Nothing
PhenoDeriv <- structure(function(
##title<<
## Method 'Deriv' to calculate phenology metrics
##description<<
## This function implements the derivative method for phenology. This is rather an internal function; please use the function \code{\link{Phenology}} to apply this method.
x,
### seasonal cycle of one year
min.mean = 0.1,
### minimum mean annual value in order to calculate phenology metrics. Use this threshold to suppress the calculation of metrics in grid cells with low average values
calc.pheno = TRUE,
### calculate phenology metrics or return NA?
plot = FALSE,
### plot results?
...
### further arguments (currently not used)
##seealso<<
## \code{\link{Phenology}}
) {
if (all(is.na(x))) return(c(sos=NA, eos=NA, los=NA, pop=NA, pot=NA, mgs=NA, rsp=NA, rau=NA, peak=NA, trough=NA, msp=NA, mau=NA))
# get statistical values
n <- length(x)
avg <- mean(x, na.rm=TRUE)
x2 <- na.omit(x)
avg2 <- mean(x2[x2 > min.mean], na.rm=TRUE)
peak <- max(x, na.rm=TRUE)
trough <- min(x, na.rm=TRUE)
ampl <- peak - trough
# get position of seasonal peak and trough
pop <- median(which(x == max(x, na.rm=TRUE)))
pot <- median(which(x == min(x, na.rm=TRUE)))
# return NA if amplitude is too low or time series has too many NA values
if (!calc.pheno) {
if (avg < min.mean) { # return for all metrics NA if mean is too low
return(c(sos=NA, eos=NA, los=NA, pop=NA, pot=NA, mgs=NA, rsp=NA, rau=NA, peak=NA, trough=NA, msp=NA, mau=NA))
} else { # return at least annual average and peak if annual mean > min.mean
return(c(sos=NA, eos=NA, los=NA, pop=pop, pot=pot, mgs=avg2, rsp=NA, rau=NA, peak=peak, trough=NA, msp=NA, mau=NA))
}
}
# calculate derivative
xd <- c(NA, diff(x))
# get SOS and EOS
soseos <- 1:length(x)
rsp <- max(xd, na.rm=TRUE)
rau <- min(xd, na.rm=TRUE)
sos <- median(soseos[xd == rsp], na.rm=TRUE)
eos <- median(soseos[xd == rau], na.rm=TRUE)
los <- eos - sos
los[los < 0] <- n + (eos[los < 0] - sos[los < 0])
# get MGS
if (sos < eos) {
mgs <- mean(x[sos:eos], na.rm=TRUE)
} else {
mgs <- mean(x[c(1:eos, sos:n)], na.rm=TRUE)
}
# get MSP, MAU
msp <- mau <- NA
if (!is.na(sos)) {
id <- (sos-10):(sos+10)
id <- id[(id > 0) & (id < n)]
msp <- mean(x[id], na.rm=TRUE)
}
if (!is.na(eos)) {
id <- (eos-10):(eos+10)
id <- id[(id > 0) & (id < n)]
mau <- mean(x[id], na.rm=TRUE)
}
metrics <- c(sos=sos, eos=eos, los=los, pop=pop, pot=pot, mgs=mgs, rsp=rsp, rau=rau, peak=peak, trough=trough, msp=msp, mau=mau)
if (plot) {
PlotPhenCycle(x, metrics=metrics, ...)
}
return(metrics)
### The function returns a vector with SOS, EOS, LOS, POP, MGS, RSP, RAU, PEAK, MSP and MAU.
}, ex=function() {
# perform time series preprocessing from first year of data
x <- TsPP(ndvi, interpolate=TRUE)[1:365]
plot(x)
# calculate phenology metrics for first year
PhenoDeriv(x, plot=TRUE)
})
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