Create a Canopy Height Model from lidar-derived Rasters in R

A common analysis using lidar data are to derive top of the canopy height values from the lidar data. These values are often used to track changes in forest structure over time, to calculate biomass, and even leaf area index (LAI). Let's dive into the basics of working with raster formatted lidar data in R!

Create a lidar-derived Canopy Height Model (CHM)

The National Ecological Observatory Network (NEON) will provide lidar-derived data products as one of its many free ecological data products. These products will come in the GeoTIFF format, which is a .tif raster format that is spatially located on the earth.

In this tutorial, we create a Canopy Height Model. The Canopy Height Model (CHM), represents the heights of the trees on the ground. We can derive the CHM by subtracting the ground elevation from the elevation of the top of the surface (or the tops of the trees).

We will use the raster R package to work with the the lidar-derived digital surface model (DSM) and the digital terrain model (DTM).

# Load needed packages
library(raster)
library(rgdal)

# set working directory to ensure R can find the file we wish to import and where
# we want to save our files. Be sure to move the download into your working directory!
wd="~/Git/data/" #This will depend on your local environment
setwd(wd)

First, we will import the Digital Surface Model (DSM). The DSM represents the elevation of the top of the objects on the ground (trees, buildings, etc).

# assign raster to object
dsm <- raster(paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/DigitalSurfaceModel/SJER2013_DSM.tif"))

# view info about the raster.
dsm

## class      : RasterLayer 
## dimensions : 5060, 4299, 21752940  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 254570, 258869, 4107302, 4112362  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## source     : /Users/olearyd/Git/data/NEON-DS-Field-Site-Spatial-Data/SJER/DigitalSurfaceModel/SJER2013_DSM.tif 
## names      : SJER2013_DSM

# plot the DSM
plot(dsm, main="Lidar Digital Surface Model \n SJER, California")

Note the resolution, extent, and coordinate reference system (CRS) of the raster. To do later steps, our DTM will need to be the same.

Next, we will import the Digital Terrain Model (DTM) for the same area. The DTM represents the ground (terrain) elevation.

# import the digital terrain model
dtm <- raster(paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/DigitalTerrainModel/SJER2013_DTM.tif"))

plot(dtm, main="Lidar Digital Terrain Model \n SJER, California")

With both of these rasters now loaded, we can create the Canopy Height Model (CHM). The CHM represents the difference between the DSM and the DTM or the height of all objects on the surface of the earth.

To do this we perform some basic raster math to calculate the CHM. You can perform the same raster math in a GIS program like QGIS.

When you do the math, make sure to subtract the DTM from the DSM or you'll get trees with negative heights!

# use raster math to create CHM
chm <- dsm - dtm

# view CHM attributes
chm

## class      : RasterLayer 
## dimensions : 5060, 4299, 21752940  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 254570, 258869, 4107302, 4112362  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## source     : memory
## names      : layer 
## values     : -1.399994, 40.29001  (min, max)

plot(chm, main="Lidar Canopy Height Model \n SJER, California")

We've now created a CHM from our DSM and DTM. What do you notice about the canopy cover at this location in the San Joaquin Experimental Range?

If, in your work you need to create lots of CHMs from different rasters, an efficient way to do this would be to create a function to create your CHMs.

# Create a function that subtracts one raster from another
# 
canopyCalc <- function(DTM, DSM) {
  return(DSM -DTM)
  }

# use the function to create the final CHM
chm2 <- canopyCalc(dsm,dtm)
chm2

## class      : RasterLayer 
## dimensions : 5060, 4299, 21752940  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 254570, 258869, 4107302, 4112362  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## source     : memory
## names      : layer 
## values     : -40.29001, 1.399994  (min, max)

# or use the overlay function
chm3 <- overlay(dsm,dtm,fun = canopyCalc) 
chm3 

## class      : RasterLayer 
## dimensions : 5060, 4299, 21752940  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 254570, 258869, 4107302, 4112362  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## source     : memory
## names      : layer 
## values     : -40.29001, 1.399994  (min, max)

As with any raster, we can write out the CHM as a GeoTiff using the writeRaster() function.

# write out the CHM in tiff format. 
writeRaster(chm,paste0(wd,"chm_SJER.tif"),"GTiff")

We've now successfully created a canopy height model using basic raster math -- in R! We can bring the chm_SJER.tif file into QGIS (or any GIS program) and look at it.

Consider going onto the next tutorial Extract Values from a Raster in R to compare this lidar-derived CHM with ground-based observations!