normalize: Normalize point cloud

In lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications

Normalize point cloud

Description

Normalize elevation or intensity values using multiple methods.

Usage

normalize_height(las, algorithm, use_class = c(2L, 9L), dtm = NULL, ...)

unnormalize_height(las)

## S4 method for signature 'LAS,ANY'
e1 - e2

normalize_intensity(las, algorithm)

Arguments

las	An object of class LAS or LAScatalog.
algorithm	(1) An algorithm for spatial interpolation. lidR has tin, kriging, knnidw or a raster representing a digital terrain model. (2) An algorithm for intensity normalization. lidR currently has range_correction.
use_class	integer vector. By default the terrain is computed by using ground points (class 2) and water points (class 9). Relevant only for a normalization without a raster DTM.
dtm	raster. If dtm is provided, then the DTM is used in place of ground points. This is different than providing a DTM in algorithm. If algorithm = dtm the dtm is subtracted naively. If algorithm = tin() and dtm = raster the ground points are not used and the DTM is interpolated as if it were made of regularly-spaced ground points.
...	normalized_height() supports add_lasattribute= TRUE to add the elevation above see level as an extra byte attribute and Wdegenerated = FALSE to silence the warning about degenerated ground points.
e1	a LAS object
e2	A raster representing a digital terrain model in format from raster, stars or terra..

Details

normalize_height

Subtract digital terrain model (DTM) from a LiDAR point cloud to create a dataset normalized with the ground at 0. The DTM can be a raster, but it can also be computed on-the-fly. In this case the algorithm does not use rasterized data and each point is interpolated. There is no inaccuracy due to the discretization of the terrain and the resolution of the terrain is virtually infinite. A new attribute 'Zref' records the former elevation values, which enables the use of unnormalize_height to restore original point elevations.

normalize_intensity

Normalize intensity values using multiple methods. The attribute 'Intensity' records the normalized intensity. An extra attribute named 'RawIntensity' records the original intensities.

Non-supported LAScatalog options

The option select is not supported and not respected because it always preserves the file format and all the attributes. select = "*" is imposed internally.

Examples

LASfile <- system.file("extdata", "Topography.laz", package="lidR")
las <- readLAS(LASfile)

# ====================
# Normalize elevation
# ====================

# First option: use a raster as DTM
# --------------------------------------

dtm <- rasterize_terrain(las, 1, knnidw(k = 6L, p = 2))
nlas <- normalize_height(las, dtm)

# restore original elevations
las <- unnormalize_height(nlas)

# operator - can be used. This is equivalent to the previous
nlas <- las - dtm

# restore original elevations
las <- unnormalize_height(las)

# Second option: interpolate each point (no discretization)
# ---------------------------------------------------------

nlas <- normalize_height(las, tin())

# operator - can be used. This is equivalent to the previous
las <- unnormalize_height(nlas)
nlas <- las - tin()

## Not run: 
# All the following syntaxes are correct
las <- normalize_height(las, knnidw())
las <- normalize_height(las, knnidw(k = 8, p = 2))
las <- las - knnidw()
las <- las - knnidw(k = 8)
las <- normalize_height(las, kriging())
las <- las - kriging(k = 8)

## End(Not run)

# ====================
# Normalize intensity
# ====================

# pmin = 15 because it is an extremely small 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))

lidR documentation built on Sept. 8, 2023, 5:10 p.m.