Description Author(s) References See Also Examples
Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty, (3) construct height-diameter models, (4) manage tree and plot coordinates, (5) estimate the aboveground biomass/carbon at the stand level with associated uncertainty. To cite BIOMASS, please use citation("BIOMASS"). See more in the article of Réjou-Méchain et al. (2017) <doi:10.1111/2041-210X.12753>.
Maintainer: Maxime Réjou-Méchain maxime.rejou@gmail.com [data contributor]
Authors:
Ariane Tanguy
Camille Piponiot
Bruno Herault bruno.herault@cirad.fr
Arthur Pere arthur.pere@cirad.fr
Guillaume Cornu gcornu@cirad.fr (0000-0002-7523-5176)
Other contributors:
Jerome Chave jerome.chave@univ-tlse3.fr [data contributor]
Ted Feldpausch T.R.Feldpausch@exeter.ac.uk [data contributor]
Réjou-Méchain M., Tanguy A., Piponiot C., Chave J., Hérault B. (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.
Useful links:
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library(BIOMASS)
# Dataset containing plot inventory data from Karnataka, India (Ramesh et al. 2010)
data(KarnatakaForest)
str(KarnatakaForest)
# Dataset containing height and diameter measurements from two 1-ha plots
# established in the lowland rainforest of French Guiana, at the Nouragues
# Ecological Research Station
data(NouraguesHD)
str(NouraguesHD)
#############################################################################
# WOOD DENSITY
# 1-RETRIEVE AND CORRECT TAXONOMY
# Checking typos in taxonomy
Taxo <- correctTaxo(genus = KarnatakaForest$genus, species = KarnatakaForest$species)
KarnatakaForest$genusCorr <- Taxo$genusCorrected
KarnatakaForest$speciesCorr <- Taxo$speciesCorrected
# Retrieving APG III Families and Orders from Genus names
APG <- getTaxonomy(KarnatakaForest$genusCorr, findOrder = T)
KarnatakaForest$familyAPG <- APG$family
KarnatakaForest$orderAPG <- APG$order
# 2-RETRIEVE WOOD DENSITY
dataWD <- getWoodDensity(
genus = KarnatakaForest$genusCorr,
species = KarnatakaForest$speciesCorr,
stand = KarnatakaForest$plotID
)
#############################################################################
# TREE HEIGHT
# Compare different local H-D models
modelHD(
D = NouraguesHD$D, H = NouraguesHD$H,
drawGraph = TRUE, useWeight = TRUE
)
# Compute the local H-D model with the lowest RSE
HDmodel <- modelHD(
D = NouraguesHD$D, H = NouraguesHD$H,
method = "log2", useWeight = TRUE
)
# Compute plot-specific H-D models
HDmodelPerPlot <- modelHD(NouraguesHD$D, NouraguesHD$H,
method = "weibull",
useWeight = T, plot = NouraguesHD$plotId
)
RSEmodels <- sapply(HDmodelPerPlot, function(x) x$RSE)
Coeffmodels <- lapply(HDmodelPerPlot, function(x) x$coefficients)
# Retrieve height data from a local HD model
dataHlocal <- retrieveH(D = KarnatakaForest$D, model = HDmodel)
# Retrieve height data from a Feldpaush et al. (2012) averaged model
dataHfeld <- retrieveH(D = KarnatakaForest$D, region = "SEAsia")
# Retrieve height data from Chave et al. (2012) equation 6
dataHchave <- retrieveH(
D = KarnatakaForest$D,
coord = cbind(KarnatakaForest$long, KarnatakaForest$lat)
)
#############################################################################
# AGB CALCULATION
KarnatakaForest$WD <- dataWD$meanWD
KarnatakaForest$H <- dataHlocal$H
KarnatakaForest$Hfeld <- dataHfeld$H
# Compute AGB(Mg) per tree
AGBtree <- computeAGB(
D = KarnatakaForest$D, WD = KarnatakaForest$WD,
H = KarnatakaForest$H
)
# Compute AGB(Mg) per plot
AGBplot <- summaryByPlot(AGBtree, KarnatakaForest$plotId)
# Compute AGB(Mg) per tree without height information (Eq. 7 from Chave et al. (2014))
AGBplotChave <- summaryByPlot(
computeAGB(
D = KarnatakaForest$D, WD = KarnatakaForest$WD,
coord = KarnatakaForest[, c("long", "lat")]
),
plot = KarnatakaForest$plotId
)
# Compute AGB(Mg) per tree with Feldpausch et al. (2012) regional H-D model
AGBplotFeld <- summaryByPlot(
computeAGB(
D = KarnatakaForest$D, WD = KarnatakaForest$WD,
H = KarnatakaForest$Hfeld
),
plot = KarnatakaForest$plotId
)
#############################################################################
# PROPAGATING ERRORS
KarnatakaForest$sdWD <- dataWD$sdWD
KarnatakaForest$HfeldRSE <- dataHfeld$RSE
# Per plot using the local HD model constructed above (modelHD)
resultMC <- AGBmonteCarlo(
D = KarnatakaForest$D, WD = KarnatakaForest$WD, errWD = KarnatakaForest$sdWD,
HDmodel = HDmodel, Dpropag = "chave2004"
)
resMC <- summaryByPlot(resultMC$AGB_simu, KarnatakaForest$plotId)
# Per plot using the Feldpaush regional HD averaged model
AGBmonteCarlo(
D = KarnatakaForest$D, WD = KarnatakaForest$WD,
errWD = KarnatakaForest$sdWD, H = KarnatakaForest$Hfeld,
errH = KarnatakaForest$HfeldRSE, Dpropag = "chave2004"
)
resMC <- summaryByPlot(resultMC$AGB_simu, KarnatakaForest$plotId)
# Per plot using Chave et al. (2014) Equation 7
resultMC <- AGBmonteCarlo(
D = KarnatakaForest$D, WD = KarnatakaForest$WD, errWD = KarnatakaForest$sdWD,
coord = KarnatakaForest[, c("long", "lat")],
Dpropag = "chave2004"
)
resMC <- summaryByPlot(resultMC$AGB_simu, KarnatakaForest$plotId)
## End(Not run)
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