Computes a series of user-defined descriptive statistics for a LiDAR dataset within
each pixel of a raster. Output is a data.table in which each line is a pixel (single grid cell),
and each column is a metric. Works both with LAS or catalog objects.
grid_metrics
is similar to lasmetrics or grid_hexametrics except it
computes metrics within each cell in a predefined grid. The grid cell coordinates are
pre-determined for a given resolution.
grid_metrics(x, func, res = 20, start = c(0, 0), splitlines = FALSE,
filter = "")
the function to be applied to each cell (see section "Parameter func")
numeric. The size of the cells. Default 20.
vector x and y coordinates for the reference raster. Default is (0,0) (see section "Parameter start").
logical. If TRUE the algorithm will compute the metrics for each flightline individually. It returns the same cells several times in overlap.
Returns a data.table
containing the metrics for each cell. The table
has the class "lasmetrics" enabling easy plotting.
The function to be applied to each cell is a classical function (see examples) that returns a labelled list of metrics. The following existing functions allows the user to compute some metrics:
Users must write their own functions to create metrics. grid_metrics
will
dispatch the LiDAR data for each cell in the user's function. The user writes their
function without considering grid cells, only a point cloud (see example).
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 raster centers: (10,10), (10,30), (30,10) etc.. When start = (-10, -10) and res = 20 grid_metrics will produce the following raster centers: (0,0), (0,20), (20,0) etc.. In Quebec (Canada) reference is (-831600, 117980) in the NAD83 coordinate system.
When the parameter x
is a LAScatalog the function processes
the entire dataset in a continuous way using a multicore process. Parallel computing is set
by default to the number of core available in the computer. The user can modify the global
options using the function catalog_options.
lidR
support .lax files. Computation speed will be significantly improved with a
spatial index.
grid_metrics
is similar to lasmetrics or grid_hexametrics except it
computes metrics within each cell in a predefined grid. 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 raster centers: (10,10), (10,30), (30,10) etc.. When start = (-10, -10) and res = 20
grid_metrics will produce the following raster centers: (0,0), (0,20), (20,0) etc.. In Quebec
(Canada) the reference is (-831600, 117980) in the NAD83 coordinate system.
# NOT RUN {
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
lidar = readLAS(LASfile)
# Canopy surface model with 4 m^2 cells
grid_metrics(lidar, max(Z), 2) %>% plot
# Mean height with 400 m^2 cells
grid_metrics(lidar, mean(Z), 20) %>% plot
# 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(lidar, myMetrics(Z, Intensity))
plot(metrics)
plot(metrics, "zwimean")
plot(metrics, "zimean")
plot(metrics, "zsqmean")
#etc.
# }
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