habtools

A collection of functions for sampling and simulating 3D surfaces and objects and estimating metrics like rugosity, fractal dimension, convexity, sphericity, circularity, second moments of area and volume, and more.

Installation

A CRAN version of habtools is forthcoming. You can also install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("jmadinlab/habtools")

Examples

There are vignettes demonstrating the use of habtools for digital elevation models (DEMs) and 3D meshes, as well as a vignette covering fractal dimension methods.

There are currently two data sets accompanying this package. horseshoe is a DEM of a coral reef in RasterLayer format, and mcap is a 3D mesh of a coral growing on a reef in mesh3d format.

The following example calculates height range, rugosity and fractal dimension of a 2 x 2 m plot of horseshoe.

library(habtools)
library(raster)

# Let's take a subset DEM of size = 2
dem <- dem_crop(horseshoe, x0 = -465, y0 = 1265, L = 2, plot = TRUE)

# height range
hr(dem)
#> [1] 1.368289

# rugosity
rg(dem, L0 = 0.0625)
#> [1] 1.75829

# fractal dimension
fd(dem, method = "hvar", keep_data = TRUE, plot=TRUE, diagnose = TRUE)

#> $D
#> [1] 2.159332
#> 
#> $data
#>        l          h
#> 1 0.0625 0.07207143
#> 2 0.1250 0.16465515
#> 3 0.2500 0.31394699
#> 4 0.5000 0.58224221
#> 5 1.0000 0.88901201
#> 6 2.0000 1.36828852
#> 
#> $lvec
#> [1] 0.0625 0.1250 0.2500 0.5000 1.0000 2.0000
#> 
#> $D_vec
#> [1] 1.808052 2.068927 2.108902 2.389417 2.377902
#> 
#> $var
#> [1] 0.2420993
#> 
#> $method
#> [1] "hvar"

The next example calculates height range, rugosity and fractal dimension for the coral colony mcap. Because 3D meshes can have more than one z coordinate for a given xy (i.e., they have overhangs), we use cube counting for fractal dimension.

library(rgl)
options(rgl.printRglwidget = TRUE)
plot3d(mcap)

# height range
hr(mcap)
#> [1] 0.2185397

# rugosity
rg(mcap, L0 = 0.045)
#> [1] 2.882813

# fractal dimension
fd(mcap, method = "cubes", keep_data = TRUE, plot=TRUE, diagnose = TRUE)
#> lvec is set to c(0.053, 0.106, 0.212, 0.423).

#> $D
#> [1] 2.315246
#> 
#> $data
#>            l   n
#> 4 0.05291204 134
#> 3 0.10582408  31
#> 2 0.21164817   8
#> 1 0.42329634   1
#> 
#> $lvec
#> [1] 0.42329634 0.21164817 0.10582408 0.05291204
#> 
#> $D_vec
#> [1] 2.111893 1.954196 3.000000
#> 
#> $var
#> [1] 0.5638126
#> 
#> $method
#> [1] "cubes"