phenofit (version 0.2.7)

FitDL: Fine fitting

Description

Fine curve fitting function is used to fit vegetation time-series in every growing season.

Usage

FitDL.Zhang(y, t = index(y), tout = t, method = "nlm", w, ...)
FitDL.AG(y, t = index(y), tout = t, method = "nlminb", w, ...)
FitDL.Beck(y, t = index(y), tout = t, method = "nlminb", w, ...)
FitDL.Elmore(y, t = index(y), tout = t, method = "nlminb", w, ...)
FitDL.Gu(y, t = index(y), tout = t, method = "nlminb", w, ...)
FitDL.Klos(y, t = index(y), tout = t, method = "BFGS", w, ...)

Arguments

y

input vegetation index time-series.

t

the corresponding doy(day of year) of y.

tout

the time of output curve fitting time-series.

method

method passed to optimx or optim function.

w

weights

...

other paraters passed to optim_pheno().

Value

• tout: The time of output curve fitting time-series.

• zs : Smoothed vegetation time-series of every iteration.

• ws : Weights of every iteration.

• par : Final optimized parameter of fine fitting.

• fun : The name of fine fitting.

References

1. Beck, P.S.A., Atzberger, C., Hogda, K.A., Johansen, B., Skidmore, A.K., 2006. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2005.10.021.

2. Elmore, A.J., Guinn, S.M., Minsley, B.J., Richardson, A.D., 2012. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob. Chang. Biol. 18, 656-674. https://doi.org/10.1111/j.1365-2486.2011.02521.x.

3. Gu, L., Post, W.M., Baldocchi, D.D., Black, TRUE.A., Suyker, A.E., Verma, S.B., Vesala, TRUE., Wofsy, S.C., 2009. Characterizing the Seasonal Dynamics of Plant Community Photosynthesis Across a Range of Vegetation Types, in: Noormets, A. (Ed.), Phenology of Ecosystem Processes: Applications in Global Change Research. Springer New York, New York, NY, pp. 35-58. https://doi.org/10.1007/978-1-4419-0026-5_2.

4. https://github.com/kongdd/phenopix/blob/master/R/FitDoubleLogGu.R

Examples

# NOT RUN {
library(phenofit)
# simulate vegetation time-series
fFUN = doubleLog.Beck
par  = c( mn  = 0.1, mx  = 0.7, sos = 50, rsp = 0.1, eos = 250, rau = 0.1)
t    <- seq(1, 365, 8)
tout <- seq(1, 365, 1)
y <- fFUN(par, t)
methods <- c("AG", "Beck", "Elmore", "Gu", "Zhang")

r <- FitDL.AG(y, t, tout)
plot(t, y)
lines(tout, r$zs$iter2, col = "red")
legend('topright', c('Original time-series', 'AG smoothed'), 
    lty = c(0, 1), pch = c(16, NA), col = c("black", "red"))
# }