TITLE: Estimating grassy volume from terrestrial LiDAR
DATE: 2021-01-15
AUTHOR: John L. Godlee
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As part of my PhD research I have been using terrestrial LiDAR to
understand woodland tree canopy traits in southern African
savannas. One of the measurements I wanted to make was an estimate
of the volume of grassy material below the canopy, so I could make
inferences about how canopy traits affect the probability of fire.
In a previous post I've already described how I processed the raw
point cloud data to produce a .csv with XYZ point coordinates, so
I'll skip straight to how I used R to estimate grassy volume. I
adapted this method from Cooper et al. (2017).
[Cooper et al. (2017)]:
https://doi.org/10.3390/rs9060531
After reading in the file with data.table::fread() the first thing
was to assign each point within a cylinder of grass to 2x2 cm 2D
bins in the XY plane, then I took the mean height of points within
each bin and estimated the volume of the column below that mean
height, assuming that the volume below the mean height was
completely filled by grass material. That's quite a big assumption
to make, but for comparison between samples it seems suitable. I
also have Disc Pasture Meter (DPM) measurements and biomass samples
on a subset of the sample locations to cross-check the estimates
from the terrestrial LiDAR.
# Read file
dat <- fread(x)
# Bin into x,y cells
dat_xy_bin <- dat %>%
mutate(
bin_x = cut(.$X, include.lowest = TRUE, labels = FALSE,
breaks = seq(floor(min(.$X)), ceiling(max(.$X)), by =
voxel_dim)),
bin_y = cut(.$Y, include.lowest = TRUE, labels = FALSE,
breaks = seq(floor(min(.$Y)), ceiling(max(.$Y)), by =
voxel_dim)))
# Take mean height of points within a column, then estimate
volume
summ <- dat_xy_bin %>%
group_by(bin_x, bin_y) %>%
summarise(volume = mean(Z, na.rm = TRUE) * voxel_dim^2)
# Sum of volumes
vol <- sum(summ$volume, na.rm = TRUE)
