# NOT RUN {
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
las = readLAS(LASfile, select = "*")
# All the predefined metrics
m1 = grid_metrics(las, ~stdmetrics(X,Y,Z,Intensity,ReturnNumber,Classification,dz=1))
# Convenient shortcut
m2 = grid_metrics(las, .stdmetrics)
# Basic metrics from intensities
m3 = grid_metrics(las, ~stdmetrics_i(Intensity))
# All the metrics from intensities
m4 = grid_metrics(las, ~stdmetrics_i(Intensity, Z, Classification, ReturnNumber))
# Convenient shortcut for the previous example
m5 = grid_metrics(las, .stdmetrics_i)
# Compute the metrics only on first return
first = lasfilterfirst(las)
m6 = grid_metrics(first, .stdmetrics_z)
# Compute the metrics with a threshold at 2 meters
over2 = lasfilter(las, Z > 2)
m7 = grid_metrics(over2, .stdmetrics_z)
# Works also with cloud_metrics and hexbin_metrics
m8 = cloud_metrics(las, .stdmetrics)
m9 = hexbin_metrics(las, .stdmetrics)
# Combine some predefined function with your own new metrics
# Here convenient shortcuts are no longer usable.
myMetrics = function(z, i, rn)
{
first = rn == 1L
zfirst = z[first]
nfirst = length(zfirst)
above2 = sum(z > 2)
x = above2/nfirst*100
# User's metrics
metrics = list(
above2aboven1st = x, # Num of returns above 2 divided by num of 1st returns
zimean = mean(z*i), # Mean products of z by intensity
zsqmean = sqrt(mean(z^2)) # Quadratic mean of z
)
# Combined with standard metrics
return( c(metrics, stdmetrics_z(z)) )
}
m10 = grid_metrics(las, ~myMetrics(Z, Intensity, ReturnNumber))
# Users can write their own convenient shorcuts like this:
.myMetrics = ~myMetrics(Z, Intensity, ReturnNumber)
m11 = grid_metrics(las, .myMetrics)
# }
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