Awesome
canopyLazR
R package to estimate leaf area density (LAD), leaf area index (LAI), and forest structural attributes from airborne LiDAR point clouds.
Information
For theory behind the package please see the citation below. Please cite with use.
Kamoske A.G., Dahlin K.M., Stark S.C., and Serbin S.P. 2019. Leaf area density from airborne LiDAR: Comparing sensors and resolutions in a forest ecosystem. Forest Ecology and Management 433, 364-375.
Corresponding Author
Dr. Aaron G. Kamoske
- [USDA Forest Service, National Adaptive Management Analyst]
- aaron.kamoske@usda.gov
Contributing Authors
Dr. Scott C. Stark
- Michigan State University, Department of Forestry
- Tropical Forestry Ecology Lab
- scott.c.stark@gmail.com
Dr. Shawn P. Serbin
- Brookhaven National Laboratory, Environmental and Climate Sciences Department
- Terrestrial Ecosystem Science and Technology (TEST) group
- sserbin@bnl.gov
Dr. Kyla M. Dahlin
- Michigan State University, Department of Geography, Environment, and Spatial Sciences
- Michigan State University, Ecology, Evolutionary Biology, and Behavior Program
- ERSAM Lab
- kdahlin@msu.edu
Installation
The easiest way to install canopyLazR is via install_github from the devtools package:
# If you haven't already installed this package and its dependencies
install.packages("devtools")
# If you alread have devtools installed or just installed it
library(devtools)
# Install canopyLazR from GitHub
install_github("akamoske/canopyLazR")
# Load the library
library(canopyLazR)
Now all functions should be available.
Downloading example data
NEON provides a teaching LiDAR dataset that is easy to download via R. We can use this file as a test dataset here. Code to download this .las file follows:
# Install missing R package if needed
list.of.packages <- c("uuid","rlas","devtools")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[, "Package"])]
if (length(new.packages)) {
print("installing : ")
print(new.packages)
install.packages(new.packages, repos = "http://cran.rstudio.com/", dependencies = TRUE)
}
# Create a scratch folder to contain example LiDAR dataset
scratch_folder <- file.path("~/scratch/neon_data/")
if (! file.exists(scratch_folder)) dir.create(scratch_folder,recursive=TRUE)
setwd(file.path(scratch_folder))
getwd()
# Download NEON example .las file
download.file(url = "https://ndownloader.figshare.com/files/7024955",
destfile = file.path(scratch_folder,"neon_lidar_example.las"),
method = "auto",
mode = "wb")
Example of usage (after installation)
Once the package is loaded into your R session, this is the an example of how to use the functions in this package to estimate LAD and LAI:
# Convert .laz or .las file into a voxelized lidar array
laz.data <- laz.to.array(laz.file.path = file.path(scratch_folder,"neon_lidar_example.las"),
voxel.resolution = 10,
z.resolution = 1,
use.classified.returns = TRUE)
# Level the voxelized array to mimic a canopy height model
level.canopy <- canopy.height.levelr(lidar.array = laz.data)
# Estimate LAD for each voxel in leveled array
lad.estimates <- machorn.lad(leveld.lidar.array = level.canopy,
voxel.height = 1,
beer.lambert.constant = NULL)
# Convert the LAD array into a single raster stack
lad.raster <- lad.array.to.raster.stack(lad.array = lad.estimates,
laz.array = laz.data,
epsg.code = 32611)
# Create a single LAI raster from the LAD raster stack
lai.raster <- raster::calc(lad.raster, fun = sum, na.rm = TRUE)
# Convert the list of LAZ arrays into a ground and canopy height raster
grd.can.rasters <- array.to.ground.and.canopy.rasters(laz.data, 32611)
# Calculate max LAD and height of max LAD
max.lad <- lad.ht.max(lad.array = lad.estimates,
laz.array = laz.data,
ht.cut = 5,
epsg.code = 32618)
# Calculate the ratio of filled and empty voxels in a given column of the canopy
empty.filled.ratio <- canopy.porosity.filled.ratio(lad.array = lad.estimates,
laz.array = laz.data,
ht.cut = 5,
epsg.code = 32618)
# Calculate the volume of filled and empty voxles in a given column of the canopy
empty.filled.volume <- canopy.porosity.filled.volume(lad.array = lad.estimates,
laz.array = laz.data,
ht.cut = 5,
xy.res = 10,
z.res = 1,
epsg.code = 32618)
# Calculate the within canopy rugosity
within.can.rugosity <- rugosity.within.canopy(lad.array = lad.estimates,
laz.array = laz.data,
ht.cut = 5,
epsg.code = 32618)
# Calculate the heights of various LAD quantiles
ht.quantiles <- lad.quantiles(lad.array = lad.estimates,
laz.array = laz.data,
ht.cut = 5,
epsg.code = 32618)
# Calculate various canopy volume metrics from Lefsky
can.volume <- canopy.volume(lad.array = lad.estimates,
laz.array = laz.data,
ht.cut = 5,
xy.res = 10,
z.res = 1,
epsg.code = 32618)
# We can calculate the depth of the euphotic zone by dividing by the volume of the voxel
euphotic.depth <- can.volume$euphotic.volume.column.raster / ( 10 * 10 * 1)
# Calculate the top of canopy rugosity volume
toc.rugos <- toc.rugosity(chm.raster = grd.can.rasters$chm.raster,
xy.res = 10,
z.res = 1)
# Plot the lai raster
plot(lai.raster)
# Plot the ground raster
plot(grd.can.rasters$ground.raster)
# Plot the canopy height raster
plot(grd.can.rasters$canopy.raster)
# Plot the canopy height model raster
plot(grd.can.rasters$chm.raster)
# Plot the max LAD raster
plot(max.lad$max.lad.raster)
# Plot the height of max LAD raster
plot(max.lad$max.lad.ht.raster)
# Plot filled voxel ratio raster
plot(empty.filled.ratio$filled.raster)
# Plot porosity voxel ratio raster
plot(empty.filled.ratio$porosity.raster)
# Plot filled voxel volume raster
plot(empty.filled.volume$filled.raster)
# Plot porosity voxel volume raster
plot(empty.filled.volume$porosity.raster)
# Plot the standard deviation of LAD within a vertical column raster
plot(within.can.rugosity$vertical.sd.lad.raster)
# Plot within canopy rugosity
plot(within.can.rugosity$rugosity.raster)
# Plot the height of the 10th quantile
plot(ht.quantiles$quantile.10.raster)
# Plot the height of the 25th quantile
plot(ht.quantiles$quantile.25.raster)
# Plot the height of the 50th quantile
plot(ht.quantiles$quantile.50.raster)
# Plot the height of the 75th quantile
plot(ht.quantiles$quantile.75.raster)
# Plot the height of the 90th quantile
plot(ht.quantiles$quantile.90.raster)
# Plot the height of the mean LAD
plot(ht.quantiles$mean.raster)
# Plot the volume of the euphotic zone for each column
plot(can.volume$euphotic.volume.column.raster)
# Plot the total leaf area in the euphotic zone for each column
plot(can.volume$euphotic.tla.column.raster)
# Plot the depth of the euphotic zone
plot(euphotic.depth)
# Plot the volume of the oligophotic zone for each column
plot(can.volume$oligophotic.volume.column.raster)
# Plot the total leaf area in the oligophotic zone for each column
plot(can.volume$oligophotic.tla.column.raster)
# Plot the volume of the empty space within a given colume
plot(can.volume$empty.volume.column.raster)
# Plot the volume of the empty space within a 3x3 moving window
plot(can.volume$empty.canopy.volume.raster)
# Plot the volume of the euphotic zone within a 3x3 moving window
plot(can.volume$filled.canopy.euphotic.raster)
# Plot the volume of the oligophotic zone within a 3x3 moving window
plot(can.volume$filled.canopy.oligophotic.raster)
# Plot the total leaf area of the euphotic zone within a 3x3 moving window
plot(can.volume$filled.canopy.euphotic.tla.raster)
# Plot the total leaf area of the oligophotic zone within a 3x3 moving window
plot(can.volume$filled.canopy.oligophotic.tla.raster)
# Plot the top of canopy rugosity volume
plot(toc.rugos)
License
This project is licensed under the GNU GPUv2 License - see the LICENSE.md file for details