| nundle.sf {NCSampling} | R Documentation |
Nundle State Forest LiDAR data
Description
LiDAR data from two strata acquired by over-flying the Nundle State Forest (SF), NSW, Australia in 2011
Usage
data(nundle.sf)
Format
A data frame with 2068 observations on the following 12 variables.
PIDnumeric vector containing unique plot IDs
heightnumeric vector containing LiDAR heights
meanhtnumeric vector containing LiDAR mean heights
mama numeric vector containing mean above mean heights
mdha numeric vector containing LiDAR mean dominant heights
pstka numeric vector containing LiDAR stocking rate
cca numeric vector containing LiDAR canopy cover
OVa numeric vector containing LiDAR occupied volume
vara numeric vector containing LiDAR height variances
Strataa factor with levels
O,Yxa numeric vector containing x-coordinates
ya numeric vector containing y-coordinates
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(nundle.sf)