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forestmangr
Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and non linear grouped data fitting, and statistical tests.
If you need any help, I’m available for consulting. If you find forestmangr useful, please consider supporting my efforts in developing this open-source R package for the forestry community!
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To install the stable CRAN version, use:
install.packages("forestmangr")
Or you can install forestmangr from github, for the latest dev version with:
# install.packages("devtools")
devtools::install_github("sollano/forestmangr")
Example
library(forestmangr)
library(dplyr)
data("exfm16")
head(exfm16)
#> # A tibble: 6 x 7
#> strata plot age DH N V B
#> <int> <int> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 1 1 26.4 12.4 1020 19.7 5.7
#> 2 1 1 38.4 17.2 1020 60.8 9.8
#> 3 1 1 51.6 19.1 1020 103. 13.9
#> 4 1 1 63.6 21.8 1020 136. 15.3
#> 5 1 2 26.4 15 900 27.3 6
#> 6 1 2 38.4 20.3 900 80 10.5
Now, we can fit a model for Site estimatation. With nls_table, we can
fit a non-linear model, extract it’s coefficients, and merge it with the
original data in one line. Here we’ll use Chapman & Richards model:
age_i <- 64
exfm16_fit <- exfm16 %>%
nls_table(DH ~ b0 * (1-exp(-b1* age))^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3), output="merge") %>%
mutate(site = DH *( ( (1- exp( -b1/age ))^b2 ) / (( 1 - exp(-b1/age_i))^b2 ))) %>%
select(-b0,-b1,-b2)
head(exfm16_fit)
#> strata plot age DH N V B site
#> 1 1 1 26.4 12.4 1020 19.7 5.7 22.48027
#> 2 1 1 38.4 17.2 1020 60.8 9.8 24.24290
#> 3 1 1 51.6 19.1 1020 103.4 13.9 22.07375
#> 4 1 1 63.6 21.8 1020 136.5 15.3 21.89203
#> 5 1 2 26.4 15.0 900 27.3 6.0 27.19388
#> 6 1 2 38.4 20.3 900 80.0 10.5 28.61226
Now, to fit Clutter’s model, we can use the fit_clutter function,
indicating the DH, B, V, site and Plot variable names:
coefs_clutter <- fit_clutter(exfm16_fit, "age", "DH", "B", "V", "site", "plot")
coefs_clutter
#> b0 b1 b2 b3 a0 a1
#> 1 1.398861 -28.84038 0.0251075 1.241779 1.883471 0.05012873
Now, say we wanted to do a Simple Random Sampling Forest Inventory, with 20% as a accepted error. First, let’s load the package and some data:
library(forestmangr)
data("exfm2")
data("exfm3")
data("exfm4")
head(exfm3,10)
#> # A tibble: 10 x 3
#> TOTAL_AREA PLOT_AREA VWB
#> <dbl> <int> <int>
#> 1 46.8 3000 41
#> 2 46.8 3000 33
#> 3 46.8 3000 24
#> 4 46.8 3000 31
#> 5 46.8 3000 10
#> 6 46.8 3000 32
#> 7 46.8 3000 62
#> 8 46.8 3000 16
#> 9 46.8 3000 66
#> 10 46.8 3000 25
First we should try a pilot inventory, to see if the number of plots sampled is enough for reaching the desired error:
sprs(exfm3, "VWB", "PLOT_AREA", "TOTAL_AREA", error = 20, pop = "fin")
#> Variables Values
#> 1 Total number of sampled plots (n) 10.0000
#> 2 Number of maximum plots (N) 156.0000
#> 3 Variance Quoeficient (VC) 53.2670
#> 4 t-student 2.2622
#> 5 recalculated t-student 2.0452
#> 6 Number of samples regarding the admited error 25.0000
#> 7 Variance (S2) 328.0000
#> 8 Standard deviation (s) 18.1108
#> 9 Mean (Y) 34.0000
#> 10 Standard error of the mean (Sy) 5.5405
#> 11 Absolute Error 12.5335
#> 12 Relative Error (%) 36.8634
#> 13 Estimated Total Value (Yhat) 5304.0000
#> 14 Total Error 1955.2326
#> 15 Inferior Confidence Interval (m3) 21.4665
#> 16 Superior Confidence Interval (m3) 46.5335
#> 17 Inferior Confidence Interval (m3/ha) 71.5549
#> 18 Superior Confidence Interval (m3/ha) 155.1118
#> 19 inferior Total Confidence Interval (m3) 3348.7674
#> 20 Superior Total Confidence Interval (m3) 7259.2326
We can see that we have 10 plots, but 15 more are needed if we want a minimum of 20% error. The exfm4 data has new samples, that we now can use to run a definitive inventory:
sprs(exfm4, "VWB", "PLOT_AREA", "TOTAL_AREA", error = 20, pop = "fin")
#> Variables Values
#> 1 Total number of sampled plots (n) 25.0000
#> 2 Number of maximum plots (N) 156.0000
#> 3 Variance Quoeficient (VC) 45.4600
#> 4 t-student 2.0639
#> 5 recalculated t-student 2.0930
#> 6 Number of samples regarding the admited error 20.0000
#> 7 Variance (S2) 226.6933
#> 8 Standard deviation (s) 15.0563
#> 9 Mean (Y) 33.1200
#> 10 Standard error of the mean (Sy) 2.7595
#> 11 Absolute Error 5.6952
#> 12 Relative Error (%) 17.1957
#> 13 Estimated Total Value (Yhat) 5166.7200
#> 14 Total Error 888.4555
#> 15 Inferior Confidence Interval (m3) 27.4248
#> 16 Superior Confidence Interval (m3) 38.8152
#> 17 Inferior Confidence Interval (m3/ha) 91.4159
#> 18 Superior Confidence Interval (m3/ha) 129.3841
#> 19 inferior Total Confidence Interval (m3) 4278.2645
#> 20 Superior Total Confidence Interval (m3) 6055.1755
The desired error was met.
The exfm2 data has a strata variable. Say we wanted to run a SRS inventory for every stand. We can do this with the .groups argument:
head(exfm2,10)
#> # A tibble: 10 x 4
#> STRATA STRATA_AREA PLOT_AREA VWB
#> <int> <dbl> <int> <dbl>
#> 1 1 14.4 1000 7.9
#> 2 1 14.4 1000 3.8
#> 3 1 14.4 1000 4.4
#> 4 1 14.4 1000 6.25
#> 5 1 14.4 1000 5.55
#> 6 1 14.4 1000 8.1
#> 7 1 14.4 1000 6.1
#> 8 1 14.4 1000 6.6
#> 9 1 14.4 1000 7.4
#> 10 1 14.4 1000 5.35
sprs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA",.groups="STRATA", error = 20, pop = "fin")
#> Variables STRATA1 STRATA2 STRATA3
#> 1 Total number of sampled plots (n) 14.0000 20.0000 23.0000
#> 2 Number of maximum plots (N) 144.0000 164.0000 142.0000
#> 3 Variance Quoeficient (VC) 24.4785 15.8269 16.7813
#> 4 t-student 2.1604 2.0930 2.0739
#> 5 recalculated t-student 2.4469 4.3027 4.3027
#> 6 Number of samples regarding the admited error 9.0000 11.0000 12.0000
#> 7 Variance (S2) 2.1829 3.6161 5.3192
#> 8 Standard deviation (s) 1.4774 1.9016 2.3063
#> 9 Mean (Y) 6.0357 12.0150 13.7435
#> 10 Standard error of the mean (Sy) 0.3752 0.3984 0.4402
#> 11 Absolute Error 0.8105 0.8339 0.9130
#> 12 Relative Error (%) 13.4288 6.9409 6.6431
#> 13 Estimated Total Value (Yhat) 869.1429 1970.4600 1951.5739
#> 14 Total Error 116.7157 136.7670 129.6455
#> 15 Inferior Confidence Interval (m3) 5.2252 11.1811 12.8305
#> 16 Superior Confidence Interval (m3) 6.8462 12.8489 14.6565
#> 17 Inferior Confidence Interval (m3/ha) 52.2519 111.8105 128.3048
#> 18 Superior Confidence Interval (m3/ha) 68.4624 128.4895 146.5647
#> 19 inferior Total Confidence Interval (m3) 752.4271 1833.6930 1821.9284
#> 20 Superior Total Confidence Interval (m3) 985.8586 2107.2270 2081.2194
We can also run a stratified random sampling inventory with this data:
strs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA", "STRATA", error = 20, pop = "fin")
#> $Table1
#> Variables STRATA 1 STRATA 2
#> 1 Plot Area 1000.0000 1000.0000
#> 2 Number of sampled plots per stratum (nj) 14.0000 20.0000
#> 3 Total number of sampled plots (n) 57.0000 57.0000
#> 4 Number of maximum plots per stratum (Nj) 144.0000 164.0000
#> 5 Number of maximum plots (N) 450.0000 450.0000
#> 6 Nj/N Ratio (Pj) 0.3200 0.3644
#> 7 Stratum sum (Eyj) 84.5000 240.3000
#> 8 Stratum quadratic sum (Eyj2) 538.3950 2955.9100
#> 9 Mean of Yi per stratum (Yj) 6.0357 12.0150
#> 10 PjSj2 0.6985 1.3179
#> 11 PjSj 0.4728 0.6930
#> 12 PjYj 1.9314 4.3788
#> 13 t-student 2.0032 2.0032
#> 14 recalculated t-student 3.1824 3.1824
#> 15 Number of samples regarding the admited error 8.0000 8.0000
#> 16 Optimal number of samples per stratum (nj optimal) 2.0000 3.0000
#> 17 Optimal number of samples (n optimal) 9.0000 9.0000
#> 18 Total value of Y per stratum (Yhatj) 869.1429 1970.4600
#> STRATA 3
#> 1 1000.0000
#> 2 23.0000
#> 3 57.0000
#> 4 142.0000
#> 5 450.0000
#> 6 0.3156
#> 7 316.1000
#> 8 4461.3350
#> 9 13.7435
#> 10 1.6785
#> 11 0.7278
#> 12 4.3368
#> 13 2.0032
#> 14 3.1824
#> 15 8.0000
#> 16 4.0000
#> 17 9.0000
#> 18 1951.5739
#>
#> $Table2
#> Variables value
#> 1 t-student 2.0032
#> 2 Standard error of the mean (Sy) 0.2339
#> 3 Stratified Variance 3.6949
#> 4 Stratified Standard Deviation 1.8936
#> 5 Variance Quoeficient (VC) 17.7851
#> 6 Stratified Mean (Y) 10.6471
#> 7 Absolute Error 0.4685
#> 8 Relative Error (%) 4.4003
#> 9 Estimated Total Value (Yhat) 4791.1768
#> 10 Total Error 210.8250
#> 11 Inferior Confidence Interval (m3) 10.1786
#> 12 Superior Confidence Interval (m3) 11.1156
#> 13 Inferior Confidence Interval (m3/ha) 101.7856
#> 14 Superior Confidence Interval (m3/ha) 111.1556
#> 15 inferior Total Confidence Interval (m3) 4580.3518
#> 16 Superior Total Confidence Interval (m3) 5002.0018
Citation
To cite this package in publications, use:
ABNT:
BRAGA S. R.; OLIVEIRA, M. L. R.; GORGENS, E. B. forestmangr: Forest Mensuration and Management. R package version 0.9.2, 2020. Disponível em: https://CRAN.R-project.org/package=forestmangr
APA:
Sollano Rabelo Braga, Marcio Leles Romarco de Oliveira and Eric Bastos Gorgens (2020). forestmangr: Forest Mensuration and Management. R package version 0.9.2. https://CRAN.R-project.org/package=forestmangr
License
This project is licensed under the MIT License - see the LICENSE file for details
Acknowledgments
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This project was developed on the Forest Management Lab, DEF, UFVJM, Diamantina/Minas Gerais - Brazil.
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This project came to be as a mean to make the life of a forestry engeneer a little easier and pratical. We’d like to thank everyone at UFVJM that has in anyway helped this project grow.
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We’d like to thank UFVJM, FAPEMIG, CNPq e CAPES for the support.