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Extract LANDFIRE CBD raster cell value for a tree list based on location

Source: R/trees_landfire_cbd.R
trees_landfire_cbd.Rd

trees_landfire_cbd() uses the input tree list (e.g. as exported by raster2trees()) with the columns treeID, tree_x, tree_y to attach LANDFIRE's Forest Canopy Bulk Density (CBD) data estimate in kilograms per cubic meter produced jointly by the U.S. Department of Agriculture and U.S. Department of the Interior. If a spatial data frame of points is the input tree list, then the columns tree_x, tree_y are not required.

LANDFIRE's Forest Canopy Bulk Density (CBD) data is attached to each tree in the tree list based on the spatial overlap with the raster data set (see references). Canopy Bulk Density is mass of flammable material per unit volume of the tree crown typically expressed in units of mass per unit volume (e.g., kilograms per cubic meter).

The simplified process for attaching the LANDFIRE CBD raster cell value to a tree is:

  • Nearest neighbor imputation is used to fill LANDFIRE data if a tree falls inside a non-forest cell in the original data

  • The LANDFIRE raster cell value in kilograms per cubic meter is applied to a tree based on spatial overlap

Usage

trees_landfire_cbd(
  tree_list,
  crs = NA,
  study_boundary = NA,
  input_landfire_dir = NULL,
  max_search_dist_m = 1000
)

Arguments

tree_list

data.frame. A data frame with the columns treeID, tree_x, tree_y, and tree_height_m. If an sf class object with POINT geometry (see sf::st_geometry_type()), the program will use the data "as-is" and only require the treeID column.

crs

string. A crs string as returned from sf::st_crs() or the EPSG code of the x,y coordinates. Defaults to the crs of the tree_list data if of class "sf".

study_boundary

sf. The boundary of the study are to define the area of the regional model. If no boundary given, regional model will be built from location of trees in the tree list.

input_landfire_dir

directory where LANDFIRE CBD data exists. Use get_landfire() first.

max_search_dist_m

number. Maximum search distance (m) to obtain forest type group data for trees in tree_list that overlap with non-forest data in the original Wilson (2023) data. Larger search distances will increase processing time and possibly result in memory issues.

Value

Returns a list of objects: tree_list = spatial data frame of individual trees with the column landfire_cell_kg_per_m3 added ; landfire_rast = raster of kilograms per cubic meter in the area.

References

Examples

 if (FALSE) { # \dontrun{
 library(tidyverse)
 # example tree list
 tl <- dplyr::tibble(
     treeID = c(1:21)
     , tree_x = rnorm(n=21, mean = 458064, sd = 11)
     , tree_y = rnorm(n=21, mean = 4450074, sd = 11)
   )
 # call the function
 tl_lf <- trees_landfire_cbd(tree_list = tl, crs = "32613")
 # what?
 tl_lf %>% class()
 # a list, but what is in it?
 tl_lf %>% names()
 # what's in the trees data?
 tl_lf$tree_list %>% dplyr::glimpse()
 # plot the tree_list spatial points
 tl_lf$tree_list %>% ggplot2::ggplot() +
  ggplot2::geom_sf(ggplot2::aes(color=landfire_cell_kg_per_m3))
 # plot the landfire cbd raster
 tl_lf$landfire_rast %>% terra::plot()
 # we can overlay these
 tl_lf$landfire_rast %>%
   terra::as.data.frame(xy = T) %>%
   ggplot2::ggplot() +
   ggplot2::geom_tile(ggplot2::aes(x=x,y=y,fill=kg_per_m3), color = "gray") +
   ggplot2::geom_sf(
     data = tl_lf$tree_list %>% sf::st_transform(terra::crs(tl_lf$landfire_rast))
     , mapping = ggplot2::aes(color=landfire_cell_kg_per_m3)
   ) +
   ggplot2::scale_color_distiller(palette = "Oranges")
 } # }