lidR (version 2.1.4)

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

• tiling_size: How much data is loaded at once. The tiling size may be slightly modified internally to ensure a strict continuous wall-to-wall output even when tiling size is equal to 0 (processing by file).

• buffer: This function guarantees a strict continuous wall-to-wall output. The buffer option is not considered.

• alignment: Align the processed clusters. The alignment may be slightly modified.

• 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

# NOT RUN {
LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las <- readLAS(LASfile)
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)
# }