Set of functions for 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.
To install the stable CRAN version, use:
Or you can install forestmangr from github, for the latest dev version with:
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 10.5Now, 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 28.61226Now, 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 0.05012873Now, 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 25First 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 Mean (Y) 34.0000
#> 8 Standard error of the mean (Sy) 5.5405
#> 9 Absolute Error 12.5335
#> 10 Relative Error (%) 36.8634
#> 11 Estimated Total Value (Yhat) 5304.0000
#> 12 Total Error 1955.2326
#> 13 Inferior Confidence Interval (m3) 21.4665
#> 14 Superior Confidence Interval (m3) 46.5335
#> 15 Inferior Confidence Interval (m3/ha) 71.5549
#> 16 Superior Confidence Interval (m3/ha) 155.1118
#> 17 inferior Total Confidence Interval (m3) 3348.7674
#> 18 Superior Total Confidence Interval (m3) 7259.2326We 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 Mean (Y) 33.1200
#> 8 Standard error of the mean (Sy) 2.7595
#> 9 Absolute Error 5.6952
#> 10 Relative Error (%) 17.1957
#> 11 Estimated Total Value (Yhat) 5166.7200
#> 12 Total Error 888.4555
#> 13 Inferior Confidence Interval (m3) 27.4248
#> 14 Superior Confidence Interval (m3) 38.8152
#> 15 Inferior Confidence Interval (m3/ha) 91.4159
#> 16 Superior Confidence Interval (m3/ha) 129.3841
#> 17 inferior Total Confidence Interval (m3) 4278.2645
#> 18 Superior Total Confidence Interval (m3) 6055.1755The 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
#> 1 Total number of sampled plots (n) 14.0000 20.0000
#> 2 Number of maximum plots (N) 144.0000 164.0000
#> 3 Variance Quoeficient (VC) 24.4785 15.8269
#> 4 t-student 2.1604 2.0930
#> 5 recalculated t-student 2.4469 4.3027
#> 6 Number of samples regarding the admited error 9.0000 11.0000
#> 7 Mean (Y) 6.0357 12.0150
#> 8 Standard error of the mean (Sy) 0.3752 0.3984
#> 9 Absolute Error 0.8105 0.8339
#> 10 Relative Error (%) 13.4288 6.9409
#> 11 Estimated Total Value (Yhat) 869.1429 1970.4600
#> 12 Total Error 116.7157 136.7670
#> 13 Inferior Confidence Interval (m3) 5.2252 11.1811
#> 14 Superior Confidence Interval (m3) 6.8462 12.8489
#> 15 Inferior Confidence Interval (m3/ha) 52.2519 111.8105
#> 16 Superior Confidence Interval (m3/ha) 68.4624 128.4895
#> 17 inferior Total Confidence Interval (m3) 752.4271 1833.6930
#> 18 Superior Total Confidence Interval (m3) 985.8586 2107.2270
#> STRATA3
#> 1 23.0000
#> 2 142.0000
#> 3 16.7813
#> 4 2.0739
#> 5 4.3027
#> 6 12.0000
#> 7 13.7435
#> 8 0.4402
#> 9 0.9130
#> 10 6.6431
#> 11 1951.5739
#> 12 129.6455
#> 13 12.8305
#> 14 14.6565
#> 15 128.3048
#> 16 146.5647
#> 17 1821.9284
#> 18 2081.2194We 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 EPjSj2 3.6949 3.6949
#> 14 EPjSj 1.8936 1.8936
#> 15 Stratified mean (Y) 10.6471 10.6471
#> 16 Variance Quoeficient (VC) 17.7851 17.7851
#> 17 t-student 2.0032 2.0032
#> 18 recalculated t-student 3.1824 3.1824
#> 19 Number of samples regarding the admited error 8.0000 8.0000
#> 20 Optimal number of samples per stratum (nj optimal) 2.0000 3.0000
#> 21 Optimal number of samples (n optimal) 9.0000 9.0000
#> 22 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 3.6949
#> 14 1.8936
#> 15 10.6471
#> 16 17.7851
#> 17 2.0032
#> 18 3.1824
#> 19 8.0000
#> 20 4.0000
#> 21 9.0000
#> 22 1951.5739
#>
#> $Table2
#> Variables value
#> 1 t-student 2.0032
#> 2 Standard error of the mean (Sy) 0.2339
#> 3 Stratified Mean (Y) 10.6471
#> 4 Absolute Error 0.4685
#> 5 Relative Error (%) 4.4003
#> 6 Estimated Total Value (Yhat) 4791.1768
#> 7 Total Error 210.8250
#> 8 Inferior Confidence Interval (m3) 10.1786
#> 9 Superior Confidence Interval (m3) 11.1156
#> 10 Inferior Confidence Interval (m3/ha) 101.7856
#> 11 Superior Confidence Interval (m3/ha) 111.1556
#> 12 inferior Total Confidence Interval (m3) 4580.3518
#> 13 Superior Total Confidence Interval (m3) 5002.0018To cite this package in publications, use:
ABNT:
BRAGA S. R.; OLIVEIRA, M. L. R.; GORGENS, E. B. forestmangr: Functions for Forest Mensuration and Management. R package version 0.9.0, 2018. Disponível em: https://CRAN.R-project.org/package=forestmangr
APA:
Sollano Rabelo Braga, Marcio Leles Romarco de Oliveira and Eric Bastos Gorgens (2018). forestmangr: Functions for Forest Mensuration and Management. R package version 0.9.0. https://CRAN.R-project.org/package=forestmangr
This project is licensed under the MIT License - see the LICENSE file for details
This project was developed on the Forest Management Lab, DEF, UFVJM, Diamantina/Minas Gerais - Brazil.
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.
We’d like to thank UFVJM, FAPEMIG, CNPq e CAPES for the support.