lidR (version 1.1.0)

stdmetrics: Predefined standard metrics function

Description

Predefined functions usable in grid_metrics and lasmetrics and their convenient shortcuts. The philosophy of the lidR package is to provide an easy way to compute user-defined metrics rather than to provide them. However, for efficiency and to save time, a set of standard metrics has been predefined. To use these functions please read the Details and Examples sections.

Usage

stdmetrics(x, y, z, i, a, rn, class, pulseID, dz = 1)
.stdmetrics
stdmetrics_z(z, dz = 1)
.stdmetrics_z
stdmetrics_i(i, z = NULL, class = NULL, rn = NULL)
.stdmetrics_i
stdmetrics_rn(rn, class = NULL)
.stdmetrics_rn
stdmetrics_pulse(pulseID, rn)
.stdmetrics_pulse
stdmetrics_ctrl(x, y, z, a)
.stdmetrics_ctrl

Arguments

x, y, z, i, a

Coordinates of the points, Intensity and ScanAngle

rn, class

ReturnNumber, Classification

pulseID

The number referencing each pulse

dz

Layer thickness for metrics requiring this data, such as entropy

Format

An object of class expression of length 1.

Details

The function names, their parameters and the output names of the metrics rely on a nomenclature chosen for brevity:

• z: refers to the elevation

• i: refers to the intensity

• rn: refers to the return number

• q: refers to quantile

• a: refers to the ScanAngle

• n: refers to a number (a count)

• p: refers to a percentage

For example the metric named zq60 refers to the elevation, quantile, 60 i.e. the 60th percentile of elevations. The metric pground refers to a percentage. It is the percentage of points classified as ground. The function stdmetric_i refers to metrics of intensity. A description of each existing metric can be found on the lidR wiki page. Some functions have optional parameters. If these parameters are not provided the function computes only a subset of existing metrics. For example stdmetrics_i requires the intensity values, but if the elevation values are provided it can compute additional metrics such as cumulative intensity at a given percentile of height. Each function has a convenient associated variable. It is the name of the function, with a dot before the name. This enables the function to be used without writing parameters. The cost of such a feature is inflexibility. It corresponds to a predefined behaviour (see examples)

See Also

grid_metrics lasmetrics

Examples

# NOT RUN {
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
lidar = readLAS(LASfile, all = TRUE)

# All the predefined functions
lidar %>% grid_metrics(stdmetrics(X,Y,Z,Intensity, ScanAngle,
                                  ReturnNumber, Classification, pulseID,
                                  dz = 1))

# Super convenient shortcut
lidar %>% grid_metrics(.stdmetrics)

# Basic metrics from intensities
lidar %>% grid_metrics(stdmetrics_i(Intensity))

# All the metrics from intensities
lidar %>% grid_metrics(stdmetrics_i(Intensity, Z, Classification, ReturnNumber))

# Super convenient shortcut
lidar %>% grid_metrics(.stdmetrics_i)

# Compute the metrics only on first return
lidar %>% lasfilterfirst %>% grid_metrics(.stdmetrics_z)

# Compute the metrics with a threshold at 2 meters
lidar %>% lasfilter(Z > 2) %>% grid_metrics(.stdmetrics_z)

# Works also with lasmetrics
lidar %>% lasmetrics(.stdmetrics)

# Combine some predefined function with your own new metrics
# Here convenient shortcuts are no longer usable.
myMetrics = function(z, i)
{
  metrics = list(
     zwimean = sum(z*i)/sum(i), # Mean elevation weighted by intensities
     zimean  = mean(z*i),       # Mean products of z by intensity
     zsqmean = sqrt(mean(z^2))  # Quadratic mean
   )

  return( c(metrics, stdmetrics_z(z)) )
}

lidar %>% grid_metrics(myMetrics(Z, Intensity))

# You can write your own convenient shorcut like this:
.myMetrics = expression(myMetrics(Z,Intensity))

lidar %>% grid_metrics(.myMetrics)
# }