metrics_texture: Canopy texture metrics

In ptompalski/lidRmetrics: Point cloud metrics for the lidR package.

Canopy texture metrics

Description

Generates a suite of GLCM (Grey-Level Co-Occurence Matrix) metrics of a canopy height model (CHM). CHM is calculated on the fly to allow easy integration with e.g. lidR::pixel_metrics function.

Usage

metrics_texture(x, y, z, pixel_size, zmin = NA, chm_algorithm = NULL, ...)

.metrics_texture

Arguments

x, y, z	X, Y, Z coordinates of a point cloud
pixel_size	pixel size
zmin	numeric. Minimum z value. If set, z values (heights) below are ignored in calculations.
chm_algorithm	Function used to generate the CHM. By default lidR::p2r(na.fill = lidR::knnidw()) is used.
...	additional parameters passed to ForestTools::glcm().

Format

An object of class formula of length 2.

Details

Function first uses the lidR::rasterize_canopy() algorithm to create a CHM. ForestTools::glcm() function is then used to calculate GLCM statistics (see package manual for details). This implementation of GLCM does not allow for missing values - after CHM is created, any missing values are converted to 0.

Value

A list. GLCM metrics.

Examples

library(lidR)
library(lidRmetrics)
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
las <- readLAS(LASfile, select = "*", filter = "-keep_random_fraction 0.5")

m1 <- cloud_metrics(las, ~metrics_texture(x = X, y = Y, z = Z, pixel_size = 1))

m2 <- pixel_metrics(las, ~metrics_texture(x = X, y = Y, z = Z, pixel_size = 1), res = 20)

ptompalski/lidRmetrics documentation built on May 7, 2024, 2:58 p.m.