#' sector_df_assess
#'
#' This is a script to calculate the deforestation rate within each sector
#' From the GLAD alerts
#'
#' @param sectors A SpatialPolygonsDataFrame describing the patrol sectors
#' @param glad_period A raster of the GLAD forest change alerts for the period of interest
#' @param sector_update Logical indicating whether the shapefile defining the sectors
#' has been updated since the function was last run. This should be set to TRUE the
#' first time the function is run.
#' @return
#' @export
#' @author Tom Swinfield
#' @details The glad_period raster can be produced automatically by the function
#' glad_by_date
#'
#' Created 17-03-08
# This function is now deprecated in favour of the sector_df_assess (BELOW)
# change_prop_calc<-function(img, poly){
# img<-crop(img, poly)
# img[is.na(values(img))]<-0
# img <- mask(img, poly, inverse=FALSE) # sets values outside of the polygon to NA
# change<-sum(na.omit(values(img))!=0)
# total<-sum(na.omit(values(img)) ==0) + change
# prop<-(change/total)*100
# return(prop)
# }
sector_df_assess<-function(sectors, glad_period, sector_update = FALSE){
if(sector_update){
# Create an object with reference to the cells relevant to each sector within the
# GLAD image:
sector_ind<-extract(glad_period, sectors, cellnumbers=TRUE)
save(sector_ind,file="data/GLAD/sector_ind")
}
else{
# If it already exists, just load it:
if(file.exists("data/GLAD/sector_ind")){
load("data/GLAD/sector_ind")
}
else{
cat("\"data/GLAD/sector_ind\" does not exist; please run sector_update first,\n")
return(-1)
}
}
# Calculate the amount of deforestation according to GLAD for the period
sector_df<-sapply(sector_ind, function(x){
x_out<<-x
img<-glad_period[x[,"cell"]]
change<-sum(na.omit(img)!=0)
total<-sum(na.omit(img) ==0) + change
prop<-(change/total)*100
}
)
return(sector_df)
}
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