rlandfire provides access to a diverse suite of spatial data layers
via the LANDFIRE Product Services
(LFPS)
API. LANDFIRE is a joint program of the USFS,
DOI, and other major partners, which provides data layers for wildfire
management, fuel modeling, ecology, natural resource management,
climate, conservation, etc. The complete list of available layers and
additional resources can be found on the LANDFIRE webpage.
Install rlandfire from CRAN:
install.packages("rlandfire")The development version of rlandfire can be installed from GitHub
with:
# install.packages("devtools")
devtools::install_github("bcknr/rlandfire")Set build_vignettes = TRUE to access this vignette in R:
devtools::install_github("bcknr/rlandfire", build_vignettes = TRUE)This package is still in development, and users may encounter bugs or unexpected behavior. Please report any issues, feature requests, or suggestions in the package’s GitHub repo.
To demonstrate rlandfire, we will explore how ponderosa pine forest
canopy cover changed after the 2020 Calwood fire near Boulder, Colorado.
library(rlandfire)
library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
library(terra)
#> terra 1.7.83
library(foreign)First, we will load the Calwood Fire perimeter data which was downloaded from Boulder County’s geospatial data hub.
boundary_file <- file.path(tempdir(), "Wildfire_History")
utils::unzip(system.file("extdata/Wildfire_History.zip", package = "rlandfire"),
exdir = boundary_file)
boundary <- st_read(file.path(boundary_file, "Wildfire_History.shp")) %>%
sf::st_transform(crs = st_crs(32613))
#> Reading layer `Wildfire_History' from data source
#> `/tmp/RtmpxbYn2L/Wildfire_History/Wildfire_History.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 1 feature and 7 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -105.3901 ymin: 40.12149 xmax: -105.2471 ymax: 40.18701
#> Geodetic CRS: WGS 84
plot(boundary$geometry, main = "Calwood Fire Boundary (2020)",
border = "red", lwd = 1.5)
We can use the function rlandfire::getAOI() to create an area of
interest (AOI) vector with the correct format for landfireAPI().
getAOI() handles several steps for us, it ensures that the AOI is
returned in the correct order (xmin, ymin, xmax, ymax) and
converts the AOI to latitude and longitude coordinates (as required by
the API) if needed.
Using the extend argument, we will increase the AOI by 1 km in all
directions to provide additional context surrounding the burned area.
This argument takes an optional numeric vector of 1, 2, or 4 elements.
aoi <- getAOI(boundary, extend = 1000)
aoi
#> [1] -105.40207 40.11224 -105.23526 40.19613Alternatively, you can supply a LANDFIRE map zone number in place of the
AOI vector. The function getZone() returns the zone number containing
an sf object or which corresponds to the supplied zone name. See
help("getZone") for more information and an example.
For this example, we are interested in canopy cover data for two years,
2019 (200CC_19) and 2022 (220CC_22), and existing vegetation type
(200EVT). All available data products, and their abbreviated names,
can be found in the products
table which can be
opened by calling viewProducts().
products <- c("200CC_19", "220CC_22", "200EVT")We can ask the API to project the data to the same CRS as our fire
perimeter data by providing the WKID for our CRS of interest and a
resolution of our choosing, in meters.
projection <- 32613
resolution <- 90We will use the edit_rule argument to filter out canopy cover data
that does not correspond to Ponderosa Pine Woodland. The edit_rule
statement should tell the API that when existing vegetation cover is
anything other than Ponderosa Pine Woodland (7054), the value of the
canopy cover layers should be set to a specified value.
To do so, we specify that when 220EVT is not equal (ne) to 7054,
the “condition,” the canopy cover layers should be set equal (st) to
1, the “change.” The edit rule syntax is explained in more depth in
the LFPS
guide.
(How the API applies edit rules can be unintuitive. For example, if we
used ‘clear value’ [cv] or set the value outside of 0-100 the edits
we want would not work. To work around this behavior, we set the values
to 1 since it is not found in the original data set.)
edit_rule <- list(c("condition","200EVT","ne",7054),
c("change", "200CC_19", "st", 1),
c("change", "220CC_22", "st", 1))Note: Edits are performed in the order that they are listed and are limited to fuel theme products (i.e., Fire Behavior Fuel Model 13, Fire Behavior Fuel Model 40, Forest Canopy Base Height, Forest Canopy Bulk Density, Forest Canopy Cover, and Forest Canopy Height).
Finally, we will provide a path to a temporary zip file. Setting the
path as a temp file is not strictly necessary because if path is left
blank landfireAPI() will save the data to a temporary folder by
default.
path <- tempfile(fileext = ".zip")Now we are able to submit a request to the LANDFIRE Product Services API
with the landfireAPI() function.
resp <- landfireAPI(products = products,
aoi = aoi,
projection = projection,
resolution = resolution,
edit_rule = edit_rule,
path = path,
verbose = FALSE)landfireAPI() will download your requested data into the folder
provided in the path argument. If you did not provide one, you can find
the path to your data in the $path element of the landfire_api
object.
resp$pathThe files returned by the LFPS API are compressed .zip files. We need
to unzip the directory before reading the .tif file. Note: all
additional metadata is included in this same directory.
lf_dir <- file.path(tempdir(), "lf")
utils::unzip(path, exdir = lf_dir)
lf <- terra::rast(list.files(lf_dir, pattern = ".tif$",
full.names = TRUE,
recursive = TRUE))Now we can reclassify the canopy cover layers to remove any values which are not classified as Ponderosa Pine, calculate the change, and plot our results.
lf$US_200CC_19[lf$US_200CC_19 == 1] <- NA
lf$US_220CC_22[lf$US_220CC_22 == 1] <- NA
change <- lf$US_220CC_22 - lf$US_200CC_19
plot(change, col = rev(terrain.colors(250)),
main = "Canopy Cover Loss - Calwood Fire (2020)",
xlab = "Easting",
ylab = "Northing")
plot(boundary$geometry, add = TRUE, col = NA,
border = "black", lwd = 2)
The LFPS REST API now embeds attributes in the GeoTIFF files for some
variables and returns a database file (.dbf) containing the full
attribute table.
To demonstrate, we will download the Existing Vegetation Cover product
from LF 2.4.0 (240EVC). Unlike in the example above we will submit a
minimal request with the default projection and resolution. We will also
allow rlandfire to save the files to a temporary directory
automatically. As mentioned above, we can find the path to the temporary
directory in the $path element of the landfire_api object returned
by landfireAPI().
resp <- landfireAPI(products = "240EVC",
aoi = aoi,
verbose = FALSE)
#> Warning in landfireAPI(products = "240EVC", aoi = aoi, verbose = FALSE): `path`
#> is missing. Files will be saved in temp directory:
#> /tmp/RtmpxbYn2L/file19829a239b8a25.zipWhen we read in and plot the EVC layer the legend will now list the
classnames for each vegetation type.
lf_cat <- file.path(tempdir(), "lf_cat")
utils::unzip(resp$path, exdir = lf_cat)
evc <- terra::rast(list.files(lf_cat, pattern = ".tif$",
full.names = TRUE,
recursive = TRUE))
plot(evc)
To access the values each classname is assigned to we can uses the
levels() function. This returns a simple two column data frame
containing both the index and active category, in our case the
vegetation cover classes.
head(levels(evc)[[1]])
#> Value CLASSNAMES
#> 1 11 Open Water
#> 2 13 Developed-Upland Deciduous Forest
#> 3 14 Developed-Upland Evergreen Forest
#> 4 15 Developed-Upland Mixed Forest
#> 5 16 Developed-Upland Herbaceous
#> 6 17 Developed-Upland ShrublandAlternatively, we can access the full attribute table using two methods.
We can use the function cats() which works similarly to levels() but
returns the full attribute table as a data frame. Alternatively, we can
read the database file using foreign::read.dbf(). Both methods return
similar results, although in this case, we see that the .dbf file
includes an additional Count column not included in the data frame
returned from cats().
# cats
attr_tbl <- cats(evc)
# Find path to database file
dbf <- list.files(lf_cat, pattern = ".dbf$",
full.names = TRUE,
recursive = TRUE)
# Read file
dbf_tbl <- foreign::read.dbf(dbf)
head(attr_tbl[[1]])
#> Value CLASSNAMES R G B RED GREEN
#> 1 11 Open Water 0 0 255 0.000000 0.000000
#> 2 13 Developed-Upland Deciduous Forest 64 61 168 0.250980 0.239216
#> 3 14 Developed-Upland Evergreen Forest 68 79 137 0.266667 0.309804
#> 4 15 Developed-Upland Mixed Forest 102 119 205 0.400000 0.466667
#> 5 16 Developed-Upland Herbaceous 122 142 245 0.478431 0.556863
#> 6 17 Developed-Upland Shrubland 158 170 215 0.619608 0.666667
#> BLUE
#> 1 1.000000
#> 2 0.658824
#> 3 0.537255
#> 4 0.803922
#> 5 0.960784
#> 6 0.843137
head(dbf_tbl)
#> Value Count CLASSNAMES R G B RED GREEN
#> 1 11 229 Open Water 0 0 255 0.000000 0.000000
#> 2 13 119 Developed-Upland Deciduous Forest 64 61 168 0.250980 0.239216
#> 3 14 337 Developed-Upland Evergreen Forest 68 79 137 0.266667 0.309804
#> 4 15 198 Developed-Upland Mixed Forest 102 119 205 0.400000 0.466667
#> 5 16 365 Developed-Upland Herbaceous 122 142 245 0.478431 0.556863
#> 6 17 181 Developed-Upland Shrubland 158 170 215 0.619608 0.666667
#> BLUE
#> 1 1.000000
#> 2 0.658824
#> 3 0.537255
#> 4 0.803922
#> 5 0.960784
#> 6 0.843137Visit the LANDFIRE webpage
for information on citing LANDFIRE data layers. The package citation
information can be viewed with citation("rlandfire").