Vignette BIOMASS
1 Load BIOMASS and datasets
Install BIOMASS (to be done once)
install.packages("BIOMASS")Load the package
library(BIOMASS)
require(knitr) # To build tables in this document## Loading required package: knitrLoad the two datasets stored in the package
data(KarnatakaForest)
str(KarnatakaForest)## 'data.frame': 61965 obs. of 8 variables:
## $ plotId : Factor w/ 96 levels "BSP1","BSP10",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ treeId : Factor w/ 65283 levels "BSP1_001","BSP1_002",..: 1 2 4 5 6 7 8 9 10 11 ...
## $ family : Factor w/ 81 levels "Acanthaceae",..: 18 61 29 64 6 64 30 29 64 26 ...
## $ genus : Factor w/ 256 levels "Acacia","Acrocarpus",..: 233 256 21 134 252 134 9 21 134 82 ...
## $ species: Factor w/ 320 levels "alata","albiflavescens",..: 28 204 163 32 16 32 161 163 32 189 ...
## $ D : num 3.5 3.82 16.87 4.14 5.41 ...
## $ lat : num 14.4 14.4 14.4 14.4 14.4 ...
## $ long : num 74.9 74.9 74.9 74.9 74.9 ...#
data(NouraguesHD)
str(NouraguesHD)## 'data.frame': 1051 obs. of 7 variables:
## $ plotId : chr "Plot1" "Plot1" "Plot1" "Plot1" ...
## $ genus : chr "indet" "Qualea" "Dicorynia" "Protium" ...
## $ species: chr "indet" "rosea" "guianensis" "cf_guianense" ...
## $ D : num 11.5 11.6 83.9 15 36.8 13.5 17.8 17.8 15.9 17.8 ...
## $ H : num 12 16 40 18 27 20 24 21 22 24 ...
## $ lat : num 4.07 4.07 4.07 4.07 4.07 ...
## $ long : num -52.7 -52.7 -52.7 -52.7 -52.7 ...Select 10 plots for illustrative purpose
selecPlot<-KarnatakaForest$plotId%in%c("BSP2","BSP12","BSP14","BSP26","BSP28","BSP30","BSP34","BSP44","BSP63","BSP65")
KarnatakaForestsub<-droplevels(KarnatakaForest[selecPlot,])2 Retrieve wood density
2.1 Check and retrieve taxonomy
First, check for any typo in the taxonomy
Taxo<-correctTaxo(genus=KarnatakaForestsub$genus,species=KarnatakaForestsub$species)## [1] "Calling http://taxosaurus.org/retrieve/a55f49e4a33763f1c378558ff50cf02f"
## [1] "Calling http://taxosaurus.org/retrieve/c9515144813c820a0ab24e209267ca6b"
## [1] "Calling http://taxosaurus.org/retrieve/87a56ea8949a2511fc8d271e2cd863d2"
## [1] "Calling http://taxosaurus.org/retrieve/16f5614d03129a9345352dd934c24e9d"
## [1] "Calling http://taxosaurus.org/retrieve/173f238d8a6fc827604610272a33ab79"
## [1] "Calling http://taxosaurus.org/retrieve/2870f350c757980c530c021d219bd12a"
## [1] "Calling http://taxosaurus.org/retrieve/1c38fec8de19cdb4abf94410a490764a"KarnatakaForestsub$genusCorr<-Taxo$genusCorrected
KarnatakaForestsub$speciesCorr<-Taxo$speciesCorrectedIf needed, retrieve APG III families and orders from genus names
APG<-getTaxonomy(KarnatakaForestsub$genusCorr, findOrder =T)
KarnatakaForestsub$familyAPG<-APG$family
KarnatakaForestsub$orderAPG<-APG$order2.2 Wood density
Retrieve wood density using the plot level average if no genus level information is available
dataWD<-getWoodDensity(genus=KarnatakaForestsub$genusCorr,
species=KarnatakaForestsub$speciesCorr,
stand=KarnatakaForestsub$plotId)## The reference dataset contains 16467 wood density values
## Your taxonomic table contains 196 taxaThe same but using the family average and adding other wood density values as references (here invented for the example)
LocalWoodDensity<-data.frame(genus=c("Ziziphus","Terminalia","Garcinia"),
species=c("oenopolia","bellirica","indica"),
wd=c(0.65,0.72,0.65))
dataWD<-getWoodDensity(genus=KarnatakaForestsub$genusCorr,
species=KarnatakaForestsub$speciesCorr,
family=KarnatakaForestsub$familyAPG,
stand=KarnatakaForestsub$plotID,
addWoodDensityData=LocalWoodDensity)## The reference dataset contains 16470 wood density values
## Your taxonomic table contains 196 taxaBelow the number of wood density value estimated at the species, genus and plot level:
# At species level
sum(dataWD$levelWD=="species")## [1] 2304# At genus level
sum(dataWD$levelWD=="genus")## [1] 2768# At plot level
sum(!dataWD$levelWD%in%c("genus","species"))## [1] 3703 Build height-diameter models
You may compare different models at once
HDmodel <- modelHD(D=NouraguesHD$D,
H =NouraguesHD$H,
drawGraph=TRUE,
useWeight=TRUE)
Which model would you like to select to model your data ?
##1 : Log 1 (blue)
##----- RSE = 4.1893 (RSElog = 0.2211)
##----- Average bias = 0.0042
##2 : Log 2 (green)
##----- RSE = 4.1017 (RSElog = 0.2194)
----- Average bias = 0.003
3 : Log 3 (red)
----- RSE = 4.1038 (RSElog = 0.2195)
----- Average bias = 0.003
4 : Weibull (orange)
----- RSE = 4.1716
----- Average bias = 0.0052
5 : Michaelis - Menten (purple)
----- RSE = 4.1662
----- Average bias = 0.0148
1: 3Compute the local H-D model with the lowest RSE
HDmodel<-modelHD(D=NouraguesHD$D,
H=NouraguesHD$H,
method="log2",
useWeight =TRUE)Compute models specific to given stands
HDmodelPerPlot <- by(NouraguesHD,NouraguesHD$plotId,
function(x) modelHD(D=x$D,H=x$H, method="weibull",useWeight =T),
simplify=FALSE)
RSEmodels<-sapply(HDmodelPerPlot,function(x) x$RSE)
Coeffmodels<-lapply(HDmodelPerPlot,function(x) x$coefficients)
ResHD<-data.frame(Plot=names(unlist(RSEmodels)),
a=round(unlist(sapply(Coeffmodels,"[",1)),3),
b=round(unlist(sapply(Coeffmodels,"[",2)),3),
c=round(unlist(sapply(Coeffmodels,"[",3)),3),
RSE=round(unlist(RSEmodels),3))
kable(ResHD, row.names = F)| Plot | a | b | c | RSE |
|---|---|---|---|---|
| Plot1 | 43.673 | 29.629 | 0.987 | 4.503 |
| Plot2 | 369.917 | 46732.555 | 0.367 | 3.865 |
4 Retrieve height data
Retrieve height data from a local Height-diameter model (Note that using a HD model built on French guianan trees for Indian trees is only for illustrative purpose here)
dataHlocal<-retrieveH(D=KarnatakaForestsub$D,
model =HDmodel)Retrieve height data from a Feldpaush et al. (2012) averaged model
dataHfeld<-retrieveH(D=KarnatakaForestsub$D,
region ="SEAsia")Retrieve height data from Chave et al. (2012) equation 6
dataHchave<-retrieveH(D=KarnatakaForestsub$D,
coord=cbind(KarnatakaForestsub$long,KarnatakaForestsub$lat))5 Estimate AGB
Organize data
KarnatakaForestsub$WD=dataWD$meanWD
KarnatakaForestsub$H=dataHlocal$H
KarnatakaForestsub$Hfeld=dataHfeld$HCompute AGB(Mg) per tree
AGBtree<-computeAGB(D=KarnatakaForestsub$D,
WD=KarnatakaForestsub$WD,
H =KarnatakaForestsub$H)Compute AGB(Mg) per plot
AGBPlotList<-by(KarnatakaForestsub, KarnatakaForestsub$plotId,
function(x) computeAGB(D=x$D,WD=x$WD,H=x$H),
simplify=F)
AGBplot<-sapply(AGBPlotList,sum) Compute AGB(Mg) per tree without height information (Eq. 7 from Chave et al. (2014))
AGBPlotListChave<-by(KarnatakaForestsub, KarnatakaForestsub$plotId,
function(x) computeAGB(D=x$D,WD=x$WD,coord =cbind(x$long, x$lat)),
simplify=F)
AGBplotChave<-sapply(AGBPlotListChave,sum) Compute AGB(Mg) per tree with Feldpausch et al. (2012) regional H-D model
AGBPlotListFeld<-by(KarnatakaForestsub, KarnatakaForestsub$plotId,
function(x) computeAGB(D=x$D,WD=x$WD,H=x$Hfeld),
simplify=F)
AGBplotFeld<-sapply(AGBPlotListFeld,sum) 6 Propagate AGB errors
Organize data
KarnatakaForestsub$sdWD=dataWD$sdWD
KarnatakaForestsub$HfeldRSE=dataHfeld$RSEPropagate error for all tree at once using the local HD model constructed above (modelHD), i.e. non-independent allometric errors will be assigned to all trees at each iteration, independently of plots.
resultMC<-AGBmonteCarlo(D=KarnatakaForestsub$D,WD=KarnatakaForestsub$WD,errWD = KarnatakaForestsub$sdWD,HDmodel=HDmodel,Dpropag ="chave2004")
meanAGBperplot<-by(resultMC$AGB_simu,KarnatakaForestsub$plotId,function(x) mean(apply(x, 2, sum)))
credperplot<-by(resultMC$AGB_simu,KarnatakaForestsub$plotId,function(x) quantile(apply(x,2,sum, na.rm = T), probs = c(0.025, 0.975)))
credinf<-sapply(credperplot,"[",1)
credsup<-sapply(credperplot,"[",2)
ord<-order(meanAGBperplot)
plot(meanAGBperplot[ord],pch=20,xlab="Plots",ylab="AGB (Mg/ha)",ylim=c(0,max(credsup)),las=1,cex.lab=1.3)
segments(1:length(ord),credinf[ord],1:length(ord),credsup[ord],col="red")
Propagate error per plot using the local HD model constructed above (modelHD), i.e. independent allometric errors will be assigned to all trees at each iteration, between plots.
resultMC<-by(KarnatakaForestsub, KarnatakaForestsub$plotId,
function(x) AGBmonteCarlo(D=x$D,WD=x$WD,H=x$H,errWD = x$sdWD,
HDmodel=HDmodel,Dpropag ="chave2004"),
simplify=F)
meanAGBperplot<-unlist(sapply(resultMC,"[",1))
credperplot<-sapply(resultMC,"[",4)
credinf<-sapply(credperplot,"[",1)
credsup<-sapply(credperplot,"[",2)
ord<-order(meanAGBperplot)
plot(meanAGBperplot[ord],pch=20,xlab="Plots",ylab="AGB (Mg/ha)",ylim=c(0,max(credsup)),las=1,cex.lab=1.3)
segments(1:length(ord),credinf[ord],1:length(ord),credsup[ord],col="red")
Per plot using the Feldpaush regional HD averaged model (code only given)
resultMC<-by(KarnatakaForestsub, KarnatakaForestsub$plotId,
function(x) AGBmonteCarlo(D=x$D,WD=x$WD,errWD=x$sdWD, H=x$Hfeld,
errH=x$HfeldRSE, Dpropag="chave2004"),
simplify=F)
meanAGBperplot<-unlist(sapply(resultMC,"[",1))
credperplot<-sapply(resultMC,"[",4)
credinf<-sapply(credperplot,"[",1)
credsup<-sapply(credperplot,"[",2)
ord<-order(meanAGBperplot)
plot(meanAGBperplot[ord],pch=20,xlab="Plots",ylab="AGB (Mg/ha)",ylim=c(0,max(credsup)),las=1,cex.lab=1.3)
segments(1:length(ord),credinf[ord],1:length(ord),credsup[ord],col="red")Per plot using the Chave et al. (2014) Equation 7 (code only given)
resultMC<-by(KarnatakaForestsub, KarnatakaForestsub$plotId,
function(x)AGBmonteCarlo(D=x$D,WD=x$WD,errWD=x$sdWD,
coord=cbind(x$long,x$lat),
Dpropag="chave2004"),
simplify=F)
meanAGBperplot<-unlist(sapply(resultMC,"[",1))
credperplot<-sapply(resultMC,"[",4)
credinf<-sapply(credperplot,"[",1)
credsup<-sapply(credperplot,"[",2)
ord<-order(meanAGBperplot)
plot(meanAGBperplot[ord],pch=20,xlab="Plots",ylab="AGB (Mg/ha)",ylim=c(0,max(credsup)),las=1,cex.lab=1.3)
segments(1:length(ord),credinf[ord],1:length(ord),credsup[ord],col="red")7 Some tricks
7.1 Mixing measured and estimated height values
If you want to use a mix of directly-measured height and of estimated ones, you may do the following steps.
1 Build a vector of H and RSE where we assume an error of 0.5 m on directly measured trees
NouraguesHD$Hmix<-NouraguesHD$H
NouraguesHD$RSEmix<-0.5
filt<-is.na(NouraguesHD$Hmix)
NouraguesHD$Hmix[filt]<- retrieveH(NouraguesHD$D,model = HDmodel)$H[filt]
NouraguesHD$RSEmix[filt]<-HDmodel$RSE2 Apply the AGBmonteCarlo by setting the height values and their errors (which depend on wether the tree was directly measured or estimated)
resultMC<-by(NouraguesHD, NouraguesHD$plotId,
function(x)AGBmonteCarlo(D=x$D,WD=x$WD,errWD=x$sdWD,
H=NouraguesHD$Hmix,errH=NouraguesHD$RSEmix,
Dpropag="chave2004"),
simplify=F)
meanAGBperplot<-unlist(sapply(resultMC,"[",1))
credperplot<-sapply(resultMC,"[",4)
credinf<-sapply(credperplot,"[",1)
credsup<-sapply(credperplot,"[",2)
ord<-order(meanAGBperplot)
plot(meanAGBperplot[ord],pch=20,xlab="Plots",ylab="AGB (Mg/ha)",ylim=c(0,max(credsup)),las=1,cex.lab=1.3)
segments(1:length(ord),credinf[ord],1:length(ord),credsup[ord],col="red")7.2 Add your tricks
Please contact Maxime (maxime.rejou@gmail.com) if you would like to add here a code that may be useful for users (code authorship will be respected)