Using lower quantile (default = 0.025) of multi-year MODIS data, determine the "winterNDVI" for each id.

Usage,
filter_winter(DT, probs = 0.025, limits = c(60L, 300L),
  doy = "DayOfYear", id = "id")

Arguments

DT

data.table of NDVI time series

probs

quantile probability to determine "winterNDVI". default is 0.025.

limits

integer vector indicating limit days of absolute winter (snow cover, etc.). default = 60 days after Jan 1 and 65 days before Jan 1.

doy

julian day column. default is 'DayOfYear'. integer type.

id

id column. default is 'id'. See details.

Value

filtered data.table with appended 'winter' column of each id's "winterNDVI" baseline value.

Details

The id argument is used to split between sampling units. This may be a point id, polygon id, pixel id, etc. depending on your analysis.

See also

Examples

# Load data.table
library(data.table)

# Read example data
ndvi <- fread(system.file("extdata", "ndvi.csv", package = "irg"))
filter_qa(ndvi, qa = 'SummaryQA', good = c(0, 1))
filter_winter(ndvi, probs = 0.025, limits = c(60L, 300L), doy = 'DayOfYear', id = 'id')
#>       id   yr DayOfYear  NDVI SummaryQA filtered winter
#>    1:  1 2002         3 -1367         3     4099   4099
#>    2:  2 2002        14  -304         3     5382   5382
#>    3:  3 2002         1   374         2     3702   3702
#>    4:  4 2002        15   635         3     5180   5180
#>    5:  5 2002         9   685         2     4621   4621
#>   ---                                                  
#> 1261:  1 2012       353   151         2     4099   4099
#> 1262:  2 2012       356   330         2     5382   5382
#> 1263:  3 2012       356   560         2     3702   3702
#> 1264:  4 2012       356  1720         2     5180   5180
#> 1265:  5 2012       356  2689         2     4621   4621