lidR (version 3.2.3)

plot_metrics: Computes metrics for each plot of a ground inventory

Description

Computes metrics for each plot of a ground inventory by 1. clipping the plots inventories, 2. computing a user's metrics to each plot, and 3. combining spatial data and metrics into one data.frame ready for statistical modelling. `plot_metrics` is basically a seamless wrapper around clip_roi, cloud_metrics, `cbind` and adequate processing settings.

Usage

plot_metrics(las, func, geometry, ...)

Arguments

las

An object of class LAS or LAScatalog.

func

formula. An expression to be applied to each cell (see also grid_metrics).

geometry

a spatial object. Many types are supported (see also clip_roi)

...

optional supplementary options (see also clip_roi)

Value

An `sp` or `sf` object depending on the input with all the metrics for each plot bound with the original input.

Working with a <code>LAScatalog</code>

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 plot_metrics function (in bold). For more details see the LAScatalog engine documentation:

• chunk size: Not relevant here.

• chunk buffer: Not relevant here.

• chunk alignment: Not relevant here.

• progress: Displays a progress estimate.

• output files: plots are extracted in memory.

• select: Read only the attributes of interest.

• filter: Read only the points of interest.

See Also

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

Examples

# NOT RUN {
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
SHPfile <- system.file("extdata", "efi_plot.shp", package="lidR")
las <- readLAS(LASfile)
inventory <- sf::st_read(SHPfile, quiet = TRUE)
inventory # contains an ID and a Value Of Interest (VOI) per plot

M <- plot_metrics(las, ~list(q85 = quantile(Z, probs = 0.85)), inventory, radius = 11.28)
model <- lm(VOI ~ q85, M)

M <- plot_metrics(las, .stdmetrics_z, inventory, radius = 11.28)

# }
# NOT RUN {
# Works with polygons as well
inventory <- sf::st_buffer(inventory, 11.28)
plot(las@header)
plot(sf::st_geometry(inventory), add = TRUE)
M <- plot_metrics(las, .stdmetrics_z, inventory)
# }