fireexposuR

The goal of fireexposuR is to provide a standardized and accessible platform for the computation and analysis of wildfire exposure. Wildfire exposure assessments are a decision support tool in wildfire management and can be applied for multiple temporal horizons and spatial extents. This package automates the methods previously documented in a series of scientific publications.

• Beverly et al. 2010
  • Introduces wildfire exposure and wildfire transmission distances for community scale assessments
• Beverly et al. 2021
  • Validation of the wildfire exposure metric at a landscape scale with observed fire history
• Beverly and Forbes 2023
  • Directional vulnerability assessment and methodology

The functions in this package require the pre-preparation of data; an accompanying paper (currently in preparation) details suggestions for data acquisition and preparation in accordance with various budget limitations and user experience levels. The initial release of the package provides a collection of functions that assist users with conducting wildfire exposure and directional vulnerability assessments for values and landscapes, and includes methods for validating the metric for an area of interest. Outputs from the functions include spatial data, tables, graphics, and maps that can be further analyzed or modified directly in R or exported for use in other applications.

Installation

You can install the development version of fireexposuR from GitHub with:

# install.packages("devtools")
devtools::install_github("heyairf/fireexposuR")

Usage example

This is a basic example which shows a workflow to assess wildfire exposure on a landscape and within an area of interest.

Data preparation

First, some example data will be generated:

library(terra)
#> terra 1.7.78
library(fireexposuR)
# read example hazard data ---------------------------------
filepath <- "extdata/hazard.tif"
haz <- rast(system.file(filepath, package = "fireexposuR"))
# read example AOI polygon ---------------------------------
filepath <- "extdata/builtsimpleexamplegeom.csv"
g <- read.csv(system.file(filepath, package = "fireexposuR"))
aoi <- vect(as.matrix(g), "polygons", crs = haz)
# ----------------------------------------------------------

Hazard data

The haz layer is a binary raster that represents wildland fuels that have the potential to generate embers up to a distance of 500 meters with a value of 1.

Area of interest

The aoi layer is a polygon representing a localized area of interest (e.g., the built environment of a community, a sensitive habitat, a campground, etc.) shown in red.

Compute exposure

Now, we will use the hazard data to compute the exposure to long-range ember transmission.

library(fireexposuR)
exp <- fire_exp(haz, tdist = "l")

Visualize exposure

Once we have an exposure raster the rest of the package functions can be used to visualize it in different ways. For a landscape, we can map exposure with a continuous scale with mapexpcont():

fire_exp_map_cont(exp)
#> <SpatRaster> resampled to 501264 cells.

We can also see how that exposure is distributed within a localized area of interest with exposure classifications using mapexpclass(). Now we can see that within our area of interest the northwest corner is a potential entry point for long-range embers from the landscape.

Note: our imaginary area of interest is in the middle of the Pacific Ocean so the base map does not provide further reference.

fire_exp_map_class(exp, classify = "local", aoi)