Area-Based Approach
Computes a series of user-defined descriptive statistics for a LiDAR dataset within each pixel of a raster (area-based approach). The grid cell coordinates are pre-determined for a given resolution, so the algorithm will always provide the same coordinates independently of the dataset. When start = (0,0) and res = 20 grid_metrics will produce the following cell centers: (10,10), (10,30), (30,10) etc. aligning the corner of a cell on (0,0). When start = (-10, -10) and res = 20 grid_metrics will produce the following cell centers: (0,0), (0,20), (20,0) etc. aligning the corner of a cell on (-10, -10).
grid_metrics(las, func, res = 20, start = c(0, 0), filter = NULL)
las |
An object of class LAS or LAScatalog. |
func |
formula. An expression to be applied to each cell (see section "Parameter func"). |
res |
numeric. The resolution of the output |
start |
vector of x and y coordinates for the reference raster. Default is (0,0) meaning that the grid aligns on (0,0). |
filter |
formula of logical predicates. Enables the function to run only on points of interest in an optimized way. See examples. |
A RasterLayer or a RasterBrick containing a numeric value in each cell. If the
RasterLayers are written on disk when running the function with a LAScatalog, a
virtual raster mosaic is returned (see gdalbuildvrt)
func
The function to be applied to each cell is a classical function (see examples) that
returns a labeled list of metrics. For example, the following function f is correctly formed.
f = function(x) {list(mean = mean(x), max = max(x))}And could be applied either on the Z coordinates or on the intensities. These two
statements are valid:
grid_metrics(las, ~f(Z), res = 20) grid_metrics(las, ~f(Intensity), res = 20)
The following existing functions allow the user to compute some predefined metrics:
But usually users must write their own functions to create metrics. grid_metrics will
dispatch the point cloud in the user's function.
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 grid_* functions (in bold). For
more details see the LAScatalog engine documentation:
chunk size: How much data is loaded at once. The chunk size may be slightly modified internally to ensure a strict continuous wall-to-wall output even when chunk size is equal to 0 (processing by file).
chunk buffer: This function guarantees a strict continuous wall-to-wall output. The
buffer option is not considered.
chunk alignment: Align the processed chunks. The alignment may be slightly modified internally to ensure a strict continuous wall-to-wall output.
progress: Displays a progress estimate.
output files: Return the output in R or write each cluster's output in a file.
Supported templates are {XLEFT}, {XRIGHT}, {YBOTTOM}, {YTOP},
{XCENTER}, {YCENTER} {ID} and, if chunk size is equal to 0 (processing
by file), {ORIGINALFILENAME}.
select: The grid_* functions usually 'know' what should be loaded
and this option is not considered. In grid_metrics this option is respected.
filter: Read only the points of interest.
Other metrics:
cloud_metrics(),
hexbin_metrics(),
point_metrics(),
tree_metrics(),
voxel_metrics()
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
las = readLAS(LASfile)
col = height.colors(50)
# === Using all points ===
# Mean height with 400 m^2 cells
metrics = grid_metrics(las, ~mean(Z), 20)
plot(metrics, col = col)
# Define your own new metrics
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(metrics)
}
metrics = grid_metrics(las, ~myMetrics(Z, Intensity))
plot(metrics, col = col)
#plot(metrics, "zwimean", col = col)
#plot(metrics, "zimean", col = col)
# === With point filters ===
# Compute using only some points: basic
first = filter_poi(las, ReturnNumber == 1)
metrics = grid_metrics(first, ~mean(Z), 20)
# Compute using only some points: optimized
# faster and uses less memory. No intermediate object
metrics = grid_metrics(las, ~mean(Z), 20, filter = ~ReturnNumber == 1)
# Compute using only some points: best
# ~50% faster and uses ~10x less memory
las = readLAS(LASfile, filter = "-keep_first")
metrics = grid_metrics(las, ~mean(Z), 20)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.