R: Intensity normalization algorithm

range_correction {lidR} | R Documentation

## Intensity normalization algorithm

### Description

This function is made to be used in normalize_intensity. It corrects intensity with a range correction according to the formula (see references):

Inorm = Iobs * (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

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

    get_range(las, sensor, gpstime = "gpstime", elevation = "Z")

### Arguments

`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 track_sensor.
`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.
`las` | an object of class LAS. `get_range()` is a regular function documented here for convenience.

### References

Gatziolis, D. (2011). Dynamic Range-based Intensity Normalization for Airborne, Discrete Return Lidar Data of Forest Canopies. Photogrammetric Engineering & Remote Sensing, 77(3), 251–259. https://doi.org/10.14358/pers.77.3.251

### Examples

    # 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 <- track_sensor(las, Roussel2020(pmin = 15))

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

    # This might be useful for some applications
    R = get_range(las, sensor)

[Package lidR version 3.2.2 Index]