tprBiomass | R Documentation |
calculate total above ground and optionally component biomass for given trees
tprBiomass(
obj,
component = NULL,
useNFI = TRUE,
interval = "none",
mono = TRUE,
Rfn = NULL
)
## S4 method for signature 'tprTrees'
tprBiomass(
obj,
component = NULL,
useNFI = TRUE,
interval = "none",
mono = TRUE,
Rfn = NULL
)
obj |
object of class 'tprTrees' |
component |
component for which biomass should be returned. If NULL, total aboveground biomass is returned, if 'all', all components are returned. See details. |
useNFI |
if |
interval |
character to indicate whether and which type of interval is
required; one of |
mono |
logical, defaults to true. If calibrated taper curve is non-monotonic at stem base, a support diameter is added. |
Rfn |
Rfn setting for residuals error matrix, defaults to
|
The available components are agb (= total aboveground biomass), stw (=stump wood), stb (=stump bark), sw (=solid wood with diameter above 7cm over bark), sb (=bark of component sw), fwb (=fine wood incl. bark) and ndl (=needles), if applicable. The needles-component is set to zero for deciduous tree species, no mass for leaves is available. One can request 'all' components to receive all components.
a vector in case agb or only one component is requested, otherwise a matrix with one row per tree
tprBiomass(tprTrees)
: method for class 'tprTrees'
Kändler, G. and B. Bösch (2012). Methodenentwicklung für die 3. Bundeswaldinventur: Modul 3 Überprüfung und Neukonzeption einer Biomassefunktion - Abschlussbericht. Im Auftrag des Bundesministeriums für Ernährung, Landwirtschaft und Verbraucherschutz in Zusammenarbeit mit dem Institut für Waldökologie und Waldinventur des Johann Heinrich von Thünen-Instituts, FVA-BW: 71.
Kaendler (2021): Biometrische Modelle für die Ermittlung des Holzvorrats, seiner Sortimentsstruktur und der oberirdischen Biomasse im Rahmen der Bundeswaldinventur. Allg. Forst- u. J.-Ztg., 191. Jg., 5/6 83
Vonderach, C., G. Kändler and C. Dormann (2018): Consistent set of additive biomass equations for eight tree species in Germany fitted by nonlinear seemingly unrelated regression. Annals of Forest Science (2018) 75:49 doi: 10.1007/s13595-018-0728-4
obj <- tprTrees(spp=c(1, 15),
Dm=list(c(30, 28), c(30, 28)),
Hm=list(c(1, 3), c(1, 3)),
Ht = rep(30, 2))
(tmp <- tprBiomass(obj, component="all"))
tprBiomass(obj, component=NULL) # aboveground biomass
component <- c("agb", "sw", "sb", "ndl")
tprBiomass(obj, component=component)
component <- c("sw", "sb", "ndl")
tprBiomass(obj, component="all")
# use NSUR-functions from Vonderach et al. 2018
# obs: currently sth=1% of tree height
# and kl=70% of tree height
tprBiomass(obj, component="all", useNFI = FALSE)
## getting confidence and prediction intervals
useNFI <- FALSE
interval <- "confidence"
component <- c("sw", "agb")
tprBiomass(obj, component, useNFI, interval)
tprBiomass(obj, component, useNFI, interval="none")
tprBiomass(obj, component, useNFI=TRUE, interval)
tprBiomass(obj, component, useNFI=TRUE, interval="none")
obj <- tprTrees(spp=15, Dm=30, Hm=1.3, Ht=27)
tprBiomass(obj, component="all", interval="confidence")
tprBiomass(obj, component="ndl", interval="confidence")
obj <- tprTrees(spp=c(1, 15), Dm=c(30, 30), Hm=c(1.3, 1.3), Ht=c(27, 27))
tprBiomass(obj, component="all", interval="confidence")
obj <- tprTrees(spp=c(1, 15), Dm=c(30, 30), Hm=c(1.3, 1.3), Ht=c(27, 27))
tprBiomass(obj, component=c("sw", "ndl"), interval="confidence")
obj <- tprTrees(spp=c(1, 15), Dm=c(30, 30), Hm=c(1.3, 1.3), Ht=c(27, 27))
tprBiomass(obj, component=c("ndl", "agb"), interval="confidence")
obj <- tprTrees(spp=c(1, 15), Dm=c(30, 30), Hm=c(1.3, 1.3), Ht=c(27, 27))
tprBiomass(obj, component=c("ndl"), interval="confidence")
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