to build a stand growth model. If the custom data contains the ID, code, AGE, H, S, BA, Bio fields, the H-model, BA-model, and Bio-model will be built simultaneously. If only the ID, code, AGE, H fields are included, only the H-model will be built.

# Use the Richards model to build a stand growth model
# interval = 5 indicates that the initial stand age interval for height classes is set to 5, number = 5 indicates that the maximum number of initial height classes is 5, and maxiter=1000 sets the maximum number of model fitting iterations to 1000
# The initial parameters for H-model fitting is set to H_start=c(a=20,b=0.05,c=1.0) by default
# The initial parameters for H-model fitting is set to BA_start=c(a=80, b=0.0001, c=8, d=0.1) by default
# The initial parameters for H-model fitting is set to Bio_start=c(a=450, b=0.0001, c=12, d=0.1) by default
forestData <- class.plot(forestData, model = "Richards",
                         interval = 5, number = 5, maxiter=1000,
                         H_start=c(a=20,b=0.05,c=1.0),
                         BA_start = c(a=80, b=0.0001, c=8, d=0.1),
                         Bio_start=c(a=450, b=0.0001, c=12, d=0.1))

The model parameter is the model used to build the H-model. Optional models include "Logistic", "Richards", "Korf", "Gompertz", "Weibull", and "Schumacher". The BA-model and Bio-model are built using the Richard model by default. interval parameter is the initial stand age interval for height classes, number parameter is the maximum number of initial height classes, and maxiter parameter is the maximum number of fitting iterations. The H_start is the initial parameter for fitting the H-model, the BA_start is the initial parameter for fitting the BA-model, and the Bio_start is the initial parameter for fitting the Bio-model. If fitting encounters errors, you can try different initial parameters as attempts.

The result returned by the class.plot() function is the forestData object, which includes Input (input data and height classes results), Hmodel (H-model results), BAmodel (BA-model results), Biomodel (Bio-model results), and output (Expressions, parameters, and precision for all models).

Figure 3. Structure of the forestData object


4.1.3 Obtain Summary Data

To understand the establishment of the model, you can use the summary(forestData) function to obtain the summary data of the forestData object. The function returns the summary.forestData object and outputs the relevant data to the screen.

The first paragraph of the output is the summary of the input data, and the second, third, and fourth paragraphs are the parameters and concise reports of the H-model, BA-model, and Bio-model, respectively.

summary(forestData)
# Output
# First paragraph
       H               S                 BA              Bio         
 Min.   : 2.00   Min.   :  68.24   Min.   : 1.387   Min.   :  5.698  
 1st Qu.: 8.10   1st Qu.: 366.37   1st Qu.: 9.641   1st Qu.: 52.326  
 Median :10.30   Median : 494.76   Median :13.667   Median : 78.502  
 Mean   :10.62   Mean   : 522.53   Mean   :14.516   Mean   : 90.229  
 3rd Qu.:12.90   3rd Qu.: 661.84   3rd Qu.:18.750   3rd Qu.:115.636  
 Max.   :19.10   Max.   :1540.13   Max.   :45.749   Max.   :344.412  

# Second paragraph
H-model Parameters:
Nonlinear mixed-effects model fit by maximum likelihood
  Model: H ~ 1.3 + a * (1 - exp(-b * AGE))^c 
  Data: data 
       AIC      BIC    logLik
  728.4366 747.2782 -359.2183

Random effects:
 Formula: a ~ 1 | LASTGROUP
               a  Residual
StdDev: 3.767163 0.7035752

Fixed effects:  a + b + c ~ 1 
      Value Std.Error  DF  t-value p-value
a 12.185054 1.7050081 313 7.146625       0
b  0.037840 0.0043682 313 8.662536       0
c  0.761367 0.0769441 313 9.895060       0
 Correlation: 
  a      b     
b -0.110       
c -0.093  0.946

Standardized Within-Group Residuals:
         Min           Q1          Med           Q3          Max 
-3.858592084 -0.719253472  0.007120413  0.761123585  3.375793806 

Number of Observations: 320
Number of Groups: 5 

Concise Parameter Report:
Model Coefficients:
       a1       a2       a3       a4       a5          b         c
 7.013778 9.575677 11.90324 14.67456 17.75801 0.03783956 0.7613666

Model Evaluations:
           pe      RMSE        R2       Var       TRE      AIC      BIC    logLik
 -0.006484677 0.6980625 0.9543312 0.4887767 0.3960163 728.4366 747.2782 -359.2183

Model Formulas:
                                       Func                  Spe
 model1:H ~ 1.3 + a * (1 - exp(-b * AGE))^c model1:pdDiag(a ~ 1)

# Third paragraph (similar data format to the second paragraph)
BA-model Parameters:

# Omitted here
......

# Fourth paragraph (similar data format to the second paragraph)
Bio-model Parameters:

# Omitted here
......

4.2 Make Graphs

After constructing the stand growth model using the class.plot() function in 4.1.2, you can use the plot() function to make graphs.

The model.type parameter specifies the model used for plotting, which include H, BA, or Bio. The plot.type parameter specifies the type of plot, which can be Curve, Residual, Scatter_Curve, or Scatter. The xlab, ylab, legend.lab, and title parameters represent the x-axis label, y-axis label, legend, and title of the graph, respectively.

# Plot the curve of the H-model
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")

# Plot the scatter plot of the BA-model
plot(forestData,model.type="BA",
     plot.type="Scatter",
     xlab="Stand age (year)",ylab=expression(paste("Basal area ( ",m^2,"/",hm^2,")")),legend.lab="Site class",
     title="The BA-model scatter plot of the mixed birch-broadleaf forest")

The sample plots produced by different plot.type values are shown in Figure 4:

Figure 4. Sample plots produced by different plot.type values


4.3 Calculate the Potential Productivity of Forest

After constructing the stand growth model using the class.plot() function in 4.1.2, the potential productivity of stand can be calculated using the potential.productivity() function. Before calculation, it is required that the BA-model and Bio-model have been developed in 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)

In the above code, the parameter code is the forest type code. The age.min and age.max represent the minimum and maximum age of the stand, and the calculation of potential productivity will be performed within this range. The left and right are the initial parameters for fitting the model. When fitting fails, try multiple initial parameters. The e is the precision of the fitting model. When the residual is less than e, the model is considered to have converged and the fitting is stopped. The maxiter is the maximum number of iterations to the fitted model. When the number of fittings equals maxiter, the model is considered to have converged and the fitting is stopped.


4.3.1 Description of Potential Productivity Output

After the calculation, the following command can be used to view and output the results:

library(dplyr)
forestData$potential.productivity %>% head(.)
# Output
    Max_GI   Max_MI       N1       D1       S0       S1       G0       G1       M0       M1 LASTGROUP AGE
1 3.949820 20.47488 9830.149 6.945724 1645.486 1800.378 33.29664 37.24646 119.5148 139.9897         1   5
2 3.348912 17.90140 8823.972 7.294578 1619.740 1748.342 33.52799 36.87690 125.2417 143.1431         1   6
3 2.906982 15.94796 8044.876 7.609892 1596.350 1705.999 33.68334 36.59033 130.1117 146.0597         1   7
4 2.568525 14.40953 7418.938 7.898755 1574.827 1670.207 33.78520 36.35373 134.3302 148.7398         1   8
5 2.300998 13.16340 6902.612 8.166065 1554.965 1639.234 33.85073 36.15173 138.0482 151.2116         1   9
6 2.084278 12.13145 6467.402 8.415423 1536.461 1611.846 33.88831 35.97259 141.3594 153.4908         1  10

The meanings of the fields in the output are as follows:

Max_GI: Maximum annual increment of stand basal area

Max_MI: Maximum annual increment of biomass

N1: Number of trees in stand at potential increment

D1: Stand average diameter at potential increment

S0: Initial stand density index

S1: Optimal stand density index at potential increment

G0: Initial stand basal area per hectare

G1: Stand basal area per hectare at potential increment (1 year later)

M0: Initial stand biomass per hectare

M1: Stand biomass per hectare at potential increment


4.4 Calculate the Realized Productivity of Forest

After constructing the stand growth model using the class.plot() function in 4.1.2, the actual or realized productivity of the stand can be calculated using the realized.productivity() function. Prior to the calculation, it is required that the BA-model and Bio-model have been obtained in the forestData object.

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

Here, the left and right parameters are the initial parameters for fitting the model. When fitting errors occur, multiple attempts with different initial parameters can be made.


4.4.1 Explanation of Realized Productivity Output Data

After the calculation is completed, the following command can be used to view and output the results:

library(dplyr)
forestData$realized.productivity %>% head(.)
# Output
  code ID AGE   H class0 LASTGROUP       BA         S       Bio        BAI        VI
1    1  1  13 2.0      1         1 4.899382 152.67461 32.671551 0.18702090 1.0034425
2    1  2  15 3.5      1         1 1.387268  68.23825  5.698105 0.07181113 0.3804467
3    1  3  20 4.2      1         1 3.388492 128.32683 22.631467 0.10764262 0.6294930
4    1  4  19 4.2      1         1 4.375324 204.93928 18.913886 0.18279397 1.0839852
5    1  5  13 4.2      2         1 1.904063  95.69713  6.511951 0.11526498 0.6028645
6    1  6  25 4.7      1         1 4.129810 153.69393 28.024739 0.10696539 0.6640617

The meaning of each field in the output results is as follows:

BAI: Realized productivity of BA

VI: Realized productivity of Bio


4.5 Details of Potential and Realized Productivity Data

After obtaining the potential and realized productivity of the stand, you can use the summary(forestData) function to obtain the summary data of the forestData object. This function returns a summary.forestData object and outputs the relevant data to the screen.

The first four sections of the output were introduced in 4.1.3, and the fifth section provides details of the potential and realized productivity data.

summary(forestData)
# Output
# First paragraph
       H               S                 BA              Bio         
 Min.   : 2.00   Min.   :  68.24   Min.   : 1.387   Min.   :  5.698  
 
# Omitted here
......

# Fifth paragraph
     Max_GI           Max_MI      
 Min.   :0.1446   Min.   : 1.216  
 1st Qu.:0.2046   1st Qu.: 1.813  
 Median :0.3023   Median : 2.562  
 Mean   :0.5477   Mean   : 4.029  
 3rd Qu.:0.5702   3rd Qu.: 4.446  
 Max.   :4.4483   Max.   :26.961   

      BAI                VI        
 Min.   :0.06481   Min.   :0.3804  
 1st Qu.:0.16296   1st Qu.:1.3086  
 Median :0.22507   Median :1.8154  
 Mean   :0.25199   Mean   :1.9743  
 3rd Qu.:0.30246   3rd Qu.:2.4227  
 Max.   :0.98168   Max.   :6.6287

5 Degraded Forest Assessment

5.1 Preprocess the Degraded Forest Data

Sample data is built into the forestat package, including three tree data of tree_1, tree_2 and tree_3 and three sample plot data of plot_1, plot_2 and plot_3. You can load and view the sample data using the following command:

# Load tree_1 tree_2 tree_3 plot_1 plot_2 plot_3 sample data in the package
# tree_1 plot_1, tree_2 plot_2, tree_3 plot_3 are the survey data in 2005, 2010 and 2015 respectively.
data(tree_1)
data(tree_2)
data(tree_3)
data(plot_1)
data(plot_2)
data(plot_3)

# View the first 6 rows of data in tree_1
head(tree_1)

# Output
  tree_number sample_plot_number inspection_type tree_species_code   plot_id
1           3                  4              11               410 700000004
2          13                  4              14               410 700000004
3          19                  4              11               420 700000004
4          26                  4              12               420 700000004
5          28                  4              12               420 700000004
6          29                  4              12               410 700000004

# View the first 6 rows of data in plot_1
head(plot_1)

# Output
  sample_plot_number sample_plot_type altitudes slope_direction slope_position gradient soil_thickness humus_thickness
1                  2               11       410               9              6        0             60               0
2                  5               11       333               3              3        4             30              10
3                  6               11       350               2              5        1             70              20
4                  7               11       395               2              3        5             75              20
5                  8               11       438               2              4        4             80              20
6                  9               11       472               7              4        5             60              25
  land_type origin dominant_tree_species average_age age_group average_diameter_at_breast_height average_tree_height
1       180      0                     0           0         0                                 0                   0
2       111     13                   620          37         2                               125                 116
3       240      0                     0           0         0                                 0                   0
4       111     13                   620          20         1                                97                 110
5       111     11                   620          75         4                               195                  97
6       111     13                   630          35         2                               120                  89
  crown_density naturalness disaster_type disaster_level standing_stock dead_wood_stock forest_cutting_stock   plot_id
1             0           0             0              0          0.000           0.000                0.000 700000002
2            85           4            20              1          4.816           0.131                0.000 700000005
3             0           0             0              0          0.000           0.000                0.000 700000006
4            60           4             0              0          1.560           0.082                0.040 700000007
5            50           4            20              1          3.665           0.464                0.013 700000008
6            60           4            20              1          4.890           0.041                1.408 700000009

The meanings of each field in the sample data are as follows:

tree_number: Tree number

sample_plot_number: Sample plot number

inspection_type: Inspection type

tree_species_code: Tree species code

plot_id: The ID of the sample plot

sample_plot_type: The type of sample plot

altitudes: Altitude

slope_direction: Slope direction

slope_position: Slope position

gradient: Gradient

soil_thickness: Soil thickness

humus_thickness: Humus thickness

land_type: The type of land

origin: Origin

dominant_tree_species: Dominant tree species

average_age: Average age

age_group: Age group

average_diameter_at_breast_height: Average diameter at breast height

average_tree_height: Average tree height

crown_density: Crown density

naturalness: Naturalness

disaster_type: Disaster type

disaster_level: Disaster level

standing_stock: Standing stock

dead_wood_stock: Dead wood stock

forest_cutting_stock: Forest cutting stock

You can also load custom data. In the custom data, 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.

# Load openxlsx package
library("openxlsx")

# Load custom data (tree_1 tree_2 tree_3 plot_1 plot_2 plot_3) from xlsx files
tree_1 <- read.xlsx("/path/to/your/folder/tree_1.xlsx", sheet = 1)
tree_2 ...
...
5.2 Calculate the Degraded Forest Grade

After loading the data, you can use the degraded_forest_preprocess() function to complete degraded forest data preprocessing, and use the calc_degraded_forest_grade() function to complete the degraded forest grade calculation.

# Degraded forest data preprocessing
plot_data <- degraded_forest_preprocess(tree_1, tree_2, tree_3,
                                        plot_1, plot_2, plot_3)

# Degraded forest grade calculation
res_data <- calc_degraded_forest_grade(plot_data)

# View calculation results
res_data

res_data includes p1, p2, p3, p4, p5, ID, referenceID, num, p1m, p2m, p3m, p4m, Z1, Z2, Z3, Z4,Z5, Z, Z_weights, Z_grade, Z_weights_grade. The meaning is as follows:

p1: Forest accumulation growth rate

p2: Forest recruitment rate

p3: Tree species reduction rate

p4: Forest canopy cover reduction rate

p5: Forest disaster level

ID: Group ID, grouped according to origin-dominant tree species-age group

referenceID: Reference object ID

num: Number of reference objects

p1m: The reference value of Forest accumulation growth rate

p2m: The reference value of forest recruitment rate

p3m: The reference value of tree species reduction rate

p4m: The reference value of forest canopy cover reduction rate

Z1: Discriminant factor Z1

Z2: Discriminant factor Z2

Z3: Discriminant factor Z3

Z4: Discriminant factor Z4

Z5: Discriminant factor Z5

Z: the sum of discriminant factor, \(Z = Z1 + Z2 + Z3 + Z4 + Z5\)

Z_weights: Comprehensive discriminant factor, the sum of discriminant factor weights, \(Z_weights = Z1 + 0.75 \times Z2 + 0.5 \times Z3 + 0.5 \times Z4 + 0.25 \times Z5\)

Z_grade: The grade of degraded forest corresponding to Z

Z_weights_grade: The grade of degraded forest corresponding to Z_weights

6 Citation

[1]
@article{lei2018methodology,
  title={Methodology and applications of site quality assessment based on potential mean annual increment.},
  author={Lei Xiangdong, Fu Liyong, Li Haikui, Li Yutang, Tang Shouzheng},
  journal={Scientia Silvae Sinicae},
  volume={54},
  number={12},
  pages={116-126},
  year={2018},
  publisher={The Chinese Society of Forestry},
  doi={10.11707/j.1001-7488.20181213}
}

[2]
@article{fu2017basal,
  title={A basal area increment-based approach of site productivity evaluation for multi-aged and mixed forests},
  author={Fu Liyong, Sharma Ram P, Zhu Guangyu, Li Haikui, Hong Lingxia, Guo Hong, Duan Guangshuang, Shen Chenchen, Lei Yuancai, Li Yutang},
  journal={Forests},
  volume={8},
  number={4},
  pages={119},
  year={2017},
  publisher={MDPI},
  doi={10.3390/f8040119}
}

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