Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/akamoske/canopyLazR
R package to estimate leaf area density (LAD) and leaf area index (LAI) from airborne LiDAR point clouds
https://github.com/akamoske/canopyLazR
Last synced: 3 months ago
JSON representation
R package to estimate leaf area density (LAD) and leaf area index (LAI) from airborne LiDAR point clouds
- Host: GitHub
- URL: https://github.com/akamoske/canopyLazR
- Owner: akamoske
- License: gpl-2.0
- Created: 2018-08-09T16:08:02.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-08-25T14:12:06.000Z (about 1 year ago)
- Last Synced: 2024-05-15T11:55:12.997Z (6 months ago)
- Language: R
- Homepage:
- Size: 44.5 MB
- Stars: 32
- Watchers: 7
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- open-sustainable-technology - canopyLazR - An R package that estimates leaf area density and leaf area index from airborne LiDAR point clouds. (Biosphere / Forest Remote Sensing)
README
# 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]
+ [email protected]
### Contributing Authors
Dr. Scott C. Stark
+ [Michigan State University, Department of Forestry](https://www.canr.msu.edu/for/)
+ [Tropical Forestry Ecology Lab](https://sites.google.com/site/scottcstarktropicalforest/)
+ [email protected]
Dr. Shawn P. Serbin
+ [Brookhaven National Laboratory, Environmental and Climate Sciences Department](https://www.bnl.gov/envsci/)
+ [Terrestrial Ecosystem Science and Technology (TEST) group](https://www.bnl.gov/testgroup)
+ [email protected]
Dr. Kyla M. Dahlin
+ [Michigan State University, Department of Geography, Environment, and Spatial Sciences](http://geo.msu.edu/)
+ [Michigan State University, Ecology, Evolutionary Biology, and Behavior Program](https://eebb.msu.edu/)
+ [ERSAM Lab](https://www.ersamlab.com/)
+ [email protected]
## 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](https://www.neonscience.org/) 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](LICENSE.md) file for details