grid_metrics
Rasterize the space and compute metrics for each cell
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.
Usage
grid_metrics(x, func, res = 20, start = c(0, 0), splitlines = FALSE,
filter = "")Arguments
- x
An object of class LAS or a catalog (see section "Use with a LAScatalog")
- func
the function to be applied to each cell (see section "Parameter func")
- res
numeric. The size of the cells. Default 20.
- start
vector x and y coordinates for the reference raster. Default is (0,0) (see section "Parameter start").
- splitlines
logical. If TRUE the algorithm will compute the metrics for each flightline individually. It returns the same cells several times in overlap.
- filter
character. Streaming filter while reading the files (see readLAS). If the input is a
LAScatalogthe function readLAS is called internally. The user cannot manipulate the lidar data directly but can use streaming filters instead.
Details
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.
Value
Returns a data.table containing the metrics for each cell. The table
has the class "lasmetrics" enabling easy plotting.
Parameter func
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).
Parameter start
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.
Use with a LAScatalog
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.
Examples
# 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.
# }