GC {leafR} | R Documentation |
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).
GC(normlas.file, threshold = 1)
normlas.file |
normalized las file |
threshold |
numerical, defines the minimum height considered to represent an echo from leaves. |
A numeric
containing the Gini coefficient (GC) calculated from the normalized LAS file
Valbuena et al. (2012) argues on why Gini is better suited to describe structural complexity the Foliage Height Diversity or the Gini-Simpon index.
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. http://dx.doi.org/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. https://doi.org/10.1016/j.rse.2016.10.024
# Get the example laz file normlas.file = system.file("extdata", "lidar_example.laz", package="leafR") GC(normlas.file, threshold =1)