---
title: 'Vignette BIOMASS'
output:
  prettydoc::html_pretty: 
    number_sections: yes
    toc: yes
    highlight: vignette
    self_contained: yes
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Vignette BIOMASS}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
test <- TRUE
CACHE <- TRUE
require(knitr)
require(BIOMASS)
```

#Load BIOMASS and datasets
**Install BIOMASS (to be done once)**
```{r, eval=F}
install.packages("BIOMASS")
```
**Load the package**
```{r, eval=F}
require(BIOMASS)
require(knitr) # To build tables in this document
```


**Load the two datasets stored in the package**
```{r, cache=CACHE}
data(KarnatakaForest)
str(KarnatakaForest)
#
data(NouraguesHD)
str(NouraguesHD)
```


**Select 10 plots for illustrative purpose**
```{r, cache=CACHE}
selecPlot <- KarnatakaForest$plotId %in% c("BSP2", "BSP12", "BSP14", "BSP26", "BSP28", "BSP30", "BSP34", "BSP44", "BSP63", "BSP65")
KarnatakaForestsub <- droplevels(KarnatakaForest[selecPlot, ])
```

#Retrieve wood density
##Check and retrieve taxonomy
**First, check for any typo in the taxonomy**
```{r eval=test, cache=CACHE}
#uncomment the following line to check & retrieve taxonomy using correctTaxo function :
#Taxo <- correctTaxo(genus = KarnatakaForestsub$genus, species = KarnatakaForestsub$species, useCache = FALSE, verbose = FALSE)
#comment the following line loading the corrected version :
data(Taxo)
KarnatakaForestsub$genusCorr <- Taxo$genusCorrected
KarnatakaForestsub$speciesCorr <- Taxo$speciesCorrected
```

**If needed, retrieve APG III families and orders from genus names**
```{r eval=test, cache=CACHE}
#uncomment the following line to retrieve APG III families and orders from genus names using getTaxonomy function :
#APG <- getTaxonomy(KarnatakaForestsub$genusCorr, findOrder = TRUE)
#comment the following line loading the corrected version :
data(APG)
KarnatakaForestsub$familyAPG <- APG$family
KarnatakaForestsub$orderAPG <- APG$order
```


## Wood density
**Retrieve wood density using the plot level average if no genus level information is available**
```{r eval=test, cache=CACHE}
dataWD <- getWoodDensity(
  genus = KarnatakaForestsub$genusCorr,
  species = KarnatakaForestsub$speciesCorr,
  stand = KarnatakaForestsub$plotId
)
```
**The same but using the family average and adding other wood density values as references (here invented for the example)**
```{r eval=test, cache=CACHE}
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
)
```
**Below the number of wood density value estimated at the species, genus and plot level:**
```{r eval=test, cache=CACHE}
# At species level
sum(dataWD$levelWD == "species")
# At genus level
sum(dataWD$levelWD == "genus")
# At plot level
sum(!dataWD$levelWD %in% c("genus", "species"))
```



#Build height-diameter models

**You may compare different models at once**
```{r echo=TRUE, cache=CACHE}
result <- modelHD(
  D = NouraguesHD$D,
  H = NouraguesHD$H,
  useWeight = TRUE
)
kable(result)
```



**Compute the local H-D model with the lowest RSE**
```{r, cache=CACHE}
HDmodel <- modelHD(
  D = NouraguesHD$D,
  H = NouraguesHD$H,
  method = "log2",
  useWeight = TRUE
)
```



**Compute models specific to given stands**

```{r, cache=CACHE}
HDmodelPerPlot <- modelHD(
  D = NouraguesHD$D, H = NouraguesHD$H, method = "weibull",
  useWeight = TRUE, plot = NouraguesHD$plotId
)
ResHD <- t(sapply(HDmodelPerPlot, function(x) c(coef(x$model), RSE = x$RSE)))
kable(ResHD, row.names = TRUE, digits = 3)
```



# 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)
```{r, cache=CACHE}
dataHlocal <- retrieveH(
  D = KarnatakaForestsub$D,
  model = HDmodel
)
```
**Retrieve height data from a Feldpaush et al. (2012) averaged model**
```{r, cache=CACHE}
dataHfeld <- retrieveH(
  D = KarnatakaForestsub$D,
  region = "SEAsia"
)
```
**Retrieve height data from  Chave et al. (2012) equation 6**
```{r, eval=F, cache=CACHE}
dataHchave <- retrieveH(
  D = KarnatakaForestsub$D,
  coord = KarnatakaForestsub[, c("long", "lat")]
)
```

# Estimate AGB
**Organize data**
```{r, cache=CACHE}
KarnatakaForestsub$WD <- dataWD$meanWD
KarnatakaForestsub$H <- dataHlocal$H
KarnatakaForestsub$Hfeld <- dataHfeld$H
```
**Compute AGB(Mg) per tree**
```{r warning=F, cache=CACHE}
AGBtree <- computeAGB(
  D = KarnatakaForestsub$D,
  WD = KarnatakaForestsub$WD,
  H = KarnatakaForestsub$H
)
```
**Compute AGB(Mg) per plot (need to be divided by plot area to get Mg/ha)**
```{r warning=F, cache=CACHE}
AGBplot <- summaryByPlot(AGBtree, KarnatakaForestsub$plotId)
```

**Compute AGB(Mg) per tree without height information (Eq. 7 from Chave et al. (2014))**
```{r warning=F, eval=F, cache=CACHE}
AGBplotChave <- summaryByPlot(
  computeAGB(
    D = KarnatakaForestsub$D, WD = KarnatakaForestsub$WD,
    coord = KarnatakaForestsub[, c("long", "lat")]
  ),
  KarnatakaForestsub$plotId
)
```
**Compute AGB(Mg) per tree with Feldpausch et al. (2012) regional H-D model**
```{r warning=F, cache=CACHE}
AGBplotFeld <- summaryByPlot(
  computeAGB(
    D = KarnatakaForestsub$D, WD = KarnatakaForestsub$WD,
    H = KarnatakaForestsub$Hfeld
  ),
  plot = KarnatakaForestsub$plotId
)
```

# Propagate AGB errors
**Organize data**
```{r, cache=CACHE }
KarnatakaForestsub$sdWD <- dataWD$sdWD
KarnatakaForestsub$HfeldRSE <- dataHfeld$RSE
```
**Propagate 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.**
```{r, cache=CACHE}
resultMC <- AGBmonteCarlo(D = KarnatakaForestsub$D, WD = KarnatakaForestsub$WD, errWD = KarnatakaForestsub$sdWD, HDmodel = HDmodel, Dpropag = "chave2004")
Res <- summaryByPlot(resultMC$AGB_simu, KarnatakaForestsub$plotId)
Res <- Res[order(Res$AGB), ]
plot(Res$AGB, pch = 20, xlab = "Plots", ylab = "AGB", ylim = c(0, max(Res$Cred_97.5)), las = 1, cex.lab = 1.3)
segments(seq(nrow(Res)), Res$Cred_2.5, seq(nrow(Res)), Res$Cred_97.5, col = "red")
```




**Using the Feldpaush regional HD averaged model (code only given)**
```{r, eval=F, cache=CACHE}
resultMC <- AGBmonteCarlo(
  D = KarnatakaForestsub$D,
  WD = KarnatakaForestsub$WD,
  errWD = KarnatakaForestsub$sdWD,
  H = KarnatakaForestsub$Hfeld,
  errH = KarnatakaForestsub$HfeldRSE,
  Dpropag = "chave2004"
)

Res <- summaryByPlot(resultMC$AGB_simu, KarnatakaForestsub$plotId)
Res <- Res[order(Res$AGB), ]
plot(Res$AGB, pch = 20, xlab = "Plots", ylab = "AGB", ylim = c(0, max(Res$Cred_97.5)), las = 1, cex.lab = 1.3)
segments(seq(nrow(Res)), Res$Cred_2.5, seq(nrow(Res)), Res$Cred_97.5, col = "red")
```

**Per plot using the Chave et al. (2014) Equation 7 (code only given)**
```{r, eval=F,cache=CACHE}
resultMC <- AGBmonteCarlo(
  D = KarnatakaForestsub$D,
  WD = KarnatakaForestsub$WD,
  errWD = KarnatakaForestsub$sdWD,
  coord = KarnatakaForestsub[, c("long", "lat")],
  Dpropag = "chave2004"
)
Res <- summaryByPlot(resultMC$AGB_simu, KarnatakaForestsub$plotId)
Res <- Res[order(Res$AGB), ]
plot(Res$AGB, pch = 20, xlab = "Plots", ylab = "AGB", ylim = c(0, max(Res$Cred_97.5)), las = 1, cex.lab = 1.3)
segments(seq(nrow(Res)), Res$Cred_2.5, seq(nrow(Res)), Res$Cred_97.5, col = "red")
```



# Some tricks

##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.

(@) Build a vector of H and RSE where we assume an error of 0.5 m on directly measured trees
```{r, cache=CACHE }
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$RSE
```
(@) Apply the AGBmonteCarlo by setting the height values and their errors (which depend on whether the tree was directly measured or estimated)
```{r eval=F, cache=CACHE}
wd <- getWoodDensity(NouraguesHD$genus, NouraguesHD$species)
resultMC <- AGBmonteCarlo(
  D = NouraguesHD$D, WD = wd$meanWD, errWD = wd$sdWD,
  H = NouraguesHD$Hmix, errH = NouraguesHD$RSEmix,
  Dpropag = "chave2004"
)
Res <- summaryByPlot(resultMC$AGB_simu, NouraguesHD$plotId)
Res <- Res[order(Res$AGB), ]
plot(Res$AGB, pch = 20, xlab = "Plots", ylab = "AGB (Mg/ha)", ylim = c(0, max(Res$Cred_97.5)), las = 1, cex.lab = 1.3)
segments(1:nrow(Res), Res$Cred_2.5, 1:nrow(Res), Res$Cred_97.5, col = "red")
```

##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) 








