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DATE: 2020-12-30
AUTHOR: John L. Godlee
Previously, measuring canopy cover with hemispherical photography only provided a 2D representation of the canopy, but with LiDAR it's possible to measure variation in canopy cover over the height of the canopy to create a canopy height profile. Here I want to describe how I used R to process the XYZ point cloud data to create a canopy height profile. I have already described in a previous post how I voxelise, clean and crop the point cloud, using PDAL[1].
# Packages library(ggplot2) library(dplyr) library(data.table) library(scico) library(zoo)
I used data.table::fread() to read the XYZ point cloud .csv files into R, as they are very large, about 500 MB, and fread() seems to do a better job at reading large files into memory.
For each file, I rounded the elevation (Z) coordinates to the nearest cm, then for each cm height bin I calculated the volume of space occupied by voxels.
I created a height foliage density profile with ggplot().
I calculated the effective number of layers according to Ehbrecht et al. 2016 (Forest Ecology and Management), which basically splits the height profile into 1 m bins and calculates the Shannon diversity index of the foliage volume occupied in each layer. Here is the function for it:
#' Effective number of layers in a point cloud distribution #' #' @param x vector of Z (elevation) coordinates #' @param binwidth width of vertical bins in units of x #' #' @return atomic vector of length one describing the effective number of layers #' in the canopy #' #' @details Uses the Shannon diversity index (Entropy) to estimate the #' "Effective Number of Layers" in the vertical profile of a point cloud #' distribution. #' #' @references #' Martin Ehbrecht, Peter Schall, Julia Juchheim, Christian Ammer, & #' Dominik Seidel (2016). Effective number of layers: A new measure for #' quantifying three-dimensional stand structure based on sampling with #' terrestrial LiDARForest Ecology and Management, 380, 212–223. #' #' @examples #' x <- rnorm(10000) #' enl(x) #' # Calculate effective number of layers in canopy ## Assign to Z slices ## Count number of points within each slice ## Calculate shannon diversity index (entropy) on vertical layer occupancy enl <- function(x, binwidth) { binz <- cut(x, include.lowest = TRUE, labels = FALSE, breaks = seq(floor(min(x)), ceiling(max(x)), by = binwidth)) n <- unlist(lapply(split(x, binz), length)) entropy <- exp(-sum(n / sum(n) * log(n / sum(n)))) return(entropy) }
I calculated the area under the curve of the foliage density profile using density() then zoo::rollmean(), a method I stole of Stack Overflow.
I also calculated the height above the ground of the peak of foliage density.
Here is the script in its entirety:
# Import data file_list <- list.files(path = "../dat/tls/height_profile", pattern = "*.csv", full.names = TRUE) # Check for output directories hist_dir <- "../img/foliage_profile" if (!dir.exists(hist_dir)) { dir.create(hist_dir, recursive = TRUE) } out_dir <- "../dat/subplot_profile" if (!dir.exists(out_dir)) { dir.create(out_dir, recursive = TRUE) } # Define parameters voxel_dim <- 0.01 z_width <- 1 cylinder_radius <- 10 # Calculate maximum 1 voxel layer volume layer_vol <- pi * cylinder_radius^2 * voxel_dim # For each subplot: profile_stat_list <- lapply(file_list, function(x) { # Get names of subplots from filenames subplot_id <- gsub("_.*.csv", "", basename(x)) plot_id <- gsub("(^[A-Z][0-9]+).*", "\\1", subplot_id) subplot <- gsub("^[A-Z][0-9]+(.*)", "\\1", subplot_id) # Read file dat <- fread(x) # Round Z coords to cm dat$z_round <- round(dat$Z, digits = 2) # Calculate volume and gap fraction bin_tally <- dat %>% group_by(z_round) %>% filter(z_round > 0) %>% tally() %>% as.data.frame() %>% mutate(vol = n * voxel_dim, gap_frac = vol / layer_vol) # Plot gap fraction density plot pdf(file = paste0(hist_dir, "/", subplot_id, "_foliage_profile.pdf"), width = 8, height = 6) print( ggplot(bin_tally, aes(x = z_round, y = gap_frac)) + geom_line() + theme_bw() + labs(x = "Elevation (m)", y = "Gap fraction") + coord_flip() ) dev.off() # Calculate effective number of layers layer_div <- enl(dat$Z, z_width) # Calculate area under curve den <- density(dat$z_round) den_df <- data.frame(x = den$x, y = den$y) auc_canopy <- sum(diff(den_df$x) * rollmean(den_df$y, 2)) # Calculate height of max peak dens_peak_height <- den_df[den_df$y == max(den_df$y), "x"] # Create dataframe from stats out <- data.frame(plot_id, subplot, layer_div, auc_canopy, dens_peak_height) # Write to file write.csv(out, file.path(out_dir, paste0(paste(plot_id, subplot, sep = "_"), "_summ.csv")), row.names = FALSE) return(out) })