Downloading LANDFIRE Data

Author

Sarah Hagen

Overview

This section walks you through downloading LANDFIRE data for your Area of Interest (AOI) using the “rlandfire” package. Alternatively, if you’ve already downloaded the LANDFIRE raster files and clipped them locally, you can skip this section and load them from the “inputs” folder along with the .csv files.

What this code does

The code below will help you obtain and manage LANDFIRE data from the LANDFIRE Product Service REST API. To do so, we use the rlandfire package developed by Mark Buckner. Mark’s site includes a useful tutorial and more information if you want to learn more about this package and what it does.

This code will:

Define your AOI using a shapefile.
Set parameters for the API call: product codes, projection (EPSG:5070), resolution, and your email.
Create a temporary file to store the downloaded data.
Call the LANDFIRE API using a retry-enabled function.
Move and rename the downloaded file to a permanent location.
Unzip the contents into a temporary folder.
Rename each unzipped file for easier access.
Clean up temporary files once processing is complete.

Note

Before running this code, ensure you have; - An internet connection - Enough time for the download to complete—it may take several minutes. - A shapefile named ‘boundary.shp’ stored in your inputs folder. - A valid email address (required by the LANDFIRE API)

1. Download BPS and EVT data using rlandfire

options(timeout = 600)  # Sets timeout to 10 minutes

# Retry-enabled download function
download_landfire_data <- function(products, aoi, projection, resolution, path, email, num_retries = 3) {
  for (i in 1:num_retries) {
    cat(paste("Attempting download (attempt", i, "of", num_retries, ")...\n"))
    
    result <- tryCatch({
      landfireAPIv2(
        products, 
        aoi, 
        projection, 
        resolution, 
        path = path,
        email = email)
    }, error = function(e) {
      message("Download failed:", conditionMessage(e))
      return(NULL)
    })
    
    if (!is.null(result)) {
      cat("Download successful!\n")
      return(result)
    } else {
      Sys.sleep(10)
    }
  }
  
  cat("Download failed after multiple retries.\n")
  return(NULL)
}

# Define AOI and parameters
aoi <- st_read("inputs/boundary.shp", quiet = TRUE)
aoi <- getAOI(aoi)

products <- c("200BPS", "240EVT")
projection <- 5070
resolution <- 30
email <- "youremail@email.com"  # Replace with your email

save_file <- tempfile(fileext = ".zip")

# Run the download
ncal <- download_landfire_data(
  products = products, 
  aoi = aoi, 
  projection = projection, 
  resolution = resolution, 
  path = save_file,
  email = email)

# Post-download processing
if (!is.null(ncal)) {
  dest_file <- file.path("rasters", "landfire_data.zip")
  file.rename(save_file, dest_file)

  temp_dir <- tempfile()
  dir.create(temp_dir)
  unzip(dest_file, exdir = temp_dir)

  unzipped_files <- list.files(temp_dir, full.names = TRUE)

  for (file in unzipped_files) {
    file_name <- basename(file)
    file_extension <- sub("^[^.]*", "", file_name)
    new_file_path <- file.path("rasters", paste0("landfire_data", file_extension))
    file.rename(file, new_file_path)
  }

  unlink(temp_dir, recursive = TRUE)
  cat("LANDFIRE data downloaded and processed successfully.\n")
} else {
  cat("LANDFIRE data download failed. Please check your connection and try again.\n")
}

2. Separate rasters from the stack

Once the data are downloaded, they will be stored as a multi-layer raster. This step separates each layer so you can work with them individually.

This code will: 1. Load the stacked raster using terra::rast(). 2. Split the raster into individual layers using assign().

# read in stacked raster
stacked_rasters <- rast("rasters/landfire_data.tif")
print(stacked_rasters)

# Reproject the entire multi-band raster to NAD83 / Conus Albers (EPSG:5070)
reprojected_landfire_raster <- project(stacked_rasters, "EPSG:5070", method="near")

# "split" downloaded raster into separate layers
for(lyr in names(reprojected_landfire_raster)) assign(lyr, reprojected_landfire_raster[[lyr]])

3. Clip the BpS and EVT data from LFPS to your aoi

Use this to clip the BpS and EVT data to the aoi boundary after you’ve downloaded it from LFPS with the rlandfire package. You will also load the BpS and EVT attribute tables.

This code will:

Crop and mask the rasters to your AOI.
Join the attribute tables to get vegetation names and fire return intervals.
Calculate acres and relative percents for each BPS and EVT.
Save the processed rasters and attribute table for future use.

Note

You’ll need the BpS and EVT attribute tables as .csv files. If you didn’t download the setup zip file, you can get them from the LANDFIRE Program Website by navigating to the individualproduct page for each layer, then scrolling down to the Attribute Tables(CSV, ADD) section and selecting the CSV file. We recommend storing that file in the inputs folder within your working directory to make the code work as expected.

# **Step 1: Process and save the BpS Raster**

# Read shapefile
shp <- st_read("inputs/boundary.shp", quiet = TRUE)
shp <- st_transform(shp, crs = 5070)

# Crop and mask the BpS_Raster to the AOI
BpS_Raster <- US_200BPS %>%
  crop(shp) %>%
  mask(shp)

plot(BpS_Raster)

# Ensure raster is a factor.
BpS_Raster <- as.factor(BpS_Raster)

# Load the BpS attribute data
bps_conus_att <- fread("inputs/LF20_BPS_220.csv")

# Get frequency as a matrix
bps_freq_matrix <- freq(BpS_Raster)

# Convert to data frame
bps_freq_table <- as.data.frame(bps_freq_matrix)

# Manually assign correct column names
colnames(bps_freq_table) <- c("layer", "VALUE", "Freq")

# Drop the 'layer' column
bps_freq_table <- bps_freq_table[, c("VALUE", "Freq")]

# Convert types
bps_freq_table$VALUE <- as.integer(bps_freq_table$VALUE)
bps_freq_table$Freq <- as.integer(bps_freq_table$Freq)

# Extract the RAT from the raster
bps_rat <- levels(BpS_Raster)[[1]]

# Now rename and convert VALUE
bps_rat <- bps_rat %>%
  rename(VALUE = US_200BPS) %>%
  mutate(VALUE = as.integer(VALUE))

# Join everything and calculate metrics
bps_rat <- bps_rat %>%
  left_join(bps_conus_att, by = "VALUE") %>%
  left_join(bps_freq_table, by ="VALUE") %>%
  filter(!is.na(Freq)) %>%
  rename(BPS_Freq = Freq) %>%
  mutate(BpS_ACRES = round((BPS_Freq * 900 / 4046.86), 0),
         BpS_REL_PCT = round((BPS_Freq / sum(BPS_Freq)), 3) * 100) %>%
  select(VALUE, BPS_NAME, GROUPVEG, FRI_ALLFIR, BPS_Freq, BpS_ACRES, BpS_REL_PCT)

# Update the raster's attribute table with the calculated metrics
levels(BpS_Raster)[[1]] <- bps_rat
activeCat(BpS_Raster) <- "BPS_NAME"

# Save the BpS Raster and its attribute table
writeRaster(BpS_Raster, "rasters/BpS_Raster.tif",
            gdal = c("COMPRESS=NONE", "TFW=YES"),
            datatype = "INT2S",
            overwrite = TRUE,
            filetype = "GTiff")

write.dbf(bps_rat, "rasters/BpS_Raster.tif.vat.dbf", factor2char = TRUE)



# **Step 2: Process and save the EVT Raster**

# Crop and mask the EVT_Raster to the AOI
EVT_Raster <- US_240EVT %>%
  crop(shp) %>%
  mask(shp)

plot(EVT_Raster)

# Ensure raster is a factor.
EVT_Raster <- as.factor(EVT_Raster)

# Load the EVT attribute data
evt_conus_att <- fread("inputs/LF23_EVT_240.csv")

# Get frequency as a matrix
evt_freq_matrix <- freq(EVT_Raster)

# Convert to data frame
evt_freq_table <- as.data.frame(evt_freq_matrix)

# Manually assign correct column names
colnames(evt_freq_table) <- c("layer", "VALUE", "Freq")

# Drop the 'layer' column
evt_freq_table <- evt_freq_table[, c("VALUE", "Freq")]

# Convert types
evt_freq_table$VALUE <- as.integer(evt_freq_table$VALUE)
evt_freq_table$Freq <- as.integer(evt_freq_table$Freq)

# Extract the RAT from the raster
evt_rat <- levels(EVT_Raster)[[1]]

# Now rename and convert VALUE
evt_rat <- evt_rat %>%
  rename(VALUE = US_240EVT) %>%
  mutate(VALUE = as.integer(VALUE))

# Calculate counts and metrics
evt_rat <- evt_rat %>%
  left_join(evt_conus_att, by = "VALUE") %>%
  left_join(evt_freq_table, by ="VALUE") %>%
  filter(!is.na(Freq)) %>%
  rename(EVT_Freq = Freq) %>%
  mutate(EVT_ACRES = round((EVT_Freq * 900 * 0.0002471), 0),
         EVT_REL_PCT = round((EVT_Freq / sum(EVT_Freq)), 3) * 100) %>%
  select(VALUE, EVT_NAME, EVT_LF, EVT_PHYS, EVT_Freq, EVT_ACRES, EVT_REL_PCT)

# Update the raster's attribute table with the calculated metrics
levels(EVT_Raster)[[1]] <- evt_rat
activeCat(EVT_Raster) <- "EVT_NAME"

# Save the EVT Raster and its attribute table
writeRaster(EVT_Raster, "rasters/EVT_Raster.tif",
            gdal = c("COMPRESS=NONE", "TFW=YES"),
            datatype = "INT2S",
            overwrite = TRUE,
            filetype = "GTiff")

write.dbf(evt_rat, "rasters/EVT_Raster.tif.vat.dbf", factor2char = TRUE)

Still have questions? LANDFIRE is here to help.

Ask the LANDFIRE Helpdesk (email link).
Search and subscribe to the LANDFIRE YouTube Channel (see tutorials, Office Hours, quick demonstrations).
Join an Office Hour (monthly meeting with open format Q & A with LANDFIRE experts).
Schedule a meeting (email link) with TNC’s LANDFIRE Team.