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data_prepare.R
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data_prepare.R
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library("zoo")
##############################
# load sapling and tree data #
##############################
tree_psp <- read.csv("data/Tree_list.csv", colClasses = "character")
sapling_psp <- read.csv("data/Yos_result2.csv",
colClasses = "character",
header = FALSE)
Tr <- subset(tree_psp, select = c("ID_PEP_MES", "ESSENCE", "DBH"))
length(1:dim(Tr)[1])
names(sapling_psp)[names(sapling_psp) == "V2"] <- "ID_PEP_MES"
names(sapling_psp)[names(sapling_psp) == "V3"] <- "ESSENCE"
names(sapling_psp)[names(sapling_psp) == "V4"] <- "DBH"
sapling_psp$V5 <- NULL
sapling_psp$V1 <- NULL
length(1:dim(sapling_psp)[1])
ps <- rbind(sapling_psp, Tr)
#######################
# load temporal plots #
#######################
tree_tmp <- read.table("data/TemporalTrees.txt",
colClasses = "character",
sep = ",",
quote = "\"")
length(1:dim(tree_tmp)[1])
head(tree_tmp)
tree_tmp$DBH <- as.numeric(tree_tmp$DBH)
tree_tmp <- subset(tree_tmp, select = c("ID_PET_MES", "ESSENCE", "DBH"))
names(tree_tmp)[names(tree_tmp) == "ID_PET_MES"] <- "ID_PEP_MES"
#######################################
# merge PSP trees with Temporal trees #
#######################################
Tree <- rbind(ps, tree_tmp)
head(tree_tmp)
length(1:dim(Tree)[1])
Tree$DBH <- as.numeric(Tree$DBH)
Tree$ESSENCE <- as.factor(Tree$ESSENCE)
Tree <- Tree[-which(Tree$DBH > 90), ]
Tree <- na.omit(Tree)
#################################################
## loading permanent and temporal sample plots ##
#################################################
y <- read.table("data/union_plots_export.txt",
colClasses = "character",
header = FALSE,
sep = ",",
quote = "\"")
colnames(y)[1] <- "ID_PEP_MES"
colnames(y)[2] <- "LATITUDE"
colnames(y)[3] <- "LONGITUDE"
colnames(y)[4] <- "SREG_ECO"
colnames(y)[5] <- "FIRE_CODE"
colnames(y)[6] <- "DOM_BIO"
colnames(y)[7] <- "ALTITUDE"
colnames(y)[8] <- "DEP_SUR"
colnames(y)[9] <- "CL_DRAI"
colnames(y)[10] <- "TYPE_ECO"
colnames(y)[11] <- "Sur_Dep"
colnames(y)[12] <- "Deposit"
colnames(y)[13] <- "Deposito"
colnames(y)[14] <- "Total_BA"
colnames(y)[15] <- "Spruce_BA"
colnames(y)[16] <- "JackPine_BA"
colnames(y)[17] <- "stands"
colnames(y)[18] <- "shannon"
all_plots <- y
all_plots <- all_plots[- which(all_plots$stands == "0"), ]
all_plots <- all_plots[- which(all_plots$stands == "RxEn"), ]
all_plots <- all_plots[- which(all_plots$stands == "RxRx"), ]
all_plots <- all_plots[- which(all_plots$stands == "EnRx"), ]
all_plots$stands <- factor(all_plots$stands)
all_plots$ID_PEP_MES <- as.factor(all_plots$ID_PEP_MES)
all_plots$stands <- as.factor(all_plots$stands)
all_plots$FIRE_CODE <- as.factor(all_plots$FIRE_CODE)
all_plots$FIRE_CODE_ <- all_plots$FIRE_CODE
######################################################################
## Create new deposit classes differentiating among 1_Till.Thick, ##
## 1_Till.Thin, M, R etc.. Differentiating the substrate thickness ##
## only for the Till group ##
######################################################################
all_plots$Dep <- ifelse(all_plots$Deposito == "1A" |
all_plots$Deposito == "1",
"1_Till.Thick",
ifelse(all_plots$Deposito == "1AY" |
all_plots$Deposito == "1AM" |
all_plots$Deposito == "M1A",
"1_Till.Thin",
ifelse(all_plots$Deposito == "2",
"2_Fluvio_glaciar",
ifelse(all_plots$Deposito == "3",
"3_Fluvial",
ifelse( all_plots$Deposito == "4",
"4_Lacustre",
ifelse(all_plots$Deposito == "5",
"5_Marins",
ifelse(all_plots$Deposito == "6",
"6_Littoraux_marins",
ifelse(all_plots$Deposito == "7",
"7_Organique",
ifelse(all_plots$Deposito == "8",
"8_Depots_pentes",
ifelse(all_plots$Deposito == "R" | all_plots$Deposito == "R1A",
"Rocheux",
"Other"))))))))))
#################################################
## Merge drainage classes 0 and 1 and 5 and 6 ##
#################################################
all_plots$Drenaje <- ifelse(all_plots$CL_DRAI == "0" | all_plots$CL_DRAI == "1",
"1_DRY",
ifelse(all_plots$CL_DRAI == "2",
"2_Bon",
ifelse(all_plots$CL_DRAI == "3",
"3_Modere",
ifelse(all_plots$CL_DRAI == "4",
"4_Imperfait",
ifelse(all_plots$CL_DRAI == "5" | all_plots$CL_DRAI == "6",
"5_WET",
"Other")))))
##########################################
## subset plots based on drainage class ##
##########################################
subsetPlot <- subset(all_plots, (Drenaje == "2_Bon" |
Drenaje == "3_Modere" |
Drenaje == "4_Imperfait")
& (Dep == "1_Till.Thin" |
Dep == "1_Till.Thick" |
Dep == "2_Fluvio_glaciar"),
select = c(1:length(all_plots)))
Plots <- subsetPlot
##########################################
## selects monospecific black spruce plots ##
##########################################
subpop <- function(reg, st, d= Plots){
res <- subset(d, FIRE_CODE %in% reg & stands%in%st,select="ID_PEP_MES")
return(res)
}
Sampled <- subpop (reg=c("A2","D4","B3","C3","E1","E3"), st=c("EnEn"), d=Plots)
##########################################
## associates plots with a tree list ##
##########################################
GetTrees <- function(T,Plots){
res <- NULL
for (i in Plots){
res<-rbind(res,subset(T,ID_PEP_MES %in% i))
}
return(res)
}
#############################################################
## generates a matrix with number of trees per diameter class ##
#############################################################
process<- function (Sampled,Tree,Plots){
Sampled2 <- Sampled[sample(1:dim(Sampled)[1], size=1, replace=T),]
Tree.List <- GetTrees (Tree,Plots=Sampled2)
as.character(Tree.List$DBH)
as.factor(Tree.List$ESSENCE)
Tree.List <- as.data.frame(lapply(Tree.List[,],function(x)rep(x,25)))
range.DBH <- c(seq(1,30, by=2), 100)
Tree.List$DBH <- as.numeric(Tree.List$DBH)
stand <- table(cut(Tree.List$DBH, breaks=range.DBH, labels=seq(1,15)))
stand[1:4] <- stand[1:4]*10
###Partition the basal area of big trees >31 cm and add number of trees that the surplus of basal area represents
basal_big_class <- 0.0707905544
BAB <- rep(0,100)
TBA <- 3.142*(Tree.List[Tree.List[,3]>31,3]/200)^2
BAB <- round(TBA/basal_big_class,digits=0)
y <- sum(BAB)
stand[15] <- stand[15]+y
res <- list(stand=stand,Sampled2=Sampled2)
return (res)
}
Plo<-numeric(length(Sampled[,1]))
List_trees <- matrix(0,length(Sampled[,1]),15)
for (i in 1:length(Sampled[,1])){
y<-process(Sampled,Tree,Plots)
List_trees[i,]<-y$stand
Plo[i]<-as.numeric(y$Sampled2)
}
##############################################
## Get intensity file for the C-2 fuel type ##
##############################################
Intensity <- read.csv("data/Sopfeu.csv",
colClasses = "character",
header = TRUE,
sep = ",")
Intensity <- Intensity[which(Intensity$JA == "1"), ]
Intensity <- Intensity[which(Intensity$INT > 1), ]
Intensity <- Intensity[which(Intensity$SupFin > 0), ]
IntensityC2 <- Intensity[which(Intensity$Comb == "C2"), ]
colnames(IntensityC2)[21] <- "FIRE_CODE"
IntensityC2 <- subset(IntensityC2, (FIRE_CODE == "A2" |
FIRE_CODE == "B3" |
FIRE_CODE == "C3" |
FIRE_CODE == "D4" |
FIRE_CODE == "E1" |
FIRE_CODE == "E3"),
select = c(1:length(IntensityC2)))
IntensityC2$Concatanate <- paste(IntensityC2$Annee,
IntensityC2$Numero,
IntensityC2$INT,
sep = "_")
#####################
## Add seasonality ##
#####################
IntensitySS <- read.csv("data/IntensityData.csv",
colClasses = "character",
header = TRUE, sep = ",")
IntensitySS <- na.omit(IntensitySS)
IntensitySS <- IntensitySS[- which(IntensitySS$INT == "0"), ]
IntensitySS <- IntensitySS[which(IntensitySS$JA == "1"), ]
IntensitySS <- IntensitySS[which(IntensitySS$Comb == "C2"), ]
IntensitySS <- IntensitySS[which(IntensitySS$Domaine == "6"), ]
strDates <- as.character(IntensitySS$Dates)
IntensitySS$Dates <- as.Date(IntensitySS$Dates, "%Y/%m/%d")
IntensitySS$date1 <- as.yearmon(IntensitySS$Dates)
IntensitySS$Season <- as.numeric(format(IntensitySS$date1, "%m"))
IntensitySS$Concatanate <- paste(IntensitySS$Annee,
IntensitySS$Numero,
IntensitySS$INT,
sep = "_")
##########################
## merge intensity data ##
##########################
Intensities <- merge(IntensitySS,
IntensityC2,
by = c("Concatanate"))
Intensities$Annee.y <- NULL
Intensities$Comb.y <- NULL
Intensities$DateRap <- NULL
Intensities$Domaine.y <- NULL
Intensities$iLat.y <- NULL
Intensities$iLon.y <- NULL
Intensities$INT.y <- NULL
Intensities$JA.y <- NULL
Intensities$Numero.y <- NULL
names(Intensities)[c(2, 3, 4, 5, 6, 7, 8, 12)] <- c("Annee", "JA", "Comb",
"iLat", "iLon", "INT",
"Domaine", "Numero")
Intensities$SprSummer <- ifelse(Intensities$Season == "5" |
Intensities$Season == "6",
"Spring", "Summer")
as.factor(Intensities$SprSummer)
############################################################
## Apply Catchpole function and size weighted distibution ##
############################################################
knownpoints <- data.frame(x <- c(0.04, 0.1, 0.13, 0.16, 0.21, 0.28, 0.37, 0.48,
0.63, 0.7, 0.8, 0.9, 1),
y <- c(1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2,
0.18, 0.15, 0.1, 0.05))
aim <- runif(24000, 0, 1)
yos <- approx(knownpoints$x, knownpoints$y, xout = aim, rule = 1)
ProportionalIntensities <- yos$y
ProportionalIntensities <- na.omit(ProportionalIntensities)
PI <- sample(ProportionalIntensities, 24000, replace = T, prob = NULL)
SpringFires <- Intensities[which(Intensities$SprSummer == "Spring"), ]
SpringFires$SupFin <- as.numeric(SpringFires$SupFin)
SpringFires$ISec<- as.numeric(SpringFires$ISec)
SpringFires$INT<-as.numeric(SpringFires$INT)
TotalFireSizeSpring <- sum(SpringFires$SupFin)
SpringFires$Weight <- SpringFires$SupFin / TotalFireSizeSpring
SpringFires$Numero<-as.numeric(SpringFires$Numero)
SpringFires<-SpringFires[which(abs(SpringFires$SupFin) == ave(SpringFires$SupFin, SpringFires$Numero,
FUN=function(x) max(abs(x)))), ]##458
SpringNumero<-sample(SpringFires$Numero,size=24000, replace=T, prob=SpringFires$Weight)
SpringNumero<-as.data.frame(SpringNumero)
colnames(SpringNumero)<-"Numero"
newdataSpring<- left_join(SpringNumero,SpringFires, by = "Numero")
newdataSpring$Catchpole <- as.numeric(newdataSpring$INT*PI)
SummerFires<-Intensities[which(Intensities$SprSummer== "Summer"),]
length(1:dim(SummerFires)[1])##374
SummerFires$SupFin<-as.numeric(SummerFires$SupFin)
SummerFires$Numero<-as.numeric(SummerFires$Numero)
TotalFireSizeSpring<-sum(SummerFires$SupFin)
SummerFires$Weight<-SummerFires$SupFin/TotalFireSizeSpring
SummerFires<-SummerFires[which(abs(SummerFires$SupFin) == ave(SummerFires$SupFin, SummerFires$Numero,
FUN=function(x) max(abs(x)))), ]
length(1:dim(SummerFires)[1])##321
SummerFires$Numero<- as.numeric(SummerFires$Numero)
SummerFires$INT<- as.numeric(SummerFires$INT)
SummerFires$ISec<-as.numeric(SummerFires$ISec)
SummerNumero<-sample(SummerFires$Numero,size=24000, replace=T, prob=SummerFires$Weight)
SummerNumero<-as.data.frame(SummerNumero)
colnames(SummerNumero)<-"Numero"
SummerNumero$Numero <- as.numeric(SummerNumero$Numero)
newdataSummer<- left_join(SummerNumero,SummerFires, by = "Numero")
newdataSummer$Catchpole <- as.numeric(newdataSummer$INT*PI)
########################
## save prepared data ##
########################
plots_file <- file("data/Plots.Rdata", "wb")
save(Plots, file = plots_file)
close(plots_file)
tree_file <- file("data/Tree.Rdata", "wb")
save(Tree, file = tree_file)
close(tree_file)
intensities_file <- file("data/Intensities.Rdata", "wb")
save(Intensities, file = intensities_file)
close(intensities_file)
spring_file <- file("data/newdataSpring.Rdata", "wb")
save(newdataSpring, file = spring_file)
close(spring_file)
summer_file = file("data/newdataSummer.Rdata", "wb")
save(newdataSummer, file = summer_file)
close(summer_file)
########################
## Verification plots ##
########################
Intensities$INT <- as.numeric(Intensities$INT)
boxplot(INT~SprSummer, data = Intensities,
ylab = "Fire intensity", ylab = "Fire season",
main = "Raw Intensities")
dev.copy(png, "plots/Fire_intensities_by_fire_season.png")
dev.off()
###########################################################################
## Compare distribution of sampled intensities weighted/unweighted cases ##
###########################################################################
IUnWeighted <- sample(Intensities$INT, size = 3000, replace = T)
IWeighted <- sample(Intensities$INT, size = 3000, replace = T,
prob = Intensities$Weight)
par(mfrow = c(1, 2))
hist(IUnWeighted, main = "Unweighted Intensities") #unweighted
hist(IWeighted, main = "Weighted Intensities")
dev.copy(png, "plots/Histograms_size_weighted.png")
dev.off()
#################################################
## Correct head fire intensity using Catchpole ##
#################################################
IUnWeightedCatch <- as.numeric(IUnWeighted * PI)
IWeightedCatch <- as.numeric(IWeighted * PI)
par(mfrow = c(1, 2))
hist(IUnWeightedCatch, main = "Unweighted w/catchpole") #unweighted
hist(IWeightedCatch, main = "Weighted w/catchpole")
dev.copy(png, "plots/Histograms_size_weighted_catchpole.png")
dev.off()