get_real_fbh {LadderFuelsR}

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

depth_metrics

Tree metrics with distances, fuel base heights, and depths (output of [get_depths()] function). An object of the class text.

step

Numeric value for the actual height bin step (in meters).

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)

• 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)
}