get_effective_gap: Effective Distances between fuel layers

In LadderFuelsR: Automated Tool for Vertical Fuel Continuity Analysis using Airborne Laser Scanning Data

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]
}

LadderFuelsR documentation built on Nov. 2, 2024, 5:06 p.m.