get_effective_gap {LadderFuelsR}

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)

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).

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

Value

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

Author(s)

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

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]
}