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Estimate tree biomass (or crown biomass) for a tree list

Source: R/trees_biomass.R
trees_biomass.Rd

trees_biomass() streamlines the process for estimating individual tree biomass in kilograms, or the component biomass of the tree crown in kilograms. Users can select one or all of the following methods available in the package for estimating biomass:

  • Tree crown biomass in kilograms:

  • Tree total above ground biomass in kilograms:

    • coming soon

If multiple methods are selected (e.g. method = c("cruz","landfire")), then the program will compile the biomass estimates and return one tree list.

Usage

trees_biomass(
  tree_list,
  crs = NA,
  study_boundary = NA,
  input_landfire_dir = NULL,
  input_foresttype_dir = NULL,
  method = NA,
  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.

input_foresttype_dir

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

method

character. one (e.g. "landfire") or multiple (e.g. c("cruz","landfire")) of the following biomass estimation methods:

  • Tree crown biomass in kilograms:

  • Tree total above ground biomass in kilograms:

    • coming soon

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_landfire = data frame of stands/cells in same projection as the LANDFIRE raster data ; stand_cell_data_cruz = data frame of stands/cells in same projection as the FIA forest type group raster data

See code in examples.

References

See references in:

Examples

 if (FALSE) { # \dontrun{
library(tidyverse)
library(sf)
# use the tree list that ships with the package
f <- system.file(package = "cloud2trees", "extdata", "crowns_poly.gpkg")
tl <- sf::st_read(f)
tl %>% dplyr::glimpse()
# call trees_biomass and get multiple biomass estimates
trees_biomass_ans <- trees_biomass(tree_list = tl, method = c("landfire","cruz"))
# what did we get back?
trees_biomass_ans %>% names()
# check out the tree list
trees_biomass_ans$tree_list %>% dplyr::glimpse()
# check out the landfire stand data
trees_biomass_ans$stand_cell_data_landfire %>% dplyr::filter(trees>0) %>% dplyr::glimpse()
# plot tree landfire crown biomass estimate
trees_biomass_ans$tree_list %>%
  ggplot2::ggplot(
    mapping = ggplot2::aes(
      x = tree_height_m
      , y = landfire_crown_biomass_kg
      , color = crown_area_m2
    )
  ) +
  ggplot2::geom_point()
# plot tree cruz crown biomass estimate
trees_biomass_ans$tree_list %>%
  ggplot2::ggplot(
    mapping = ggplot2::aes(
      x = tree_height_m
      , y = cruz_crown_biomass_kg
      , color = crown_area_m2
    )
  ) +
  ggplot2::geom_point()
# plot tree landfire vs. cruz crown biomass estimate
trees_biomass_ans$tree_list %>%
  ggplot2::ggplot(
    mapping = ggplot2::aes(
      x = landfire_crown_biomass_kg, y = cruz_crown_biomass_kg
    )
  ) +
  ggplot2::geom_abline(lwd = 1.5) +
  ggplot2::geom_smooth(method = "lm", se=F, color = "gray", linetype = "dashed") +
  ggplot2::geom_point(ggplot2::aes(color = tree_height_m)) +
  ggplot2::scale_x_continuous(
    limits = c(0
      , max(trees_biomass_ans$tree_list$cruz_crown_biomass_kg)
    )
  ) +
  ggplot2::scale_y_continuous(
    limits = c(0
      , max(trees_biomass_ans$tree_list$cruz_crown_biomass_kg)
    )
  )
# get the projection for the stand cell data
epsg_code <- trees_biomass_ans$stand_cell_data_landfire$rast_epsg_code[1] %>% as.numeric()
# plot the stand cell data with trees overlaid
trees_biomass_ans$stand_cell_data_landfire %>%
  dplyr::filter(trees>0) %>%
  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 = trees_biomass_ans$tree_list %>% sf::st_transform(crs = epsg_code)
    , ggplot2::aes(color = cruz_crown_biomass_kg)
  ) +
  ggplot2::labs(
    fill="stand kg/m3", color = "landfire\ncrown 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()
 } # }