lidR (version 1.4.2)

grid_metrics: Rasterize the space and compute metrics for each cell

Description

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 LAScatalog the function readLAS is called internally. The user cannot manipulate the lidar data directly but can use streaming filters instead.

Value

Returns a data.table containing the metrics for each cell. The table has the class "lasmetrics" enabling easy plotting.

Parameter <code>func</code>

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:

• stdmetrics

• entropy

• VCI

• LAD

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 <code>start</code>

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 <code>LAScatalog</code>

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.

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.

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.
# }