get_gaps_fbhs: Gaps and Fuel layers Base Height (FBH)

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

Gaps and Fuel layers Base Height (FBH)

Description

This function calculates gaps and fuel layers base height (FBH) as the difference in percentiles between consecutive LAD values along the vertical tree profile (VTP). Negative differences are linked to gaps and positive differences to fuel base height.It has been updated for reordering input columns.

Usage

get_gaps_fbhs (LAD_profiles, step=1, min_height=1.5,
perc_gap= 25, perc_base= 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.
step	Numeric value for the actual height bin step (in meters).
min_height	Numeric value for the actual minimum base height (in meters).
perc_gap	Numeric value of the percentile threshold used to identify gaps (default percentile 25th).
perc_base	Numeric value of the percentile threshold used to identify fuels layers base height (default percentile 25th).
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

• cbh - Height of the fuel layer base height (m)

• gap - Height of gap between fuel layers (m)

• gap_lad: LAD value in the gap height

• gap_perc - Percentage of LAD in the gap height

• cbh_lad - LAD value in the fuel base height

• cbh_perc - Percentage of LAD in the fuel base height

• max_height - Maximum height of the tree profile

Value

A data frame giving the height of gaps and fuel layers bases in meters.

Author(s)

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

Examples

library(magrittr)
library(dplyr)

# 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)

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

metrics_precentile_list1<-list()

for (i in levels(trees_name2)) {
tree1 <- LAD_profiles |> dplyr::filter(treeID == i)
metrics_precentil <- get_gaps_fbhs(tree1, step=1,
min_height=1.5,
perc_gap= 25,perc_base= 25,
verbose=TRUE)
metrics_precentile_list1[[i]] <- metrics_precentil
}

metrics_all_percentil <- dplyr::bind_rows(metrics_precentile_list1)
metrics_all_percentil$treeID <- factor(metrics_all_percentil$treeID)

# Remove the row with all NA values from the original data frame
# First remove "treeID" and "treeID1" columns
no_treeID <- metrics_all_percentil[, -which(names(metrics_all_percentil) == c("treeID","treeID1"))]

# Check if any row has all NA values
NA_or_zero <- apply(no_treeID, 1, function(row) all(is.na(row) | row == 0))

# Get the row index with all NA values
row_index <- which(NA_or_zero)

# Remove the row with all NA values from the original data frame
if (length(row_index) > 0) {
gap_cbh_metrics <- metrics_all_percentil[-row_index, ]
} else {
gap_cbh_metrics <- metrics_all_percentil
}

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