training: Nundle State Forest LiDAR data

In NCSampling: Nearest Centroid (NC) Sampling

Description

Contains LiDAR data for 200 plots from two strata acquired by over-flying the Nundle State Forest (SF), NSW, Australia in 2011

Usage

data(training)

Format

A data frame with 200 observations on the following 10 variables.

OV

a numeric vector containing LiDAR occupied volume

height

numeric vector containing LiDAR heights

cc

a numeric vector containing LiDAR canopy cover

pstk

a numeric vector containing LiDAR stocking rate

var

a numeric vector containing LiDAR height variances

x

a numeric vector containing x-coordinates

y

a numeric vector containing y-coordinates

Strata

a factor with levels O Y

PID

numeric vector containing unique plot IDs

plot_type

a factor with levels B C T

Details

The LiDAR variables were calculated as outlined in Turner et al. (2011).

Source

Forestry Corporation of NSW

References

Melville G, Stone C, Turner R (2015). Application of LiDAR data to maximize the efficiency of inventory plots in softwood plantations. New Zealand Journal of Forestry Science, 45:9,1-16. doi:10.1186/s40490-015-0038-7.

Stone C, Penman T, Turner R (2011). Determining an optimal model for processing lidar data at the plot level: results for a Pinus radiata plantation in New SouthWales, Australia. New Zealand Journal of Forestry Science, 41, 191-205.

Turner R, Kathuria A, Stone C (2011). Building a case for lidar-derived structure stratification for Australian softwood plantations. In Proceedings of the SilviLaser 2011 conference, Hobart, Tasmania, Australia.

Examples

data(training)

NCSampling documentation built on May 1, 2019, 10:15 p.m.