Functionality
The package aims at providing functions to evaluate TapeR-base taper
curves with respect to diameter at given height, height of given
diameter, estimating double bark thickness, providing volumes of total
taper curve and parts of it, estimating total aboveground biomass and
components of it. The following section provide examples on the data
structure and how the functions can be used.
S4-classes
The package establishes two S4-classes, which define requirements on
the data applied to the functions. The tprTrees-class obviously
defines trees, but with a focus on sectional measurements. With that,
one can provide as many diameters (and their measurement heights) to
precisely follow its taper curve. But similarly, one can only provide
one diameter and its measurement height for subsequent use of the
package functions.
For estimating roundwood assortments, there is an additional S4-class
parSort which defines the sorting parameters.
handled tree species
The tree species list is taken from the predecessor taper curve model
BDAT. Indeed, there are only eight taper curve models, but the
other tree species are mapped to these eight. For future releases this
might chance if further data leads to the development of additional
taper curve models.
## Lade nötiges Paket: TapeS
## This is TapeS 0.12.1
## Option 'TapeS_Rfn' set to 'sig2'. See '?setTapeSoptions' for details.
tprSpeciesCode(inSp = NULL, outSp = NULL)
## ID kurz lang short long scientific
## 1 1 Fi Fichte NS Norway spruce Picea abies
## 2 2 SF Sitka-Fichte SS Sitka spruce Picea sitchensis
## 3 3 Ta Tanne ESF European silver fir Abies alba
## 4 4 KT Kuestentanne GF Grand fir Abies grandis
## 5 5 Kie Kiefer SP Scotch pine Pinus sylvestris
## 6 6 SK Schwarzkiefer AUP Austrian pine Pinus nigra
## 7 7 WK Weymouthskiefer WEP Weymouth pine Pinus strobus
## 8 8 DG Douglasie DF Douglas fir Pseudotsuga menziesii
## 9 9 La Laerche XLA larch Larix spp.
## 10 10 EL Europ. Laerche ELA European larch Larix decidua
## 11 11 JL Jap. Laerche JLA Japanese larch Larix kaempferi
## 12 12 Th Thuja RC Western red cedar Thuja plicata
## 13 13 Ts Tsuga WH Western hemlock Tsuga heterophylla
## 14 14 SN sNB XC other conifers Coniferales trees
## 15 15 Bu Buche BE beech Fagus sylvatica
## 16 16 HB Hainbuche HBM hornbeam Carpinus betulus
## 17 17 Ei Eiche OK oak (robur/petraea) Quercus spp.
## 18 18 RE Roteiche ROK Red oak Quercus rubra
## 19 19 Pa Pappel XPO poplar Populus spp.
## 20 20 BP Balsampappel BPO Balsam poplar Populus balsamifera
## 21 21 Es Esche AH ash Fraxinus excelsior
## 22 22 Ah Ahorn XAH maple Acer spp.
## 23 23 BA Bergahorn SY sycamore Acer pseudoplatanus
## 24 24 SA Spitzahorn NOM Norway maple Acer platanoides
## 25 25 FA Feldahorn FM Field maple Acer campestre
## 26 26 Bi Birke XBI birch Betula spp.
## 27 27 Li Linde LI lime tree Tilia spp.
## 28 28 Er Erle AR alder Alnus spp.
## 29 29 Kir Kirsche WCH Wild cherry Prunus avium
## 30 30 Ul Ulme EM elm Ulmus spp.
## 31 31 Ro Robinie BL black locust Robinia pseudoacacia
## 32 32 El Elsbeere WST Wild service tree Sorbus torminalis
## 33 33 Ka Kastanie SC Sweet chestnut Castanea sativa
## 34 34 We Weide XWL willow Salix spp.
## 35 35 LB sLB XB other broadleaves Magnoliopsida trees
## 36 36 VB Vogelbeere ROW rowan Sorbus aucuparia
## 37 37 BU_ Buche_ BE_ beech_ Fagus sylvatica_
## 38 38 Ei_ Eiche_ OK_ oak_ Quercus spp._
## 39 39 REi_ Roteiche_ ROK_ Red oak_ Quercus rubra_
species mapping
Beside the taper curve models, mapping between species is required
also for double bark thickness, biomass functions (total aboveground and
component biomass), minimum cutting diameter, the amount of crown wood
in deciduous tree species and the volume for small trees according to
FAO.
BaMap(Ba = NULL, type = NULL)
## Schaftform Rinde Az uvDh mAS Biomasse BmComp VolFao Formigkeit
## 1 1 1 1 0 0 1 1 1 1
## 2 1 1 1 0 0 1 1 1 1
## 3 2 2 3 0 0 2 2 2 2
## 4 2 2 3 0 0 2 2 2 2
## 5 4 4 4 0 0 4 3 4 4
## 6 4 8 4 0 0 4 3 4 4
## 7 4 9 4 0 0 4 3 4 4
## 8 3 3 1 0 0 3 4 3 3
## 9 5 6 5 0 0 5 4 5 5
## 10 5 6 5 0 0 5 4 5 5
## 11 5 7 5 0 0 5 4 5 5
## 12 1 1 1 0 0 1 1 1 1
## 13 1 1 1 0 0 1 1 1 1
## 14 1 2 1 0 0 1 1 1 1
## 15 6 10 6 1 1 6 5 6 6
## 16 6 18 6 1 1 9 6 7 6
## 17 7 11 7 2 2 7 6 7 7
## 18 8 14 7 2 2 7 6 7 8
## 19 8 25 6 2 2 17 5 6 8
## 20 8 25 6 2 2 17 5 6 8
## 21 6 17 6 2 2 8 8 7 6
## 22 6 15 6 1 1 10 7 7 6
## 23 6 15 6 1 1 10 7 7 6
## 24 6 15 6 1 1 11 7 7 6
## 25 6 21 6 1 1 10 7 7 6
## 26 6 21 6 1 1 15 5 6 9
## 27 6 16 6 1 1 12 5 6 6
## 28 6 28 6 1 1 16 5 6 6
## 29 6 26 6 1 1 6 5 6 6
## 30 6 20 6 1 1 14 5 6 6
## 31 6 19 7 2 2 13 5 6 6
## 32 6 23 6 1 1 6 5 6 6
## 33 7 11 6 1 1 6 5 6 7
## 34 6 12 6 1 1 18 5 6 6
## 35 6 12 6 1 1 6 5 6 6
## 36 6 26 7 2 1 6 5 6 6
## 37 9 10 6 1 1 6 5 6 6
## 38 10 11 7 2 2 7 6 7 7
## 39 11 14 7 2 2 7 6 7 8
Defining a tprTrees object
require(TapeS)
obj <- tprTrees(spp=c(1, 3),
Hm=list(c(1.3, 5, 7), c(1.3)),
Dm=list(c(27, 23.5, 22.4), c(27)),
Ht=c(27, 30))
Hx <- c(1.3, 5, 7)
Diameter estimation
Diameter estimation from the developed taper curves via the empirical
best linear unbiased predictor (EBLUP, see Kublin et al. (2013)) not
necessarily
passed through the observations. This behaviour can be forced if the
residual variance is set to zero. The package offers to set an option
‘TapeS_R0’ which, by default, is set to FALSE and uses full information
from the models.
If one wants to force the taper curves through given (measured)
diameters, one can do so by setting TapeS::setTapeSoptions(). See
further down.
tprDiameter(obj, Hx = Hx) # R0=TRUE, taper curve through measurements
## [,1] [,2] [,3]
## [1,] 26.98207 23.53026 22.38791
## [2,] 27.00847 24.30814 23.31787
tprDiameter(obj, Hx = Hx, bark = FALSE)
## [,1] [,2] [,3]
## [1,] 25.51535 22.21057 21.11821
## [2,] 25.32629 22.77738 21.84264
tprDiameter(obj, Hx = Hx, interval = "prediction")
## tree Hx lwr EDx upr
## [1,] 1 1.3 25.69324 26.98207 28.27089
## [2,] 1 5.0 22.38707 23.53026 24.67345
## [3,] 1 7.0 21.21585 22.38791 23.55997
## [4,] 2 1.3 25.07738 27.00847 28.93955
## [5,] 2 5.0 21.90235 24.30814 26.71393
## [6,] 2 7.0 20.82588 23.31787 25.80986
The diameter are evaluated at position Hx for each tree. If only one
diameter is required, e.g. for relative height, the parameter cp=FALSE
(no cartesian product) can be used
tprDiameter(obj, Hx = 0.3*Ht(obj), interval = "prediction", cp=FALSE)
## tree Hx lwr EDx upr
## [1,] 1 8.1 20.51562 21.76156 23.00750
## [2,] 2 9.0 19.75799 22.38323 25.00847
Height estimation
tprHeight(obj, Dx = c(10, 9, 8, 7))
## [,1] [,2] [,3] [,4]
## [1,] 21.68562 22.37393 23.01733 23.62004
## [2,] 25.14243 25.79791 26.40098 26.95868
tprHeight(obj, Dx = c(10, 9, 8, 7), bark = FALSE)
## [,1] [,2] [,3] [,4]
## [1,] 21.16237 21.93210 22.64808 23.31524
## [2,] 24.56976 25.32251 26.00833 26.63661
Estimation of double bark thickness
tprBark(obj, Hx = c(1, 2, 3))
## [,1] [,2] [,3]
## [1,] 1.512889 1.404739 1.371391
## [2,] 1.712393 1.632778 1.592503
Volume estimation
tprVolume(obj) # default is Vfm
## [1] 0.7155448 0.8559027
tprVolume(obj, AB = list(A=0, B=7), iAB=c("h", "dob"), bark=TRUE) # same
## [1] 0.7155448 0.8559027
## [1] 0.7155448 0.8559027
## [1] 0.7155448 0.8559027
Efm(obj, stH = 0.01) # default
## [1] 0.6148924 0.7297650
## tree vol
## 1 1 0.5569941
## 2 2 0.6604166
## [1] 0.6977114 0.8395405
Vfm_phys(obj) # takes a while
## [1] 0.7238518 0.8588418
## [1] 0.6205634 0.7317585
tprVolume(obj, AB = list(A=0.01*Ht(obj), B=7, sl=0.01), iAB = c("H", "Dob"), bark=FALSE)
## [1] 0.6205634 0.7317585
Estimation of roundwood assortments
tprAssortment(obj) ## default assortment parameters
## tree sort height length mdm zdm vol
## 3 1 sth 0.27 19.000000 18.847735 11.721560 0.530104882
## 5 1 ih01 19.46 1.000000 10.961870 10.384459 0.009437547
## 6 1 nvd 20.46 3.160039 8.385469 6.051613 0.017451670
## 31 2 sth 0.30 19.000000 19.850787 14.712666 0.588029258
## 51 2 ih01 19.49 4.000000 12.854161 10.818101 0.051908368
## 61 2 nvd 23.49 3.468678 8.670170 5.987748 0.020479017
pars <- parSort(stH=0.2, Lxh=c(1, 1.5), fixN=2, fixL=4)
tprAssortment(obj, pars = pars)
## tree sort height length mdm zdm vol
## 1 1 hx 0.200 1.000000 27.305190 25.115321 0.05855720
## 2 1 fix01 1.200 4.000000 22.508322 21.382661 0.15916080
## 3 1 fix02 5.240 4.000000 20.260404 19.146289 0.12895734
## 4 1 sth 9.280 10.900000 15.870577 10.710667 0.21562635
## 7 1 nvd 20.289 3.331039 8.501393 6.051613 0.01890818
## 11 2 hx 0.200 1.500000 25.222573 24.090990 0.07494798
## 21 2 fix01 1.700 4.000000 22.719310 21.733041 0.16215866
## 31 2 fix02 5.740 4.000000 20.796086 19.876919 0.13586671
## 41 2 sth 9.780 13.700000 16.647170 10.829273 0.29818878
## 71 2 nvd 23.617 3.341678 8.582446 5.987748 0.01933199
Biomass estimation
total aboveground biomass
tprBiomass(obj) # bwi-biomass
## [1] 313.5984 394.2241
component biomass
For this package the biomass functions of Vonderach et al. (2018)
have been extended to include predictors D03 and crown length for the
prediction of several components. Although these models can estimate
absolute biomass, in this context they are used to predict the share of
each component, which is multiplied by total aboveground biomass.
tprBiomass(obj, component = c("sw", "sb", "ndl"))
## sw sb ndl
## 1 237.0407 21.92367 16.23462
## 2 252.2461 39.56637 54.81818
tprBiomass(obj, component = c("all"))
## stw stb sw sb fwb ndl agb
## 1 9.770014 0.9030712 237.0407 21.92367 27.72627 16.23462 313.5984
## 2 6.089138 1.1174934 252.2461 39.56637 40.38675 54.81818 394.2241
Option: Taper curve evaluation
In TapeR-package, the taper curve of a tree with given diameter- and
height measurements is as a default realised using the estimated best
linear unbiased predictor (see Kublin et al. 2013, p.987). This means,
that all diameters and volume predictions are estimated using the
intrinsic error structure of the fitted taper curve model. Hence the
taper curve not necessarily passes through the measured diameter.
For the possibility of forcing the taper curves through the measured
diameters, the TapeR-package has been extended to allow for explicitly
determining the assumed residual variance. See
?TapeR::resVar.
To apply a modification of the residual variance in TapeS, one can
set a special package option (all called functions react to this option)
or define the options as a parameter in each function. The package
option is set by TapeS_Rfn = list(fn=“zero”), here with a
zero-function to force interpolation.
setTapeSoptions(Rfn = list(fn="zero"))
tprDiameter(obj, Hx=1.3)
## [1] 27 27
setTapeSoptions(Rfn = list(fn="sig2"))
tprDiameter(obj, Hx=1.3)
## [1] 26.98207 27.00847
By default, this option is set to “sig2” on load of the package,
hence, the residual variance of the model is used (default behaviour of
TapeR). Other functions are also available, e.g. “linear”, “laglinear”,
“biliniar”, “dnorm” and “dlnorm”. Most function allow for defining a
point of zero variance. The latter two are based on the normal and
log-normal distribution and allow for a standard deviation term.
Please note: setting a zero variance should not be overstrained. It
is mainly implemented to preserve diameter in breast height (dbh)
measurements for trees of e.g. inventory data. If one is about to apply
the TapeS-functions to sectional data, where a lot of diameter
measurements are available, the TapeS_Rfn-Option should left as
is or set to sig2 via TapeS::setTapeSoptions(Rfn =
list(fn=“sig2”)).
Option: improve monotonicity of taper curves
Depending on the size and measurements of (especially small) trees,
it sometimes might happen, that the calibrated taper curves have an
increasing diameter sequence at the stem foot:
plot(tprTrees(spp=3, Dm=7.9, Hm=1.3, Ht=12, ), mono=FALSE)
To avoid such implausible taper curves, internally an additional
support “measurement” at 1% of tree height is added to the set of
measurements. This extra measurement is based on the ratio between 5%
and 1% of tree height of the population average curve. With that, taper
curves usually are monotonically decreasing:
plot(tprTrees(spp=3, Dm=7.9, Hm=1.3, Ht=12, ), mono=TRUE)
By default, mono is set to TRUE (must not be given) via
options(“TapeS_mono”), hence, all non-monotonic curves (check at
initialisation), are amended by an additional support diameter.