MODIS (version 1.2.11)

smooth.spline.raster: Filter Time Series Imagery with a Cubic Spline

Description

This function uses the stats::smooth.spline() function to filter a vegetation index time series of satellite data.

Usage

smooth.spline.raster(
  x,
  w = NULL,
  t = NULL,
  groupYears = TRUE,
  timeInfo = orgTime(x),
  df = 6,
  outDirPath = "./",
  ...
)

Value

The filtered data and a text file with the dates of the output layers.

Arguments

x

RasterBrick (or RasterStack) or character vector of file names, sorted 'Vegetation index'.

w

RasterBrick (or RasterStack) with weighting information, e.g. derived from makeWeights().

t

In case of MODIS composite, the corresponding 'composite_day_of_the_year' RasterBrick (or RasterStack).

groupYears

logical. If TRUE (default), output files are grouped by years.

timeInfo

Result from orgTime().

df

numeric, yearly degree of freedom value passed to stats::smooth.spline(). If set as character (i.e., df = "6"), it is not adapted to the time series length but used as a fixed value (see Details).

outDirPath

Output path, defaults to the current working directory.

...

Arguments passed to raster::writeRaster(). Note that 'filename' is created automatically.

Author

Matteo Mattiuzzi

Details

numeric values of 'df' (e.g., df = 6) are treated as yearly degrees of freedom. Here, the length of the input time series is not relevant since df is adapted to it with: df * ('length of _input_ timeserie in days' / 365). The input length can differ from the output because of the 'pillow' argument in orgTime().

character values of 'df' (e.g., df = "6"), on the other hand, are not adopted to the length of the input time series.

Currently tested on MODIS and Landsat data. With M*D13 data, it is also possible to use the 'composite_day_of_the_year' layer and the 'VI_Quality' layer.

See Also

whittaker.raster(), raster::raster().

Examples

if (FALSE) {
# The full capacity of the following functions is currently available only 
# with M*D13 data.
# !! The function is very new, double check the result!!

# You need to extract the: 'vegetation index', 'VI_Quality layer', 
# and 'composite day of the year' layer.
# runGdal(product="MOD13A2",begin="2004340",extent="sicily",end="2006070",
# job="fullCapa",SDSstring="101000000010")
# Afterward extract it to: 
options("MODIS_outDirPath")

# the only obligatory dataset is "x" (vegetatino index), get the 'vi' data on
# the source directory: 
path <- paste0(options("MODIS_outDirPath"),"/fullCapa")
vi <- preStack(path=path, pattern="*_NDVI.tif$")

# `orgTime()` detects timing information of the input data and generates 
# based on the arguments the output date information. For spline functions 
# (in general) the start and end of the time series is always problematic. 
# So there is the argument "pillow" (default 75 days) that adds
# (if available) some more layers on the two endings.

timeInfo <- orgTime(vi,nDays=16,begin="2005001",end="2005365",pillow=40)

# now re-run "preStack" with two differences, 'files' (output of the first 
# `preStack()` call) and the 'timeInfo'.
# Here only the data needed for the filtering is extracted:
vi <- preStack(files=vi,timeInfo=timeInfo)

smooth.spline.raster(x=vi,timeInfo=timeInfo)

# Filter with weighting and time information:
# if the files are M*D13 you can use also use quality layers and the 
# composite day of the year:
w <- stack(preStack(path=path, pattern="*_VI_Quality.tif$", timeInfo=timeInfo))
w <- makeWeights(w,bitShift=2,bitMask=15,threshold=6)
# you can also pass only the names
t <- preStack(path=path, pattern="*_composite_day_of_the_year.tif$", timeInfo=timeInfo)

smooth.spline.raster(x=vi,w=w,t=t,timeInfo=timeInfo)
}