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)
}
Run the code above in your browser using DataLab