Download LANDFIRE data into R

Skills learned

Use this page to:

Download and prepare LANDFIRE data using the rlandfire package (developed by Mark Buckner), including setting up a project environment and managing spatial data files
Define an area of interest, interact with the LANDFIRE API, and organize downloaded data for future analysis

Datasets used

Set up

To run the code below you will need to:

Set up a new R-Studio project, i.e. “r_landfire_demos”
Create two directories, “inputs” and “outputs”
Download “bps_demo.zip” (clicking will initiate the download that will likely land in your “C:” directory) which contains:
- LANDFIRE Map Zone 35 shapefile
- BpS attributes file as a .csv
Extract the “bps_demo.zip” files into the “inputs” sub-directory of your r-studio project.
Create and save a new r-script with a name such as “bps_demo_code”.

Once this set up is complete, you should be able to copy/paste the code below into the r script you created above.

Code to load packages and inputs

# install packages if needed
  # install.packages("foreign")
  # install.packages("rlandfire")
  # install.packages("sf")
  # install.packages("terra")
  # install.packages("tidyverse")

# load packages

library(foreign)
library(rlandfire)
library(sf)
library(terra)
library(tidyverse)


# read in Area of Interest (aoi) shapefile, plot to check

shp <- st_read("inputs/map_zone_35.shp", quiet = TRUE) %>% 
  st_transform(crs = 5070) %>%
  st_union() %>%
  st_sf()




# plot the shape for fun (not much to look at on it's own!)
plot(shp)

Download manage and load data

To obtain and manage LANDFIRE data we need to:

Define the Area of Interest (AOI): The code gets the area of interest from a shapefile.
Set Parameters for the API Call: It specifies the product code, coordinate system (EPSG:4326), resolution (30 meters), and your email address for the API.
Create a Temporary File: A temporary file is created to save the downloaded data.
Call the LANDFIRE API: The API is called with the specified parameters, and the data is saved to the temporary file.
Make sure the downloaded data lands in a place where you can find and use it easily
- Define the Destination Path: The destination path for the downloaded file is set.
- Move and Rename the File: The temporary file is moved to the destination path and renamed.
- Create a Temporary Directory for Unzipping: A temporary directory is created to unzip the downloaded file.
- Unzip the File: The file is unzipped into the temporary directory.
- Get the List of Unzipped Files: A list of unzipped files is obtained.
- Rename Each Unzipped File: Each unzipped file is renamed to “landfire_data” followed by its original extension.
- Clean Up the Temporary Directory: The temporary directory and its contents are deleted.

The code below does that in roughly 2 minutes with an internet download speed of ~450mbs. Your times may vary.

aoi <- getAOI(shp)

products <-  c("200BPS", "240EVT")  # note, you can download multiple datasets at once
projection <- 5070  
resolution <- 30
email <- "your_email@wah.org" # REPLACE WITH YOUR E-MAIL ADDRESS PLEASE! 

# R specific arguments
save_file <- tempfile(fileext = ".zip")

# call API
ncal <- landfireAPIv2(
  products, 
  aoi, 
  projection, 
  resolution, 
  path = save_file,
  email = email)


# define the destination path
dest_file <- file.path("inputs", "landfire_data.zip")

# move and rename the file
file.rename(save_file, dest_file)

# create a temporary directory for unzipping
temp_dir <- tempfile()
dir.create(temp_dir)

# unzip the file into the temporary directory
unzip(dest_file, exdir = temp_dir)

# get the list of unzipped files
unzipped_files <- list.files(temp_dir, full.names = TRUE)

# rename each unzipped file to "landfire_data" with its full original extension
for (file in unzipped_files) {
  file_name <- basename(file)
  file_extension <- sub("^[^.]*", "", file_name)  # Extract the full extension
  new_file_path <- file.path("inputs", paste0("landfire_data", file_extension))
  file.rename(file, new_file_path)
}

# clean up the temporary directory
unlink(temp_dir, recursive = TRUE)

Next steps

You should see a set of files with “landfire” in the name. Next go to the Prepare rasters page to learn how to load them into R, clip/mask and view them.

More resources

Mark Buckner’s rlandfire site also has a useful tutorial.
There is also a Python package for downloading LANDFIRE data, landfire-python. If you try this please let us know!