Estimate tree crown biomass for a tree list based on Cruz et al. (2003)

trees_biomass_cruz() uses the input tree list (e.g. as exported by raster2trees()) with the columns treeID, tree_x, tree_y to attempt to attach tree crown biomass in kilogram estimates based on the Cruz et al. (2003) equations (see references) and the FIA forest type group.

If a spatial data frame of points is the input tree list, then the columns tree_x, tree_y are not required. If the FIA forest type group data named forest_type_group_codeis not in the input tree list, then the function calls trees_type() to attempt to attach the FIA forest type group. Other required columns include:

crown_area_m2, tree_height_m (e.g. as exported by raster2trees())
tree_cbh_m (e.g. as exported by trees_cbh())
and one of dbh_cm, dbh_m, or basal_area_m2 (e.g. as exported by trees_dbh())

The Cruz et al. (2003) study developed models to predict canopy fuel stratum at the stand level for four coniferous forest types common in the western US: Douglas-fir, ponderosa pine, lodgepole pine, and mixed conifer. Models for other forests types are currently lacking which limits the scope of this methodology. If the tree list has trees that are in a FIA forest type group not represented in the list above, then the return data will be blank

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 process for estimating tree crown biomass in kilograms is:

Nearest neighbor imputation is used to fill the FIA forest type data if a tree falls inside a non-forest cell in the original data
The LANDFIRE estimate of CBD is distributed across the individual trees that fall in a raster cell by:
1. At the stand level (i.e. raster cell), aggregate the tree level data within the stand to obtain:
- mean_crown_length_m = mean(crown_length_m), where tree crown_length_m = tree_height_m - tree_cbh_m
- sum_crown_volume_m3 = sum(crown_volume_m3), where tree crown_volume_m3 = (4/3) * pi * ((crown_length_m/2)) * ((crown_dia_m/2)^2)
1. At the stand level (i.e. raster cell), determine the area of the stand that overlaps (overlap_area_m2) with the AOI defined as the study_boundary parameter (see below) or the bounding box of all the trees
2. At the stand level (i.e. raster cell), use the Cruz equations (Table 4; see reference) to estimate of CBD in kilograms per cubic meter (cruz_stand_kg_per_m3)
3. At the stand level (i.e. raster cell), get canopy fuel loading (CFL) in kilograms per square meter (kg_per_m2 = mean_crown_length_m * cruz_stand_kg_per_m3)
4. At the stand level (i.e. raster cell), get the stand biomass in kilograms (biomass_kg = kg_per_m2 * overlap_area_m2)
5. At the stand level (i.e. raster cell), the single tree CBD in kilograms per cubic meter will be a constant (cruz_tree_kg_per_m3 = biomass_kg / sum_crown_volume_m3)
6. Attach the the single tree CBD in kilograms per cubic meter to the tree level based on raster cell spatial overlap
7. Calculate individual tree crown mass in kilograms as cruz_crown_biomass_kg = cruz_tree_kg_per_m3 * crown_volume_m3

Usage

trees_biomass_cruz(
  tree_list,
  crs = NA,
  study_boundary = NA,
  input_foresttype_dir = NULL,
  max_crown_kg_per_m3 = 2
)

Arguments

tree_list

data.frame. A data frame with the columns treeID, tree_x, and tree_y. 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. Other required columns include:

crown_area_m2, tree_height_m (e.g. as exported by raster2trees())
tree_cbh_m (e.g. as exported by trees_cbh())
and one of dbh_cm, dbh_m, or basal_area_m2 (e.g. as exported by trees_dbh())

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 area to define the area of interest which may extend beyond the space with trees. If no boundary given, the AOI will be built from location of trees in the tree list.

input_foresttype_dir

directory where Forest Type Groups data exists. Use get_foresttype() first.

max_crown_kg_per_m3

numeric. the maximum CBD of the tree crown in kilograms per cubic meter. Values above this limit will be set at the median value for the area using only stands that have CBD values lower than this limit. The default value of 2 kilograms per cubic meter was based on Mell et al. (2009) who found the dry bulk density of the tree crown was 2.6 kilograms per cubed meter using Douglas-fir trees grown on Christmas tree farms. Set this parameter to a large value (e.g. 1e10) or NULL to avoid limiting tree crown CBD.

Value

Returns a list of objects: tree_list = spatial data frame of individual trees ; stand_cell_data = data frame of stands/cells in same projection as the FIA forest type group raster data

See code in examples.

References

Forest Type Groups of the Continental United States Wilson, B.T. (2023). Forest Type Groups of the Continental United States.
doi:10.1071/WF02024 Cruz, M.G, M.E. Alexander, and R.H. Wakimoto. 2003. Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. Int. J. Wildland Fire. 12(1):39-50.
https://doi.org/10.1016/j.combustflame.2009.06.015 Mell, W., Maranghides, A., McDermott, R., & Manzello, S. L. (2009). Numerical simulation and experiments of burning douglas fir trees. Combustion and Flame, 156(10), 2023-2041.

Examples

 if (FALSE) { # \dontrun{
library(tidyverse)
library(sf)
my_n <- 111
# fake tree list
tl <- dplyr::tibble(
    treeID = c(1:my_n)
    , tree_x = rnorm(n=my_n, mean = 458064, sd = 33)
    , tree_y = rnorm(n=my_n, mean = 4450074, sd = 33)
    , tree_height_m = rnorm(n=my_n, mean = 10, sd = 7)
  ) %>%
  dplyr::mutate(
    tree_height_m = ifelse(tree_height_m<1.37, 1.37, tree_height_m) # above DBH
    , crown_area_m2 = 0.47+(0.49*tree_height_m)
    , tree_cbh_m = 0.72+(0.53*tree_height_m)
    , dbh_cm = -2.3+(2.14*tree_height_m)
  )
# how does our fake tree list look?
tl %>% dplyr::glimpse()
tl %>% ggplot2::ggplot() + ggplot2::geom_point(ggplot2::aes(x = dbh_cm, y = tree_height_m))
# call the function
tl_cruz <- trees_biomass_cruz(tree_list = tl, crs = "32613")
# what is in it?
tl_cruz %>% names()
# look at the trees
tl_cruz$tree_list %>% dplyr::glimpse()
# tree FIA forest type groups
tl_cruz$tree_list %>%
  sf::st_drop_geometry() %>%
  dplyr::count(forest_type_group_code, forest_type_group)
# look at the stand
tl_cruz$stand_cell_data %>% dplyr::filter(!is.na(trees)) %>% dplyr::glimpse()
# get the projection for the stand cell data
epsg_code <- tl_cruz$stand_cell_data$rast_epsg_code[1] %>% as.numeric()
 # plot the stand cell data with trees overlaid
 tl_cruz$stand_cell_data %>%
   ggplot2::ggplot() +
   ggplot2::geom_tile(ggplot2::aes(x=x,y=y,fill = cruz_stand_kg_per_m3), color = "gray44") +
   ggplot2::geom_text(ggplot2::aes(x=x,y=y,label = trees), color = "white") +
   ggplot2::geom_sf(
     data = tl_cruz$tree_list %>% sf::st_transform(crs = epsg_code)
     , ggplot2::aes(color = cruz_crown_biomass_kg)
   ) +
   ggplot2::labs(fill="stand kg/m3", color = "tree crown kg", caption = "# trees shown in cell") +
   ggplot2::scale_fill_viridis_c(option = "rocket", na.value = "gray", direction = -1) +
   ggplot2::scale_color_viridis_c(option = "viridis", na.value = "gray22", begin = 0.6) +
   ggplot2::theme_void()
 } # }