View source: R/AGBmonteCarlo.R
AGBmonteCarlo | R Documentation |
Propagation of the errors throughout the steps needed to compute AGB or AGC.
AGBmonteCarlo(
D,
WD = NULL,
errWD = NULL,
H = NULL,
errH = NULL,
HDmodel = NULL,
coord = NULL,
Dpropag = NULL,
n = 1000,
Carbon = FALSE,
Dlim = NULL,
plot = NULL
)
D |
Vector of tree diameters (in cm) |
WD |
Vector of wood density estimates (in g/cm3) |
errWD |
Vector of error associated to the wood density estimates (should be of the same size as |
H |
(option 1) Vector of tree heights (in m). If set, |
errH |
(if |
HDmodel |
(option 2) Model used to estimate tree height from tree diameter (output from |
coord |
(option 3) Coordinates of the site(s), either a vector giving a single site (e.g. c(longitude, latitude)) or a matrix/dataframe with two columns (e.g. cbind(longitude, latitude)). The coordinates are used to predict height-diameter allometry with bioclimatic variables. |
Dpropag |
This variable can take three kind of values, indicating how to propagate the errors on diameter measurements:
a single numerical value or a vector of the same size as |
n |
Number of iterations. Cannot be smaller than 50 or larger than 1000. By default |
Carbon |
(logical) Whether or not the propagation should be done up to the carbon value (FALSE by default). |
Dlim |
(optional) Minimum diameter (in cm) for which above ground biomass should be calculated (all diameter below
|
plot |
(optional) Plot ID, must be either one value, or a vector of the same length as D. This argument is used to build stand-specific HD models. |
See Rejou-Mechain et al. (2017) for all details on the error propagation procedure.
Returns a list with (if Carbon is FALSE):
meanAGB
: Mean stand AGB value following the error propagation
medAGB
: Median stand AGB value following the error propagation
sdAGB
: Standard deviation of the stand AGB value following the error propagation
credibilityAGB
: Credibility interval at 95\
AGB_simu
: Matrix with the AGB of the trees (rows) times the n iterations (columns)
Maxime REJOU-MECHAIN, Bruno HERAULT, Camille PIPONIOT, Ariane TANGUY, Arthur PERE
Chave, J. et al. (2004). Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society B: Biological Sciences, 359(1443), 409-420.
Rejou-Mechain et al. (2017). BIOMASS: An R Package for estimating above-ground biomass and its uncertainty in tropical forests. Methods in Ecology and Evolution, 8 (9), 1163-1167.
# Load a database
data(NouraguesHD)
data(NouraguesTrees)
# Modelling height-diameter relationship
HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2")
# Retrieving wood density values
NouraguesWD <- getWoodDensity(NouraguesTrees$Genus, NouraguesTrees$Species,
stand = NouraguesTrees$Plot
)
# Propagating errors with a standard error for Wood density
resultMC <- AGBmonteCarlo(
D = NouraguesTrees$D, WD = NouraguesWD$meanWD,
errWD = NouraguesWD$sdWD, HDmodel = HDmodel
)
# If only the coordinates are available
coord <- c(-52.683213,4.083024 )
resultMC <- AGBmonteCarlo(
D = NouraguesTrees$D, WD = NouraguesWD$meanWD,
errWD = NouraguesWD$sdWD, coord = coord
)
# Propagating errors with a standard error in wood density in all plots at once
NouraguesTrees$meanWD <- NouraguesWD$meanWD
NouraguesTrees$sdWD <- NouraguesWD$sdWD
resultMC <- by(
NouraguesTrees, NouraguesTrees$Plot,
function(x) AGBmonteCarlo(
D = x$D, WD = x$meanWD, errWD = x$sdWD,
HDmodel = HDmodel, Dpropag = "chave2004"
)
)
meanAGBperplot <- unlist(sapply(resultMC, "[", 1))
credperplot <- sapply(resultMC, "[", 4)
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