# CBH estimation using the breaking point method and the LAD percentage below and above the CBH 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. ```r get_cum_break(LAD_profiles, cbh_metrics, threshold=75, min_height= 1.5, 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 data frame. - `cbh_metrics`: CBH metrics based on three criteria: maximum LAD percentage, maximum distance and last distance (output of [get_cbh_metrics()] function). An object of the class data frame. - `threshold`: Numeric value of the LAD percentage below or above the breaking point to set the CBH (default 75). - `min_height`: Numeric value for the actual minimum base height (in meters). - `verbose`: Logical, indicating whether to display informational messages (default is TRUE). ## Returns 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. ## 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 ## Examples ```r 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) } ``` ## See Also `get_cbh_metrics` ## Author(s) Olga Viedma, Carlos Silva, JM Moreno and A.T. Hudak

get_cum_break function