lidR (version 2.2.5)

lasrangecorrection: Normalize intensity with a range correction

Description

Normalize intensity with a range correction according to the formula (see references): $$I_{norm} = I_{obs} (\frac{R}{Rs})^f)$$ To achieve the range correction the position of the sensor must be known at different discrete times. Using the 'gpstime' of each point, the position of the sensor is interpolated from the reference and a range correction is applied.

Usage

lasrangecorrection(
  las,
  sensor,
  Rs,
  f = 2.3,
  gpstime = "gpstime",
  elevation = "Z"
)

Arguments

las

An object of class LAS or LAScatalog.

sensor

SpatialPointsDataDrame object containing the coordinates of the sensor at different timepoints t. The time and elevation are stored as attributes (default names are 'gpstime' and 'Z'). It can be computed with sensor_tracking.

Rs

numeric. Range of reference.

f

numeric. Exponent. Usually between 2 and 3 in vegetation contexts.

gpstime, elevation

character. The name of the attributes that store the gpstime of the position and the elevation of the sensor respectively. If elevation = NULL the Z coordinates are searched in the third column of the coordinates matrix of the SpatialPointsDataFrame. This is useful if read from a format that supports 3 coordinates points.

Value

An object of class LAS. The attribute 'Intensity' records the normalised intensity. An extra attribute named 'RawIntensity' records the original intensities.

References

Gatziolis, D. (2013). Dynamic Range-based Intensity Normalization for Airborne, Discrete Return Lidar Data of Forest Canopies. Photogrammetric Engineering & Remote Sensing, 77(3), 251<U+2013>259. https://doi.org/10.14358/pers.77.3.251

Examples

# NOT RUN {
# A valid file properly populated
LASfile <- system.file("extdata", "Topography.laz", package="lidR")
las <- readLAS(LASfile)

# pmin = 15 because it is an extremely tiny file
# strongly decimated to reduce its size. There are
# actually few multiple returns
sensor <- sensor_tracking(las, pmin = 15)

# Here the effect is virtually null because the size of
# the sample is too tiny to notice any effect of range
las <- lasrangecorrection(las, sensor, Rs = 2000)
# }