Package 'forestGYM'

Title: Forest Growth and Yield Model Based on Clutter Model
Description: The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021)<doi:10.1007/s10342-021-01380-1>. Guera O, Silva J, Ferreira R, et al(2019)<doi:10.1590/2179-8087.038117>.
Authors: Zongzheng Chai [aut, cre]
Maintainer: Zongzheng Chai <[email protected]>
License: GPL-2
Version: 1.0.0
Built: 2025-02-11 05:16:27 UTC
Source: https://github.com/cran/forestGYM

Help Index

Construction of stand growth model based on Clutter model.

Description

Construction of stand growth model based on Clutter model.

Usage

clutter_mod(growthdata,object="coef")

Arguments

growthdata

The data used to construct the stand growth model is in the format of data.frame and includes at least t1, t2, G1, G2, M1, M2, and SI. For specific meanings, see standgrowth.

object

object is a type of fitted model object. It has methods for the generic functions anova, coef, confint, deviance, df.residual, fitted, formula, logLik, predict, print, profile, residuals, summary, vcov and weights.see Details of nls function.

Details

Construction of stand growth model based on Clutter model.

Value

The returned data format is a list, data summary for Clutter model.

Author(s)

Zongzheng Chai, [email protected]

References

Clutter, J. L. (1963) Compatible Growth For Loblolly by the Southeastern, Forest Science, 9(3), pp. 354–371. Sullivan, A. D. and Clutter, J. L. (1972) A Simultaneous Growth and Yield for Loblolly Pine, Forest Science, 18(1), pp. 76–86.

Examples

data(standgrowth)
clutter_mod(growthdata=standgrowth,object="coef")

Data summary for stand growth prediction of Clutter model integrating simulated logging.

Description

At the determined final harvest period, through the setting of different logging periods and the determination of logging intensities for different cutting periods, the Clutter model is used to realize stand growth prediction.

Usage

clutter_pre(b0,b1,b2,b3,a0,a1,
                   B1,SI,t1,growth_years,
                   thinning_years,thinning_intensity)

Arguments

b0

Regression coefficients of Clutter model.

b1

Regression coefficients of Clutter model.

b2

Regression coefficients of Clutter model.

b3

Regression coefficients of Clutter model.

a0

Regression coefficients of Clutter model.

a1

Regression coefficients of Clutter model.

SI

Site index

t1

Initial stand age,the unit is year.

B1

Basal area in t1, the unit is m2/ha.

growth_years

The final logging period is the main cutting period of the stand,the unit is year.

thinning_years

Different logging periods,the value is between t1 and growth_years,the unit is year.

thinning_intensity

Logging intensities corresponding to the thinning_years,the value is betwee 0 and 1.

Details

Both growth_years and thinning_years should be integers, the value of thinning_years is between t1 and growth_years,the unit is year.

Value

The returned data format is a list, data summary for stand growth prediction of Clutter model integrating simulated logging.

Author(s)

Zongzheng Chai, [email protected]

References

Clutter, J. L. (1963) Compatible Growth For Loblolly by the Southeastern, Forest Science, 9(3), pp. 354–371. Sullivan, A. D. and Clutter, J. L. (1972) A Simultaneous Growth and Yield for Loblolly Pine, Forest Science, 18(1), pp. 76–86.

Examples

clutter_simulation(b0=2.0137,b1=0.0795,b2=-16.9509,b3=0.7924,
                   a0=1.1656,a1=0.1376,
                   B1=3.1,SI=12,t1=10,growth_years=30,
                   thinning_years=c(15,25),thinning_intensity=c(0.1,0.5))

Stand growth prediction of Clutter model based on optimal logging.

Description

Through the enumeration method, achieve the optimal volume growth based on independent simulated logging.

Usage

clutter_simopt(b0,b1,b2,b3,a0,a1,
              B1,SI,t1,growth_years,
              times,smallest_interval,
              thinning_intensity)

Arguments

b0

Regression coefficients of Clutter model.

b1

Regression coefficients of Clutter model.

b2

Regression coefficients of Clutter model.

b3

Regression coefficients of Clutter model.

a0

Regression coefficients of Clutter model.

a1

Regression coefficients of Clutter model.

SI

Site index

t1

Initial stand age,the unit is year.

B1

Basal area in t1, the unit is m2/ha.

growth_years

The final logging period is the main cutting period of the stand,the unit is year.

times

Logging times.

smallest_interval

Smallest interval among Logging times (times).

thinning_intensity

Range of logging intensities,the value is betwee 0 and 1.

Details

Through the enumeration method, achieve the optimal volume growth based on independent simulated logging.

Value

The returned data format is a list, data summary for the optimal volume growth based on independent simulated logging.

Author(s)

Zongzheng Chai, [email protected]

References

Clutter, J. L. (1963) Compatible Growth For Loblolly by the Southeastern, Forest Science, 9(3), pp. 354–371. Sullivan, A. D. and Clutter, J. L. (1972) A Simultaneous Growth and Yield for Loblolly Pine, Forest Science, 18(1), pp. 76–86.

Examples

clutter_simopt(b0=2.0137,b1=0.0795,b2=-16.9509,b3=0.7924,
               a0=1.1656,a1=0.1376,
               B1=3.1,SI=12,t1=10,
               growth_years=30,
               times=2,smallest_interval=5,
               thinning_intensity=seq(0.1,0.3,0.1))

Stand growth prediction of Clutter model integrating simulated logging.

Description

At the determined final harvest period, through the setting of different logging periods and the determination of logging intensities for different cutting periods, the Clutter model is used to realize stand growth prediction.

Usage

clutter_simulation(b0,b1,b2,b3,a0,a1,
                   B1,SI,t1,growth_years,
                   thinning_years,thinning_intensity)

Arguments

b0

Regression coefficients of Clutter model.

b1

Regression coefficients of Clutter model.

b2

Regression coefficients of Clutter model.

b3

Regression coefficients of Clutter model.

a0

Regression coefficients of Clutter model.

a1

Regression coefficients of Clutter model.

SI

Site index

t1

Initial stand age,the unit is year.

B1

Basal area in t1, the unit is m2/ha.

growth_years

The final logging period is the main cutting period of the stand,the unit is year.

thinning_years

Different logging periods,the value is between t1 and growth_years,the unit is year.

thinning_intensity

Logging intensities corresponding to the thinning_years,the value is betwee 0 and 1.

Details

Both growth_years and thinning_years should be integers, the value of thinning_years is between t1 and growth_years,the unit is year.

Value

The returned data format is a list, representing the changes in stand basal area and volume growth in different logging periods.

Author(s)

Zongzheng Chai, [email protected]

Examples

clutter_simulation(b0=2.0137,b1=0.0795,b2=-16.9509,b3=0.7924,
                   a0=1.1656,a1=0.1376,
                   B1=3.1,SI=12,t1=10,growth_years=30,
                   thinning_years=c(15,25),thinning_intensity=c(0.1,0.5))

Estimation of stand volume growth dynamic based on Clutter model.

Description

The dynamic prediction of stand volume in a specified prediction year is based on the Clutter model.

Usage

estV(b0,b1,b2,b3,a0,a1,B1,t1,t2,SI)

Arguments

b0

Regression coefficients of Clutter model.

b1

Regression coefficients of Clutter model.

b2

Regression coefficients of Clutter model.

b3

Regression coefficients of Clutter model.

a0

Regression coefficients of Clutter model.

a1

Regression coefficients of Clutter model.

SI

Site index

t1

Initial stand age,the unit is year.

t2

Stand age in the future period corresponding to volume prediction,the unit is year.

B1

Basal area in t1, the unit is m2/ha.

Details

Both t1 and t2 should be integers, the value of t2 should be bigger than t1,the unit is year.

Value

prediction results of stand volume in a specified prediction year is based on the Clutter model.

Author(s)

Zongzheng Chai, [email protected]

References

Clutter, J. L. (1963) Compatible Growth For Loblolly by the Southeastern, Forest Science, 9(3), pp. 354–371. Sullivan, A. D. and Clutter, J. L. (1972) A Simultaneous Growth and Yield for Loblolly Pine, Forest Science, 18(1), pp. 76–86.

Examples

#Volume prediction for a specific year.
estV(b0=2.0137,b1=0.0795,b2=-16.9509,b3=0.7924,
     a0=1.1656,a1=0.1376,
     B1=3.1,t1=10,t2=100,SI=12)

#Volume prediction for several specific years.
estV(b0=2.0137,b1=0.0795,b2=-16.9509,b3=0.7924,
     a0=1.1656,a1=0.1376,
     B1=3.1,t1=10,t2=c(15,30,46,85),SI=12)

#Volume prediction for continuous years.
estV(b0=2.0137,b1=0.0795,b2=-16.9509,b3=0.7924,
            a0=1.1656,a1=0.1376,
            B1=3.1,t1=10,t2=11:100,SI=12)

Calculation of annal and mean increment of stand volume.

Description

Calculation of annal and mean increment of stand volume based on growth dynamic data of stand volume

Usage

increment(Vpre)

Arguments

Vpre

Growth dynamic data of stand volume, the data format is the data.frame.

Details

Growth dynamic data of stand volume, the data format is the data.frame.

Value

Data included the annal and mean increment of stand volume.

Author(s)

Zongzheng Chai, [email protected]

References

NULL

Examples

Vdyn<-estV(b0=2.0137,b1=0.0795,b2=-16.9509,b3=0.7924,
     a0=1.1656,a1=0.1376,
     B1=3.1,t1=10,t2=11:100,SI=12)
increment(Vpre=Vdyn$Value)

Data for construction of stand growth model.

Description

The forest survey data of two periods typically contain valuable information for analyzing forest growth and changes.

Usage

data("standgrowth")

Format

A data frame with 330 observations on the following 16 variables from the forest survey data of two periods

plot

Id of forest plot.

SI

Site index

t1

Time period 1, the unit is year.

D1

Average DBH in t1, the unit is cm.

H1

Average tree height in t1, the unit is m.

DH1

Top height in t1, the unit is m.

N1

Stand density in t1, the unit is N/ha.

G1

Basal area in t1, the unit is m2/ha.

M1

Volume in t1, the unit is m3/ha.

t2

Time period 2, the unit is year.

D2

Average DBH in t2, the unit is cm.

H2

Average tree height in t2, the unit is m.

DH2

Top height in t2, the unit is m.

N2

Stand density in t2, the unit is N/ha.

G2

Basal area in t2, the unit is m2/ha.

M2

Volume in t2, the unit is m3/ha.

Details

The forest survey data of two periods typically contain valuable information for analyzing forest growth and changes.

Author(s)

Zongzheng Chai, [email protected]

Examples

data(standgrowth)
standgrowth

Integrated results of clutter_simulation function.

Description

Integrated results of clutter_simulation function.

Usage

Vres(x)

Arguments

x

Results of clutter_simulation function.

Details

Integrated results of clutter_simulation function and to make the data presentation more intuitive and easy to understand.

Value

prediction results of stand volume prediction.

Author(s)

Zongzheng Chai, [email protected]

Examples

Vresult<-clutter_simulation(b0=2.0137,b1=0.0795,b2=-16.9509,b3=0.7924,
                       a0=1.1656,a1=0.1376,
                       B1=3.1,SI=12,t1=10,growth_years=30,
                       thinning_years=c(15,25),thinning_intensity=c(0.1,0.5))
Vres(Vresult)