Area-Based Approach

Description

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).

Usage

grid_metrics(
  las,
  func,
  res = 20,
  start = c(0, 0),
  filter = NULL,
  by_echo = "all"
)

Arguments

Value

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)

Parameter 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:

• stdmetrics

• entropy

• VCI

• LAD

But usually users must write their own functions to create metrics. grid_metrics will dispatch the point cloud in the user's function.

Working with 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

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.

See Also

Other metrics: cloud_metrics(), plot_metrics(), point_metrics(), tree_metrics(), voxel_metrics()

Examples

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)

# === Split by echo type ===
func = ~list(avgI = mean(Intensity))
echo = c("all", "first","multiple")
metrics <- grid_metrics(las, func , 20, by_echo = echo)
plot(metrics, col = heat.colors(25))