R/quatre_montagnes-data.R

In lidaRtRee: Forest Analysis with Airborne Laser Scanning (LiDAR) Data

#' @name quatre_montagnes
#' 
#' @title Field plot inventory in the Quatre Montagnes area (France)
#' 
#' @description Dataset of forest parameters measured in the field on 96 circular plots of 15 m radius. Metrics derived from airborne laser scanning (ALS) point clouds have also been extracted and calculated for those plots.
#' 
#' @docType data
#'
#' @usage data(quatre_montagnes)
#'
#' @format A \code{data.frame} with 76 columns:
#' \enumerate{
#' \item \code{plotId} id of field plot
#' \item \code{X} easting coordinate (epsg: 2154)
#' \item \code{Y} northing coordinate (epsg: 2154)
#' \item \code{clusterId} id of cluster, plots were inventoried in groups of 4
#' \item \code{G_m2_ha} basal area in m2 per ha
#' \item \code{N_ha} number of trees per ha
#' \item \code{D_mean_cm} mean tree diameter at breast height (1.3 m) in cm
#' \item \code{stratum} forest ownership (public or private)
#' \item \code{[, 9:60]} point cloud metrics computed from ALS, see \code{\link{aba_metrics}}
#' \item \code{[, 61:73]} metrics derived from tree segmentation in ALS data, see \code{\link{std_tree_metrics}}
#' \item \code{[, 74:76]} terrain statistics, see \code{\link{terrain_points_metrics}}
#' }
#'
#' @keywords datasets
#'
#' @references Monnet, J.-M. 2021. Tutorial on modeling forest parameters with ALS data. ABA data preparation \url{https://lidar.pages.mia.inra.fr/lidaRtRee/articles/area-based.1.data.preparation.html}
#'
#' @examples
#' data(quatre_montagnes)
#' summary(quatre_montagnes)
NULL
"quatre_montagnes"