Phenology: Calculate phenology metrics in time series

In greenbrown: Land Surface Phenology and Trend Analysis

Description

This function calculates from time series annual metrics of vegetation phenology:

• sos start of season

• eos end of season

• los length of season

• pop position of peak value (maximum)

• pot position of trough value (minimum)

• mgs mean growing season value

• peak peak value (maximum)

• trough trough value (minimum)

• msp mean spring value

• mau mean autumn value

• rsp rate of spring greenup (not all methods)

• rau rate of autumn senescence rates (not all methods)

The calculation of these metrics is performed in three steps and by using different methods:

• Step 1: Filling of permanent (winter) gaps. See FillPermanentGaps

• Step 2: Time series smoothing and interpolation. See TsPP

• Step 3: Detection of phenology metrics. Phenology metrics are estimated from the gap filled, smoothed and interpolated time series. This can be done by threshold methods (PhenoTrs) or by using the derivative of the time series (PhenoDeriv).

• Step 4: Correction of annual DOY (day of year) time series. sos, eos, pop, and pot time series are corrected to not jump between years and ouliers are removed. See (CorrectDOY).

Usage

Phenology(Yt, approach = c("White", 
    "Trs", "Deriv"), 
    min.mean = 0.1, trs = NULL, 
    fpg = FillPermanentGaps, 
    tsgf = "TSGFspline", 
    interpolate = TRUE, 
    min.gapfrac = 0.2, 
    lower = TRUE, fillval = NA, 
    fun = min, method = c("Elmore", 
        "Beck"), check.seasonality = 1:3, 
    backup = NULL, ...)

Arguments

Yt	univariate time series of class ts
approach	Approach to be used to calculate phenology metrics from smoothed time series. 'White' by sclaing annual cycles between 0 and 1 (White et al. 1997, see PhenoTrs); 'Trs' for simple thresholds (PhenoTrs); 'Deriv' by using the derivative of the smoothed function (PhenoDeriv).
min.mean	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
trs	threshold to be used to determine SOS and EOS if method 'Trs' is used. If method 'Trs' is used but trs is NULL than trs will be computed from the long-term mean of Yt.
fpg	Filling of permanent gaps: If NULL, permanent gaps will be not filled, else the function FillPermanentGaps will be applied.
tsgf	Temporal smoothing and gap filling: Function to be used for temporal smoothing, gap filling and interpolation of the time series. If NULL, this step will be not applied. Otherwise a function needs to be specified. Exisiting functions that can be applied are TSGFspline, TSGFlinear, TSGFdoublelog, TSGFphenopix
interpolate	Should the smoothed and gap filled time series be interpolated to daily values?
min.gapfrac	How often has an observation to be NA to be considered as a permanent gap? (fraction of time series length) Example: If the month January is 5 times NA in a 10 year time series (= 0.5), then the month January is considered as permanent gap if min.gapfrac = 0.4.
lower	For filling of permanent gaps: fill lower gaps (TRUE), upper gaps (FALSE) or lower and upper gaps (NULL)
fillval	For filling of permanent gaps: constant fill values for gaps. If NA the fill value will be estimated from the data using fun.
fun	For filling of permanent gaps: function to be used to compute fill values. By default, minimum.
method	If 'tsgf' is TSGFdoublelog: Which kind of double logistic curve should be used to smooth the data? 'Elmore' (Elmore et al. 2012, see FitDoubleLogElmore) or 'Beck' (Beck et al. 2006, see FitDoubleLogBeck) .
check.seasonality	Which methods in Seasonality should indicate TRUE (i.e. time series has seasonality) in order to calculate phenology metrics? 1:3 = all methods should indicate seasonality, Set to NULL in order to not perform seasonality checks.
backup	Which backup algorithm should be used instead of TSGFdoublelog for temporal smoothing and gap filling if the time series has no seasonality? If a time series has no seasonal pattern, the fitting of double logistic functions is not meaningful. In this case another method can be used. Default: NULL (returns NA - no smoothing), other options: "TSGFspline", "TSGFlinear"
...	further arguments (currently not used)

Details

This function allows to calculate phenology metrics on time series. This method can be applied to gridded (raster) data using the function PhenologyRaster.

Value

The function returns a "Phenology" object with the following components

• method Selected method.

• series gap-filled, smoothed and daily interpolated time series

• sos annual time series of start of season

• eos annual time series of end of season

• los annual time series of length of season

• pop annual time series of position of peak (maximum)

• pot annual time series of position of trough (minimum)

• mgs annual time series of mean growing season values

• peak annual time series of peak value

• trough annual time series of trough value

• msp annual time series of mean spring value

• mau annual time series of mean autumn value

• rsp annual time series of spring greenup rates (only for methods 'Deriv' and 'Logistic')

• rau annual time series of autumn senescence rates (only for methods 'Deriv' and 'Logistic')

Author(s)

Matthias Forkel <[e-mail]> [aut, cre]

References

Beck, P.S.A., C. Atzberger, K.A. Hodga, B. Johansen, A. Skidmore (2006): Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. - Remote Sensing of Environment 100:321-334.
Elmore, A.J., S.M. Guinn, B.J. Minsley and A.D. Richardson (2012): Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. - Global Change Biology 18, 656-674.
White M.A., P.E. Thornton and S.W. Running (1997): A continental phenology model for monitoring vegetation responses to interannual climatic variability. - Global Biogeochemical Cycles 11, 217-234.

See Also

PhenologyRaster, TSGFspline, TSGFdoublelog, FitDoubleLogElmore, FitDoubleLogBeck

Examples

# introduce some missing values in NDVI time series
winter <- (1:length(ndvi))[cycle(ndvi) == 1 | cycle(ndvi) == 2 | cycle(ndvi) == 12]
ndvi[sample(winter, length(winter)*0.5)] <- NA
plot(ndvi)

# spline fit and threshold
spl.trs <- Phenology(ndvi, tsgf="TSGFspline", approach="White") 
spl.trs
plot(spl.trs)	# default plot: start of season, end of season, position of peak
plot(spl.trs, type=c("los")) # length of season

# plot mean spring, growing season, autumn and peak values
plot(spl.trs, type=c("msp", "mgs", "mau", "peak")) 

# gap-filled and smoothed time series that was used to estimate phenology metrics
plot(spl.trs$series, col="red"); lines(ndvi) 


# # calculate phenology metrics using different smoothing methods and approaches
# #-----------------------------------------------------------------------------
# 
# # linear interpolation/running median + threshold
# lin.trs <- Phenology(ndvi, tsgf="TSGFlinear", approach="White") 
# 
# # linear interpolation/running median + derivative
# lin.deriv <- Phenology(ndvi, tsgf="TSGFlinear", approach="Deriv") 
# 
# # spline + threshold
# spl.trs <- Phenology(ndvi, tsgf="TSGFspline", approach="White") 
# 
# # spline + derivative
# spl.deriv <- Phenology(ndvi, tsgf="TSGFspline", approach="Deriv") 
# 
# # double logistic fit + threshold
# beck.trs <- Phenology(ndvi, tsgf="TSGFdoublelog", method="Beck", approach="White") 
# 
# # double logistic fit + derivative
# beck.deriv <- Phenology(ndvi, tsgf="TSGFdoublelog", method="Beck", approach="Deriv") 
# 
# # double logistic fit + threshold
# elmore.trs <- Phenology(ndvi, tsgf="TSGFdoublelog", method="Elmore", approach="White") 
# 
# # double logistic fit + derivative
# elmore.deriv <- Phenology(ndvi, tsgf="TSGFdoublelog", method="Elmore", approach="Deriv") 
# 
# # compare results: SOS and EOS
# type <- c("sos", "eos")
# require(RColorBrewer)
# cols <- brewer.pal(10, "Paired")
# nms <- c("Lin+Trs", "Lin+Deriv", "Spline+Trs", "Spline+Deriv", "DoubleLog1+Trs", 
#  "DoubleLog1+Deriv", "DoubleLog2+Trs", "DoubleLog2+Deriv")
# plot(lin.trs, col=cols[1], type=type, ylim=c(1, 365))
# plot(lin.deriv, col=cols[2], type=type, add=TRUE)
# plot(spl.trs, col=cols[3], type=type, add=TRUE)
# plot(spl.deriv, col=cols[4], type=type, add=TRUE)
# plot(beck.trs, col=cols[7], type=type, add=TRUE)
# plot(beck.deriv, col=cols[8], type=type, add=TRUE)
# plot(elmore.trs, col=cols[9], type=type, add=TRUE)
# plot(elmore.deriv, col=cols[10], type=type, add=TRUE)
# legend("center", nms, text.col=cols, ncol=3, bty="n")
# 
# cor(cbind(lin.trs$sos, lin.deriv$sos, spl.trs$sos, spl.deriv$sos, beck.trs$sos, 
#    beck.deriv$sos, elmore.trs$sos, elmore.deriv$sos), use="pairwise.complete.obs")
# cor(cbind(lin.trs$eos, lin.deriv$eos, spl.trs$eos, spl.deriv$eos, beck.trs$eos, 
#    beck.deriv$eos, elmore.trs$eos, elmore.deriv$eos), use="pairwise.complete.obs")
# 
# # compare results: LOS
# type <- c("los")
# plot(lin.trs, col=cols[1], type=type, ylim=c(130, 365))
# plot(lin.deriv, col=cols[2], type=type, add=TRUE)
# plot(spl.trs, col=cols[3], type=type, add=TRUE)
# plot(spl.deriv, col=cols[4], type=type, add=TRUE)
# plot(beck.trs, col=cols[7], type=type, add=TRUE)
# plot(beck.deriv, col=cols[8], type=type, add=TRUE)
# plot(elmore.trs, col=cols[9], type=type, add=TRUE)
# plot(elmore.deriv, col=cols[10], type=type, add=TRUE)
# legend("bottom", nms, text.col=cols, ncol=5, bty="n")
# 
# # compare results: MSP, PEAK, TROUGH
# type <- c("msp", "peak", "trough")
# plot(lin.trs, col=cols[1], type=type, ylim=c(0.17, 0.37))
# plot(lin.deriv, col=cols[2], type=type, add=TRUE)
# plot(spl.trs, col=cols[3], type=type, add=TRUE)
# plot(spl.deriv, col=cols[4], type=type, add=TRUE)
# plot(beck.trs, col=cols[7], type=type, add=TRUE)
# plot(beck.deriv, col=cols[8], type=type, add=TRUE)
# plot(elmore.trs, col=cols[9], type=type, add=TRUE)
# plot(elmore.deriv, col=cols[10], type=type, add=TRUE)
# legend("bottom", nms, text.col=cols, ncol=5, bty="n")

greenbrown documentation built on Dec. 18, 2020, 3:02 p.m.