lidR: grid_canopy – R documentation

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grid_canopy

Digital Surface Model

Description

Creates a digital surface model (DSM) using several possible algorithms. If the user provides a normalised point cloud, the output is indeed a canopy height model (CHM).

Usage

grid_canopy(las, res, algorithm)

Arguments

`las`	An object of class LAS or LAScatalog.
`res`	numeric. The resolution of the output `Raster`. Can optionally be a `RasterLayer`. In that case the `RasterLayer` is used as the layout.
`algorithm`	function. A function that implements an algorithm to compute a digital surface model. `lidR` implements p2r, dsmtin, pitfree (see respective documentation and examples).

Value

A RasterLayer containing a numeric value in each cell. If the RasterLayers are written on disk when running the function with a LAScatalog, a virtual raster mosaic is returned (see gdalbuildvrt)

Working with a `LAScatalog`

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 grid_* functions (in bold). For more details see the LAScatalog engine documentation:

chunk size: How much data is loaded at once. The chunk size may be slightly modified internally to ensure a strict continuous wall-to-wall output even when chunk size is equal to 0 (processing by file).
chunk buffer: This function guarantees a strict continuous wall-to-wall output. The buffer option is not considered.
chunk alignment: Align the processed chunks. The alignment may be slightly modified internally to ensure a strict continuous wall-to-wall output.
progress: Displays a progress estimate.
output files: Return the output in R or write each cluster's output in a file. Supported templates are {XLEFT}, {XRIGHT}, {YBOTTOM}, {YTOP}, {XCENTER}, {YCENTER} {ID} and, if chunk size is equal to 0 (processing by file), {ORIGINALFILENAME}.
select: The grid_* functions usually 'know' what should be loaded and this option is not considered. In grid_metrics this option is respected.
filter: Read only the points of interest.

Examples

LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las <- readLAS(LASfile, filter = "-inside 481280 3812940 481330 3812990")
col <- height.colors(50)

# Points-to-raster algorithm with a resolution of 1 meter
chm <- grid_canopy(las, res = 1, p2r())
plot(chm, col = col)

# Points-to-raster algorithm with a resolution of 0.5 meters replacing each
# point by a 20-cm radius circle of 8 points
chm <- grid_canopy(las, res = 0.5, p2r(0.2))
plot(chm, col = col)

# Basic triangulation and rasterization of first returns
chm <- grid_canopy(las, res = 0.5, dsmtin())
plot(chm, col = col)

# Khosravipour et al. pitfree algorithm
chm <- grid_canopy(las, res = 0.5, pitfree(c(0,2,5,10,15), c(0, 1.5)))
plot(chm, col = col)

lidR

Airborne LiDAR Data Manipulation and Visualization for Forestry Applications

v3.1.2

GPL-3

Authors

Jean-Romain Roussel [aut, cre, cph], David Auty [aut, ctb] (Reviews the documentation), Florian De Boissieu [ctb] (Fixed bugs and improved catalog features), Andrew Sánchez Meador [ctb] (Implemented wing2015() for segment_snags()), Bourdon Jean-François [ctb] (Contributed to Roussel2020() for track_sensor()), Gatziolis Demetrios [ctb] (Implemented Gatziolis2019() for track_sensor())

Initial release

2021-03-11