GC {leafR}

Gini coefficient of foliage structural diversity

Description

Calculates the Gini coefficient (GC) from individual LIDAR returns (i.e. without voxelization), as described for the L-coefficient of variation (equivalent to Gini) in Valbuena et al. (2017).

Usage

GC(normlas.file, threshold = 1)

Arguments

normlas.file

normalized las file

threshold

numerical, defines the minimum height considered to represent an echo from leaves.

Value

A numeric containing the Gini coefficient (GC) calculated from the normalized LAS file

Note

Valbuena et al. (2012) argues on why Gini is better suited to describe structural complexity the Foliage Height Diversity or the Gini-Simpon index.

References

Valbuena R., Packalen P., Martín-Fernández S., Maltamo M. (2012) Diversity and equitability ordering profiles applied to the study of forest structure. Forest Ecology and Management 276: 185–195. doi: 10.1016/j.foreco.2012.03.036 Valbuena R., Maltamo M., Mehtätalo L., Packalen P. (2017) Key structural features of Boreal forests may be detected directly using L-moments from airborne lidar data. Remote Sensing of Environment. 194: 437–446. doi: 10.1016/j.rse.2016.10.024

Examples

# Get the example laz file
normlas.file = system.file("extdata", "lidar_example.laz", package="leafR")

GC(normlas.file, threshold =1)