lidR (version 1.2.1)

grid_metrics: Rasterize the space and compute metrics for each cell

Description

Computes a series of descriptive statistics defined by the user for a LiDAR dataset within each pixel of a raster. Output is a data.frame in which each line is a pixel (single grid cell), and each column is a metric.

Usage

grid_metrics(.las, func, res = 20, start = c(0, 0), splitlines = FALSE,
  debug = FALSE)

Arguments

.las

An object of class LAS

func

the function to be applied to each cell

res

numeric. The size of the cells. Default 20.

start

vector x and y coordinates for the reference raster. Default is (0,0).

splitlines

logical. If TRUE the algorithm will compute the metrics for each flightline individually. It returns the same cells several times in overlap.

debug

logical. If you encounter a non trivial error try debug = TRUE.

Value

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

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) reference is (-831600, 117980) in the NAD83 coordinate system. The function to be applied to each cell is a classical function (see examples) that returns a labelled list of metrics. The following existing function can help 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 cloud of points (see example).

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.
# }

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