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#' @references
#' 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.
#'
#'
#' @examples
#' \donttest{
#' 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$genusCorr, findOrder = TRUE)
#' KarnatakaForest$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 = TRUE, 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)
#' }
#' @keywords internal
"_PACKAGE"
.onLoad <- function(libname, pkgname) {
}
.onAttach <- function(libname, pkgname) {
basePath <- cachePath()
if(attr(basePath, "source")=="temp") {
packageStartupMessage(
"Using temporary cache",
"\n It is recommended to use a permanent cache to avoid to re-download files on each session.",
"\n See function createCache() or BIOMASS.cache option."
)
}
if(attr(basePath, "source")=="data") {
packageStartupMessage(
"Using user data cache ", basePath,
"\n To clear or remove cache see function clearCache()."
)
}
}
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