Compute the percentile value of each height

Description

This function calculates the percentile value of each height

Usage

calculate_gaps_perc (LAD_profiles,min_height=1.5)

Arguments

Value

A data frame giving the percentile value of each height.

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))

percentile_list1<-list()

for (i in levels(trees_name2)) {
tree1 <- LAD_profiles |> dplyr::filter(treeID == i)
percentiles <- calculate_gaps_perc (tree1,min_height=1.5)
percentile_list1[[i]] <- percentiles
}
gaps_perc <- dplyr::bind_rows(percentile_list1)
gaps_perc$treeID <- factor(gaps_perc$treeID)

Methods to estimated the Crown Base Height of a tree: maximum LAD percentage, maximum distance and the last distance

Description

This function determines the CBH of a segmented tree using three criteria: maximum LAD percentage, maximum distance and the last distance.

Usage

get_cbh_metrics(effective_LAD, min_height= 1.5, hdepth1_height = 2.5, verbose=TRUE)

Arguments

Details

List of tree metrics:

• treeID: tree ID with strings and numeric values

• treeID1: tree ID with only numeric values

• 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

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

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

• Hcbh_Hdptf - Percentage of LAD values comprised in each effective fuel layer

• maxlad_Hcbh - Height of the CBH of the segmented tree based on the maximum LAD percentage

• maxlad1_Hcbh - Height of the CBH from the second fuel layer when the maximum LAD occurred in the first fuel layer but its depth <= "hdepth1_height"

• max_Hcbh - Height of the CBH of the segmented tree based on the maximum distance found in its profile

• last_Hcbh - Height of the CBH of the segmented tree based on the last distance found in its profile

• maxlad_ - Values of distance and fuel depth and their corresponding heights at the maximum LAD percentage

• maxlad1_ - Values of distance and fuel depth and their corresponding heights for the second fuel layer when the maximum LAD occurred in the first fuel layer but its depth <= "hdepth1_height"

• max_ - Values of distance and fuel depth and their corresponding heights at the maximum distance

• last_ - Values of distance and fuel depth and their corresponding heights at the last distance

• nlayers - Number of effective fuel layers

• max_height - Maximum height of the tree profile

Value

A data frame giving the Crown Base Height (CBH) of a tree using three criteria: maximum LAD percentage, maximum distance and the last distance. For the case of maximum LAD CBH, the output gives the actual CBH with maximum LAD and also, the CBH from the second fuel layer when the first fuel layer has the maximum LAD but its depth is lesser than the value indicated in the parameter "hdepth1_height".

Author(s)

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

See Also

get_layers_lad

Examples

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

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

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

cbh_dist_list <- list()

for (i in levels(trees_name2)) {
tree1 <- effective_LAD |> dplyr::filter(treeID == i)
cbh_dist_metrics <- get_cbh_metrics(tree1, min_height= 1.5,  hdepth1_height = 2.5, verbose=TRUE)
cbh_dist_list[[i]] <- cbh_dist_metrics
}

# Combine the individual data frames
cbh_metrics <- dplyr::bind_rows(cbh_dist_list)

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

# Specify prefixes
desired_order <- c("treeID", "Hcbh", "dptf","effdist","dist", "Hdist", "Hdptf", "max_","last_",
"maxlad_","maxlad1_", "nlayers")

# 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)
}
# Reorder values
cbh_metrics <- cbh_metrics[, new_order]
}

CBH estimation using the breaking point method and the LAD percentage below and above the CBH

Description

This function calculates the crown base height (CBH) of the vertical tree profile (VTP) using a segmented regression model fitted to the cumulative LAD values as a function of height.The function also calculates the percentage of LAD values below and above the identified CBH or breaking point.

Usage

get_cum_break(LAD_profiles, cbh_metrics, threshold=75, min_height= 1.5, verbose=TRUE)

Arguments

Details

# List of tree metrics:

• treeID: tree ID with strings and numeric values

• treeID1: tree ID with only numeric values

• Hcbh_brpt: Height of the CBH based on the breaking point method (m)

• below_hcbhbp: Percentage of LAD values below the CBH or breaking point

• above_hcbhbp: Percentage of LAD values above the CBH or breaking point

• bp_hcbh: Height of the CBH based on the breaking point method or on the maximum LAD criterium if there is not breaking point (m)

• bp_Hdptf: Height of the canopy layer depth using the breaking point method or the maximum LAD criterium (m)

• bp_dptf: Depth of the CBH using the breaking point method or the maximum LAD criterium (m)

• bp_Hdist: Height of the distance between the CBH and the ground using the breaking point method or the maximum LAD criterium (m)

• bp_effdist: Distance between the CBH and the ground using the breaking point method or the maximum LAD criterium (m)

• bp_lad: Percentage of LAD comprised by the canopy layer

• cumlad: Cumulative LAD values at the CBH or breaking point

• nlayers - Number of effective fuel layers

• max_height - Maximum height of the tree profile

Value

A data frame identifying the CBH of the vertical tree profile (VTP) based on the breaking point method and the percentage of LAD values below and above the identified CBH or breaking point.

Author(s)

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

See Also

get_cbh_metrics

Examples

library(magrittr)
library(segmented)
library(gdata)
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)

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

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

cum_LAD_metrics_list <- list()

for (i in levels(trees_name2)) {
# Filter data for each tree
tree1 <- LAD_profiles |> dplyr::filter(treeID == i)
tree2 <- cbh_metrics |> dplyr::filter(treeID == i)

# Get cumulative LAD metrics for each tree
cum_LAD_metrics <- get_cum_break(tree1, tree2, threshold=75, min_height= 1.5, verbose=TRUE)
cum_LAD_metrics_list[[i]] <- cum_LAD_metrics
}

# Combine the individual data frames
cummulative_LAD <- dplyr::bind_rows(cum_LAD_metrics_list)
}

Fuels depth in meters

Description

This function calculates fuels depth as the difference between gaps interleaved between fuel layers minus one step if the fuel depths are greater than one step.

Usage

get_depths (LAD_profiles, distance_metrics,step= 1,min_height= 1.5, verbose=TRUE)

Arguments

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 layers base height (m)

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

• dist: Distance between consecutive fuel layers (m)

• Hdist - Height of the distance between consecutive fuel layers (m)

• depth - Depth of fuel layers (m)

• Hdepth - Height of the depth of fuel layers (m)

• max_height - Maximum height of the tree profile

Value

A data frame giving fuel layers depth and the height of the depths in meters.

Author(s)

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

See Also

get_distance

Examples

library(magrittr)
library(dplyr)

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

metrics_depth_list <- list()

for (i in levels(LAD_profiles$treeID)){

tree1 <- LAD_profiles |> dplyr::filter(treeID == i)
tree2 <- distance_metrics |> dplyr::filter(treeID == i)

# Get depths for each tree
metrics_depth <- get_depths(tree1, tree2,step= 1,min_height= 1.5, verbose=TRUE)
metrics_depth_list[[i]] <- metrics_depth
}

# Combine the individual data frames
depth_metrics <- dplyr::bind_rows(metrics_depth_list)
}

Distances between fuel layers

Description

This function calculates distances (and their heights) between fuel layers as the difference between consecutive gaps and fuel bases (the gap height always must be lower than the fuel base height).

Usage

get_distance (gap_cbh_metrics,gaps_perc,step=1,min_height=1.5,verbose=TRUE)

Arguments

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 layers base height (m)

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

• dist: Distance between consecutive fuel layers (m)

• Hdist - Height of the distance between consecutive fuel layers (m)

• max_height - Maximum height of the tree profile

Value

A data frame giving distances (and their heights) between fuel layers in meters.

Author(s)

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

See Also

get_gaps_fbhs

calculate_gaps_perc

Examples

library(magrittr)
library(gdata)
library(dplyr)

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

# Before running this example, make sure to run calculate_gaps_perc().
LadderFuelsR::gaps_perc$treeID <- factor(LadderFuelsR::gaps_perc$treeID)

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

metrics_distance_list <- list()

for (i in levels(trees_name2)) {

# Filter data for each tree
tree1 <- gap_cbh_metrics |> dplyr::filter(treeID == i)
tree2 <- gaps_perc |> dplyr::filter(treeID == i)
# Get distance metrics for each tree
metrics_distance <- get_distance(tree1, tree2, step=1, min_height=1.5)
metrics_distance_list[[i]] <- metrics_distance
}
# Combine the individual data frames
distance_metrics <- dplyr::bind_rows(metrics_distance_list)
}

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

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]
}

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

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
}

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 (default 10 the depth of the remaining ones (second output).

Usage

get_layers_lad(LAD_profiles, effective_distances,
threshold=10, step = 1, min_height= 1.5, verbose=TRUE)

Arguments

Details

• treeID: tree ID with strings and numeric values

• treeID1: tree ID with only numeric values

• 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

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

• Hdist: Height of the distance (> any number of steps) between consecutive fuel layers (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, JM Moreno and A.T. Hudak

See Also

get_renamed_df

get_effective_gap

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=10,
step = 1,min_height= 1.5,
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)
}

Plots the Crown Base Height (CBH) based on the maximum LAD percentage criterium

Description

This function plots the CBH of a segmented tree based on the fuel layer with the maximum LAD percentage.

Usage

get_plots_cbh_LAD(LAD_profiles,cbh_metrics,min_height=1.5)

Arguments

Value

A plot drawing the Crown Base Height (CBH) of the fuel layer with the maximum Leaf Area Density (LAD) percentage.

Author(s)

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

See Also

get_layers_lad

Examples

library(ggplot2)
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)

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

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

# Generate plots for fuels LAD metrics
plots_trees_LAD <- get_plots_cbh_LAD(LAD_profiles, cbh_metrics, min_height=1.5)
}

Plot of the Crown Base Height (CBH) based on the breaking point method

Description

This function plots the crown base height (CBH) based on breaking point over the cummulative LAD values and gives the LAD percentage of the canopy layer

Usage

get_plots_cbh_bp(LAD_profiles, cummulative_LAD, min_height = 1.5)

Arguments

Value

A plot of the Crown Base Height (CBH) based on the breaking point method and Leaf Area Density (LAD) percentage of the canopy layer.

Author(s)

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

See Also

get_cum_break

Examples

library(ggplot2)
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)

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

# Generate cumulative LAD plots
plots_cbh_bp <- get_plots_cbh_bp(LAD_profiles, cummulative_LAD,min_height = 1.5)
}

Plots the Crown Base Height (CBH) based on the last distance criterium

Description

This function plots the CBH of a segmented tree based on the fuel layer located at the last distance.

Usage

get_plots_cbh_lastdist(LAD_profiles, cbh_metrics, min_height=1.5)

Arguments

Value

A plot drawing the Crown Base Height (CBH) of the fuel layer located at the last distance.

Author(s)

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

See Also

get_cbh_metrics

Examples

library(ggplot2)
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)

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

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

# Generate plots for CBH based on the fuel layer at the last distance
plots_cbh_lastdist <- get_plots_cbh_lastdist(LAD_profiles, cbh_metrics, min_height=1.5)
}

Plots the Crown Base Height (CBH) based on the maximum distance criterium

Description

This function plots the CBH of a segmented tree based on the fuel layer located at the maximum distance.

Usage

get_plots_cbh_maxdist(LAD_profiles, cbh_metrics, min_height=1.5)

Arguments

Value

A plot drawing the Crown Base Height (CBH) of the fuel layer located at the maximum distance.

Author(s)

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

See Also

get_cbh_metrics

Examples

library(ggplot2)
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)

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

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

# Generate plots for fuels LAD metrics
plots_cbh_maxdist <- get_plots_cbh_maxdist(LAD_profiles, cbh_metrics, min_height=1.5)
}

Plots of fuel layers with LAD percentage > 10

Description

This function plots fuel layers with LAD percentage greater than 10.

Usage

get_plots_effective(LAD_profiles, effective_LAD, min_height = 1.5)

Arguments

Value

A plot drawing fuel layers with LAD percentage greater than 10.

Author(s)

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

See Also

get_layers_lad

Examples

library(ggplot2)
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)

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

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

# Generate plots for fuels LAD metrics
plots_trees_LAD <- get_plots_cbh_LAD(LAD_profiles, effective_LAD, min_height = 1.5)
}

Plots of tree profiles with gaps and fuel layers base height (fbh)

Description

This function plots gaps and fuel layers base height (fbh) in the vertical tree profile (VTP).

Usage

get_plots_gap_fbh (LAD_profiles,gap_cbh_metrics,min_height=1.5)

Arguments

Value

A plot drawing by lines the height of gaps and fuel layers bases in tiff format.

Author(s)

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

See Also

get_gaps_fbhs

Examples

library(ggplot2)
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)

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

# Generate plots for gaps and fbhs
plots_gaps_fbhs <- get_plots_gap_fbh(LAD_profiles, gap_cbh_metrics, min_height=1.5)
}

Effective fuel layers depth

Description

This function recalculates fuel layers depth after considering distances greater than the actual height bin step.

Usage

get_real_depths (effective_fbh, step=1, min_height=1.5, verbose=TRUE)

Arguments

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)

• Hdist - Height of the distance between consecutive fuel layers (m)

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

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

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

• max_height - Maximum height of the tree profile

Value

A data frame giving new fuel layers depth after considering distances greater than the actual height bin step.

Author(s)

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

See Also

get_renamed0_df

get_real_fbh

Examples

library(magrittr)
library(tidyr)
library(dplyr)

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

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

depth_metrics_corr_list <- list()

for (i in levels(trees_name2)){
# Filter data for each tree
tree3 <- effective_fbh |> dplyr::filter(treeID == i)
# Get real depths for each tree
depth_metrics_corr <- get_real_depths(tree3, step=1, min_height=1.5,verbose=TRUE)
depth_metrics_corr_list[[i]] <- depth_metrics_corr
}

# Combine depth values for all trees
effective_depth <- dplyr::bind_rows(depth_metrics_corr_list)

# Reorder columns
original_column_names <- colnames(effective_depth)

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

# 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_depth <- effective_depth[, new_order]
}

Fuels base height recalculation after after considering distances greater than any number of height bin steps

Description

This function reshapes fuel layers after removing distances equal to any number of height bin steps, keeping the first "base height" from those consecutive ones separated by such distance.

Usage

get_real_fbh(depth_metrics, step= 1, number_steps = 1, min_height=1.5, verbose=TRUE)

Arguments

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)

• Hdist - Height of the distance between consecutive fuel layers (m)

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

• depth - Depth of fuel layers (m)

• Hdepth - Height of the depth of fuel layers (m)

• max_height - Maximum height of the tree profile

Value

A data frame giving the first "base height" from those consecutive ones separated by the number of height bin steps indicated in the function. The function returns new fuel layers separated by distances greater than the indicated number of steps.

Author(s)

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

See Also

get_depths

Examples

library(magrittr)
library(dplyr)
#Before running this example, make sure to run get_depths()
if (interactive()) {
depth_metrics <- get_depths()
LadderFuelsR::depth_metrics$treeID <- factor(LadderFuelsR::depth_metrics$treeID)

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

fbh_corr_list <- list()

for (i in levels(trees_name2)){
# Filter data for each tree
tree3 <- depth_metrics |> dplyr::filter(treeID == i)
# Get real fbh for each tree
fbh_corr <- get_real_fbh(tree3, step= 1, number_steps = 1, min_height=1.5, verbose=TRUE)
# Store fbh values in a list
fbh_corr_list[[i]] <- fbh_corr
}

# Combine fbh values for all trees
effective_fbh <- dplyr::bind_rows(fbh_corr_list)
effective_fbh$treeID <- factor(effective_fbh$treeID)
}

Rename and reorder columns (I)

Description

This function reorders columns and appends numeric suffixes. Don´t run it. It is an internal function.

Usage

get_renamed0_df (df)

Arguments

Value

No return value. The function is called for side effects.

Examples

library(dplyr)
# get_renamed0_df function reorders columns and appends numeric suffixes

Rename and reorder columns (II)

Description

This function deals with concatenated column names, reorders columns and appends numeric suffixes. Don´t run it. It is an internal function.

Usage

get_renamed_df (df)

Arguments

Value

No return value. The function is called for side effects.

Examples

library(dplyr)
# get_renamed_df concatenates column names, reorders columns and appends numeric suffixes