get_layers_lad {LadderFuelsR} | R Documentation |
Leaf Area Density (LAD) percentage comprised in each effective fuel layer
Description
This function calculates the percentage of Leaf Area Density (LAD) within each fuel layer (first output) and removes those fuel layers with LAD percentage less than a specified threshold, recalculating the distances and the depth of the remaining ones (second output).
Usage
get_layers_lad(LAD_profiles, effective_distances, threshold=25, verbose=TRUE)
Arguments
LAD_profiles |
Original tree Leaf Area Density (LAD) profile (output of [lad.profile()] function in the leafR package). An object of the class text. |
effective_distances |
Tree metrics of fuel layers separated by distances greater than 1 m (output of [get_effective_gap()] function). An object of the class text. |
threshold |
Numeric value for the minimum required LAD percentage in a fuel layer. The default threshold is 25. |
verbose |
Logical, indicating whether to display informational messages (default is TRUE). |
Details
treeID: tree ID with strings and numeric values
treeID1: tree ID with only numeric values
Hcbh - Height of the base of each effective fuel layer (m)
Hdist - Height of the distance between consecutive fuel layers (m)
effdist - Distance between consecutive fuel layers (m)
dptf - Depth of the effective fuel layers (m) at distances greater than 1 m
Hdptf - Height of the depth of fuel layers (m) at distances greater than 1 m
Hcbh_Hdptf - Percentage of LAD values comprised in each effective fuel layer
max_height - Maximum height of the tree profile
nlayers - Number of effective fuel layers
Value
A data frame identifying the fuel layers with their corresponding LAD percentage.
Author(s)
Olga Viedma, Carlos Silva and JM Moreno
See Also
Examples
library(magrittr)
library(gdata)
library(dplyr)
library(stringr)
# LAD profiles derived from normalized ALS data after applying [lad.profile()] function
LAD_profiles <- read.table(system.file("extdata", "LAD_profiles.txt", package = "LadderFuelsR"),
header = TRUE)
LAD_profiles$treeID <- factor(LAD_profiles$treeID)
# Before running this example, make sure to run get_effective_gap().
if (interactive()) {
effective_distances <- get_effective_gap()
LadderFuelsR::effective_distances$treeID <- factor(LadderFuelsR::effective_distances$treeID)
trees_name1 <- as.character(effective_distances$treeID)
trees_name2 <- factor(unique(trees_name1))
LAD_metrics1 <- list()
LAD_metrics2 <- list()
for (i in levels(trees_name2)) {
# Filter data for each tree
tree1 <- LAD_profiles |> dplyr::filter(treeID == i)
tree2 <- effective_distances |> dplyr::filter(treeID == i)
# Get LAD metrics for each tree
LAD_metrics <- get_layers_lad(tree1, tree2, threshold=25, verbose=TRUE)
LAD_metrics1[[i]] <- LAD_metrics$df1
LAD_metrics2[[i]] <- LAD_metrics$df2
}
all_LAD <- dplyr::bind_rows(LAD_metrics1)
effective_LAD <- dplyr::bind_rows(LAD_metrics2)
}