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Kangaroo Island Wildfire - Final.R
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#Travis Zalesky
#GIST 601B - Remote Sensing
#Final: Australian Wildfires Kangaroo Island
#11/28/2023
#Objectives:
### 1. Showcase my remote sensing knowledge and skills.
### 2. Describe the impacts and recovery of the "Black Summer" fires on Kangaroo
### Island, Australia.
### 3. Use machine learning to classify land cover pre and post fire.
### 4. Calculate dNBR and describe long term recovery, 3 years post fire.
#Packages
#install.packages("terra")
library(terra)
#Set WD
wd <- choose.dir()
setwd(wd)
#getwd() #check wd
dir() #explore directory
#Prefered file sturcture.
subfolder_names <- c("Fire", "Fire/Data", "Fire/Output", "Prefire1", "Prefire1/Data", "Prefire1/Output", "Prefire2", "Prefire2/Data", "Prefire2/Output",
"Postfire1", "Postfire1/Data", "Postfire1/Output", "Postfire2", "Postfire2/Data", "Postfire2/Output", "2023", "2023/Data", "2023/Output",
"MOD13Q1")
for (name in subfolder_names) {
dir.create(name)
}
#Download Images from EarthExplorer according to table in ReadMe file.
##Place downloaded data files in the appropriate folder using your prefered method.
###First Image -----
sub <- "Fire"
dir(paste(sub, "Data", sep = "/"))
tarFile <- list.files(paste(sub, "Data", sep = "/"), pattern = ".tar")
untar(paste(sub, "Data", tarFile, sep = "/"),
exdir = paste(sub, "Data", sep = "/"))
dir(paste(sub, "Data", sep = "/"))
ls <- list.files(paste(sub, "Data", sep = "/"), pattern = "B\\d+.TIF")
ls
im1 <- rast(paste(sub, "Data", ls, sep = "/"))
plotRGB(im1, r = 4, g = 3, b = 2, stretch = "lin")
e <- ext(633959, 785100, -4050000, -3930811) #e should be the same for all images
plotRGB(im1, r = 4, g = 3, b = 2, stretch = "lin")
plot(e, add = T)
im1_e <- crop(im1, e)
plotRGB(im1_e, r = 4, g = 3, b = 2, stretch = "lin")
writeRaster(im1_e, filename = paste(sub, "Data/20200109_Crop.tif", sep = "/"),
overwrite = T)
png(paste(sub, "Output/True_Color1.png", sep = "/"), bg = 0)
plotRGB(im1_e, r = 4, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im1_e, r=8, g=3, b=2, stretch = "lin")
png(paste(sub, "Output/Thermal1.png", sep = "/"), bg = 0)
plotRGB(im1_e, r = 8, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im1_e, r = 8, g = 4, b = 3, stretch = "lin")
png(paste(sub, "Output/Thermal2.png", sep = "/"), bg = 0)
plotRGB(im1_e, r = 8, g = 4, b = 3, stretch = "lin")
dev.off()
###Initial Prefire -----
sub <- "Prefire1"
dir(paste(sub, "Data", sep = "/"))
tarFile <- list.files(paste(sub, "Data", sep = "/"), pattern = ".tar")
untar(paste(sub, "Data", tarFile, sep = "/"),
exdir = paste(sub, "Data", sep = "/"))
dir(paste(sub, "Data", sep = "/"))
ls <- list.files(paste(sub, "Data", sep = "/"), pattern = "B\\d+.TIF")
ls
im2 <- rast(paste(sub, "Data", ls, sep = "/"))
plotRGB(im2, r = 4, g = 3, b = 2, smooth = T, stretch = "lin")
e <- ext(633959, 785100, -4000000, -3930811) #e should be the same for all images
plotRGB(im2, r = 4, g = 3, b = 2, stretch = "lin")
plot(e, add = T)
im2_e <- crop(im2, e)
plotRGB(im2_e, r = 4, g = 3, b = 2, stretch = "lin")
writeRaster(im2_e, filename = paste(sub, "Data/20190223_Crop.tif", sep = "/"))
png(paste(sub, "Output/True_Color1.png", sep = "/"), bg = 0)
plotRGB(im2_e, r = 4, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im2_e, r=8, g=3, b=2, stretch = "lin")
png(paste(sub, "Output/Thermal1.png", sep = "/"), bg = 0)
plotRGB(im2_e, r = 8, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im2_e, r = 8, g = 4, b = 3, stretch = "lin")
png(paste(sub, "Output/Thermal2.png", sep = "/"), bg = 0)
plotRGB(im2_e, r = 8, g = 4, b = 3, stretch = "lin")
dev.off()
### Clip Raster in ArcGIS Pro
### Save output in appropriate Data folder using YYYYMMDD_Clip.tif filenaming convention.
im2_clip <- rast(paste(sub, "Data/20190223_Clip.tif", sep = "/"))
plotRGB(im2_clip, r = 4, g = 3, b = 2, stretch = "lin")
#Calculate Indices
##NBR = (NIR-SWIR2)/(NIR+SWIR2)
initNBR_pre <- (im2_clip[[5]]-im2_clip[[7]])/(im2_clip[[5]]+im2_clip[[7]])
plot(initNBR_pre, axes = F)
##NDVI = (NIR-Red)/(NIR+Red)
initNDVI_pre <- (im2_clip[[5]]-im2_clip[[4]])/(im2_clip[[5]]+im2_clip[[4]])
plot(initNDVI_pre, axes = F)
im2_clip <- c(im2_clip, initNBR_pre, initNDVI_pre)
names(im2_clip) <- c("B1", "B2", "B3", "B4", "B5", "B6", "B7", "B10", "NBR", "NDVI")
writeRaster(im2_clip, filename = paste(sub, "Data/20200210_ClipPlus.tif", sep = "/"))
###Initial Postfire -----
sub <- "Postfire1"
dir(paste(sub, "Data", sep = "/"))
tarFile <- list.files(paste(sub, "Data", sep = "/"), pattern = ".tar")
untar(paste(sub, "Data", tarFile, sep = "/"),
exdir = paste(sub, "Data", sep = "/"))
dir(paste(sub, "Data", sep = "/"))
ls <- list.files(paste(sub, "Data", sep = "/"), pattern = "B\\d+.TIF")
ls
im3 <- rast(paste(sub, "Data", ls, sep = "/"))
plotRGB(im3, r = 4, g = 3, b = 2, smooth = T, stretch = "lin")
e <- ext(633959, 785100, -4000000, -3930811)
plotRGB(im3, r = 4, g = 3, b = 2, stretch = "lin")
plot(e, add = T)
im3_e <- crop(im3, e)
plotRGB(im3_e, r = 4, g = 3, b = 2, stretch = "lin")
writeRaster(im3_e, filename = paste(sub, "Data/20200210_Crop.tif", sep = "/"),
overwrite = T)
png(paste(sub, "Output/True_Color1.png", sep = "/"), bg = 0)
plotRGB(im3_e, r = 4, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im3_e, r=8, g=3, b=2, stretch = "lin")
png(paste(sub, "Output/Thermal1.png", sep = "/"), bg = 0)
plotRGB(im3_e, r = 8, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im3_e, r = 8, g = 4, b = 3, stretch = "lin")
png(paste(sub, "Output/Thermal2.png", sep = "/"), bg = 0)
plotRGB(im3_e, r = 8, g = 4, b = 3, stretch = "lin")
dev.off()
### Clip Raster in ArcGIS Pro
### Save output in appropriate Data folder using YYYYMMDD_Clip.tif filenaming convention.
im3_clip <- rast(paste(sub, "Data/20200210_Clip.tif", sep = "/"))
plotRGB(im3_clip, r = 4, g = 3, b = 2, stretch = "lin")
#Calculate Indicies
##NBR = (NIR-SWIR2)/(NIR+SWIR2)
initNBR_post <- (im3_clip[[5]]-im3_clip[[7]])/(im3_clip[[5]]+im3_clip[[7]])
plot(initNBR_post, axes = F)
##NDVI = (NIR-Red)/(NIR+Red)
initNDVI_post <- (im3_clip[[5]]-im3_clip[[4]])/(im3_clip[[5]]+im3_clip[[4]])
plot(initNDVI_post, axes = F)
im3_clip <- c(im3_clip, initNBR_post, initNDVI_post)
names(im3_clip) <- c("B1", "B2", "B3", "B4", "B5", "B6", "B7", "B10", "NBR", "NDVI")
writeRaster(im3_clip, filename = paste(sub, "Data/20200210_ClipPlus.tif", sep = "/"))
### Initial dNBR ----
initdNBR <- initNBR_pre - initNBR_post
plot(initdNBR, axes = F)
writeRaster(initdNBR, filename = "Postfire1/Output/dNBR.tif")
## Reclassify and calculate burn severity in ArcGIS Pro
###Extended Prefire -----
sub <- "Prefire2"
dir(paste(sub, "Data", sep = "/"))
tarFile <- list.files(paste(sub, "Data", sep = "/"), pattern = ".tar")
untar(paste(sub, "Data", tarFile, sep = "/"),
exdir = paste(sub, "Data", sep = "/"))
dir(paste(sub, "Data", sep = "/"))
ls <- list.files(paste(sub, "Data", sep = "/"), pattern = "B\\d+.TIF")
ls
im4 <- rast(paste(sub, "Data", ls, sep = "/"))
plotRGB(im4, r = 4, g = 3, b = 2, stretch = "lin")
e <- ext(633959, 785100, -4000000, -3930811)
plotRGB(im4, r = 4, g = 3, b = 2, stretch = "lin")
plot(e, add = T)
im4_e <- crop(im4, e)
plotRGB(im4_e, r = 4, g = 3, b = 2, stretch = "lin")
writeRaster(im4_e, filename = paste(sub, "Data/20191208_Crop.tif", sep = "/"))
png(paste(sub, "Output/True_Color1.png", sep = "/"), bg = 0)
plotRGB(im4_e, r = 4, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im4_e, r=8, g=3, b=2, stretch = "lin")
png(paste(sub, "Output/Thermal1.png", sep = "/"), bg = 0)
plotRGB(im4_e, r = 8, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im4_e, r = 8, g = 4, b = 3, stretch = "lin")
png(paste(sub, "Output/Thermal2.png", sep = "/"), bg = 0)
plotRGB(im4_e, r = 8, g = 4, b = 3, stretch = "lin")
dev.off()
### Clip Raster in ArcGIS Pro
### Save output in appropriate Data folder using YYYYMMDD_Clip.tif filenaming convention.
im4_clip <- rast(paste(sub, "Data/20191208_Clip.tif", sep = "/"))
plotRGB(im4_clip, r = 4, g = 3, b = 2, stretch = "lin")
#Calculate Indicies
##NBR = (NIR-SWIR2)/(NIR+SWIR2)
extNBR_pre <- (im4_clip[[5]]-im4_clip[[7]])/(im4_clip[[5]]+im4_clip[[7]])
plot(extNBR_pre, axes = F)
##NDVI = (NIR-Red)/(NIR+Red)
extNDVI_pre <- (im4_clip[[5]]-im4_clip[[4]])/(im4_clip[[5]]+im4_clip[[4]])
plot(extNDVI_pre, axes = F)
im4_clip <- c(im4_clip, extNBR_pre, extNDVI_pre)
names(im4_clip) <- c("B1", "B2", "B3", "B4", "B5", "B6", "B7", "B10", "NBR", "NDVI")
writeRaster(im4_clip, filename = paste(sub, "Data/20191208_ClipPlus.tif", sep = "/"))
###Extended Postfire -----
sub <- "Postfire2"
dir(paste(sub, "Data", sep = "/"))
tarFile <- list.files(paste(sub, "Data", sep = "/"), pattern = ".tar")
untar(paste(sub, "Data", tarFile, sep = "/"),
exdir = paste(sub, "Data", sep = "/"))
dir(paste(sub, "Data", sep = "/"))
ls <- list.files(paste(sub, "Data", sep = "/"), pattern = "B\\d+.TIF")
ls
im5 <- rast(paste(sub, "Data", ls, sep = "/"))
plotRGB(im5, r = 4, g = 3, b = 2, stretch = "lin")
e <- ext(633959, 785100, -4000000, -3930811)
plotRGB(im5, r = 4, g = 3, b = 2, stretch = "lin")
plot(e, add = T)
im5_e <- crop(im5, e)
plotRGB(im5_e, r = 4, g = 3, b = 2, stretch = "lin")
writeRaster(im5_e, filename = paste(sub, "Data/20201226_Crop.tif", sep = "/"),
overwrite = T)
png(paste(sub, "Output/True_Color1.png", sep = "/"), bg = 0)
plotRGB(im5_e, r = 4, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im5_e, r=8, g=3, b=2, stretch = "lin")
png(paste(sub, "Output/Thermal1.png", sep = "/"), bg = 0)
plotRGB(im5_e, r = 8, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im5_e, r = 8, g = 4, b = 3, stretch = "lin")
png(paste(sub, "Output/Thermal2.png", sep = "/"), bg = 0)
plotRGB(im5_e, r = 8, g = 4, b = 3, stretch = "lin")
dev.off()
### Clip Raster in ArcGIS Pro
### Save output in appropriate Data folder using YYYYMMDD_Clip.tif filenaming convention.
im5_clip <- rast(paste(sub, "Data/20201226_Clip.tif", sep = "/"))
plotRGB(im5_clip, r = 4, g = 3, b = 2, stretch = "lin")
#Calculate Indicies
##NBR = (NIR-SWIR2)/(NIR+SWIR2)
extNBR_post <- (im5_clip[[5]]-im5_clip[[7]])/(im5_clip[[5]]+im5_clip[[7]])
plot(extNBR_post, axes = F)
##NDVI = (NIR-Red)/(NIR+Red)
extNDVI_post <- (im5_clip[[5]]-im5_clip[[4]])/(im5_clip[[5]]+im5_clip[[4]])
plot(extNDVI_post, axes = F)
im5_clip <- c(im5_clip, initNBR_post, initNDVI_post)
names(im5_clip) <- c("B1", "B2", "B3", "B4", "B5", "B6", "B7", "B10", "NBR", "NDVI")
writeRaster(im5_clip, filename = paste(sub, "Data/20200210_ClipPlus.tif", sep = "/"))
### Extended dNBR ----
extdNBR <- extNBR_pre - extNBR_post
plot(extdNBR, axes = F)
writeRaster(extdNBR, filename = "Postfire2/Output/dNBR.tif")
## Reclassify and calculate burn severity in ArcGIS Pro
### 2023 (5.42% land cloud cover) ----
sub <- "2023"
dir(paste(sub, "Data", sep = "/"))
tarFile <- list.files(paste(sub, "Data", sep = "/"), pattern = ".tar")
untar(paste(sub, "Data", tarFile, sep = "/"),
exdir = paste(sub, "Data", sep = "/"))
dir(paste(sub, "Data", sep = "/"))
ls <- list.files(paste(sub, "Data", sep = "/"), pattern = "B\\d+.TIF")
ls
im6 <- rast(paste(sub, "Data", ls, sep = "/"))
plotRGB(im6, r = 4, g = 3, b = 2, stretch = "lin")
e <- ext(633959, 785100, -4000000, -3930811)
plotRGB(im6, r = 4, g = 3, b = 2, stretch = "lin")
plot(e, add = T)
im6_e <- crop(im6, e)
plotRGB(im6_e, r = 4, g = 3, b = 2, stretch = "lin")
writeRaster(im6_e, filename = paste(sub, "Data/20231008_Crop.tif", sep = "/"),
overwrite = T)
png(paste(sub, "Output/True_Color1.png", sep = "/"), bg = 0)
plotRGB(im6_e, r = 4, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im6_e, r=8, g=3, b=2, stretch = "lin")
png(paste(sub, "Output/Thermal1.png", sep = "/"), bg = 0)
plotRGB(im6_e, r = 8, g = 3, b = 2, stretch = "lin")
dev.off()
plotRGB(im6_e, r = 8, g = 4, b = 3, stretch = "lin")
png(paste(sub, "Output/Thermal2.png", sep = "/"), bg = 0)
plotRGB(im6_e, r = 8, g = 4, b = 3, stretch = "lin")
dev.off()
### Clip Raster in ArcGIS Pro
### Save output in appropriate Data folder using YYYYMMDD_Clip.tif filenaming convention.
im6_clip <- rast(paste(sub, "Data/20231008_Clip.tif", sep = "/"))
plotRGB(im6_clip, r = 4, g = 3, b = 2, stretch = "lin")
#Calculate Indicies
##NBR = (NIR-SWIR2)/(NIR+SWIR2)
extNBR_2023 <- (im6_clip[[5]]-im6_clip[[7]])/(im6_clip[[5]]+im6_clip[[7]])
plot(extNBR_2023, axes = F)
##NDVI = (NIR-Red)/(NIR+Red)
extNDVI_2023 <- (im6_clip[[5]]-im6_clip[[4]])/(im6_clip[[5]]+im6_clip[[4]])
plot(extNDVI_2023, axes = F)
im6_clip <- c(im6_clip, initNBR_post, initNDVI_post)
names(im6_clip) <- c("B1", "B2", "B3", "B4", "B5", "B6", "B7", "B10", "NBR", "NDVI")
writeRaster(im6_clip, filename = paste(sub, "Data/20231008_ClipPlus.tif", sep = "/"))
### 2023 dNBR ----
extdNBR_2023 <- extNBR_pre - extNBR_2023
plot(extdNBR_2023, axes = F)
writeRaster(extdNBR_2023, filename = "2023/Output/dNBR.tif")
## Reclassify and calculate burn severity in ArcGIS Pro
###MOD13Q1 -----
#install.packages("tidyverse")
#install.packages("lubridate")
#install.packages("zoo")
library(tidyverse)
library(lubridate)
library(zoo)
sub <- "MOD13Q1"
ls <- list.files(path = sub, pattern = ".csv")
df <- read.csv(paste(sub, ls, sep = "/"))%>%
select('date', "min", "max", "sum", "range", "mean", "variance",
"standard_deviation", "tot_pixels")%>%
mutate(date = as.Date(mdy(date)),
across(2:9, function(x) as.numeric(as.character(x))))%>%
filter(if_any(everything(), ~ !is.na(.)))%>%
mutate(mean48 = rollmean(mean, 3, fill = NA),
dayNum = as.numeric(date - min(date)))
source("ggplot theme.R")
ggplot(subset(df, date >= ymd("2012-01-01")), aes(date, mean48))+
#stat_smooth(col = "black", se = F, fullrange=TRUE, linewidth = 0.7)+ #optional trendline
#geom_vline(xintercept = mdy("12/20/2019"), col = "red", linewidth = 1)+ #optional vertical line indicating date of fire
geom_line(linewidth = 1)+
labs(title = "MODIS/Terra Vegetation Indices",
subtitle = "Kangaroo Island",
y = "48 Day Mean MOD13Q1")
ggsave(filename = "MOD13Q1/MOD13Q1_48Day.png")
#Some MOD13Q1 statistics
prefireMax <- max(subset(df, date > ymd("2012-01-01") & date <= ymd("2019-12-01"))$mean48, na.rm = T)
prefireMin <- min(subset(df, date > ymd("2012-01-01") & date <= ymd("2019-12-01"))$mean48, na.rm = T)
prefireMean <- mean(subset(df, date > ymd("2012-01-01") & date <= ymd("2019-12-01"))$mean48, na.rm = T)
fireMin <- min(subset(df, date >= ymd("2019-12-01"))$mean48, na.rm = T)
postfireMax <- max(subset(df, date >= ymd("2019-12-01"))$mean48, na.rm = T)
min(subset(df, year(date) == "2023")$mean48, na.rm = T)
### dNBR reclass figures -----
colors <- c("darkgreen", "green", "gold3", "tomato", "red", "red4")
initdNBR_reclass <- rast("Postfire1/Data/InitdNBR.tif")
png("Postfire1/Output/dNBR Reclass.png", width = 1500, height = 660)
plot(initdNBR_reclass, col = colors, axes = F, legend = "topright",
background = "#A1CDEF",
plg=list( # parameters for drawing legend
title.cex = 2, # Legend title size
cex = 2 # Legend text size
),
mar=c(1,0,5,0),
main = "Initial dNBR", cex.main = 5)
dev.off()
png("Postfire1/Output/dNBR Reclass Alt.png", width = 1500, height = 660)
plot(initdNBR_reclass, col = colors, axes = F, legend = NA,
background = "#A1CDEF", mar=c(1,0,5,0),
main = "Initial dNBR", cex.main = 5)
dev.off()
extdNBR_reclass <- rast("Postfire2/Data/ExtdNBR.tif")
png("Postfire2/Output/dNBR Reclass.png", width = 1500, height = 660)
plot(extdNBR_reclass, col = colors, axes = F, legend = "topright",
background = "#A1CDEF",
plg=list( # parameters for drawing legend
title = "dNBR",
title.cex = 2, # Legend title size
cex = 2 # Legend text size
),
mar=c(1,0,5,0),
main = "Initial dNBR", cex.main = 5)
dev.off()
png("Postfire2/Output/dNBR Reclass Alt.png", width = 1500, height = 660)
plot(extdNBR_reclass, col = colors, axes = F, legend = NA,
background = "#A1CDEF", mar=c(1,0,5,0),
main = "Initial dNBR", cex.main = 5)
dev.off()
dNBR2023_reclass <- rast("2023/Data/2023dNBR_Reclass.tif")
png("2023/Output/dNBR Reclass.png", width = 1500, height = 660)
plot(dNBR2023_reclass, col = colors, axes = F, legend = "topright",
background = "#A1CDEF",
plg=list( # parameters for drawing legend
title = "dNBR",
title.cex = 2, # Legend title size
cex = 2 # Legend text size
),
mar=c(1,0,5,0), #mar=c(bottom, left, top, right)
main = "Initial dNBR", cex.main = 5)
dev.off()
png("2023/Output/dNBR Reclass Alt.png", width = 1500, height = 660)
plot(dNBR2023_reclass, col = colors, axes = F, legend = NA,
background = "#A1CDEF", mar=c(1,0,5,0),
main = "Initial dNBR", cex.main = 5)
dev.off()
#This takes about 14 seconds to render.
png("Reclass Comp.png", width = 3000, height = 1320)
par(mfrow = c(2,2))
plot(initdNBR_reclass, col = colors, axes = F, legend = NA,
background = "#A1CDEF", mar=c(1,2,5,1), #mar=c(bottom, left, top, right)
main = "Initial dNBR", cex.main = 7)
plot(extdNBR_reclass, col = colors, axes = F, legend = NA,
background = "#A1CDEF", mar=c(1,1,5,2),#mar=c(bottom, left, top, right)
main = "Extended dNBR", cex.main = 7)
plot(dNBR2023_reclass, col = colors, axes = F, #legend = "right",
background = "#A1CDEF", mar=c(2,2,5,1),#mar=c(bottom, left, top, right)
plg=list( # parameters for drawing legend
cex = 8 # Legend text size
),
main = "Long Term Recovery", cex.main = 7)
dev.off()
### RF classification ----------
#install.packages("sf")
#install.packages("randomForest")
#install.packages("beepr")
library(sf)
library(randomForest)
#library(beepr) #optional package to alert user when code finishes.
#Use provided training Shapefile, created in ArcGIS Pro.
train <- vect(paste("Postfire1/Data",
list.files(path = "Postfire1/Data", pattern = ".shp"), sep ="/"))
im <- rast(paste("Postfire1/Data",
list.files(path = "Postfire1/Data", pattern = "ClipPlus.tif"), sep ="/"))
initdNBR <- rast(paste("Postfire1/Data",
list.files(path = "Postfire1/Data", pattern = "dNBR.tif$"), sep ="/"))
initdNBR <- project(initdNBR, im)
im <- c(im, initdNBR)
names(im)
names(im)[11] <- "dNBR"
plotRGB(im, r=4,g=3,b=2, stretch='lin')
plot(train, add = TRUE, col = "red")
train <- project(train, im)
# partition the training data into training and validation
source("classify_codes.R") #Code provided by Professor Dr. Matthew Marcus, UoA.
train_val <- crossvalidsp(train, att = "Classname")
## run a random forest classification using the partitioned training data
# Extract predictor values based on the geometry of the training data
extracted_values <- terra::extract(im, train_val[[2]])
training_data <- as.data.frame(train_val[[2]])
training_data$ID <- 1:nrow(as.data.frame(training_data)) # inserts column of integers, needed for matching in next step
# Match class names to integers in new DF
extracted_values$Classname <- training_data$Classname[match(extracted_values$ID, training_data$ID)]
# Ensure the class column is formatted as a factor
extracted_values$Classname <- as.factor(extracted_values$Classname)
extracted_values <- extracted_values[-1]
# run the random forest - This can take several minutes!
{start <- Sys.time() #Optional argument for calculating runtime.
rf <- randomForest(formula = Classname ~ ., data = extracted_values,
ntree = 1000, na.action = na.omit,
importance = TRUE, type = "classification")
stop <- Sys.time() #Optional argument for calculating runtime.
print(stop-start) #Print runtime.
#beep("mario") #Optional sound to alert user from package beepr
}
# Use the random forest to generate a raster with the predict function - This can take several minutes!
{start <- Sys.time() #Optional argument for calculating runtime.
classification <- terra::predict(object = im, model = rf, type = "class",
na.rm = TRUE)
stop <- Sys.time() #Optional argument for calculating runtime.
print(stop-start) #Print runtime.
#beep("mario") #Optional sound to alert user from package beepr
}
col <- c("grey10", "darkred", "olivedrab", "khaki", "ghostwhite", "plum", "blue2")
plot(classification, col = col, axes = F, main = "Random Forest Classification")
png(filename = "Postfire1/Output/RF_Classification.png",
width = 1500, height = 660)
plot(classification, col = col, axes = F, legend = "topright",
main = "Random Forest Classification", cex.main = 5,
plg=list( # parameters for drawing legend
title.cex = 2, # Legend title size
cex = 2 # Legend text size
),
mar=c(1,0,5,0),
background = "#A1CDEF",)
dev.off()
# validate the map
val <- validateMap(classification, val = train_val[[1]], sampsize = 1000, class_col = "Classname")
val
accs <- getAccus(val)
## plot your accuracy
df <- accs%>%
dplyr::filter(Class != "overall")%>%# drop overall
mutate(Class = replace(Class, Class == "Pasture/Agriculture", "Pasture/ Agriculture")) #For improved text wrapping in ggplot.
vals <- c("#00CC00", "#377EB8") # green and blue codes
labs <- c("Producer's Accuracy","User's Accuracy" )
#Set plotting theme
source("ggplot Theme.R")
accPlot <- ggplot(df, aes(x = Class, y = Accuracy, fill = factor(type))) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
labs(title = "Random Forest Classification Accuracy",
x = "Classes",
y = "Accuracy %")
scale_fill_manual(values = vals,
labels = function(x) str_wrap(labs, width = 10)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))+
theme(axis.text.x = element_text(angle = 65, hjust = 1, vjust = 1),
legend.title = element_blank())
print(accPlot)
ggsave(accPlot, path = "Postfire1/Output", filename = "accPlot.png", height = 6, width = 9)