diff --git a/scripts/create_loss_layer.R b/scripts/create_loss_layer.R
new file mode 100644
index 0000000..bbb3126
--- /dev/null
+++ b/scripts/create_loss_layer.R
@@ -0,0 +1,149 @@
+library(terra)
+library(dplyr)
+library(tidyr)
+
+
+######################### data setup ######################### 
+lt_data_dir <- file.path("rasters/lt_nbr_ftv_1985_2021_combo6")
+lt_name <- "lt_nbr_1985_2021_combo6" 
+ftv_file_name <- "lt_nbr_ftv_1985_2021_combo6" 
+vertex_file_name <- "lt_nbr_is_vertex_1985_2021_combo6" 
+dir.create(paste0(lt_data_dir, "/ftv_tiles"))
+dir.create(paste0(lt_data_dir, "/vertex_tiles"))
+dir.create(paste0(lt_data_dir, "/masked_ftv"))
+dir.create(paste0(lt_data_dir, "/losses"))
+
+######################### merge tiles function ######################### 
+#ftv stacks from LT are often very large, and as such are split into several tiles. This function will stitch those tiles back together
+#if you have a single ftv file, you can skip this step
+#requires gdal installation
+merge_tiles <- function(source_dir, dest_file, pattern = ".*tif") {
+  vrt_file <- gsub("(tiff|tif)", "vrt", dest_file)
+  
+  files <- list.files(source_dir, full.names = TRUE, pattern = pattern)
+  index_file <- file.path(source_dir, "files.txt")
+  writeLines(files, index_file)
+  system(
+    glue::glue("gdalbuildvrt {vrt_file} -input_file_list {index_file}")
+  )
+  system(
+    glue::glue(
+      paste(
+        "gdal_translate -co COMPRESS=DEFLATE -co PREDICTOR=3 -co TILED=YES",
+        "-co BIGTIFF=YES {vrt_file} {dest_file}"
+      )
+    )
+  )
+  unlink(index_file)
+  unlink(vrt_file)
+  return(dest_file)
+}
+
+merge_tiles(lt_data_dir, paste0(lt_data_dir, "/", ftv_file_name, "_all.tif"), pattern = "lt_nbr_ftv.*tif" )
+
+
+######################### retile ftv and vertex layers ######################### 
+# load merged ftv and vertex layers
+ftv <- terra::rast(paste0(lt_data_dir, "/", ftv_file_name, "_all.tif"))
+names(ftv) <- 1985:2022
+is_vertex <- terra::rast(paste0(lt_data_dir, "/", vertex_file_name, ".tif"))
+names(is_vertex) <- 1985:2022
+
+
+#analysis requires much smaller tiles, can adjust size of tiles based on processing ability of your computer
+tile_grid <- terra::rast(
+  extent = terra::ext(ftv),
+  ncols = 30, 
+  nrows = 30
+)
+
+terra::makeTiles(
+  ftv,
+  tile_grid,
+  path.expand(paste0(lt_data_dir, '/ftv_tiles/ftv_', ".tif")),
+  overwrite = TRUE
+)
+
+terra::makeTiles(
+  is_vertex,
+  tile_grid,
+  path.expand(paste0(lt_data_dir, '/vertex_tiles/is_vertex_', ".tif")),
+  overwrite = TRUE
+)
+
+######################### mask smoothed ftv traj with vertices  ######################### 
+#removing pixels that are not breakpoints in spectral trajectory
+vertex_files <- list.files(paste0(lt_data_dir,"/vertex_tiles/"), pattern = "is_vertex_.*.tif", full.names = TRUE)
+ftv_files <- list.files(paste0(lt_data_dir,"/ftv_tiles/"), pattern = "ftv_.*.tif", full.names = TRUE)
+
+lapply(1:length(ftv_files), \(i) {
+  message(paste0("Working on iteration ", i))
+  vertex_file <- vertex_files[i]
+  ftv_file <- ftv_files[i]
+  index <- stringr::str_split(gsub(pattern = "\\.tif", "", basename(vertex_file)), "_")[[1]] |> tail(1)
+  masked_ftv <- file.path(paste0(lt_data_dir,"/masked_ftv/masked_ftv_", index, ".tif"))
+  
+  if (!file.exists(masked_ftv)){
+    terra::mask(terra::rast(ftv_file), terra::rast(vertex_file), maskvalues = 0, filename = masked_ftv)
+  } 
+})
+
+######################### create all loss layer  ######################### 
+#using masked ftv to identify decreasing segments
+#value of previous vertex greater than value of current vertex
+
+detect_loss <- function(model, data, ...){
+  # Collect these for later
+  years <- names(data)
+  
+  # Build a place holder data.frame
+  results_container <- data.frame(
+    matrix(nrow = nrow(data), ncol = length(years))
+  )
+  names(results_container) <- years
+  
+  # Compute magnitudes for loss vertices, with > 1 segment
+  data <- data |> 
+    mutate(id = 1:nrow(data)) |> # keep track of pixel order
+    pivot_longer(-id, names_to = "year") |>
+    mutate(year = as.numeric(year) + 1) |> # LT defaults adds a year to the vertex date to assign year of disturbance
+    group_by(id) |>
+    filter(!is.na(value)) |>
+    filter(n() > 2) |>
+    mutate(change = value - lead(value)) |>
+    filter(change > 0) |> 
+    select(-value) |>
+    ungroup() |>
+    pivot_wider(names_from = "year", values_from = "change")
+  
+  # Pad the results, filling in the years that don't have disturbances
+  fill_years <- setdiff(years, names(data))
+  fill_cols <- lapply(fill_years, \(fy) {
+    return(rep(NA, nrow(data)))
+  })
+  names(fill_cols) <- fill_years
+  data <- bind_cols(data, fill_cols) |>
+    dplyr::relocate(all_of(years))
+  
+  # Insert results in the correct location
+  results_container[data$id, ] <- data |> select(-id)
+  return(results_container)
+}
+
+#use loss funtction to create loss tiles
+masked_ftvs <- list.files(paste0(lt_data_dir, "/masked_ftv/"), full.names = TRUE)
+lapply(masked_ftvs, \(masked_file) {
+  index <- stringr::str_split(gsub(pattern = "\\.tif", "", basename(masked_file)), "_")[[1]] |> tail(1)
+  losses <- file.path(paste0(lt_data_dir,"/losses/losses", index , ".tif")) 
+  
+  if (!file.exists(losses)){
+    terra::predict(terra::rast(masked_file), list(), detect_loss, filename = losses)
+  } 
+})
+
+#merge loss tiles into new loss.tif 
+merge_tiles(paste0(lt_data_dir, "/losses"), paste0(lt_data_dir,"/losses/", lt_name,"_all_losses.tif"), pattern = "*.tif" )
+all_losses <- rast("rasters/lt_nbr_rt1_1985-2022/losses/rt1_1985_2022_all_losses.tif")
+names(all_losses) <- 1985:2022
+all_losses <- subset(all_losses, 6:38) #remove pre 1990 years, optional step
+writeRaster(all_losses, paste0(lt_data_dir,"/losses/", lt_name,"_all_losses.tif"), overwrite = TRUE)
diff --git a/scripts/harvest_brick.R b/scripts/harvest_brick.R
new file mode 100644
index 0000000..52145fc
--- /dev/null
+++ b/scripts/harvest_brick.R
@@ -0,0 +1,59 @@
+library(terra)
+######################## set up######################## 
+years <- 1990:2021 #study years
+harvest_directory <- file.path("../GIS_Files/harvest_rasters")
+dir.create(paste0(harvest_directory, "/harv_layers"))
+harv_records <- vect("../GIS_Files/albers_combined_harv_records.gpkg")
+#need study area bounds
+harv_boundaries <- vect("../GIS_Files/combined_tract_boundaries.gpkg")
+
+crs <- labrador.client::get_cafri_crs()
+
+
+######################## rasterize harvests  ######################## 
+harv_records$value <- 1 #dummy value for creating harvest raster
+
+#use your LT output as reference raster here, want harvest layer to have same extent
+ref_raster <- rast("rasters/combo12_all_losses2.tif")[[1]]
+values(ref_raster) <- NA
+
+lapply(1:length(years), \(i) {
+  message(paste0("Working on iteration ", i))
+  
+  harv_subset <- terra::subset(harv_records,
+                               (years[i] >= harv_records$start_date & years[i] <= harv_records$end_date)
+                               | as.integer(harv_records$start_date) + 1 == years[i]
+                               | as.integer(harv_records$end_date) + 1== years[i]
+                               | as.integer(harv_records$start_date) - 1 == years [i]
+                               | as.integer(harv_records$end_date) - 1 == years[i] )
+  
+  harv_file <- file.path(paste0(harvest_directory, "/harv_layers/harv_layer_", years[i], ".tif"))
+  harv_exists <- file.exists(harv_file)
+  
+  if (!harv_exists & nrow(harv_subset) != 0){
+    terra::rasterize(harv_subset, ref_raster, field = "value", filename = harv_file)
+  } else if (!harv_exists){
+    writeRaster(ref_raster, filename = harv_file)
+  }
+})
+
+######################## combine havrvest rasters into single harvest brick layer ######################## 
+rasters <- list.files(paste0(harvest_directory, "/harv_layers/"), full.names = TRUE)
+harv_raster <- rast(rasters)
+names(harv_raster) <- years
+harv_raster[is.na(harv_raster)] <- 0
+writeRaster(harv_raster, paste0(harvest_directory,"/harvest_brick.tif"), overwrite = TRUE)
+harv_raster <- rast(paste0(harvest_directory,"/harvest_brick.tif"))
+
+######################## mask harvest brick with study area boundaries ######################## 
+#terra throws a fit if the crs info doesn't match exactly, just making the labels match here, its the same crs
+crs(harvest_rast) <- crs
+
+#dummy value for making raster
+harv_boundaries$rast_value <- 1
+harv_boundaries <- rasterize(harv_boundaries, harv_raster, field = "rast_value")
+#areas outside harvests set to NA
+harv_raster <- mask(harv_raster, harv_boundaries) 
+
+writeRaster(harv_raster, paste0(harvest_directory,"/harvest_brick.tif"), overwrite = TRUE)
+rast
\ No newline at end of file
diff --git a/scripts/multi_disturbance_accuracy_assesment.R b/scripts/multi_disturbance_accuracy_assesment.R
new file mode 100644
index 0000000..e5e1e4b
--- /dev/null
+++ b/scripts/multi_disturbance_accuracy_assesment.R
@@ -0,0 +1,222 @@
+library(terra)
+library(exactextractr)
+library(dplyr)
+library(future)
+library(future.apply)
+library(progressr)
+
+
+############################################# set up #############################################
+#this accuracy assessment process is designed to match a an annual disturbance raster stack to polygon reference data
+#replace reference data with your own polygon data for best results
+#required data: LT output, target event polygons, raster of event polygons, polygons of study area bounds (tracts)
+
+#adjusts allowable ram usaage
+terra::terraOptions(memfrac=0.85)
+
+study_begin <- 1990
+study_end <- 2021
+crs <- labrador.client::get_cafri_crs()
+
+#load data
+crs <-  labrador.client::get_cafri_crs()
+tracts <- vect("../GIS_Files/combined_tract_boundaries.gpkg")
+harvests <- vect("../GIS_Files/albers_combined_harv_records.gpkg") |> terra::as.data.frame(geom = "WKT")
+harvest_rast <- rast("../GIS_Files/harvest_rasters/harvest_brick.tif")
+losses <- rast("rasters/combo12_all_losses2.tif")
+#terra throws a fit if the crs info doesn't match exactly, just making the labels match here, its the same crs 
+crs(losses) <- crs
+
+#data names
+lt_output_name <- "combo_12"
+
+############################################# functions ###########################################
+
+#harvest accuracy assessment function
+#this function assesses the area within harvests
+#assesses accuracy for each harvest event
+
+polygon_acc_assesment <- function(harvest, loss_layer){
+  
+  # Assumes loss_layer is passed as a filename - important for parallelization with terra
+  loss_layer <- terra::rast(loss_layer) |> terra::crop(harvest)
+  start_year <- harvest$start_date
+  end_year <- harvest$end_date
+  
+  #assigning buffer years, each harvest gets +- 1 year buffer window, unless they align with the end of the study period
+  if (end_year == 2021) {
+    harv_years <- c((as.numeric(start_year)-1):(as.numeric(end_year)))
+  } else if (start_year == 1990) {
+    harv_years <- c((as.numeric(start_year)):(as.numeric(end_year) + 1))
+  } else {
+    harv_years <- c((as.numeric(start_year)-1):(as.numeric(end_year) +1))
+  }
+  
+  #subset loss layer so that only the layers for the harvest years remain
+  harv_year_losses <- subset(loss_layer, as.character(harv_years))
+  # exact extract wont count NA values, so this makes counting TPs easy
+  harv_year_losses[harv_year_losses < 0] <- NA 
+  
+  TP <- exact_extract(harv_year_losses, harvest, 'count')
+  #add up TP for all years of the harvest
+  TP <- sum(TP)
+  #FN = total pixels in the harvest - TPs
+  #the number of pixels in the harvest is constant so loss_layer[[1]] calls the first layer of the loss object to count them
+  FN <- exact_extract(loss_layer[[1]], harvest, 'count')
+  FN <- FN - TP
+  
+  results <- as.data.frame(TP) |> cbind(FN)
+  
+  #this method essentially takes an overhead view of the harvest 
+  #instead of treating each year individually, we look at the harvest as a whole
+  #to get the right number of pixels for the multi year stack, multiply by the number of years in the harvest
+  results <- results * as.numeric(length(harv_years)) 
+  return(results)
+}
+
+# tract accuracy assessment function
+#this function looks at areas outside your harvests but within your study area boundaries
+#assess accuracy on an annual basis
+
+tract_acc_assesment <- function(tract_losses_file_path, i, tract){
+  #going one year at a time
+  # Assumes loss file  is passed as a filename - important for parallelization with terra
+  year_layer <- terra::rast(tract_losses_file_path, lyrs=i) |>
+    terra::crop(tract)
+  
+  #FP = total number of disturbances detected, since outside reference data area any detection are automatic FP
+  #TN = total number of pixels - FPS
+  #however to count this easily first need total number of pixels
+  total_pixels <- exact_extract(year_layer, tract, 'count')
+  
+  #set all the 0 pixels to NA now
+  #couldn't have done this before now without making it difficult to calculate the total number of pixels inside the tracts
+  year_layer[year_layer <= 0] <- NA
+  
+  #count FP
+  FP <- exact_extract(year_layer, tract, 'count')
+  
+  #count TN
+  TN <- total_pixels - FP
+  
+  results <- as.data.frame(sum(TN)) |> cbind(sum(FP))
+  return(results)
+}
+
+
+####################################### reference data formatting ####################################### 
+
+#match reference data dates to LT output dates, check validity of harvest dates
+harvests <- harvests %>% filter(!(is.na(start_date)| is.na(end_date))) %>% 
+  filter(!(end_date < start_date)) %>% 
+  filter(!(start_date < study_begin | end_date > study_end | start_date > study_end))
+harvests <- vect(harvests, geom = "geometry")
+#terra also looses the crs information when moving from a vector -> dataframe -> vector so need to reassign it here
+crs(harvests) <- crs
+
+
+####################################### harvest polygon accuracy assessment  ####################################### 
+# tallying TP and FN
+
+#create new loss layer that only has values for areas within harvests
+#duplicate loss layer so don't overwrite original 
+polygon_losses <- losses
+#set all NA to -1, use -1 not zero in case there were any very small disturbance magnitudes
+polygon_losses[is.na(polygon_losses)] <- -1 
+#mask so all areas outside harvest polygons are NA, areas inside harvest are now either disturbance magnitude or -1
+polygon_losses <- mask(polygon_losses, harvests) 
+#need to write new losses file for polygon acc assessment function
+writeRaster(polygon_losses, paste0(lt_output_name, "_polygon_assesment_loss_layer.tif"), overwrite = TRUE) 
+#read file path back
+loss_layer <- file.path(paste0(lt_output_name, "_polygon_assesment_loss_layer.tif"))
+
+#harvests have to be sf object to work with parallelization
+harvests <- sf::st_as_sf(harvests) 
+
+#make empty data frame for results
+polygon_results <- data.frame(TP=0, FN = 0)
+names(polygon_results) <- c("TP", "FN")
+
+#run polygon assessment function
+future::plan('multisession')
+
+progressr::with_progress({
+  p <- progressr::progressor(along = 1:nrow(harvests))
+  polygon_results <- future.apply::future_lapply(seq_len(nrow(harvests)), \(i) {
+    p(message = paste0("Processing harvest: ", (i)))
+    harvest <- harvests[i,]
+    polygon_acc_assesment(harvest, loss_layer)
+  })
+})
+  
+polygon_results <- dplyr::bind_rows(polygon_results)
+
+#write.csv(polygon_results, paste0(lt_output_name, "_multidisturbance_polygon_accuracy.csv"), row.names = FALSE)
+
+
+####################################### asses area outside harvest polygons #######################################
+#tallies FP and TN
+
+
+##### make tract brick #######
+#can use any raster with same extent as template
+tract_rast <- fasterize::fasterize(sf::st_as_sf(tracts), raster::raster(losses)) 
+#convert to spatRaster
+tract_rast <- rast(tract_rast)
+
+###### recrop loss layer #####
+tract_losses <- crop(losses, tract_rast) 
+#focused on losses outside harvest areas now
+#set areas outside of the tracts to -1, -1 just dummy value
+tract_losses <- mask(tract_losses, tract_rast, updatevalue = -1) 
+#set any NA pixels inside tracts to 0
+tract_losses[is.na(tract_losses)] <- 0
+#set outside values back to NA, now areas outside tracts are NA and areas inside are either the loss magnitude or 0
+tract_losses[tract_losses == -1] <- NA
+#remove harvested areas from loss layer
+#the combination of mask_values = 0 and inverse = true means we are keeping the areas outside the harvests
+tract_losses <- mask(tract_losses, harvest_rast, maskvalues = 0, inverse = TRUE)
+crs(harvest_rast) <- crs
+#write tract loss layer for tract accuracy assessment function
+writeRaster(swiss_cheese, paste0(lt_output_name, "_tract_loss_layer.tif"), overwrite = TRUE)
+
+#tract layer has to be sf object for exact extract functions
+tracts <- sf::st_as_sf(tracts)
+
+#create blank df for results
+tract_results <- data.frame(TN=0, FP = 0)
+names(tract_results) <- c("TN", "FP")
+
+#load tract losses file path, has to be file path for function
+tract_losses_file_path <- file.path(paste0(lt_output_name, "_tract_loss_layer.tif"))
+
+years <- names(rast(tract_losses))
+
+
+#run tract accuracy assesment function
+future::plan('multisession')
+
+progressr::with_progress({
+  p <- progressr::progressor(along = 1:length(names))
+  tract_results <- future.apply::future_lapply(seq_len(length(names)), \(i) {
+    p(message = paste0("Processing layer: ", (i)))
+    tract_acc_assesment(tract_losses, i, tracts)
+  })
+})
+
+tract_results <- dplyr::bind_rows(tract_results)
+#write.csv(tract_results, paste0(lt_output_name, "_multidisturbance_tract_accuracy.csv"), row.names = FALSE)
+
+
+####################### combine results ##########################
+
+full_results <- cbind(sum(polygon_results[['TP']]), sum(polygon_results[['FP']]), sum(tract_results[['sum(TN)']]), sum(tract_results[['sum(FP)']]))
+full_results <- as.data.frame(full_results)
+names(full_results) <- c("TP", "FN", "TN", "FP")
+
+#write.csv(full_results, paste0(lt_output_name, "_multidisturbance__accuracy.csv"), row.names = FALSE)
+
+precision <- full_results$TP / (full_results$TP + full_results$FP)
+recall <- full_results$TP / (full_results$TP + full_results$FN)
+f1 <- 2 * (recall * precision)/(recall + precision)
+accuracy <- (full_results$TP + full_results$TN)/(full_results$TN + full_results$FN + full_results$TP + full_results$FP)
diff --git a/scripts/single_disturbance_accuracy_assesment.R b/scripts/single_disturbance_accuracy_assesment.R
new file mode 100644
index 0000000..2993758
--- /dev/null
+++ b/scripts/single_disturbance_accuracy_assesment.R
@@ -0,0 +1,101 @@
+library(terra)
+library(dplyr)
+
+#this accuracy assessment process is designed to match a disturbance raster to polygon reference data, replace reference data with your own polygon data for best results
+#required data: LT output, target event polygons, polygons of study area bounds (tracts)
+
+################################## set beginning and end of study window ##################################
+#our reference data has different time ranges so we need to handle them separately for whole script, if you have just one area could simplify
+uhw_study_begin <- 2010
+uhw_study_end <- 2019
+hwf_study_begin <- 1990
+hwf_study_end <- 2019
+
+################################## load reference data ##################################
+harvests <- vect("../GIS_Files/combined_harvest_records.gpkg") #%>% project(labrador.client::get_cafri_crs()) #match reference data to disturbance raster crs
+#remove harvests that start or end after the end of lt output
+harvests <- harvests[harvests$start_date <= uhw_study_end & harvests$end_date < uhw_study_end] 
+harvests$start_date <- as.numeric(harvests$start_date)
+harvests$end_date <- as.numeric(harvests$end_date)
+
+#out reference data has different ranges of harvest dates, have to be handled separately
+HWF_harvests <- harvests[harvests$Property == "HWF"]
+UHW_harvests <- harvests[harvests$Property == "UHW"]
+
+#tract data is outer bounds of study area, for us here it is the property boundaries for our harvest record data
+tracts <- vect("../GIS_Files/combined_tract_boundaries.gpkg")
+tracts <- project(tracts, "EPSG:26918")
+uhw_tracts <- tracts[tracts$Ownership == "UHW" ]
+hwf_tracts <- tracts[tracts$Ownership == "HWF" ]
+
+################################## load and adjust disturbance raster ##################################
+disturbances <- rast("rasters/tuning_combos/lt_gee_nbr_greatest_tuning_recoverythreshold_75.tif")
+
+#remove disturbances that occur after end of reference data, +1 is becuase we use +- 1 year buffer window for accuracy assesment below
+disturbances[disturbances > (uhw_study_end +1)] <- 0 
+#clip/mask rasters to extent of harvest polygons
+uhw_disturbances <- crop(disturbances, uhw_tracts)
+uhw_disturbances <- mask(uhw_disturbances, uhw_tracts)
+hwf_disturbances <- crop(disturbances, hwf_tracts)
+hwf_disturbances <- mask(hwf_disturbances, hwf_tracts)
+#adjust start dates of disturbance rasters to match reference data
+uhw_disturbances[uhw_disturbances < uhw_study_begin] <- 0
+hwf_disturbances[hwf_disturbances < hwf_study_begin] <- 0
+
+#create harvest start and end date rasters 
+uhw_start_date <- rasterize(UHW_harvests, uhw_disturbances, field = 'start_date', background = NA)
+uhw_end_date <- rasterize(UHW_harvests, uhw_disturbances, field = 'end_date', background = NA)
+
+hwf_start_date <- rasterize(HWF_harvests, hwf_disturbances, field = 'start_date', background = NA)
+hwf_end_date <- rasterize(HWF_harvests, hwf_disturbances, field = 'end_date', background = NA)
+
+#set no data values to zero
+uhw_disturbances[is.na(uhw_disturbances)] <- 0
+uhw_start_date[is.na(uhw_start_date)] <- 0
+uhw_end_date[is.na(uhw_end_date)] <- 0
+
+hwf_disturbances[is.na(hwf_disturbances)] <- 0
+hwf_start_date[is.na(hwf_start_date)] <- 0
+hwf_end_date[is.na(hwf_end_date)] <- 0
+
+################################## accuracy assesments ##################################
+
+#UHW accuracy assessment calculations, note +/- 1 year buffer window
+#create blank raster with same extent to map accuracy assesment values onto
+uhw_accuracy_assessment <- uhw_disturbances
+uhw_accuracy_assessment[!is.na(uhw_accuracy_assessment)] <- NA
+#accuracy assessment
+uhw_accuracy_assessment[uhw_disturbances == 0 & uhw_start_date == 0] <- 1 #no start date, no harvest, TN
+uhw_accuracy_assessment[uhw_disturbances == 0 & uhw_start_date > 1] <- 2 #FN
+uhw_accuracy_assessment[uhw_disturbances != 0 & (uhw_disturbances >= (uhw_start_date - 1)) & (uhw_disturbances <= (uhw_end_date + 1))] <- 3 #TP
+uhw_accuracy_assessment[uhw_disturbances > 0 & (uhw_disturbances <= (uhw_start_date - 1) | uhw_disturbances >= (uhw_end_date + 1))] <- 4 #FP
+
+uhw_accuracy_assessment <- crop(uhw_accuracy_assessment, uhw_tracts)
+uhw_accuracy_assessment <- mask(uhw_accuracy_assessment, uhw_tracts)
+
+#HWF accuracy assessment, note +/- 1 year buffer window
+#create blank raster with same extent to map accuracy assesment values onto
+hwf_accuracy_assessment <- hwf_disturbances
+hwf_accuracy_assessment[!is.na(hwf_accuracy_assessment)] <- NA
+#accuracy assessment
+hwf_accuracy_assessment[hwf_disturbances == 0 & hwf_start_date == 0] <- 1
+hwf_accuracy_assessment[hwf_disturbances == 0 & hwf_start_date > 1] <- 2
+hwf_accuracy_assessment[hwf_disturbances != 0 & (hwf_disturbances >= (hwf_start_date - 1)) & (hwf_disturbances <= (hwf_end_date + 1))] <- 3
+hwf_accuracy_assessment[hwf_disturbances > 0 & (hwf_disturbances <= (hwf_start_date - 1) | hwf_disturbances >= (hwf_end_date + 1))] <- 4
+
+hwf_accuracy_assessment <- crop(hwf_accuracy_assessment, hwf_tracts)
+hwf_accuracy_assessment <- mask(hwf_accuracy_assessment, hwf_tracts)
+
+################################## calculate accuracy metrics ##################################
+#accuracy metrics
+freq_hwf <- freq(hwf_accuracy_assessment)
+freq_uhw <- freq(uhw_accuracy_assessment)
+
+TN <- freq_hwf[1,3] + freq_uhw[1,3]
+FN <- freq_hwf[2,3] + freq_uhw[2,3]
+TP <- freq_hwf[3,3] + freq_uhw[3,3]
+FP <- freq_hwf[4,3] + freq_uhw[4,3]
+precision <- TP/(TP + FP)
+recall <- TP/(TP + FN)
+F1 <- 2 * (recall * precision)/(recall + precision)
+accuracy <- (TP + TN)/(TN + FN + TP + FP)