R/AGBChange.R

In GabrielaLopez/BiomasaFP: Tools for analysing data downloaded from ForestPlots.net

#' @title Function to estimate AGB change between first and last census (by PlotViewID).
#' @description This function estimates biomass change between the first and last census by time elapsed. AGB is estimated by providing a biomass equation from the ones included in the package.
#' Results are displayed by PlotViewID.
#' @param xdataset a dataset with diameter (use mergefp)
#' @param AGBEquation the AGB equation used to produce the summary
#' @param dbh a diameter (in mm) to estimate biomass
#' @author Gabriela Lopez-Gonzalez
#' @references Chave J, Andalo C, Brown S, et al. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145 (1):87-99. doi:10.1007/s00442-005-0100-x.
#' 
#' Chave J, Rejou-Mechain M, Burquez A et al. 2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology 20: 3177-3190. doi: 10.1111/gcb.12629
#' 
#' Feldpausch TR, Banin L, Phillips OL, Baker TR, Lewis SL et al. 2011. Height-diameter allometry of tropical forest trees. Biogeosciences 8 (5):1081-1106. doi:10.5194/bg-8-1081-2011.
#' 
#' Chave J, Coomes DA, Jansen S, Lewis SL, Swenson NG, Zanne AE. 2009. Towards a worldwide wood economics spectrum. Ecology Letters 12(4): 351-366. http://dx.doi.org/10.1111/j.1461-0248.2009.01285.x
#' 
#' Zanne AE, Lopez-Gonzalez G, Coomes DA, Ilic J, Jansen S, Lewis SL, Miller RB, Swenson NG, Wiemann MC, Chave J. 2009. Data from: Towards a worldwide wood economics spectrum. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.234

#' @export

AGBch <- function (xdataset, AGBEquation, dbh ="D4"){
        AGBData <- AGBEquation (xdataset, dbh) 
        AGBAlive <-aggregate (cbind(AGBind,  Alive, AGBind/PlotArea) ~ PlotCode + PlotViewID + PlotArea+ Census.No + Census.Mean.Date +LatitudeDecimal + LongitudeDecimal, data = AGBData, FUN=sum )
        SummaryB<-AGBAlive
        SummaryB <- SummaryB[order(SummaryB$PlotViewID, SummaryB$Census.No, decreasing=FALSE), ]
        #Estimating Change
     
        fc<-SummaryB [SummaryB $Census.No==1,]
        maxCensusNo <-aggregate(Census.No ~ PlotViewID, data = SummaryB , max)
        lc<- merge(SummaryB , maxCensusNo, by = c('PlotViewID', 'Census.No'))
        lcs <- lc[,c('Census.No','Census.Mean.Date','PlotViewID','V3')]
        colnames(lcs) <- c('LastCensus.No','Last.Census.Mean.Date', 'PlotViewID','LastAGB_ha' )
        
        AGBCh<-merge(fc, lcs, by= 'PlotViewID')
        #AGBCh
        AGBCh$AGBChange <- (AGBCh$LastAGB_ha - AGBCh$V3)/(AGBCh$Last.Census.Mean.Date-AGBCh$Census.Mean.Date)
        #AGBCh
        AGBCha <- AGBCh[, c('PlotViewID', 'PlotCode','PlotArea', 'LatitudeDecimal','LongitudeDecimal', 'Census.Mean.Date','LastCensus.No','Last.Census.Mean.Date', 'AGBChange')]
        AGBCha
        
        
}