keltoskytoi / canopyLazR

R package to estimate leaf area density (LAD) and leaf area index (LAI) from airborne LiDAR point clouds

canopyLazR

R package to estimate leaf area density (LAD) and leaf area index (LAI) 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

Aaron G. Kamoske, PhD Candidate

• Michigan State University, Department of Geography, Environment, and Spatial Sciences
• ERSAM Lab
• [e-mail]

Contributing Authors

Dr. Scott C. Stark

• Michigan State University, Department of Forestry
• Tropical Forestry Ecology Lab
• [e-mail]

Dr. Shawn P. Serbin

• Brookhaven National Laboratory, Environmental and Climate Sciences Department
• Terrestrial Ecosystem Science and Technology (TEST) group
• [e-mail]

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
• [e-mail]

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:

# List all the files in the path
laz.files.list <- list.files("./", pattern=c("\\.laz$|.las$"), full.names = TRUE)

# Remove any files that are less than 500kb - because these do not contain data
laz.files.list <- laz.files.list[sapply(laz.files.list, file.size) > 5000]

#-----------------------------------------------------------------------------------
# For memory storage these is the best way to run the package
#-----------------------------------------------------------------------------------

For (i in 1:length(laz.files.list)) {
  # Convert .laz or .las file into a voxelized lidar array
  laz.data <- laz.to.array(laz.file.path = laz.files.list[i], 
                           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)
  
  # Generate a quick raster plot of the resulting total canopy LAI values for each pixel
  plot(lai.raster)
  
  # 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)
  
  # 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)
  
  # All files will need to be saved before starting the next loop, write those to disc
}

License

This project is licensed under the GNU GPUv2 License - see the LICENSE.md file for details