Andrew Plowright
This short guide will demonstrate how to use the ForestTools package to detect and outline trees from a canopy height model (CHM) derived from a photogrammetric point cloud.
Begin by installing ForestTools.
install.packages("ForestTools")A sample CHM is included in the ForestTools package. It represents a small 1.5 hectare swath of forest in the Kootenay Mountains, British Columbia.
# Attach the 'ForestTools' and 'terra' libraries
library(ForestTools)
library(terra)
library(sf)
# Load sample canopy height model
chm <- terra::rast(kootenayCHM)View the CHM using the plot function. The cell values are equal to the
canopy’s height above ground.
# Remove plot margins (optional)
par(mar = rep(0.5, 4))
# Plot CHM (extra optional arguments remove labels and tick marks from the plot)
plot(chm, xlab = "", ylab = "", xaxt='n', yaxt = 'n')
Dominant treetops can be detected using vwf. This function implements
the variable window filter algorithm developed by Popescu and Wynne
(2004). In short, a moving window scans the CHM and tags a given cell as
treetop if it is found to be the highest within the window. The size of
the window itself changes dynamically depending on the height of the
cell on which it is centered. This is to account for varying crown
sizes, with tall trees assumed to have wide crowns and vice versa.
Therefore, the first step is to define the function that will define
the dynamic window size. Essentially, this function should take a
CHM cell value (i.e.: the height of the canopy above ground at that
location) and return the radius of the search window. Here, we will
define a simple linear equation, but any function with a single input
and output will work. We do not wish for the vwf to tag low-lying
underbrush or other spurious treetops, and so we also set a minimum
height of 2 m using the minHeight argument. Any cell with a lower
value will not be tagged as a treetop.
# Function for defining dynamic window size
lin <- function(x){x * 0.05 + 0.6}
# Detect treetops
ttops <- vwf(chm, winFun = lin, minHeight = 2)We can now plot these treetops on top of the CHM.
# Plot CHM
plot(chm, xlab = "", ylab = "", xaxt='n', yaxt = 'n')
# Add dominant treetops to the plot
plot(ttops$geometry, col = "blue", pch = 20, cex = 0.5, add = TRUE)
The ttops object created by vwf in this example contains the spatial
coordinates of each detected treetop, as well as two default attributes:
height and winRadius. These correspond to the tree’s height above
ground and the radius of the moving window where the tree was located.
Note that winRadius is not necessarily equivalent to the tree’s
crown radius.
# Get the mean treetop height
mean(ttops$height)## [1] 5.404217
Canopy height models often represent continuous, dense forests, where
tree crowns abut against each other. Outlining discrete crown shapes
from this type of forest is often referred to as canopy segmentation,
where each crown outline is represented by a segment. Once a set of
treetops have been detected from a canopy height model, the mcws
function can be used for this purpose.
The mcws function implements the watershed algorithm from the
imager library.
Watershed algorithms are frequently used in topographical analysis to
outline drainage basins. Given the morphological similarity between an
inverted canopy and a terrain model, this same process can be used to
outline tree crowns. However, a potential problem is the issue of
oversegmentation, whereby branches, bumps and other spurious treetops
are given their own segments. This source of error can be mitigated by
using a variant of the algorithm known as marker-controlled watershed
segmentation (Beucher & Meyer, 1993), whereby the watershed algorithm
is constrained by a set of markers – in this case, treetops.
The mcws function also takes a minHeight argument, although this
value should be lower than that which was assigned to vwf. For the
latter, minHeight defines the lowest expected treetop, whereas for the
former it should correspond to the height above ground of the fringes of
the lowest trees.
# Create crown map
crowns_ras <- mcws(treetops = ttops, CHM = chm, minHeight = 1.5)
# Plot crowns
plot(crowns_ras, col = sample(rainbow(50), nrow(unique(chm)), replace = TRUE), legend = FALSE, xlab = "", ylab = "", xaxt='n', yaxt = 'n')
By default, mcws returns a raster, where each crown is given a unique
cell value. Depending on the intended purpose of the crown map, it may
be preferable to store these outlines as polygons. This can be
accomplished by setting the format argument to “polygons”. As an added
benefit, these polygons will inherit the attributes of the treetops from
which they were generated, such as height.
# Create polygon crown map
crowns_poly <- mcws(treetops = ttops, CHM = chm, format = "polygons", minHeight = 1.5)
# Plot CHM
plot(chm, xlab = "", ylab = "", xaxt='n', yaxt = 'n')
# Add crown outlines to the plot
plot(crowns_poly$geometry, border = "blue", lwd = 0.5, add = TRUE)
Assuming that each crown has a roughly circular shape, we can use the crown’s area to compute its average circular diameter.
# Compute area and diameter
crowns_poly[["area"]] <- st_area(crowns_poly)
crowns_poly[["diameter"]] <- sqrt(crowns_poly[["area"]]/ pi) * 2
# Mean crown diameter
mean(crowns_poly$diameter)## 2.882985 [m]
Popescu, S. C., & Wynne, R. H. (2004). Seeing the trees in the forest. Photogrammetric Engineering & Remote Sensing, 70(5), 589-604.
Beucher, S., and Meyer, F. (1993). The morphological approach to segmentation: the watershed transformation. Mathematical morphology in image processing, 433-481.