LadderFuelsR (version 0.0.6)

get_effective_gap: Effective Distances between fuel layers

Description

This function recalculates the distance between fuel layers after considering distances greater than any number of height bin steps.

Usage

get_effective_gap(effective_depth, number_steps = 1, min_height= 1.5, verbose=TRUE)

Value

A data frame giving the effective distances (> any number of steps) between consecutive fuel layers.

Arguments

effective_depth

Tree metrics with the recalculated depth values after considering distances greater than the actual height bin step (output of [get_real_depths()] function). An object of the class data frame.

number_steps

Numeric value for the number of height bin steps that can be jumped to reshape fuels layers.

min_height

Numeric value for the actual minimum base height (in meters).

verbose

Logical, indicating whether to display informational messages (default is TRUE).

Author

Olga Viedma, Carlos Silva, JM Moreno and A.T. Hudak

Details

List of tree metrics:

• treeID: tree ID with strings and numeric values

• treeID1: tree ID with only numeric values

• dist: Distance between consecutive fuel layers (m)

• dptf: Depth of fuel layers (m) after considering distances greater than the actual height bin step

• effdist: Effective distance between consecutive fuel layers (m) after considering distances greater than any number of steps

• Hcbh: Base height of each fuel separated by a distance greater than the certain number of steps

• Hdist: Height of the distance (> any number of steps) between consecutive fuel layers (m)

• Hdptf: Height of the depth of fuel layers (m) after considering distances greater than the actual step

• max_height: Maximum height of the tree

See Also

get_real_depths

Examples

library(magrittr)
library(stringr)
library(dplyr)

# Before running this example, make sure to run get_real_depths().
if (interactive()) {
effective_depth <- get_real_depths()
LadderFuelsR::effective_depth$treeID <- factor(LadderFuelsR::effective_depth$treeID)

trees_name1 <- as.character(effective_depth$treeID)
trees_name2 <- factor(unique(trees_name1))

corr_distance_metrics_list <- list()

for (i in levels(trees_name2)) {
tree1 <- effective_depth |> dplyr::filter(treeID == i)
corr_distance_metrics <- get_effective_gap(tree1, number_steps = 1, min_height= 1.5, verbose=TRUE)
corr_distance_metrics_list[[i]] <- corr_distance_metrics
}

# Combine the individual data frames
effective_distances <- dplyr::bind_rows(corr_distance_metrics_list)

# Get original column names
original_column_names <- colnames(effective_distances)

# Specify prefixes
desired_order <- c("treeID", "Hcbh", "dptf","effdist","dist", "Hdist", "Hdptf", "max_")

# Identify unique prefixes
prefixes <- unique(sub("^([a-zA-Z]+).*", "\\1", original_column_names))
# Initialize vector to store new order
new_order <- c()

# Loop over desired order of prefixes
for (prefix in desired_order) {
 # Find column names matching the current prefix
matching_columns <- grep(paste0("^", prefix), original_column_names, value = TRUE)
# Append to the new order
new_order <- c(new_order, matching_columns)
}
effective_distances <- effective_distances[, new_order]
}