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Subtract digital terrain model (DTM) from LiDAR point cloud to create a dataset normalized with
the ground at 0. The DTM can originate from an external file or can be computed by the user. 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.
How well the edges of the dataset are interpolated depends on the interpolation method used.
Thus, a buffer around the region of interest is always recommended to avoid edge effects.
The attribute Z of the returned LAS object is the normalized elevation. A new attribute 'Zref' records
the former elevation values, which enables the use of lasunormalize to restore original point elevations.
lasnormalize(las, algorithm, na.rm = FALSE, use_class = c(2L, 9L), ...)lasunnormalize(las)
# S4 method for LAS,RasterLayer
-(e1, e2)
# S4 method for LAS,`function`
-(e1, e2)
An object of class LAS or LAScatalog.
a spatial interpolation function. lidR have tin,
kriging, knnidw or a RasterLayer representing a digital terrain
model (can be computed with grid_terrain)
logical. When using a RasterLayer as DTM, by default the function fails if a point
fall in an empty pixel because a Z elevation cannot be NA. If na.rm = TRUE points with an
elevation of NA are filtered. Be careful this creates a copy of the point cloud.
integer vector. By default the terrain is computed by using ground points (class 2) and water points (class 9). Relevant only for a normalisation without a raster DTM.
If algorithm is a RasterLayer, ... is propagated to
extract. Typically one may use method = "bilinerar".
a LAS object
RasterLayer representing a digital terrain model (can be
computed with grid_terrain) or a spatial interpolation function. lidR has tin,
kriging, and knnidw.
If the input is a LAS object, return a LAS object. If the input is a
LAScatalog, returns a LAScatalog.
This section appears in each function that supports a LAScatalog as input.
In lidR when the input of a function is a LAScatalog the
function uses the LAScatalog processing engine. The user can modify the engine options using
the available options. A careful reading of the
engine documentation is recommended before processing LAScatalogs. Each
lidR function should come with a section that documents the supported engine options.
The LAScatalog engine supports .lax files that significantly improve the computation
speed of spatial queries using a spatial index. Users should really take advantage a .lax files,
but this is not mandatory.
Supported processing options for a LAScatalog (in bold). For more details see the
LAScatalog engine documentation:
chunk size: How much data is loaded at once.
chunk buffer*: Mandatory to get a continuous output without edge effects. The buffer is always removed once processed and will never be returned either in R or in files.
chunk alignment: Align the processed chunks.
progress: Displays a progression estimation.
output files*: Mandatory because the output is likely to be too big to be returned
in R and needs to be written in las/laz files. Supported templates are {XLEFT}, {XRIGHT},
{YBOTTOM}, {YTOP}, {XCENTER}, {YCENTER} {ID} and, if
chunk size is equal to 0 (processing by file), {ORIGINALFILENAME}.
select: The function will write files equivalent to the original ones. Thus select = "*"
and cannot be changed.
filter: Read only points of interest.
# NOT RUN {
LASfile <- system.file("extdata", "Topography.laz", package="lidR")
las <- readLAS(LASfile)
plot(las)
# First option: use a RasterLayer as DTM
# =======================================================
dtm <- grid_terrain(las, 1, knnidw(k = 6L, p = 2))
las <- lasnormalize(las, dtm)
plot(dtm)
plot(las)
# restore original elevations
las <- lasunnormalize(las)
plot(las)
# operator - can be used. This is equivalent to the previous
las <- las - dtm
plot(las)
# restore original elevations
las <- lasunnormalize(las)
# Second option: interpolate each point (no discretization)
# =========================================================
las <- lasnormalize(las, tin())
plot(las)
# operator - can be used. This is equivalent to the previous
las <- lasunnormalize(las)
las <- las - tin()
# }
# NOT RUN {
# All the following syntaxes are correct
las <- lasnormalize(las, knnidw())
las <- lasnormalize(las, knnidw(k = 8, p = 2))
las <- las - knnidw()
las <- las - knnidw(k = 8)
las <- lasnormalize(las, kriging())
las <- las - kriging(k = 8)
# }
# NOT RUN {
# }
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