PhenologyRaster {greenbrown} | R Documentation |
This function calculates metrics of vegetation phenology on multi-temporal raster data. See 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 treshold methods (PhenoTrs
) or by using the derivative of the time series (PhenoDeriv
).
PhenologyRaster(r, start = c(1982, 1), freq = 12, 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"), backup = NULL, check.seasonality = 1:3, ...)
r |
multi-layer raster object of class |
start |
beginning of the time series (i.e. the time of the first observation). The default is c(1982, 1), i.e. January 1982 which is the usual start date to compute trends on long-term series of satellite observations of NDVI. See |
freq |
The frequency of observations. The default is 12 for monthly observations. Use 24 for bi-monthly observations, 365 for daily observations or 1 for annual observations. See |
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 |
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 |
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 |
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 |
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", "TSGFssa", "TSGFlinear" |
check.seasonality |
Which methods in |
... |
additional arguments as for |
The function returns a RasterBrick with different phenology metrics statistics. The layers are named:
SOS.
start of season in year x
EOS.
end of season in year x
LOS.
length of season in year x
POP.
position of peak in year x
POT.
position of trough in year x
MGS.
mean growing season value in year x
PEAK.
peak value in year x
TROUGH.
trough value in year x
MSP.
mean spring value in year x
MAU.
mean autumn value in year x
RSP.
rate of spring greenup in year x (only if approach is 'Deriv')
RAU.
rate of autumn senescence in year x (only if approach is 'Deriv')
The number of years in the input raster will define the number of layers in the result.
Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [aut, cre]
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.
Phenology
, PhenologyNCDF
, NamesPhenologyRaster
# load a multi-temporal raster dataset of Normalized Difference Vegetation Index data(ndvimap) plot(ndvimap, 8) # calculate phenology metrics (this can take some time!) phenmap <- PhenologyRaster(ndvimap, start=c(1982, 1), freq=12, tsgf="TSGFspline", approach="Deriv") # Select method by defining 'tsgf' (temporal smoothing and gap filling) and # by 'approach' (method to summarize phenology metrics). # See \code{\link{Phenology}} for examples and a comparison of methods. plot(phenmap) plot(phenmap, grep("SOS.1982", names(phenmap))) # start of season 1982 plot(phenmap, grep("EOS.1982", names(phenmap))) # end of season 1982 plot(phenmap, grep("LOS.1982", names(phenmap))) # length of season 1982 plot(phenmap, grep("POP.1982", names(phenmap))) # position of peak value 1982 plot(phenmap, grep("POT.1982", names(phenmap))) # position of trough value 1982 plot(phenmap, grep("MGS.1982", names(phenmap))) # mean growing season value 1982 plot(phenmap, grep("PEAK.1982", names(phenmap))) # peak value 1982 plot(phenmap, grep("TROUGH.1982", names(phenmap))) # trough value 1982 plot(phenmap, grep("MSP.1982", names(phenmap))) # mean spring value 1982 plot(phenmap, grep("MAU.1982", names(phenmap))) # mean autumn value 1982 plot(phenmap, grep("RSP.1982", names(phenmap))) # rate of spring greenup 1982 plot(phenmap, grep("RAU.1982", names(phenmap))) # rate of autumn senescence 1982 # calculate trends on length of season using TrendRaster losmap <- subset(phenmap, grep("LOS", names(phenmap))) plot(losmap) lostrend <- TrendRaster(losmap, start=c(1982, 1), freq=1) plot(lostrend) # classify trends in length of season lostrend.cl <- TrendClassification(lostrend) plot(lostrend.cl, col=brgr.colors(3), breaks=c(-1.5, -0.5, 0.5, 1.5)) # only a few pixels have a positive trend in the length of growing season