Description Usage Arguments Details Value Author(s) References Examples
Propagation of the errors throughought the steps needed to compute AGB or AGC.
1 2 3 |
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% of the stand AGB value following the error propagation
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # Load a database
data(NouraguesHD)
data(KarnatakaForest)
# Modelling height-diameter relationship
HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2")
# Retrieving wood density values
KarnatakaWD <- getWoodDensity(KarnatakaForest$genus, KarnatakaForest$species,
stand = KarnatakaForest$plotId
)
# Propagating errors with a standard error in wood density in one plot
filt <- KarnatakaForest$plotId == "BSP20"
resultMC <- AGBmonteCarlo(
D = KarnatakaForest$D[filt], WD = KarnatakaWD$meanWD[filt],
errWD = KarnatakaWD$sdWD[filt], HDmodel = HDmodel
)
str(resultMC)
# If only the coordinates are available
lat <- KarnatakaForest$lat[filt]
long <- KarnatakaForest$long[filt]
coord <- cbind(long, lat)
## Not run:
resultMC <- AGBmonteCarlo(
D = KarnatakaForest$D[filt], WD = KarnatakaWD$meanWD[filt],
errWD = KarnatakaWD$sdWD[filt], coord = coord
)
str(resultMC)
## End(Not run)
# Propagating errors with a standard error in wood density in all plots at once
KarnatakaForest$meanWD <- KarnatakaWD$meanWD
KarnatakaForest$sdWD <- KarnatakaWD$sdWD
## Not run:
resultMC <- by(
KarnatakaForest, KarnatakaForest$plotId,
function(x) AGBmonteCarlo(
D = x$D, WD = x$meanWD, errWD = x$sdWD,
HDmodel = HDmodel, Dpropag = "chave2004"
)
)
meanAGBperplot <- unlist(sapply(resultMC, "[", 1))
credperplot <- sapply(resultMC, "[", 4)
## End(Not run)
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