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Estimate tree crown biomass for a tree list using LANDFIRE data

Source: R/trees_biomass_landfire.R
trees_biomass_landfire.Rd

trees_biomass_landfire() uses the input tree list (e.g. as exported by raster2trees()) with the columns treeID, tree_x, tree_y to attach an estimate of tree crown biomass using LANDFIRE's Forest Canopy Bulk Density (CBD) data 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. 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())

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 process for estimating tree crown biomass in kilograms 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 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), get the LANDFIRE estimate of CBD in kilograms per cubic meter (landfire_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 * landfire_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 (landfire_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 landfire_crown_biomass_kg = landfire_tree_kg_per_m3 * crown_volume_m3

Usage

trees_biomass_landfire(
  tree_list,
  crs = NA,
  study_boundary = NA,
  input_landfire_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_landfire_dir

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

See code in examples.

References

Examples

 if (FALSE) { # \dontrun{
library(tidyverse)
library(sf)
my_n <- 122
# 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_landfire <- trees_biomass_landfire(tree_list = tl, crs = "32613")
# what is in it?
tl_landfire %>% names()
# look at the trees
tl_landfire$tree_list %>% dplyr::glimpse()
# look at the stand
tl_landfire$stand_cell_data %>% dplyr::filter(!is.na(trees)) %>% dplyr::glimpse()
# the stand CBD
 tl_landfire$stand_cell_data %>%
   dplyr::filter(trees>0) %>%
   sf::st_drop_geometry() %>%
   dplyr::count(landfire_stand_kg_per_m3)
# get the projection for the stand cell data
epsg_code <- tl_landfire$stand_cell_data$rast_epsg_code[1] %>% as.numeric()
# plot the stand cell data with trees overlaid
tl_landfire$stand_cell_data %>%
  ggplot2::ggplot() +
  ggplot2::geom_tile(ggplot2::aes(x=x,y=y,fill = landfire_stand_kg_per_m3), color = "gray44") +
  ggplot2::geom_text(ggplot2::aes(x=x,y=y,label = trees), color = "white") +
  ggplot2::geom_sf(
    data = tl_landfire$tree_list %>% sf::st_transform(crs = epsg_code)
    , ggplot2::aes(color = landfire_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()
 } # }