simHemiphoto {hemiphoto2LAI} | R Documentation |
Generate a random hemiphoto of gap vs non-gap for a chosen leaf angle distribution model.
simHemiphoto(ladType, ladParameter, option=list(),...)
ladType |
a three-leter string to specify one of the 19 LAD models supported (e.g., "es1","uni",and "lan"). Type |
ladParameter |
a vector of two elements specifying the parameter values of the chosen LAD type |
option |
(optional). If absent, |
... |
additional parameters, not used currently but reserved for future extension |
The output is a LIST object,comprising the following elements:
imageTHETA |
a square matrix of dimension |
imageGAP |
a square matrix of dimension |
LAI |
the LAI value used in the simulation. It should be the same as the inputted LAI as specified by option$LAI. |
PAR |
a copy of the input LAD parameters. |
THETA |
a vector consisting of 200 zenith angles ranging from 0 to pi/2. It is used to characterize the LAD curve used in simulation. |
G |
a vector consisting of 200 LAD value evalauted at the zenith agnles of |
Zhao et al. (2019). How to better estimate leaf area index and leaf angle distribution from digital hemispherical photography? Switching to a binary nonlinear regression paradigm (under review)
library(hemiphoto2LAI) #--------------------------------Example 1--------------------------------# opt=list() #Create an empty list to append individual parameters opt$LAI =3.0 #LAI of the simulated canopy opt$imageSize=400 #The size of the resulting hemiphoto opt$scaledParameter=0 #If set to 0, the ladParamter input should be valid values #falling within the expected range of the chosen LAD. If set # to 1, ladParamter is two numbers within [0, 1] to # be scaled to the valid ranges of the chosen LAD model. result=simHemiphoto("es1", c(0.1, NA), opt) #*****************************End of Example 1****************************# #\donttest #{ LAI=estimateLAI(result$imageTHETA,result$imageGAP) #}