rugosity.within.canopy: Convert LAD estimates into two rasters - standard deviation...

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 two rasters - standard deviation of LAD in a given column and canopy rugosity

Description

This function reads in a the LAD estimates that were previously calculated, finds the standard deviation of LAD estimates within a given column, then uses a 3x3 moving window to find the standard deviation of the previously calculated vertical standard deviations in the moving window. This value is then saved in a new matrix, so that the value calculated by the moving window is not used in subsequent calcluations. This is the Hardiman approach to calcluating canopy rugosity to look at within canopy variation of LAD.

Usage

rugosity.within.canopy(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

Because the second half of this funtion uses a 3x3 moving window, the outside rows and columns are lost due to insufficient data. If you are mosaicing multiple rasters together in later steps, this needs to be taken into account. I am working on a function that will pass a moving window over a large raster, but it is still in development.

These forest structure attributes are based off calculations from:

Hardiman, B. S., Bohrer, G., Gough, C. M., Vogel, C. S., & Curtis, P. S. (2011). The role of canopy structural complexity in wood net primary production of a maturing northern deciduous forest. Ecology, 92, 1818-1827. https://doi.org/10.1890/10-2192.1

Value

A list containing max LAD and height of max LAD rasters.

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