R/AGBfluxes_functions.R
In ervanSTRI/AGBflux_pack: Set of functions to format, correct and compute AGB fluxes from CTFS-formated data.
Defines functions
error
error
data.correction
data.prep
Documented in
data.correction
data.prep
#' CTFS-formated data preparation
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Main routine to format, detect major obvious errors, and gap-fill those errors in CTFS-formated data
#' @param site the full name of your site (in lower case); e.g., 'barro colorado island'
#' @param stem TRUE or FALSE, using the stem data (stem=TRUE) rather than the tree data (i.e. called 'full', stem=FALSE)
#' @param taper.correction TRUE or FALSE, apply Cushman et al (2014) taper correction
#' @param fill.missing TRUE or FALSE, interpolate missing DBH values
#' @param use.palm.allometry TRUE or FALSE, if TRUE, compute biomass of palm trees using a palm-specific allometric model from Goodman et al. (2013)
#' @param flag.stranglers TRUE or FALSE, individuals of known strangler fig species greater than 'dbh.stranglers' are flagged
#' @param dbh.stranglers (optional) minimal diameter (in mm) of strangler figs, default = 500
#' @param maxrel a numeric value: the threshold for flagging major errors in productivity, applied as absval(individual tree productivity)>maxrel*(average productivity per hectare)
#' @param output.errors TRUE or FALSE, output all records for trees with major errors in productivity to a csv file
#' @param DATA_path the pathname where the data are located
#' @param exclude.interval NULL by default. If needed a vector (e.g. c(1,2)) indicating which census interval(s) must be discarded from computation due, for instance, to a change in measurement protocol
#' @return a data.table (data.frame) with all relevant variables.
#' @import data.table # load data.table package
#' @export
data.prep <- function(site,stem,taper.correction,fill.missing,use.palm.allometry,flag.strangler,dbh.stranglers,maxrel,graph.problem.trees,output.errors,DATA_path,exclude.interval=NULL) {
site <- tolower(site)
INDEX <- match(tolower(site),site.info$site)
if (is.na(INDEX)) { stop("Site name should be one of the following: \n",paste(levels(factor(site.info$site)),collapse=" - ")) }
if(!exists("DATA_path")){
DATA_path <- paste0(path_folder,"/data/")
}
files <-list.files(DATA_path)
ifelse(stem,files <- files[grep("stem",files)], files <- files [grep("full",files)])
ifelse(!dir.exists(file.path(paste0(path_folder,"/output"))), dir.create(file.path(paste0(path_folder,"/output"))), FALSE)
# Create the receiving data.frame
df <- data.frame("treeID"=NA, "stemID"=NA, "tag"=NA, "StemTag"=NA, "sp"=NA, "quadrat"=NA, "gx"=NA, "gy"=NA,"dbh"=NA,"hom"=NA, "ExactDate"=NA, "DFstatus"=NA, "codes"=NA, "date"=NA,"status"=NA,"CensusID"=NA,"year"=NA)
for (i in 1:length(files)) {
temp <- setDT(LOAD(paste(DATA_path,files[i],sep="/")))
temp$CensusID <- i
temp[,year:= round(mean(as.numeric(format(as.Date(date, origin='1960-1-1'),'%Y')),na.rm=T))]
if(any(is.na(match(names(df),names(temp))))) {
ID <- which(is.na(match(names(df),names(temp))))
stop(paste0("The data must have a column named ",names(df)[ID],collapse=" - "))
}
temp <- temp[,match(names(df),names(temp)),with=FALSE]
df <- rbind(df,temp)
}
rm(temp)
table(df$year,df$CensusID)
df <- df[-1,]
df <- data.correction(df,taper.correction,fill.missing,stem)
print("Step 1: data correction done.")
df <- computeAGB(df,"dbh2",site,use.palm.allometry)
print("Step 2: AGB calculation done.")
# save(df,file=paste0(path_folder,"/output/",site,"_rawcensus_corrected.Mar18.Rdata"))
# stem=T;taper.correction=T;fill.missing=T;palm=T;flag.stranglers=T;maxrel=0.2;graph.problem.trees=F;output.errors=F;exclude.interval=1;assign("dbh.stranglers",500)
#df <- LOAD("C:/Users/Ervan/Documents/Work/R/CTFS/Code_compil/biomass_fluxes_BCI/output/barro colorado island_rawcensus_corrected.Mar18.Rdata")
DF <- format.interval(df,flag.stranglers,dbh.stranglers)
print("Step 3: data formating done.")
DF <- flag.errors(DF,site,flag.stranglers=flag.stranglers,maxrel=maxrel,graph.problem.trees=graph.problem.trees,output.errors=output.errors,exclude.interval=exclude.interval)
print("Step 4: errors flagged. Saving corrected data into output folder.")
save(DF,file=paste0(path_folder,"/output/",site,"_85-15_20perc_Sep18.Rdata"))
save(DF,file=paste0(path_folder,"/output/",site,"_85-15_20perc_notaper_May18.Rdata"))
# save(DF,file=paste0(path_folder,"/output/",site,"_census_intervals_20perc_nobrokenstems.Rdata"))
rm(list=setdiff(ls(), c("DF","path_folder","SITE", lsf.str())))
return(DF)
}
#' Trim and correct data
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Stack all censuses together and correct DBH, if required
#' @param taper.correction TRUE or FALSE, are you willing to apply Cushman et al (2014) taper correction?
#' @param fill.missing TRUE or FALSE, are you willing to extrapolate missing DBH from surrounding DBH?
#' @param stem is the function applied over stem (stem=TRUE) or tree (stem=FALSE) data?
#' @import data.table # load data.table package
#' @return a data.table (data.frame) with all relevant variables.
#' @export
data.correction <- function(df,taper.correction,fill.missing,stem) {
if (stem) {
df[,"id" :=paste(treeID,stemID,sep="-")] # creat a unique tree-stem ID
df <- df[order(id,CensusID)]
} else { df[,"id" := treeID]}
df[,status1:=normal.stat(.SD),by=id] # check that the status is consistent over all censuses (e.g. can't be dead and alive at next census)
df <- df[!status1%in%c("P","Dr")] # discard all priors & replicated dead trees
# Add average date of census when missing for alive trees (mandatory for interpolating DBHs)
df[,"year":=round(mean(as.numeric(format(as.Date(ExactDate, origin='1960-1-1'),'%Y')),na.rm=T)),by=CensusID] # Assign 1 year per census
DATE <- df[,.(date=mean(date,na.rm=T)),by=year]
df <- within(df,date[is.na(date)] <- DATE$date[match(df[is.na(date),"year"]$year,DATE$year)])
# remove stems without any measurement in any census
NO.MEASURE <- df[,all(is.na(dbh)),by=id]
df <- df[!id%in%NO.MEASURE$treeID[NO.MEASURE$V1]]
# Taper correction or missing values: -> fill gaps for missing values
system.time(df[, c("dbh2","hom2") := corDBH(.SD,taper.correction=taper.correction,fill.missing=fill.missing), by=id]) # might be time consuming (25 minutes for BCI)
table(is.na(df$dbh2),df$status1)
NO.MEASURE <- df[,all(is.na(dbh2)),by=treeID]
table(NO.MEASURE$V1)
df <- df[!treeID%in%NO.MEASURE$treeID[NO.MEASURE$V1]] # remove trees without any measurement
return(df)
}
#' Data correction
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Perform two mains tasks: (a) apply a taper correction when POM is > 130 cm, and (b) linear interpolation values when missing DBHs. Interpolation of missing values is done by averaging surrounding available DBH values.
#' @param DF a data.table
#' @param taper.correction TRUE or FALSE, are you willing to apply Cushman et al (2014) taper correction?
#' @param fill.missing TRUE or FALSE, are you willing to extrapolate missing DBH from surrounding DBH?
#' @import data.table # load data.table package
#' @return a data.table (data.frame) with all relevant variables.
#' @export
# Debug
# for (i in levels(factor(df$id))) {
# corDBH(df[id==i],taper.correction==T,fill.missing==T)
# }
# DT <- df[id==i]
corDBH <- function(DT,taper.correction,fill.missing) {
dbh2 <- DT[status1=="A",'dbh'][[1]]
hom2 <- DT[status1=="A",round(hom*100)/100] # round hom to be at 1.3 meter (avoiding odd rounding)
loc <- which(is.na(dbh2))
DATE <- DT[status1=="A","date"][[1]]
hom2[is.na(hom2) & !is.na(dbh2)] <- 1.3
# 1. Apply Cushman's correction to trees with POM changed
if (taper.correction & any(hom2 > 1.3,na.rm=T)) {
hom2[is.na(hom2)] <- 1.3
if (taper.correction & any(hom2 > 1.3,na.rm=T)) {
loc1 <- hom2!=1.3 & !is.na(dbh2)
if(!any(is.na(loc1))) {
dbh2[loc1] <- round(dbh2[loc1]*exp(0.0247*(hom2[loc1]-1.3)),1)
hom2[loc1] <- 1.3
}}
}
# 2. Fill gaps
if (fill.missing & any(is.na(dbh2))) {
# dead stem
if (any(grep("D",DT$status1))) {
ifelse(length(dbh2)%in%loc,RULE<-2,RULE<-1) # if last value is NA, last valid DBH measure is replicated (RULE=2)
tryCatch(approx(DATE,dbh2,rule=RULE,xout=DT$date[loc])$y,error=function(e) print(unique(paste("Stem id",DT$id,"generates a problem."))))
if(any(is.na(approx(DATE,dbh2,rule=RULE,xout=DATE[loc])$y))) { print(DT$id)}
dbh2[loc] <- approx(DATE,dbh2,rule=RULE,xout=DATE[loc])$y
hom2[loc] <- approx(DATE,hom2,rule=RULE,xout=DATE[loc])$y
dbh2 <- c(dbh2,NA) # add NA at year of death
hom2 <- c(hom2,NA)
} # end of dead stems
else {
# alive stems
ifelse(length(dbh2)%in%loc,RULE<-2,RULE<-1) # if last value is NA, last valid BDBH measure is replicated (RULE=2)
tryCatch(approx(DT$date,dbh2,rule=RULE,xout=DT$date[loc])$y,error=function(e) print(paste("Stem id",DT$id,"generates a problem.")))
dbh2[loc] <- approx(DT$date,dbh2,rule=RULE,xout=DT$date[loc])$y
hom2[loc] <- approx(DT$date,hom2,rule=RULE,xout=DT$date[loc])$y
} # end of alive stems
# Resprouts
RESP <- grep("\\bR\\b",DT$codes[is.na(DT$dbh)])
if(length(RESP)>0) {
dbh2[RESP] <- NA
hom2[RESP] <- 1.30
}
} else {# end of fill.missing
# Replicate dbh & hom if no NA values or fill.missing = FALSE
dbh2 <- DT[,'dbh'][[1]]
hom2 <- DT[,round(hom*100)/100]
hom2[is.na(hom2)] <- 1.3
if (taper.correction & any(hom2 > 1.3,na.rm=T)) {
loc1 <- hom2!=1.3 & !is.na(dbh2)
if(!any(is.na(loc1))) {
dbh2[loc1] <- round(dbh2[loc1]*exp(0.0247*(hom2[loc1]-1.3)),1)
hom2[loc1] <- 1.3
}}
} # end if fill.missing = F or no missing dbh
return(list(dbh2,hom2))
}
#' Biomass computation
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Allocate wood density and compute above-ground biomass using the updated model of Chave et al. (2014), given in Rejou-Mechain et al. (2017). Palm trees (palm=T) are computed using a different allometric model (Goodman et al. 2013).
#' @param df the data.frame on which AGB shall be computed
#' @param DBH the name of the variable to be used (e.g. "dbh" or "dbh_corrected")
#' @param site provide the full name of your site (in lower case) i.e. 'barro colorado island'
#' @param use.palm.allometry TRUE or FALSE, if TRUE, biomass of palm trees is computed through a specific allometric model (Goodman et al. 2013). Only valid for South America.
#' @param DATA_path allows to provide a different path where the data is located
#' @import data.table # load data.table package
#' @return a data.table (data.frame) with all relevant variables.
#' @export
computeAGB <- function(df,DBH,site,use.palm.allometry) {
if(!exists("DATA_path")){
DATA_path <<- paste0(path_folder,"/data/")
}
## Allocate wood density
df <- density.ind(df,site,wsg=WSG)
# Compute biomass
df$agb <- AGB.comp(site,DBH, df$wsg,H = NULL)
# Compute biomass for palms
if (use.palm.allometry) {
agbPalm <- function(D) { exp(-3.3488 + 2.7483*log(D/10) + ((0.588)^2)/2)/1000 }
if(is.na(match("family",tolower(names(df))))) {
SP <- LOAD(paste0(DATA_path,list.files(DATA_path)[grep("spptable",list.files(DATA_path))]))
trim <- function (x) gsub("^\\s+|\\s+$", "", x)
SP$genus <- trim(substr(SP$Latin,1,regexpr(" ",SP$Latin)))
SP$species <- trim(substr(SP$Latin,regexpr(" ",SP$Latin),50))
SP <- SP[,c("sp","genus","species","Family")]
SP$name <- paste(SP$genus,SP$species,sep=" ")
names(SP) <- c("sp","genus","species","Family","name")
df <- merge(df,SP,by="sp",all.x=T)
df['Family'=="Arecaceae","agb":= agbPalm(dbh2)]
} else {
df['Family'=="Arecaceae","agb":= agbPalm(dbh2)]
}
}
return(df)
}
#' Format census intervals
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Create census intervals (i.e. put consecutive census side by side), assign status by tree (i.e. alive (A),dead (D), recruited (R) or resprout (Rsp))
#' @param df a data.table
#' @param flag.stranglers TRUE or FALSE, individuals of known strangler fig species greater than 'dbh.stranglers' are flagged
#' @param dbh.stranglers (optional) minimal diameter (in mm) of strangler figs, default = 500
#' @import data.table # load data.table package
#' @return a formated data.table.
#' @export
format.interval <- function(df,flag.stranglers,dbh.stranglers,code.broken=NULL) {
YEAR <- unique(df$year)
# Receiveing data set
DF2 <- data.table("treeID"=NA,"dbh1"=NA,"dbhc1"=NA,"status1"=NA,"code1"=NA,"hom1"=NA,"sp"=NA,"wsg"=NA,"agb1"=NA,"date1"=NA,"dbh2"=NA,"dbhc2"=NA,"status2"=NA,"code2"=NA,"hom2"=NA,"agb2"=NA, "date2"=NA, "broken"=NA, "agbl"=NA, "agb1.surv"=NA, "interval"=NA, "year"=NA)
for (j in 1:(length(YEAR)-1)) { # 4 minutes to run
i1 <- df[year==YEAR[j] & status1 != "D", .I[which.max(dbh2)], by = treeID] # keep only information for the biggest alive stem per treeID
A1 <- df[i1$V1,c("treeID","dbh","dbh2","status1","codes","hom","agb","id","sp","wsg")]
names(A1) <- c("treeID","dbh1","dbhc1","status1","code1","hom1","agb","id1","sp","wsg")
B1 <- df[year==YEAR[j] & status1 != "D",list("agb1"=sum(agb,na.rm=T),"date1"=mean(date,na.rm=T)),by=treeID]
BB <- merge(B1,A1,by="treeID",all.x=T)
cens1 <- BB[,c("treeID","dbh1","dbhc1","status1","code1","hom1","agb","id1","sp","wsg","agb1","date1")]
i2 <- df[year==YEAR[j+1] & status1 != "D" , .I[which.max(dbh2)], by = treeID]
A2 <- df[i2$V1,c("treeID","dbh","dbh2","hom","status1","codes","id")]
names(A2) <- c("treeID","dbh2","dbhc2","hom2","status2","code2","id2")
B2 <- df[year==YEAR[j+1] ,list("agb2"=sum(agb[status1 != "D"],na.rm=T),"date2"=mean(date,na.rm=T)),by=treeID]
BB2 <- merge(B2,A2,by="treeID",all.x=T)
cens2 <- BB2[,c("treeID","dbh2","dbhc2","status2","code2","hom2","agb2","date2","id2")]
cens2 <- within(cens2,status2[is.na(status2)] <- "D")
ID <- data.table(treeID=unique(c(cens1$treeID,cens2$treeID)))
ID <- merge(ID,cens1,by='treeID',all.x=T)
ID <- merge(ID,cens2,by='treeID',all.x=T)
ID[,"broken":=ifelse(id1!=id2 & dbhc2/dbhc1<0.8 & sp!= "oenoma" & dbhc1>100 & hom2<=hom1,1,0)] # flag large broken main stems to be added to losses
ID[broken==1,"agbl":=agb]
ID[broken==1,"agb1.surv":=agb1-agbl]
# ID$broken <- 0 ## when not flaggigng broken stems
# ID$agbl <- as.numeric(NA) ## when not flaggigng broken stems
# ID$agb1.surv <- as.numeric(NA) ## when not flaggigng broken stems
# if(exists("code.broken")) {
# idx <- ID[broken==1,.I[!grepl(paste0("\\b",code.broken,"\\b"),code2) ]]
# } else {
# idx <- ID[broken==1,.I[!grepl("\\bR\\b",code2) ]]
# }
#ID[broken==1,] <- within(ID[broken==1,],code2[idx] <- "broken")
ID[,c("agb","id1","id2"):=NULL]
ID$interval=j
ID$year=YEAR[j+1]
DF2 <- rbind(DF2,ID)
}
DF2 <- DF2[-1,]
# Add coordinates & other mandatory information
COORD <- df[,.(gx=round(10*gx[!is.na(gx)])/10,gy=round(10*gy[!is.na(gx)])/10,quadrat=quadrat,name=name),by=treeID]
COORD <- unique(COORD[!duplicated(COORD$treeID)])
DF2 <- merge(DF2,COORD,by="treeID",all.x=T)
# Add average date of census when missing
DATE <- df[,.(date=mean(date,na.rm=T)),by=.(year,quadrat)][order(year)]
DATE[,"year2" := c(year[2:length(year)],NA),by=quadrat] # year of previous census
DATE[,"ID1":=paste(year,quadrat,sep="-")]
DATE[,"ID2":=paste(year2,quadrat,sep="-")]
DF2[,"ID":= paste(DF2$year,DF2$quadrat,sep="-")]
DF2 <- within(DF2,date1[is.na(date1)] <- DATE$date[match(DF2[is.na(date1),ID],DATE$ID2)])
DF2 <- within(DF2,date2[is.na(date2)] <- DATE$date[match(DF2[is.na(date2),ID],DATE$ID1)])
DF2$int <- (DF2$date2 - DF2$date1)/365.5 # census interval in days
# Update status for recruited trees
DF2[, nrow := seq_len(.N), by = treeID]
DF2 <- within(DF2,status1[is.na(status1) & !is.na(dbh2) & nrow==1] <- "P") # recruited trees or Z code (=?)
DF2[,nrow:= NULL]
# Assign status
DF2[, c("agbl","code","dHOM") := assign.status(.SD), by=treeID]
# Remove unnecessary rows
DF2[code=="D", nrow := seq_len(.N), by = treeID]
DF2 <- within(DF2,nrow[is.na(nrow)] <- 0)
DF2 <- DF2[nrow<2,] # keeps only 1 line for dead trees
DF2[,"nrow":=NULL]
# Compute annualized fluxes
DF2[code%in%c("A","AC","B"),prod.g := (agb2-agb1)/int,by=treeID] # annual prod for alive trees & multi-stems
DF2[code=="B",prod.g := (agb2-agb1.surv)/int,by=treeID] # annual prod for alive trees & multi-stems
DF2[code%in%c("R","Rsp"),prod.r:=agb2/int,by=treeID] # annual prod for resprouts and recruits
DF2[code=="D",agbl:= agb1,by=treeID]
DF2[,loss:=agbl/int,by=treeID] # annualized loss for dead trees
# Flag large strangler figs
if(flag.stranglers) {
DF2$ficus <- 0
ficus$name <- paste(ficus$Genus,ficus$Species,sep=" ")
FIC <- match(DF2$name,ficus$name[ficus$Strangler=="Yes"])
if(!exists("dbh.stranglers")) {assign("dbh.stranglers",500) }
DF2 <- within(DF2,ficus[!is.na(FIC) & dbhc1>dbh.stranglers]<-1)
}
return(DF2)
}
#' Flag major errors
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Identify trees with major errors in DBH measurments. A major error correspond to a relative individal productivity (or growth) is above a given percentage (set by 'maxrel') of the mean productivity computed at a site. Additionnaly, flagged trees that died at next census interval are also flagged. Option to see DBH measurement (=draw.graph) of flagged trees or print a csv (output.errors) are given.
#' @param DF a data.table
#' @param site provide the full name of your site (in lower case) i.e. 'barro colorado island'
#' @param strangler TRUE or FALSE, if TRUE, strangler figs tree are flagged (upon a list to published soon)
#' @param maxrel a numeric value setting the threshold over which relative productivity is assumed to be too high (usually set at 20 percents)
#' @param output.errors TRUE or FALSE, output all records for trees with major errors in productivity to a csv file
#' @param exclude.interval a vector (i.e. c(1,2)) indicating if a set of census intervals must be discarded from computation due for instance to a change in protocol of measurment
#' @import data.table # load data.table package
#' @return a data.table (data.frame) with all relevant variables.
#' @export
#'
flag.errors <- function(DF,site,flag.stranglers,maxrel,graph.problem.trees,output.errors,exclude.interval) {
mean.prod <- determine.mean.prod(DF,site,flag.stranglers,exclude.interval)
DF[,prod.rel:=as.numeric(NA),]
DF[,prod.rel:=prod.g/mean.prod] # relative contribution to average total productivity
DF[,error:=0]
DF <- within(DF,error[prod.rel>maxrel & dHOM==0 & code!="D"] <- 1)
DF <- within(DF,error[prod.rel<(-maxrel) & dHOM==0 & code!="D"] <- -1)
# Flag census after a major error
POSI <- DF[,.I[error!=0]+1,]
POSI2 <- DF[POSI,.I[error==0 & dHOM==0 & code=="D"],]
DF[,error.loss:=0]
DF <- within(DF,error.loss[POSI[POSI2]] <- 1) # flag consecutive census
if (flag.stranglers) {
DF <- within(DF,error.loss[ficus==1 & code=="D"] <- 1) # flag dead strangler figs
}
# ID <- DF[error!=0 & !code%in%c("D","R"),nrow(.SD)>=1,by=treeID]
# ID <- ID[V1==T,treeID]
ID <- unique(DF[error!=0,treeID])
if (length(ID)/12 > 10) {
A <- menu(c("Y", "N"), title="There are more than 144 trees (10 pages) to be printed. Do you want to print them?")
ifelse(A==1,graph.problem.trees<-T,graph.problem.trees<-F)
}
if(graph.problem.trees) { # Plot trees with large major error
YEAR <- levels(factor(DF$year))
CX=2
a=0
i = 0
GRAPH = list()
for (n in 1:length(ID)){
i = i + 1
DF[,year:=as.numeric(year)]
DF$dbh1 <- round(DF$dbh1,1)
DF[,"y1":=year-round(int)]
aa <- ID[n]
X <- DF[treeID==aa & !code%in%c("R")][order(year)]
X$point <- 0
X$point[X$error!=0] <- 1
Y <- DF[treeID==aa & !code%in%c("D","R")][order(year)]
YY <- Y[,.(year=max(year),name=unique(name),d2=round(dbhc2[year==max(year)],1),d02=round(dbh2[year==max(year)],1),hom2=round(hom2[year==max(year)],2),y1=unique(y1)),by=treeID]
Y$line <- 0
Y$line[Y$dHOM==0] <- 1
Y$point <- 0
Y$point[Y$error!=0] <- 1
GRAPH[[i]] = ggplot(X,aes(x=y1,y=dbhc1)) + geom_point(size=2) + geom_segment(data=Y,aes(x=y1,y=dbhc1,xend=year,yend=dbhc2,linetype=as.factor(line))) + geom_point(data=X[point==1],aes(x=year,y=dbhc2),col=2) + labs(title=paste0(unique(X$name)," (",ID[n],")"), x=" ",y="dbh (mm)") + geom_text(data=Y,aes(x=y1,y=dbh1-(0.05*dbh1)),label=round(Y$hom1,2),cex=CX) + geom_text(data=YY,aes(x=year,y=d02-(0.05*d02)),label=round(YY$hom2,2),cex=CX) + geom_text(data=Y,aes(x=year,y=0.3*max(dbhc2)),label=Y$dbh1,cex=CX,angle=90,vjust=1) + geom_text(data=YY,aes(x=year,y=0.3*max(d2)),label=YY$d02,cex=CX,angle=90,vjust=1) + theme(plot.title = element_text(size=5*CX,face="bold"),axis.title.y= element_text(size=5*CX,,face="bold"),axis.text.y = element_text(size=4*CX),axis.text.x = element_text(size=4*CX,vjust=0,angle=30),panel.background = element_blank(),strip.text = element_text(size = 4*CX,face="bold"),strip.background = element_rect("lightgrey"),panel.spacing = unit(0.1, "lines")) + scale_linetype_manual(values=c('0'="dashed",'1'="solid")) + guides(linetype=F,colour=F) + scale_x_continuous(limits=c(min(as.numeric(YEAR))-3,max(as.numeric(YEAR))+3),breaks = as.numeric(YEAR)) + scale_y_continuous(limits=c(0.2*max(YY$d2),max(X$dbhc2,X$dbhc1)))
if (i %% 15 == 0 | i<15) { ## print 8 plots on a page
a=a+1
plot(do.call(grid.arrange, GRAPH))
ggsave(do.call(grid.arrange, GRAPH),file=paste0(path_folder,"/output/trees_with_major_errors_",a,".pdf"),width = 29.7, height = 20.1, units = "cm")
GRAPH = list() # reset plot
i = 0 # reset index
}}
} # end of graph
if (length(ID)==0){
print(paste0("No tree productivity above",maxrel, "% or below",-maxrel,"% of mean productivity at your plot. You may eventually want to try a lower threshold."))
}
if (output.errors & length(ID)>0){
write.csv(DF[treeID%in%ID],file=paste0(path_folder,"/output/trees_with_major_errors.csv"))
}
return(DF)
} # end of major.error
#' Set mean productivity
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Set mean productivity (Mg/ha/yr) across all census intervals for a given site
#' @param DF a data.table
#' @param site provide the full name of your site (in lower case) i.e. 'barro colorado island'
#' @param strangler TRUE or FALSE, if TRUE, strangler figs tree are flagged (upon a list to published soon)
#' @param exclude.interval a vector (i.e. c(1,2)) indicating if a set of census intervals must be discarded from computation due for instance to a change in protocol of measurment
#' @return mean productivity in Mg/ha/yr.
#' @export
determine.mean.prod <- function(DF,site,flag.stranglers,exclude.interval) {
AREA <- site.info$size[match(site,site.info$site)]
if (missing(exclude.interval)){
ifelse(flag.stranglers,
PRODA <- data.table(DF)[ficus!=1,.(prod=rec.flux(sum(agb1,na.rm=T),sum(agb2[code!="D"],na.rm=T),sum(agb1[code%in%c("A","AC")],na.rm=T),AREA,mean(int,na.rm=T))), by=interval],
PRODA <- data.table(DF)[, .(prod=rec.flux(sum(agb1,na.rm=T),sum(agb2[code!="D"],na.rm=T),sum(agb1[code%in%c("A","AC")],na.rm=T),AREA,mean(int,na.rm=T))), by=interval])
} else {
ifelse(flag.stranglers,
PRODA <- data.table(DF)[ficus!=1 & !interval%in%c(exclude.interval),.(prod=rec.flux(sum(agb1,na.rm=T),sum(agb2[code!="D"],na.rm=T),sum(agb1[code%in%c("A","AC")],na.rm=T),AREA,mean(int,na.rm=T))), by=interval],
PRODA <- data.table(DF)[!interval%in%c(exclude.interval),.(prod=rec.flux(sum(agb1,na.rm=T),sum(agb2[code!="D"],na.rm=T),sum(agb1[code%in%c("A","AC")],na.rm=T),AREA,mean(int,na.rm=T))), by=interval])
}
mPROD <- mean((PRODA$prod))
return(mPROD)
}
#' Loess
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description a wrapper to get smoothed predictions of AGB fluxes using a loess function (library 'locfit')
#' @param x a data.table
#' @param var the name of the variable to be smoothed again intial AGB
#' @param range the range of initial AGB to be used for prediction (i.e. 5th and 95th percentiles of the whole distribution)
#' @return a smoothed prediction of the variable of interest
#' @export
loess.fun <- function(x,var,range,weights=NULL) {
if (is.null(weights)) {
fit <- locfit(var ~ lAGB, data=x)}
else {fit <- locfit(var ~ lAGB, data=x,weights=weights)}
pred <- predict(fit,newdata=list(lAGB=range))
return(data.frame(lAGB=range,y=as.numeric(pred)))
}
# loess.fun <- function(x,var) {
# fit <- locfit(var ~ lAGB, data=x)
# Xrange = as.numeric(quantile(x,seq(0.01,1,.01),na.rm=T)) # 100 values to estimate the smooth spline
# pred <- predict(fit,newdata=list(lAGB=Xrange))
# return(data.frame(lAGB=Xrange,y=as.numeric(pred)))
# }
#' Normalized tree status
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Check the consistency of stem/tree status over time (i.e. a tree that is 'alive' at last census can not be 'dead' inbetween)
#' @param x a data.table
#' @return a data.table (data.frame) with a new colum "status1" where values can be "P"(prior),"A"(alive),"D"(dead) and "Dr"(dead replicated).
#' @export
#'
normal.stat <-function(DT) {
if(!"status"%in%names(DT)) {
DT$status <- NA
DT$status[DT$DFstatus=="alive"] <- "A"
DT$status[DT$DFstatus=="dead"] <- "D"
}
STAT <- rep("A",nrow(DT))
if (all(is.na(DT$dbh))) {
STAT <- rep("Dr",nrow(DT))
} else if (any(is.na(DT$dbh))|any(grep("\\bD\\b",DT$status))) { # look for dbh=NA or dead (D) status
locA <- which(DT$status=="A" & !is.na(DT$dbh))
if (length(locA)!=0) {
if (min(locA) >1) {
STAT[is.na(DT$dbh)][1:min(locA)-1] <- "P" } # Prior (to be discarded)
if (max(locA) < nrow(DT)) {
STAT[(max(locA)+1)] <- "D" }
if (max(locA)+2 <= nrow(DT)) {
STAT[(max(locA)+2):nrow(DT)] <- "Dr" } # Dead replicated (to be discarded)
} # end of loca>0
}
return(STAT)
}
#' Assign wood density
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Assign wood density using CTFS wood density data base (WSG)
#' @param df a data.table
#' @param site provide the full name of your site (in lower case) i.e. 'barro colorado island'
#' @param wsgdata a list of tree species (mnenomic) and corresponding wood density
#' @param denscol the variable to be return ("wsg" by default). No other option is implemented.
#' @return a data.table (data.frame) with all relevant variables.
#' @export
density.ind <- function (DAT, site, wsgdata, denscol = "wsg") {
if(!exists("DATA_path")){
DATA_path <<- paste0(path_folder,"/data/")
}
#Add genus & species to data
SP <- LOAD(paste(DATA_path,list.files(DATA_path)[grep("spptable",list.files(DATA_path))],sep="/"))
SP <- subset(SP,,c("sp","Genus","Species","Family"))
SP$name = paste(SP$Genus,SP$Species,sep=" ")
# Import & format CTFS wood data base
wsgdatamatch = which(WSG$site %in% site.info$wsg.site.name[site.info$site==tolower(site)])
if (length(wsgdatamatch) == 0) stop("Site name doesn't match!")
wsg = unique(WSG[wsgdatamatch,c("genus","species","spwood") ])
names(wsg) <- c("genus","species","wd")
wsg <- wsg[!is.na(wsg$wd),]
# Assign WD by taxon
A <- invisible(getWoodDensity(SP$Genus, SP$Species, stand = rep(site,nrow(SP)), family = NULL, region = "World",addWoodDensityData = wsg))
A$name <- paste(A$genus,A$species,sep=" ")
SP <- unique(merge(SP,A[,c("name","meanWD")],by="name",all.x=T))
names(SP) <- c("name", "sp", "genus", "species", "Family", "wsg")
if (any(grep("name",names(DAT)))) {
DT <- merge(DAT,SP,by="name",all.x=T)
} else {
DT <- merge(DAT,SP,by="sp",all.x=T)
}
# Allocate mean WD to species not in the list
if (any(is.na(DT$wsg))){
print(paste0("There are ",nrow(DT[is.na(wsg)])," individuals without WD values. Plot-average value (", round(mean(DT$wsg, na.rm=T),2),") was assigned."))
DT <- within(DT,wsg[is.na(wsg)] <- mean(wsg, na.rm=T))
}
return(DT)
}
#' AGB computation
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Compute above-ground biomass using the updated model of Chave et al. (2014), given in Rejou-Mechain et al. (2017)
#' @param site provide the full name of your site (in lower case) i.e. 'barro colorado island'
#' @param D a column with tree diameters (in mm)
#' @param WD a column with wood density values
#' @param H a column with tree heights (optional)
#' @return a vector with AGB values (in Mg)
#' @export
AGB.comp <- function (site,D,WD, H = NULL) {
if (length(D) != length(WD))
stop("D and WD have different lenghts")
if (is.null(site)) {
stop("You must specified your site") }
if (!is.null(H)) {
if (length(D) != length(H))
stop("H and WD have different length")
if (any(is.na(H)) & !any(is.na(D)))
warning("NA values are generated for AGB values because of missing information on tree height, \nyou may construct a height-diameter model to overcome that issue (see ?HDFunction and ?retrieveH)")
if (any(is.na(D)))
warning("NA values in D")
AGB <- (0.0673 * (WD * H * (D/10)^2)^0.976)/1000
}
else {
INDEX <- match(tolower(site),site.info$site)
if (is.na(INDEX)) { stop("Site name should be one of the following: \n",paste(levels(factor(site.info$site)),collapse=" - ")) }
E <- site.info$E[INDEX]
AGB <- exp(-2.023977 - 0.89563505 * E + 0.92023559 *
log(WD) + 2.79495823 * log(D/10) - 0.04606298 *
(log(D/10)^2))/1000
}
return(AGB)
}
#' Load object
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description a wrapper to softly load R objects in the Global environment
#' @param saveFile the path to the object to be loaded
#' @return import the object
#' @export
LOAD <- function(saveFile) {
env <- new.env()
load(saveFile, envir=env)
loadedObjects <- objects(env, all=TRUE)
stopifnot(length(loadedObjects)==1)
env[[loadedObjects]]
}
#' Assign status
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Assign status alive ("A"), alive with POM changed ("AC"), dead ("D"), recruited ("R") or resprout ("Rsp") to trees, and check for consistency over time (i.e. avoid resurrection)
#' @param DF a data.table
#' @return update the column 'status1' with consistent information.
#' @export
# # # Debug
# DT <- DF2[treeID==391]
# DT <- DF2[treeID== 230867 ][order(year)]
# DT <- DF2[treeID==5637]
# assign.status(DT)
assign.status <- function(DT) {
code <- rep("A",nrow(DT))
agbl <- DT$agbl
code[code=="A" & is.na(DT$dbhc1) & DT$status1=="P"] <- "R"
code[code=="A" & DT$broken==1] <- "B" # Broken
code[code=="A" & DT$code2=="R"] <- "Rsp" #
code[code=="A" & is.na(DT$status1) & !is.na(DT$status2)] <- "Rsp" # resprouted trees poses a problem only the first year, are alive/dead after
code[code=="A" & grep("MAIN",DT$code1) & grep("SPROUT",DT$code2)] <- "Rsp" # resprouted trees poses a problem only the first year, are alive/dead after
if(tail(DT$status2,1)=="D") {code[length(code)] <- "D"}
if (any(code=="A")){
loc <- max(which(code%in%c("A","Rsp")))
if (any(code[1:loc]=="D")) {
loc2 <- which(code[1:loc]=="D")
code[loc2] <- "B"
agbl[loc2] <- DT$agb1[loc2]
}
}
dHOM <- DT$hom2 - DT$hom1
dHOM[is.na(dHOM)] <- 0
code[code=="A" & dHOM!=0] <- "AC" # trees with POM changed are not accounted for in productivity
return(list(agbl,code,dHOM))
}
# assign.status2 <- function(DT) {
# code <- rep("A",nrow(DT))
# code[DT$status1=="P"] <- "R"
# code[is.na(DT$dbhc1) & DT$status2=="A" & code!="R"] <- "Rsp" # resprouted trees poses a problem only the first year, are alive/dead after
# code[DT$status2=="D"] <- "D"
# if(any(code=="A")){
# loc <- max(which(code%in%c("A","Rsp")))
# if (any(code[1:loc]=="D")) {
# code[which(code[1:loc]=="D")] <- "A"
# dbh2
# }}
# dHOM <- DT$hom2 - DT$hom1
# dHOM[is.na(dHOM)] <- 0
# code[code=="A" & dHOM!=0] <- "AC" # trees with POM changed are not accounted for in productivity
# return(list(code,dHOM))
# }
#' Create quadrats
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Creat a grid where all trees are allocated to a given quadrat of size (= grid size).
#' @param census a data.frame where trees have relative X-Y coordinates.
#' @param grid_size the size of the grid (in meter)
#' @param x the identifier of X coordinates (i.e. 'gx')
#' @param y the identifier of Y coordinates (i.e. 'gy')
#' @param make.squared does the coordinates needs to
#' @return add three columns to the data.frame: quadrat's number, centroids X and Y.
#' @export
create.quadrats=function(census,grid_size,x="gx",y="gy",fit.in.plot) {
X <- census[,grep(x,names(census)),with=F][[1]]
Y <- census[,grep(y,names(census)),with=F][[1]]
if (any(is.na(X))|any(is.na(Y))) {
warning(paste(length(X[is.na(X)])," trees without coordinates were discarded."))
census <- census[!is.na(X) & !is.na(Y)]
X <- census[,grep(x,names(census)),with=F][[1]]
Y <- census[,grep(y,names(census)),with=F][[1]]
}
minx=0
miny=0
maxx=max(X,na.rm=T)
maxy=max(Y,na.rm=T)
if(exists("fit.in.plot")) {
maxx <- round(max(X)/10)*10
maxy <- round(max(Y)/10)*10
}
x1=X
x1[X<=0]=0.1
x1[X>=maxx]=maxx-0.1
y1=Y
y1[Y<=0]=0.1
y1[Y>=maxy]=maxy-0.1
# specify grid size for division into quadrats
w_grid = ceiling(maxx)/grid_size;
h_grid = ceiling(maxy)/grid_size;
n_quadrat = w_grid*h_grid;
# for now, only allow grid sizes that fit neatly
if ( max(x1,na.rm=T) > maxx | min(x1,na.rm=T) < 0 | max(y1,na.rm=T) > maxy | min(y1,na.rm=T) < 0 )
{
stop('Some trees are outside the plot boundaries. Consider fit.in.plot=T')
}
if ( round(w_grid) != w_grid | round(h_grid) != h_grid )
{
stop('Plot width and height must be divisible by grid_size');
}
census$quad<-(ceiling((maxy-miny)/grid_size))*(floor((x1-minx)/grid_size))+(floor((y1-miny)/grid_size)+1) ## identifiant de cellule unique 1->100 en partant de 0,0
if ( max(census$quad,na.rm=T) != n_quadrat )
{
stop(paste('Quadrat numbering error: expected ',n_quadrat,' quadrats; got ',max(census$quad,na.rm=T),sep=''))
}
census$centroX<-(floor(x1/grid_size)*grid_size)+(grid_size/2)
census$centroY<-(floor(y1/grid_size)*grid_size)+(grid_size/2)
# census[,.(max(gx)-min(gx),max(gy)-min(gy)),by=quad]
return(census)
}
#' Unbiased recruitment flux
#' @author Helene Muller-Laudau (hmullerlandau@gmail.com )
#' @description Compute unbiased recruitment rate (i.e. account for unmeasured recruitment)
#' @param A0 biomass at initial census.
#' @param A1 biomass of alive trees at final census.
#' @param S1 initial biomass of individuals that survived to time t
#' @param time cenusus interval in year
#' @return absolute recruitment flux in % per year
#' @export
rec.flux <- function(A0,A1,S1,area,time) {
rec <- log(A1/S1)*(A1-A0)/(area*time*log(A1/A0))
ifelse(rec==Inf,0,rec)
return(rec)
}
#' Unbiased loss flux
#' @author Helene Muller-Laudau (hmullerlandau@gmail.com )
#' @description Compute unbiased recruitment rate (i.e. account for unmeasured recruitment)
#' @param A0 biomass at initial census.
#' @param A1 biomass of alive trees at final census.
#' @param S1 initial biomass of individuals that survived to time t
#' @param time cenusus interval in year
#' @return absolute recruitment flux in % per year
#' @export
loss.flux <- function(A0,A1,S1,area,time) {
LO <- (log(A0/S1)/log(A1/A0))*((A1-A0)/(area*time))
ifelse(LO==Inf,0,LO)
return(LO)
}
trim.growth=function(cens,slope=0.006214,intercept=.9036,err.limit=4,maxgrow=75,dbhunit='mm')
{
if(dbhunit=='cm') intercept=intercept/10
stdev.dbh1=slope*cens$dbhc1+intercept
growth=(cens$dbhc2-cens$dbhc1)/cens$int
bad.neggrow=which(cens$dbhc2<=(cens$dbhc1-err.limit*stdev.dbh1))
bad.posgrow=which(growth>maxgrow)
accept=rep(TRUE,length(cens$dbhc1))
accept[bad.neggrow]=FALSE
accept[bad.posgrow]=FALSE
accept[is.na(growth)]=FALSE
accept[is.na(cens$dbhc1) | is.na(cens$dbhc2) | cens$dbhc2<=0]=FALSE
growth[!accept] <- NA
return(growth)
}
ervanSTRI/AGBflux_pack documentation built on Oct. 15, 2021, 6:44 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions error error data.correction data.prep
Documented in data.correction data.prep
#' CTFS-formated data preparation
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Main routine to format, detect major obvious errors, and gap-fill those errors in CTFS-formated data
#' @param site the full name of your site (in lower case); e.g., 'barro colorado island'
#' @param stem TRUE or FALSE, using the stem data (stem=TRUE) rather than the tree data (i.e. called 'full', stem=FALSE)
#' @param taper.correction TRUE or FALSE, apply Cushman et al (2014) taper correction
#' @param fill.missing TRUE or FALSE, interpolate missing DBH values
#' @param use.palm.allometry TRUE or FALSE, if TRUE, compute biomass of palm trees using a palm-specific allometric model from Goodman et al. (2013)
#' @param flag.stranglers TRUE or FALSE, individuals of known strangler fig species greater than 'dbh.stranglers' are flagged
#' @param dbh.stranglers (optional) minimal diameter (in mm) of strangler figs, default = 500
#' @param maxrel a numeric value: the threshold for flagging major errors in productivity, applied as absval(individual tree productivity)>maxrel*(average productivity per hectare)
#' @param output.errors TRUE or FALSE, output all records for trees with major errors in productivity to a csv file
#' @param DATA_path the pathname where the data are located
#' @param exclude.interval NULL by default. If needed a vector (e.g. c(1,2)) indicating which census interval(s) must be discarded from computation due, for instance, to a change in measurement protocol
#' @return a data.table (data.frame) with all relevant variables.
#' @import data.table # load data.table package
#' @export
data.prep <- function(site,stem,taper.correction,fill.missing,use.palm.allometry,flag.strangler,dbh.stranglers,maxrel,graph.problem.trees,output.errors,DATA_path,exclude.interval=NULL) {
site <- tolower(site)
INDEX <- match(tolower(site),site.info$site)
if (is.na(INDEX)) { stop("Site name should be one of the following: \n",paste(levels(factor(site.info$site)),collapse=" - ")) }
if(!exists("DATA_path")){
DATA_path <- paste0(path_folder,"/data/")
}
files <-list.files(DATA_path)
ifelse(stem,files <- files[grep("stem",files)], files <- files [grep("full",files)])
ifelse(!dir.exists(file.path(paste0(path_folder,"/output"))), dir.create(file.path(paste0(path_folder,"/output"))), FALSE)
# Create the receiving data.frame
df <- data.frame("treeID"=NA, "stemID"=NA, "tag"=NA, "StemTag"=NA, "sp"=NA, "quadrat"=NA, "gx"=NA, "gy"=NA,"dbh"=NA,"hom"=NA, "ExactDate"=NA, "DFstatus"=NA, "codes"=NA, "date"=NA,"status"=NA,"CensusID"=NA,"year"=NA)
for (i in 1:length(files)) {
temp <- setDT(LOAD(paste(DATA_path,files[i],sep="/")))
temp$CensusID <- i
temp[,year:= round(mean(as.numeric(format(as.Date(date, origin='1960-1-1'),'%Y')),na.rm=T))]
if(any(is.na(match(names(df),names(temp))))) {
ID <- which(is.na(match(names(df),names(temp))))
stop(paste0("The data must have a column named ",names(df)[ID],collapse=" - "))
}
temp <- temp[,match(names(df),names(temp)),with=FALSE]
df <- rbind(df,temp)
}
rm(temp)
table(df$year,df$CensusID)
df <- df[-1,]
df <- data.correction(df,taper.correction,fill.missing,stem)
print("Step 1: data correction done.")
df <- computeAGB(df,"dbh2",site,use.palm.allometry)
print("Step 2: AGB calculation done.")
# save(df,file=paste0(path_folder,"/output/",site,"_rawcensus_corrected.Mar18.Rdata"))
# stem=T;taper.correction=T;fill.missing=T;palm=T;flag.stranglers=T;maxrel=0.2;graph.problem.trees=F;output.errors=F;exclude.interval=1;assign("dbh.stranglers",500)
#df <- LOAD("C:/Users/Ervan/Documents/Work/R/CTFS/Code_compil/biomass_fluxes_BCI/output/barro colorado island_rawcensus_corrected.Mar18.Rdata")
DF <- format.interval(df,flag.stranglers,dbh.stranglers)
print("Step 3: data formating done.")
DF <- flag.errors(DF,site,flag.stranglers=flag.stranglers,maxrel=maxrel,graph.problem.trees=graph.problem.trees,output.errors=output.errors,exclude.interval=exclude.interval)
print("Step 4: errors flagged. Saving corrected data into output folder.")
save(DF,file=paste0(path_folder,"/output/",site,"_85-15_20perc_Sep18.Rdata"))
save(DF,file=paste0(path_folder,"/output/",site,"_85-15_20perc_notaper_May18.Rdata"))
# save(DF,file=paste0(path_folder,"/output/",site,"_census_intervals_20perc_nobrokenstems.Rdata"))
rm(list=setdiff(ls(), c("DF","path_folder","SITE", lsf.str())))
return(DF)
}
#' Trim and correct data
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Stack all censuses together and correct DBH, if required
#' @param taper.correction TRUE or FALSE, are you willing to apply Cushman et al (2014) taper correction?
#' @param fill.missing TRUE or FALSE, are you willing to extrapolate missing DBH from surrounding DBH?
#' @param stem is the function applied over stem (stem=TRUE) or tree (stem=FALSE) data?
#' @import data.table # load data.table package
#' @return a data.table (data.frame) with all relevant variables.
#' @export
data.correction <- function(df,taper.correction,fill.missing,stem) {
if (stem) {
df[,"id" :=paste(treeID,stemID,sep="-")] # creat a unique tree-stem ID
df <- df[order(id,CensusID)]
} else { df[,"id" := treeID]}
df[,status1:=normal.stat(.SD),by=id] # check that the status is consistent over all censuses (e.g. can't be dead and alive at next census)
df <- df[!status1%in%c("P","Dr")] # discard all priors & replicated dead trees
# Add average date of census when missing for alive trees (mandatory for interpolating DBHs)
df[,"year":=round(mean(as.numeric(format(as.Date(ExactDate, origin='1960-1-1'),'%Y')),na.rm=T)),by=CensusID] # Assign 1 year per census
DATE <- df[,.(date=mean(date,na.rm=T)),by=year]
df <- within(df,date[is.na(date)] <- DATE$date[match(df[is.na(date),"year"]$year,DATE$year)])
# remove stems without any measurement in any census
NO.MEASURE <- df[,all(is.na(dbh)),by=id]
df <- df[!id%in%NO.MEASURE$treeID[NO.MEASURE$V1]]
# Taper correction or missing values: -> fill gaps for missing values
system.time(df[, c("dbh2","hom2") := corDBH(.SD,taper.correction=taper.correction,fill.missing=fill.missing), by=id]) # might be time consuming (25 minutes for BCI)
table(is.na(df$dbh2),df$status1)
NO.MEASURE <- df[,all(is.na(dbh2)),by=treeID]
table(NO.MEASURE$V1)
df <- df[!treeID%in%NO.MEASURE$treeID[NO.MEASURE$V1]] # remove trees without any measurement
return(df)
}
#' Data correction
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Perform two mains tasks: (a) apply a taper correction when POM is > 130 cm, and (b) linear interpolation values when missing DBHs. Interpolation of missing values is done by averaging surrounding available DBH values.
#' @param DF a data.table
#' @param taper.correction TRUE or FALSE, are you willing to apply Cushman et al (2014) taper correction?
#' @param fill.missing TRUE or FALSE, are you willing to extrapolate missing DBH from surrounding DBH?
#' @import data.table # load data.table package
#' @return a data.table (data.frame) with all relevant variables.
#' @export
# Debug
# for (i in levels(factor(df$id))) {
# corDBH(df[id==i],taper.correction==T,fill.missing==T)
# }
# DT <- df[id==i]
corDBH <- function(DT,taper.correction,fill.missing) {
dbh2 <- DT[status1=="A",'dbh'][[1]]
hom2 <- DT[status1=="A",round(hom*100)/100] # round hom to be at 1.3 meter (avoiding odd rounding)
loc <- which(is.na(dbh2))
DATE <- DT[status1=="A","date"][[1]]
hom2[is.na(hom2) & !is.na(dbh2)] <- 1.3
# 1. Apply Cushman's correction to trees with POM changed
if (taper.correction & any(hom2 > 1.3,na.rm=T)) {
hom2[is.na(hom2)] <- 1.3
if (taper.correction & any(hom2 > 1.3,na.rm=T)) {
loc1 <- hom2!=1.3 & !is.na(dbh2)
if(!any(is.na(loc1))) {
dbh2[loc1] <- round(dbh2[loc1]*exp(0.0247*(hom2[loc1]-1.3)),1)
hom2[loc1] <- 1.3
}}
}
# 2. Fill gaps
if (fill.missing & any(is.na(dbh2))) {
# dead stem
if (any(grep("D",DT$status1))) {
ifelse(length(dbh2)%in%loc,RULE<-2,RULE<-1) # if last value is NA, last valid DBH measure is replicated (RULE=2)
tryCatch(approx(DATE,dbh2,rule=RULE,xout=DT$date[loc])$y,error=function(e) print(unique(paste("Stem id",DT$id,"generates a problem."))))
if(any(is.na(approx(DATE,dbh2,rule=RULE,xout=DATE[loc])$y))) { print(DT$id)}
dbh2[loc] <- approx(DATE,dbh2,rule=RULE,xout=DATE[loc])$y
hom2[loc] <- approx(DATE,hom2,rule=RULE,xout=DATE[loc])$y
dbh2 <- c(dbh2,NA) # add NA at year of death
hom2 <- c(hom2,NA)
} # end of dead stems
else {
# alive stems
ifelse(length(dbh2)%in%loc,RULE<-2,RULE<-1) # if last value is NA, last valid BDBH measure is replicated (RULE=2)
tryCatch(approx(DT$date,dbh2,rule=RULE,xout=DT$date[loc])$y,error=function(e) print(paste("Stem id",DT$id,"generates a problem.")))
dbh2[loc] <- approx(DT$date,dbh2,rule=RULE,xout=DT$date[loc])$y
hom2[loc] <- approx(DT$date,hom2,rule=RULE,xout=DT$date[loc])$y
} # end of alive stems
# Resprouts
RESP <- grep("\\bR\\b",DT$codes[is.na(DT$dbh)])
if(length(RESP)>0) {
dbh2[RESP] <- NA
hom2[RESP] <- 1.30
}
} else {# end of fill.missing
# Replicate dbh & hom if no NA values or fill.missing = FALSE
dbh2 <- DT[,'dbh'][[1]]
hom2 <- DT[,round(hom*100)/100]
hom2[is.na(hom2)] <- 1.3
if (taper.correction & any(hom2 > 1.3,na.rm=T)) {
loc1 <- hom2!=1.3 & !is.na(dbh2)
if(!any(is.na(loc1))) {
dbh2[loc1] <- round(dbh2[loc1]*exp(0.0247*(hom2[loc1]-1.3)),1)
hom2[loc1] <- 1.3
}}
} # end if fill.missing = F or no missing dbh
return(list(dbh2,hom2))
}
#' Biomass computation
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Allocate wood density and compute above-ground biomass using the updated model of Chave et al. (2014), given in Rejou-Mechain et al. (2017). Palm trees (palm=T) are computed using a different allometric model (Goodman et al. 2013).
#' @param df the data.frame on which AGB shall be computed
#' @param DBH the name of the variable to be used (e.g. "dbh" or "dbh_corrected")
#' @param site provide the full name of your site (in lower case) i.e. 'barro colorado island'
#' @param use.palm.allometry TRUE or FALSE, if TRUE, biomass of palm trees is computed through a specific allometric model (Goodman et al. 2013). Only valid for South America.
#' @param DATA_path allows to provide a different path where the data is located
#' @import data.table # load data.table package
#' @return a data.table (data.frame) with all relevant variables.
#' @export
computeAGB <- function(df,DBH,site,use.palm.allometry) {
if(!exists("DATA_path")){
DATA_path <<- paste0(path_folder,"/data/")
}
## Allocate wood density
df <- density.ind(df,site,wsg=WSG)
# Compute biomass
df$agb <- AGB.comp(site,DBH, df$wsg,H = NULL)
# Compute biomass for palms
if (use.palm.allometry) {
agbPalm <- function(D) { exp(-3.3488 + 2.7483*log(D/10) + ((0.588)^2)/2)/1000 }
if(is.na(match("family",tolower(names(df))))) {
SP <- LOAD(paste0(DATA_path,list.files(DATA_path)[grep("spptable",list.files(DATA_path))]))
trim <- function (x) gsub("^\\s+|\\s+$", "", x)
SP$genus <- trim(substr(SP$Latin,1,regexpr(" ",SP$Latin)))
SP$species <- trim(substr(SP$Latin,regexpr(" ",SP$Latin),50))
SP <- SP[,c("sp","genus","species","Family")]
SP$name <- paste(SP$genus,SP$species,sep=" ")
names(SP) <- c("sp","genus","species","Family","name")
df <- merge(df,SP,by="sp",all.x=T)
df['Family'=="Arecaceae","agb":= agbPalm(dbh2)]
} else {
df['Family'=="Arecaceae","agb":= agbPalm(dbh2)]
}
}
return(df)
}
#' Format census intervals
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Create census intervals (i.e. put consecutive census side by side), assign status by tree (i.e. alive (A),dead (D), recruited (R) or resprout (Rsp))
#' @param df a data.table
#' @param flag.stranglers TRUE or FALSE, individuals of known strangler fig species greater than 'dbh.stranglers' are flagged
#' @param dbh.stranglers (optional) minimal diameter (in mm) of strangler figs, default = 500
#' @import data.table # load data.table package
#' @return a formated data.table.
#' @export
format.interval <- function(df,flag.stranglers,dbh.stranglers,code.broken=NULL) {
YEAR <- unique(df$year)
# Receiveing data set
DF2 <- data.table("treeID"=NA,"dbh1"=NA,"dbhc1"=NA,"status1"=NA,"code1"=NA,"hom1"=NA,"sp"=NA,"wsg"=NA,"agb1"=NA,"date1"=NA,"dbh2"=NA,"dbhc2"=NA,"status2"=NA,"code2"=NA,"hom2"=NA,"agb2"=NA, "date2"=NA, "broken"=NA, "agbl"=NA, "agb1.surv"=NA, "interval"=NA, "year"=NA)
for (j in 1:(length(YEAR)-1)) { # 4 minutes to run
i1 <- df[year==YEAR[j] & status1 != "D", .I[which.max(dbh2)], by = treeID] # keep only information for the biggest alive stem per treeID
A1 <- df[i1$V1,c("treeID","dbh","dbh2","status1","codes","hom","agb","id","sp","wsg")]
names(A1) <- c("treeID","dbh1","dbhc1","status1","code1","hom1","agb","id1","sp","wsg")
B1 <- df[year==YEAR[j] & status1 != "D",list("agb1"=sum(agb,na.rm=T),"date1"=mean(date,na.rm=T)),by=treeID]
BB <- merge(B1,A1,by="treeID",all.x=T)
cens1 <- BB[,c("treeID","dbh1","dbhc1","status1","code1","hom1","agb","id1","sp","wsg","agb1","date1")]
i2 <- df[year==YEAR[j+1] & status1 != "D" , .I[which.max(dbh2)], by = treeID]
A2 <- df[i2$V1,c("treeID","dbh","dbh2","hom","status1","codes","id")]
names(A2) <- c("treeID","dbh2","dbhc2","hom2","status2","code2","id2")
B2 <- df[year==YEAR[j+1] ,list("agb2"=sum(agb[status1 != "D"],na.rm=T),"date2"=mean(date,na.rm=T)),by=treeID]
BB2 <- merge(B2,A2,by="treeID",all.x=T)
cens2 <- BB2[,c("treeID","dbh2","dbhc2","status2","code2","hom2","agb2","date2","id2")]
cens2 <- within(cens2,status2[is.na(status2)] <- "D")
ID <- data.table(treeID=unique(c(cens1$treeID,cens2$treeID)))
ID <- merge(ID,cens1,by='treeID',all.x=T)
ID <- merge(ID,cens2,by='treeID',all.x=T)
ID[,"broken":=ifelse(id1!=id2 & dbhc2/dbhc1<0.8 & sp!= "oenoma" & dbhc1>100 & hom2<=hom1,1,0)] # flag large broken main stems to be added to losses
ID[broken==1,"agbl":=agb]
ID[broken==1,"agb1.surv":=agb1-agbl]
# ID$broken <- 0 ## when not flaggigng broken stems
# ID$agbl <- as.numeric(NA) ## when not flaggigng broken stems
# ID$agb1.surv <- as.numeric(NA) ## when not flaggigng broken stems
# if(exists("code.broken")) {
# idx <- ID[broken==1,.I[!grepl(paste0("\\b",code.broken,"\\b"),code2) ]]
# } else {
# idx <- ID[broken==1,.I[!grepl("\\bR\\b",code2) ]]
# }
#ID[broken==1,] <- within(ID[broken==1,],code2[idx] <- "broken")
ID[,c("agb","id1","id2"):=NULL]
ID$interval=j
ID$year=YEAR[j+1]
DF2 <- rbind(DF2,ID)
}
DF2 <- DF2[-1,]
# Add coordinates & other mandatory information
COORD <- df[,.(gx=round(10*gx[!is.na(gx)])/10,gy=round(10*gy[!is.na(gx)])/10,quadrat=quadrat,name=name),by=treeID]
COORD <- unique(COORD[!duplicated(COORD$treeID)])
DF2 <- merge(DF2,COORD,by="treeID",all.x=T)
# Add average date of census when missing
DATE <- df[,.(date=mean(date,na.rm=T)),by=.(year,quadrat)][order(year)]
DATE[,"year2" := c(year[2:length(year)],NA),by=quadrat] # year of previous census
DATE[,"ID1":=paste(year,quadrat,sep="-")]
DATE[,"ID2":=paste(year2,quadrat,sep="-")]
DF2[,"ID":= paste(DF2$year,DF2$quadrat,sep="-")]
DF2 <- within(DF2,date1[is.na(date1)] <- DATE$date[match(DF2[is.na(date1),ID],DATE$ID2)])
DF2 <- within(DF2,date2[is.na(date2)] <- DATE$date[match(DF2[is.na(date2),ID],DATE$ID1)])
DF2$int <- (DF2$date2 - DF2$date1)/365.5 # census interval in days
# Update status for recruited trees
DF2[, nrow := seq_len(.N), by = treeID]
DF2 <- within(DF2,status1[is.na(status1) & !is.na(dbh2) & nrow==1] <- "P") # recruited trees or Z code (=?)
DF2[,nrow:= NULL]
# Assign status
DF2[, c("agbl","code","dHOM") := assign.status(.SD), by=treeID]
# Remove unnecessary rows
DF2[code=="D", nrow := seq_len(.N), by = treeID]
DF2 <- within(DF2,nrow[is.na(nrow)] <- 0)
DF2 <- DF2[nrow<2,] # keeps only 1 line for dead trees
DF2[,"nrow":=NULL]
# Compute annualized fluxes
DF2[code%in%c("A","AC","B"),prod.g := (agb2-agb1)/int,by=treeID] # annual prod for alive trees & multi-stems
DF2[code=="B",prod.g := (agb2-agb1.surv)/int,by=treeID] # annual prod for alive trees & multi-stems
DF2[code%in%c("R","Rsp"),prod.r:=agb2/int,by=treeID] # annual prod for resprouts and recruits
DF2[code=="D",agbl:= agb1,by=treeID]
DF2[,loss:=agbl/int,by=treeID] # annualized loss for dead trees
# Flag large strangler figs
if(flag.stranglers) {
DF2$ficus <- 0
ficus$name <- paste(ficus$Genus,ficus$Species,sep=" ")
FIC <- match(DF2$name,ficus$name[ficus$Strangler=="Yes"])
if(!exists("dbh.stranglers")) {assign("dbh.stranglers",500) }
DF2 <- within(DF2,ficus[!is.na(FIC) & dbhc1>dbh.stranglers]<-1)
}
return(DF2)
}
#' Flag major errors
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Identify trees with major errors in DBH measurments. A major error correspond to a relative individal productivity (or growth) is above a given percentage (set by 'maxrel') of the mean productivity computed at a site. Additionnaly, flagged trees that died at next census interval are also flagged. Option to see DBH measurement (=draw.graph) of flagged trees or print a csv (output.errors) are given.
#' @param DF a data.table
#' @param site provide the full name of your site (in lower case) i.e. 'barro colorado island'
#' @param strangler TRUE or FALSE, if TRUE, strangler figs tree are flagged (upon a list to published soon)
#' @param maxrel a numeric value setting the threshold over which relative productivity is assumed to be too high (usually set at 20 percents)
#' @param output.errors TRUE or FALSE, output all records for trees with major errors in productivity to a csv file
#' @param exclude.interval a vector (i.e. c(1,2)) indicating if a set of census intervals must be discarded from computation due for instance to a change in protocol of measurment
#' @import data.table # load data.table package
#' @return a data.table (data.frame) with all relevant variables.
#' @export
#'
flag.errors <- function(DF,site,flag.stranglers,maxrel,graph.problem.trees,output.errors,exclude.interval) {
mean.prod <- determine.mean.prod(DF,site,flag.stranglers,exclude.interval)
DF[,prod.rel:=as.numeric(NA),]
DF[,prod.rel:=prod.g/mean.prod] # relative contribution to average total productivity
DF[,error:=0]
DF <- within(DF,error[prod.rel>maxrel & dHOM==0 & code!="D"] <- 1)
DF <- within(DF,error[prod.rel<(-maxrel) & dHOM==0 & code!="D"] <- -1)
# Flag census after a major error
POSI <- DF[,.I[error!=0]+1,]
POSI2 <- DF[POSI,.I[error==0 & dHOM==0 & code=="D"],]
DF[,error.loss:=0]
DF <- within(DF,error.loss[POSI[POSI2]] <- 1) # flag consecutive census
if (flag.stranglers) {
DF <- within(DF,error.loss[ficus==1 & code=="D"] <- 1) # flag dead strangler figs
}
# ID <- DF[error!=0 & !code%in%c("D","R"),nrow(.SD)>=1,by=treeID]
# ID <- ID[V1==T,treeID]
ID <- unique(DF[error!=0,treeID])
if (length(ID)/12 > 10) {
A <- menu(c("Y", "N"), title="There are more than 144 trees (10 pages) to be printed. Do you want to print them?")
ifelse(A==1,graph.problem.trees<-T,graph.problem.trees<-F)
}
if(graph.problem.trees) { # Plot trees with large major error
YEAR <- levels(factor(DF$year))
CX=2
a=0
i = 0
GRAPH = list()
for (n in 1:length(ID)){
i = i + 1
DF[,year:=as.numeric(year)]
DF$dbh1 <- round(DF$dbh1,1)
DF[,"y1":=year-round(int)]
aa <- ID[n]
X <- DF[treeID==aa & !code%in%c("R")][order(year)]
X$point <- 0
X$point[X$error!=0] <- 1
Y <- DF[treeID==aa & !code%in%c("D","R")][order(year)]
YY <- Y[,.(year=max(year),name=unique(name),d2=round(dbhc2[year==max(year)],1),d02=round(dbh2[year==max(year)],1),hom2=round(hom2[year==max(year)],2),y1=unique(y1)),by=treeID]
Y$line <- 0
Y$line[Y$dHOM==0] <- 1
Y$point <- 0
Y$point[Y$error!=0] <- 1
GRAPH[[i]] = ggplot(X,aes(x=y1,y=dbhc1)) + geom_point(size=2) + geom_segment(data=Y,aes(x=y1,y=dbhc1,xend=year,yend=dbhc2,linetype=as.factor(line))) + geom_point(data=X[point==1],aes(x=year,y=dbhc2),col=2) + labs(title=paste0(unique(X$name)," (",ID[n],")"), x=" ",y="dbh (mm)") + geom_text(data=Y,aes(x=y1,y=dbh1-(0.05*dbh1)),label=round(Y$hom1,2),cex=CX) + geom_text(data=YY,aes(x=year,y=d02-(0.05*d02)),label=round(YY$hom2,2),cex=CX) + geom_text(data=Y,aes(x=year,y=0.3*max(dbhc2)),label=Y$dbh1,cex=CX,angle=90,vjust=1) + geom_text(data=YY,aes(x=year,y=0.3*max(d2)),label=YY$d02,cex=CX,angle=90,vjust=1) + theme(plot.title = element_text(size=5*CX,face="bold"),axis.title.y= element_text(size=5*CX,,face="bold"),axis.text.y = element_text(size=4*CX),axis.text.x = element_text(size=4*CX,vjust=0,angle=30),panel.background = element_blank(),strip.text = element_text(size = 4*CX,face="bold"),strip.background = element_rect("lightgrey"),panel.spacing = unit(0.1, "lines")) + scale_linetype_manual(values=c('0'="dashed",'1'="solid")) + guides(linetype=F,colour=F) + scale_x_continuous(limits=c(min(as.numeric(YEAR))-3,max(as.numeric(YEAR))+3),breaks = as.numeric(YEAR)) + scale_y_continuous(limits=c(0.2*max(YY$d2),max(X$dbhc2,X$dbhc1)))
if (i %% 15 == 0 | i<15) { ## print 8 plots on a page
a=a+1
plot(do.call(grid.arrange, GRAPH))
ggsave(do.call(grid.arrange, GRAPH),file=paste0(path_folder,"/output/trees_with_major_errors_",a,".pdf"),width = 29.7, height = 20.1, units = "cm")
GRAPH = list() # reset plot
i = 0 # reset index
}}
} # end of graph
if (length(ID)==0){
print(paste0("No tree productivity above",maxrel, "% or below",-maxrel,"% of mean productivity at your plot. You may eventually want to try a lower threshold."))
}
if (output.errors & length(ID)>0){
write.csv(DF[treeID%in%ID],file=paste0(path_folder,"/output/trees_with_major_errors.csv"))
}
return(DF)
} # end of major.error
#' Set mean productivity
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Set mean productivity (Mg/ha/yr) across all census intervals for a given site
#' @param DF a data.table
#' @param site provide the full name of your site (in lower case) i.e. 'barro colorado island'
#' @param strangler TRUE or FALSE, if TRUE, strangler figs tree are flagged (upon a list to published soon)
#' @param exclude.interval a vector (i.e. c(1,2)) indicating if a set of census intervals must be discarded from computation due for instance to a change in protocol of measurment
#' @return mean productivity in Mg/ha/yr.
#' @export
determine.mean.prod <- function(DF,site,flag.stranglers,exclude.interval) {
AREA <- site.info$size[match(site,site.info$site)]
if (missing(exclude.interval)){
ifelse(flag.stranglers,
PRODA <- data.table(DF)[ficus!=1,.(prod=rec.flux(sum(agb1,na.rm=T),sum(agb2[code!="D"],na.rm=T),sum(agb1[code%in%c("A","AC")],na.rm=T),AREA,mean(int,na.rm=T))), by=interval],
PRODA <- data.table(DF)[, .(prod=rec.flux(sum(agb1,na.rm=T),sum(agb2[code!="D"],na.rm=T),sum(agb1[code%in%c("A","AC")],na.rm=T),AREA,mean(int,na.rm=T))), by=interval])
} else {
ifelse(flag.stranglers,
PRODA <- data.table(DF)[ficus!=1 & !interval%in%c(exclude.interval),.(prod=rec.flux(sum(agb1,na.rm=T),sum(agb2[code!="D"],na.rm=T),sum(agb1[code%in%c("A","AC")],na.rm=T),AREA,mean(int,na.rm=T))), by=interval],
PRODA <- data.table(DF)[!interval%in%c(exclude.interval),.(prod=rec.flux(sum(agb1,na.rm=T),sum(agb2[code!="D"],na.rm=T),sum(agb1[code%in%c("A","AC")],na.rm=T),AREA,mean(int,na.rm=T))), by=interval])
}
mPROD <- mean((PRODA$prod))
return(mPROD)
}
#' Loess
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description a wrapper to get smoothed predictions of AGB fluxes using a loess function (library 'locfit')
#' @param x a data.table
#' @param var the name of the variable to be smoothed again intial AGB
#' @param range the range of initial AGB to be used for prediction (i.e. 5th and 95th percentiles of the whole distribution)
#' @return a smoothed prediction of the variable of interest
#' @export
loess.fun <- function(x,var,range,weights=NULL) {
if (is.null(weights)) {
fit <- locfit(var ~ lAGB, data=x)}
else {fit <- locfit(var ~ lAGB, data=x,weights=weights)}
pred <- predict(fit,newdata=list(lAGB=range))
return(data.frame(lAGB=range,y=as.numeric(pred)))
}
# loess.fun <- function(x,var) {
# fit <- locfit(var ~ lAGB, data=x)
# Xrange = as.numeric(quantile(x,seq(0.01,1,.01),na.rm=T)) # 100 values to estimate the smooth spline
# pred <- predict(fit,newdata=list(lAGB=Xrange))
# return(data.frame(lAGB=Xrange,y=as.numeric(pred)))
# }
#' Normalized tree status
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Check the consistency of stem/tree status over time (i.e. a tree that is 'alive' at last census can not be 'dead' inbetween)
#' @param x a data.table
#' @return a data.table (data.frame) with a new colum "status1" where values can be "P"(prior),"A"(alive),"D"(dead) and "Dr"(dead replicated).
#' @export
#'
normal.stat <-function(DT) {
if(!"status"%in%names(DT)) {
DT$status <- NA
DT$status[DT$DFstatus=="alive"] <- "A"
DT$status[DT$DFstatus=="dead"] <- "D"
}
STAT <- rep("A",nrow(DT))
if (all(is.na(DT$dbh))) {
STAT <- rep("Dr",nrow(DT))
} else if (any(is.na(DT$dbh))|any(grep("\\bD\\b",DT$status))) { # look for dbh=NA or dead (D) status
locA <- which(DT$status=="A" & !is.na(DT$dbh))
if (length(locA)!=0) {
if (min(locA) >1) {
STAT[is.na(DT$dbh)][1:min(locA)-1] <- "P" } # Prior (to be discarded)
if (max(locA) < nrow(DT)) {
STAT[(max(locA)+1)] <- "D" }
if (max(locA)+2 <= nrow(DT)) {
STAT[(max(locA)+2):nrow(DT)] <- "Dr" } # Dead replicated (to be discarded)
} # end of loca>0
}
return(STAT)
}
#' Assign wood density
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Assign wood density using CTFS wood density data base (WSG)
#' @param df a data.table
#' @param site provide the full name of your site (in lower case) i.e. 'barro colorado island'
#' @param wsgdata a list of tree species (mnenomic) and corresponding wood density
#' @param denscol the variable to be return ("wsg" by default). No other option is implemented.
#' @return a data.table (data.frame) with all relevant variables.
#' @export
density.ind <- function (DAT, site, wsgdata, denscol = "wsg") {
if(!exists("DATA_path")){
DATA_path <<- paste0(path_folder,"/data/")
}
#Add genus & species to data
SP <- LOAD(paste(DATA_path,list.files(DATA_path)[grep("spptable",list.files(DATA_path))],sep="/"))
SP <- subset(SP,,c("sp","Genus","Species","Family"))
SP$name = paste(SP$Genus,SP$Species,sep=" ")
# Import & format CTFS wood data base
wsgdatamatch = which(WSG$site %in% site.info$wsg.site.name[site.info$site==tolower(site)])
if (length(wsgdatamatch) == 0) stop("Site name doesn't match!")
wsg = unique(WSG[wsgdatamatch,c("genus","species","spwood") ])
names(wsg) <- c("genus","species","wd")
wsg <- wsg[!is.na(wsg$wd),]
# Assign WD by taxon
A <- invisible(getWoodDensity(SP$Genus, SP$Species, stand = rep(site,nrow(SP)), family = NULL, region = "World",addWoodDensityData = wsg))
A$name <- paste(A$genus,A$species,sep=" ")
SP <- unique(merge(SP,A[,c("name","meanWD")],by="name",all.x=T))
names(SP) <- c("name", "sp", "genus", "species", "Family", "wsg")
if (any(grep("name",names(DAT)))) {
DT <- merge(DAT,SP,by="name",all.x=T)
} else {
DT <- merge(DAT,SP,by="sp",all.x=T)
}
# Allocate mean WD to species not in the list
if (any(is.na(DT$wsg))){
print(paste0("There are ",nrow(DT[is.na(wsg)])," individuals without WD values. Plot-average value (", round(mean(DT$wsg, na.rm=T),2),") was assigned."))
DT <- within(DT,wsg[is.na(wsg)] <- mean(wsg, na.rm=T))
}
return(DT)
}
#' AGB computation
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Compute above-ground biomass using the updated model of Chave et al. (2014), given in Rejou-Mechain et al. (2017)
#' @param site provide the full name of your site (in lower case) i.e. 'barro colorado island'
#' @param D a column with tree diameters (in mm)
#' @param WD a column with wood density values
#' @param H a column with tree heights (optional)
#' @return a vector with AGB values (in Mg)
#' @export
AGB.comp <- function (site,D,WD, H = NULL) {
if (length(D) != length(WD))
stop("D and WD have different lenghts")
if (is.null(site)) {
stop("You must specified your site") }
if (!is.null(H)) {
if (length(D) != length(H))
stop("H and WD have different length")
if (any(is.na(H)) & !any(is.na(D)))
warning("NA values are generated for AGB values because of missing information on tree height, \nyou may construct a height-diameter model to overcome that issue (see ?HDFunction and ?retrieveH)")
if (any(is.na(D)))
warning("NA values in D")
AGB <- (0.0673 * (WD * H * (D/10)^2)^0.976)/1000
}
else {
INDEX <- match(tolower(site),site.info$site)
if (is.na(INDEX)) { stop("Site name should be one of the following: \n",paste(levels(factor(site.info$site)),collapse=" - ")) }
E <- site.info$E[INDEX]
AGB <- exp(-2.023977 - 0.89563505 * E + 0.92023559 *
log(WD) + 2.79495823 * log(D/10) - 0.04606298 *
(log(D/10)^2))/1000
}
return(AGB)
}
#' Load object
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description a wrapper to softly load R objects in the Global environment
#' @param saveFile the path to the object to be loaded
#' @return import the object
#' @export
LOAD <- function(saveFile) {
env <- new.env()
load(saveFile, envir=env)
loadedObjects <- objects(env, all=TRUE)
stopifnot(length(loadedObjects)==1)
env[[loadedObjects]]
}
#' Assign status
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Assign status alive ("A"), alive with POM changed ("AC"), dead ("D"), recruited ("R") or resprout ("Rsp") to trees, and check for consistency over time (i.e. avoid resurrection)
#' @param DF a data.table
#' @return update the column 'status1' with consistent information.
#' @export
# # # Debug
# DT <- DF2[treeID==391]
# DT <- DF2[treeID== 230867 ][order(year)]
# DT <- DF2[treeID==5637]
# assign.status(DT)
assign.status <- function(DT) {
code <- rep("A",nrow(DT))
agbl <- DT$agbl
code[code=="A" & is.na(DT$dbhc1) & DT$status1=="P"] <- "R"
code[code=="A" & DT$broken==1] <- "B" # Broken
code[code=="A" & DT$code2=="R"] <- "Rsp" #
code[code=="A" & is.na(DT$status1) & !is.na(DT$status2)] <- "Rsp" # resprouted trees poses a problem only the first year, are alive/dead after
code[code=="A" & grep("MAIN",DT$code1) & grep("SPROUT",DT$code2)] <- "Rsp" # resprouted trees poses a problem only the first year, are alive/dead after
if(tail(DT$status2,1)=="D") {code[length(code)] <- "D"}
if (any(code=="A")){
loc <- max(which(code%in%c("A","Rsp")))
if (any(code[1:loc]=="D")) {
loc2 <- which(code[1:loc]=="D")
code[loc2] <- "B"
agbl[loc2] <- DT$agb1[loc2]
}
}
dHOM <- DT$hom2 - DT$hom1
dHOM[is.na(dHOM)] <- 0
code[code=="A" & dHOM!=0] <- "AC" # trees with POM changed are not accounted for in productivity
return(list(agbl,code,dHOM))
}
# assign.status2 <- function(DT) {
# code <- rep("A",nrow(DT))
# code[DT$status1=="P"] <- "R"
# code[is.na(DT$dbhc1) & DT$status2=="A" & code!="R"] <- "Rsp" # resprouted trees poses a problem only the first year, are alive/dead after
# code[DT$status2=="D"] <- "D"
# if(any(code=="A")){
# loc <- max(which(code%in%c("A","Rsp")))
# if (any(code[1:loc]=="D")) {
# code[which(code[1:loc]=="D")] <- "A"
# dbh2
# }}
# dHOM <- DT$hom2 - DT$hom1
# dHOM[is.na(dHOM)] <- 0
# code[code=="A" & dHOM!=0] <- "AC" # trees with POM changed are not accounted for in productivity
# return(list(code,dHOM))
# }
#' Create quadrats
#' @author Ervan Rutishauser (er.rutishauser@gmail.com)
#' @description Creat a grid where all trees are allocated to a given quadrat of size (= grid size).
#' @param census a data.frame where trees have relative X-Y coordinates.
#' @param grid_size the size of the grid (in meter)
#' @param x the identifier of X coordinates (i.e. 'gx')
#' @param y the identifier of Y coordinates (i.e. 'gy')
#' @param make.squared does the coordinates needs to
#' @return add three columns to the data.frame: quadrat's number, centroids X and Y.
#' @export
create.quadrats=function(census,grid_size,x="gx",y="gy",fit.in.plot) {
X <- census[,grep(x,names(census)),with=F][[1]]
Y <- census[,grep(y,names(census)),with=F][[1]]
if (any(is.na(X))|any(is.na(Y))) {
warning(paste(length(X[is.na(X)])," trees without coordinates were discarded."))
census <- census[!is.na(X) & !is.na(Y)]
X <- census[,grep(x,names(census)),with=F][[1]]
Y <- census[,grep(y,names(census)),with=F][[1]]
}
minx=0
miny=0
maxx=max(X,na.rm=T)
maxy=max(Y,na.rm=T)
if(exists("fit.in.plot")) {
maxx <- round(max(X)/10)*10
maxy <- round(max(Y)/10)*10
}
x1=X
x1[X<=0]=0.1
x1[X>=maxx]=maxx-0.1
y1=Y
y1[Y<=0]=0.1
y1[Y>=maxy]=maxy-0.1
# specify grid size for division into quadrats
w_grid = ceiling(maxx)/grid_size;
h_grid = ceiling(maxy)/grid_size;
n_quadrat = w_grid*h_grid;
# for now, only allow grid sizes that fit neatly
if ( max(x1,na.rm=T) > maxx | min(x1,na.rm=T) < 0 | max(y1,na.rm=T) > maxy | min(y1,na.rm=T) < 0 )
{
stop('Some trees are outside the plot boundaries. Consider fit.in.plot=T')
}
if ( round(w_grid) != w_grid | round(h_grid) != h_grid )
{
stop('Plot width and height must be divisible by grid_size');
}
census$quad<-(ceiling((maxy-miny)/grid_size))*(floor((x1-minx)/grid_size))+(floor((y1-miny)/grid_size)+1) ## identifiant de cellule unique 1->100 en partant de 0,0
if ( max(census$quad,na.rm=T) != n_quadrat )
{
stop(paste('Quadrat numbering error: expected ',n_quadrat,' quadrats; got ',max(census$quad,na.rm=T),sep=''))
}
census$centroX<-(floor(x1/grid_size)*grid_size)+(grid_size/2)
census$centroY<-(floor(y1/grid_size)*grid_size)+(grid_size/2)
# census[,.(max(gx)-min(gx),max(gy)-min(gy)),by=quad]
return(census)
}
#' Unbiased recruitment flux
#' @author Helene Muller-Laudau (hmullerlandau@gmail.com )
#' @description Compute unbiased recruitment rate (i.e. account for unmeasured recruitment)
#' @param A0 biomass at initial census.
#' @param A1 biomass of alive trees at final census.
#' @param S1 initial biomass of individuals that survived to time t
#' @param time cenusus interval in year
#' @return absolute recruitment flux in % per year
#' @export
rec.flux <- function(A0,A1,S1,area,time) {
rec <- log(A1/S1)*(A1-A0)/(area*time*log(A1/A0))
ifelse(rec==Inf,0,rec)
return(rec)
}
#' Unbiased loss flux
#' @author Helene Muller-Laudau (hmullerlandau@gmail.com )
#' @description Compute unbiased recruitment rate (i.e. account for unmeasured recruitment)
#' @param A0 biomass at initial census.
#' @param A1 biomass of alive trees at final census.
#' @param S1 initial biomass of individuals that survived to time t
#' @param time cenusus interval in year
#' @return absolute recruitment flux in % per year
#' @export
loss.flux <- function(A0,A1,S1,area,time) {
LO <- (log(A0/S1)/log(A1/A0))*((A1-A0)/(area*time))
ifelse(LO==Inf,0,LO)
return(LO)
}
trim.growth=function(cens,slope=0.006214,intercept=.9036,err.limit=4,maxgrow=75,dbhunit='mm')
{
if(dbhunit=='cm') intercept=intercept/10
stdev.dbh1=slope*cens$dbhc1+intercept
growth=(cens$dbhc2-cens$dbhc1)/cens$int
bad.neggrow=which(cens$dbhc2<=(cens$dbhc1-err.limit*stdev.dbh1))
bad.posgrow=which(growth>maxgrow)
accept=rep(TRUE,length(cens$dbhc1))
accept[bad.neggrow]=FALSE
accept[bad.posgrow]=FALSE
accept[is.na(growth)]=FALSE
accept[is.na(cens$dbhc1) | is.na(cens$dbhc2) | cens$dbhc2<=0]=FALSE
growth[!accept] <- NA
return(growth)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.