-blue.svg){kind=icon}
README by Peter Biber
Many methods from the fields of forest growth and yield, as well as forest inventory, that belong to the core contents of forest related education were not widely available in a contemporary form so far. To cover this deficit, and to continue doing so in the long run is the driving idea behind the creation and development of the package ForestElementsR. The concept of the package is mainly based on experiences and procedures made and developed at the former Chair of Forest Growth and Yield at the Technical University of Munich. With ForestElementsR, we want to provide generic algorithms and data structures for use with forest mensurational data within a consistent framework. The functions and objects included are a collection of widely usable tools; more specialized applications should be implemented in separate packages that build on this foundation.
With ForestElementsR we would like to address a broad audience. Practitioners might want to apply it for many purposes from using single functions in a “forest pocket calculator” style to evaluate forest stand surveys by arranging functions in a workchain way. Scientists will hopefully find the package useful for carrying out routine evaluations of forest data (like volume estimates, site indexing) that are regular prerequisites for in-dephth scientific analyzes. Students could profit from applying ForestElementR’s functionality to the extensive set of example data that comes with the package. We are confident that this would help them to develop an understanding and intuition about quantitative key methods in forestry as well as about the quantities themselves.
Before we proceed, let us put out an Important Warning: While the functions and objects we provide with ForestElemensR behave exactly as described in the documentation, professional training in the fields of forestry, forest management, forest science is required to apply them correctly and to understand what their output actually means. If you are a hobby forester, you are more than welcome to make use of this package, but be sure to consult a professional before you draw important conclusions from what you get out of your evaluations. We also would like to point out another trivial wisdom that, however, seems to be ignored on a regular basis: If your data are bad, don’t expect to get good results.
In order to work with ForestElementsR you require an installation of R (version >= 4.3.0) on your system. The installation files for R itself are available here. The released version of ForestElementsR can be installed from CRAN with:
install.packages("ForestElementsR")Within this README file, we can only provide a limited set of examples with brief explanations of how to use ForestElementsR. The package, however, comes with three vignettes that provide a detailed introduction and information about important features. See the section “More Information” at the bottom of this file for how to access these vignettes.
One of the ways the package can be used is a “pocket calculator workstyle”. Assume, we have rough information about a Norway spruce forest stand as follows: The number of trees is about 820 per ha, the mean diameter is 25 cm, and the mean height is 22.5 m at a stand age of 60 years. An estimate of the stand volume is easily obtained as follows:
library(ForestElementsR)## Lade nötiges Paket: sf
## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE# Estimate the mean tree's merchantable wood volume over bark in m³ with the
# function v_gri; species_id = 1 relates to Norway spruce (see vignette about
# tree species codings)
v_mean <- v_gri(species_id = 1, dbh_cm = 25, height_m = 22.5)
v_mean## [1] 0.5392409v_mean * 820 # Stand volume per ha (820 trees per ha) ## [1] 442.1775As we also know the stand’s age and its mean height, we can determine its site index according to an appropriate yield table:
si <- site_index(
age = 60,
size = 22.5,
ytable = fe_ytable_spruce_gehrhardt_moderate_1921,
si_variable = "h_q_m"
)
si## [1] 1.486842Using this site index, we can estimate the stand’s current annual increment by looking it up in the yield table:
# Current annual volume increment in m³/ha/yr
iv <- ytable_lookup(age = 60, si = si, variable = "pai_m3_ha_yr",
ytable = fe_ytable_spruce_gehrhardt_moderate_1921)
iv## [1] 16.84474As the information above also allows to calculate the stand’s basal area, the stocking level (i.e. a measure of stand density related to the yield table of interest) can be determined. This in turn is useful for adjusting the increment estimate above:
# Calculate the stand's basal area in m²/ha (dbh^2 * pi/4 * stem number)
ba <- (25/100)^2 * pi/4 * 820
ba## [1] 40.25166sl <- stocking_level(ba, age = 60, si = si,
ytable = fe_ytable_spruce_gehrhardt_moderate_1921)
sl## [1] 0.9094797# As the stocking level < 1, we correct the increment estimate above
iv_corr <- iv * sl
iv_corr## [1] 15.31995Assume, we have measured the stem diameters at breast height of some trees in the stand we were dealing with above, and we would like to have estimates for their individual heights. As we have information about the stand’s mean height, mean diameter and its age, we can use a standard height curve system for this task:
# Measured stem diameters at breast height (cm)
dbh <- c(19.1, 22.4, 21.3, 27.8, 23.5, 26.6, 31.2, 25.1, 33.7, 17.2)
# Height estimates (m) with a standard height curve system, species_id = 1
# refers to Norway spruce (check documentation of the function h_standard_bv and
# the vignette about tree species codings)
h_est <- h_standard_bv(species_id = 1,
dbh_cm = dbh,
age_yr = 60,
d_q_cm = 25,
h_q_m = 22.5)
h_est## [1] 19.83220 21.44096 20.94121 23.47997 21.90867 23.07813 24.49230 22.53766
## [9] 25.13652 18.73660With the height estimates being available, the single tree volumes
can be easily obtained by using the function v_gri:
# Standing merchantable wood volumes over bark (m³)
v <- v_gri(species_id = 1, dbh_cm = dbh, height_m = h_est)
v## [1] 0.2747285 0.4122321 0.3633952 0.6935922 0.4640041 0.6252607 0.9043768
## [8] 0.5444442 1.0749596 0.2080623While the volume values above represent standing volumes over bark,
practitioners often prefer to work with under bark values that are, in
addition, reduced by harvest losses. For obtaining these, the function
v_red_harvest_ubark can be applied to the original volume
values:
# Reduced volumes to under bark values and accounting for harvest losses
v_red_harvest_ubark(species_id = 1, v_orig_m3 = v)## [1] 0.2225301 0.3339080 0.2943501 0.5618097 0.3758433 0.5064612 0.7325452
## [8] 0.4409998 0.8707173 0.1685304Besides the “pocket calculator style”, another way of working with
ForestElementsR is using the classes it provides for
representing data and running evaluations on these. One example data set
that comes with the package is
mm_forest_1_fe_stand_spatial; it represents a plot in a
mixed mountain forest that has been subsequently surveyed several times
on a single tree basis. It belongs to the family of
fe_stand objects (see vignette The Package
ForestElementsR). For such objects, one single function
(stand_sums_static) calculates the most important stand
level sum and mean variables in one pass:
# Display species names in scientific notation
po <- options(fe_spec_lang = "sci")
# mm_forest_1_fe_stand_spatial is an example stand that comes with
# ForestElementsR. Its single trees can be accessed and viewed as follows:
mm_forest_1_fe_stand_spatial$trees## # A tibble: 353 × 12
## tree_id species_id layer_key time_yr age_yr dbh_cm height_m
## <chr> <tm_wwk_lng> <dbl> <int> <dbl> <dbl> <dbl>
## 1 3 Picea abies 1 1975 NA 44.1 30.9
## 2 6 Picea abies 1 1975 NA 60.7 33.6
## 3 8 Picea abies 1 1975 NA 38.4 29.2
## 4 12 Picea abies 1 1975 NA 34.6 27.8
## 5 17 Picea abies 1 1975 NA 36.9 28.7
## 6 18 Picea abies 1 1975 NA 28.5 24.9
## 7 22 Picea abies 1 1975 NA 52.1 32.5
## 8 25 Picea abies 1 1975 NA 54.1 32.8
## 9 35 Picea abies 1 1975 NA 37.8 29.0
## 10 41 Picea abies 1 1975 NA 62.3 33.7
## # ℹ 343 more rows
## # ℹ 5 more variables: crown_base_height_m <dbl>, crown_radius_m <dbl>,
## # removal <lgl>, ingrowth <lgl>, n_rep_ha <dbl># Stand sum and mean values on species level
sts_sta <- mm_forest_1_fe_stand_spatial |> stand_sums_static()
sts_sta |> print(n = Inf)## # A tibble: 22 × 9
## # Groups: time_yr [5]
## time_yr species_id stem_number_ha basal_area_m2_ha d_q_cm d_dom_cm
## <int> <tm_wwk_lng> <dbl> <dbl> <dbl> <dbl>
## 1 1975 Picea abies 143. 21.4 43.6 60.3
## 2 1975 Abies alba 101. 6.16 27.8 42.5
## 3 1975 Fagus sylvatica 161. 3.83 17.4 29.5
## 4 1975 Acer pseudoplatanus 101. 3.55 21.1 27.2
## 5 1984 Picea abies 71.6 14.1 50.2 64.0
## 6 1984 Abies alba 53.7 3.47 28.7 46.6
## 7 1984 Fagus sylvatica 89.5 3.22 21.4 36.7
## 8 1984 Acer pseudoplatanus 59.7 2.54 23.3 31.4
## 9 1995 Picea abies 71.6 15.8 52.9 67.6
## 10 1995 Abies alba 41.8 3.87 34.4 50.2
## 11 1995 Fagus sylvatica 89.5 4.39 25.0 41.4
## 12 1995 Acer pseudoplatanus 53.7 2.72 25.4 34.2
## 13 2004 Picea abies 77.6 16.4 51.9 68.6
## 14 2004 Abies alba 47.7 4.62 35.1 53.5
## 15 2004 Fagus sylvatica 101. 5.26 25.7 42.8
## 16 2004 Fraxinus excelsior 41.8 0.196 7.73 8.94
## 17 2004 Acer pseudoplatanus 71.6 3.05 23.3 35.4
## 18 2015 Picea abies 41.8 6.65 45.0 71.5
## 19 2015 Abies alba 59.7 5.70 34.9 57.2
## 20 2015 Fagus sylvatica 149. 5.41 21.5 39.7
## 21 2015 Fraxinus excelsior 101. 0.634 8.92 11.5
## 22 2015 Acer pseudoplatanus 95.5 3.25 20.8 36.4
## # ℹ 3 more variables: h_q_m <dbl>, h_dom_m <dbl>, v_m3_ha <dbl># Stand volume sums of all species
sts_sta |> dplyr::group_by(time_yr) |> dplyr::summarise(v_m3_ha = sum(v_m3_ha))## # A tibble: 5 × 2
## time_yr v_m3_ha
## <int> <dbl>
## 1 1975 444.
## 2 1984 327.
## 3 1995 363.
## 4 2004 409.
## 5 2015 282.Similarly, the stand level volume and basal area increments can be
obtained in one go when subsequent surveys are represented in the
fe_stand object of interest:
# Stand level increments on species level
sts_dyn <- mm_forest_1_fe_stand_spatial |> stand_sums_dynamic()## Joining with `by = join_by(species_id, time_yr)`
## Joining with `by = join_by(time_yr, species_id)`
## Joining with `by = join_by(time_yr, species_id)`sts_dyn |> print(n = Inf)## # A tibble: 22 × 4
## time_yr species_id iba_m2_ha_yr iv_m3_ha_yr
## <int> <tm_wwk_lng> <dbl> <dbl>
## 1 1975 Picea abies NA NA
## 2 1984 Picea abies 0.176 3.94
## 3 1995 Picea abies 0.146 1.52
## 4 2004 Picea abies 0.0745 1.58
## 5 2015 Picea abies 0.0337 0.311
## 6 1975 Abies alba NA NA
## 7 1984 Abies alba 0.0598 1.10
## 8 1995 Abies alba 0.0532 0.574
## 9 2004 Abies alba 0.0825 1.49
## 10 2015 Abies alba 0.0981 1.59
## 11 1975 Fagus sylvatica NA NA
## 12 1984 Fagus sylvatica 0.101 1.72
## 13 1995 Fagus sylvatica 0.107 1.33
## 14 2004 Fagus sylvatica 0.0964 1.32
## 15 2015 Fagus sylvatica 0.170 2.13
## 16 1975 Acer pseudoplatanus NA NA
## 17 1984 Acer pseudoplatanus 0.0354 0.794
## 18 1995 Acer pseudoplatanus 0.0289 0.0709
## 19 2004 Acer pseudoplatanus 0.0369 0.671
## 20 2015 Acer pseudoplatanus 0.0577 0.462
## 21 2004 Fraxinus excelsior NA NA
## 22 2015 Fraxinus excelsior 0.0399 0.175# Stand level increment sums of all species
sts_dyn |>
dplyr::group_by(time_yr) |>
dplyr::summarise(iv_m3_ha_yr = sum(iv_m3_ha_yr, na.rm = TRUE))## # A tibble: 5 × 2
## time_yr iv_m3_ha_yr
## <int> <dbl>
## 1 1975 0
## 2 1984 7.56
## 3 1995 3.49
## 4 2004 5.06
## 5 2015 4.67# Reset species display language to previous setting
options(po)For more information and details, we currently provide three
vignettes that come with the package. We suggest to read the main
vignette, The Package ForestElementsR, first. The other two
vignettes Tree Species Codings in ForestElementsR, and
Yield Tables in ForestElementsR provide in-detail information
about two of the package’s major features.
WithbrowseVignettes("ForestElementsR") you obtain a link to
each available vignette. You can also call each vignette directly with
one of the following commands:
vignette("forestelementsr_package")
vignette("tree_species_codings")
vignette("yield_tables")The authors would like to thank the Bavarian Ministry for Nutrition, Agriculture, Forestry, and Tourism for funding the projects FeNEU: Ein innovatives Instrument für die forstliche Planung in Bayern (E062) and Ertragskundliche Betreuung der langfristigen Versuche (W007).