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Calculate competition metrics for a tree list

Source: R/trees_competition.R
trees_competition.Rd

trees_competition() uses the input tree list (e.g. as exported by raster2trees()) with the columns treeID, tree_x, tree_y, and tree_height_m to calculate competition metrics at the tree level.

Competition metrics returned include:

  • Distance to the nearest neighbor (comp_dist_to_nearest_m)

  • Trees per ha within a 5m radius (comp_trees_per_ha)

  • The relative tree height (comp_relative_tree_height = height_tree / height_max) within a 5m radius where a value of 1 indicates the tallest tree.

Usage

trees_competition(
  tree_list,
  crs = NA,
  competition_buffer_m = 5,
  study_boundary = NA,
  search_dist_max = 10
)

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 and tree_height_m columns.

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".

competition_buffer_m

number. Set buffer around tree (m) to calculate competition metrics

study_boundary

sf. If you want to scale per ha calculations, provide the geography of the study boundary

search_dist_max

number. Maximum search distance (m) to nearest tree. Larger search distances will increase processing time and possibly result in memory issues. If no competition trees are found within this distance, the return column comp_dist_to_nearest_m = search_dist_max parameter.

Value

Returns a spatial data frame of individual trees.

References

https://doi.org/10.3390/f13122077 Tinkham et al. (2022). Modeling the missing DBHs: Influence of model form on UAV DBH characterization. Forests, 13(12), 2077.

Examples

 if (FALSE) { # \dontrun{
 # 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)
     , tree_height_m = exp(rgamma(n = 21, shape = (7/4)^2, rate = (4^2)/7))
   )
 # call the function
 tl_comp <- trees_competition(tree_list = tl, crs = "32613")
 # what?
 tl_comp %>% class()
 tl_comp %>% dplyr::select(tidyselect::starts_with("comp_")) %>% dplyr::glimpse()
 tl_comp %>% ggplot2::ggplot() + ggplot2::geom_sf(ggplot2::aes(color=comp_dist_to_nearest_m))
 } # }