lad.quantiles: Convert LAD estimates into six rasters explaining the height...

In akamoske/LiDARforestR: R library for estimating leaf area density (LAD) and leaf area index (LAI) from airborne LiDAR point clouds

Convert LAD estimates into six rasters explaining the height distribution of LAD within the canopy - the heigth of the 10th, 25th, 50th, 75th, 90th quantiles aas well as the mean

Description

This function reads in a the LAD estimates that were previously calculated, calculates the height where a host of different quantiles and the mean occcur and return a raster showing the height where each quantile occurs within a given vertical column.

Usage

lad.quantiles(lad.array, laz.array, ht.cut, epsg.code)

Arguments

lad.array	LAD estimate array that was generated using the machorn.lad function.
laz.array	Voxelized LiDAR array that was generated using the laz.to.array function. This contains spatial information for all arrays.
ht.cut	Height that calculations will exclude. This is to remove understory LAD estimates from further calculations. If 5 is entered then all voxels 5 meters and above will be included. Enter 0 if you want to include all calculations
epsg.code	EPSG code so that the rasters can be projected into the appropriate projection

Details

These forest structure attributes are based off calculations from:

Shi, Y., Wang, T., Skidmore, A.K., and Heurich, M. (2018). Important LiDAR metrics for discriminating forest tree species in Central Europe. ISPRS Journal of Photogrammetry and Remote Sensing, 137, 163-174. https://doi.org/10.1016/j.isprsjprs.2018.02.002

Value

A list containing the quantile and mean rasters.

akamoske/LiDARforestR documentation built on Aug. 31, 2023, 1:33 a.m.