Description Usage Arguments Details Value See Also Examples
Using lower quantile (default = 0.025) of multi-year MODIS data, determine the "winterNDVI" for each id.
1 2 3 4 5 6 7 | filter_winter(
DT,
probs = 0.025,
limits = c(60L, 300L),
doy = "DayOfYear",
id = "id"
)
|
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 is c(60, 300): 60 days after Jan 1 and 65 days before Jan 1. |
doy |
julian day column. default is 'DayOfYear'. |
id |
id column. default is 'id'. See 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.
filtered data.table with appended 'winter' column of each id's "winterNDVI" baseline value.
Other filter:
filter_ndvi()
,
filter_qa()
,
filter_roll()
,
filter_top()
1 2 3 4 5 6 7 | # Load data.table
library(data.table)
# Read example data
ndvi <- fread(system.file("extdata", "sampled-ndvi-MODIS-MOD13Q1.csv", package = "irg"))
filter_qa(ndvi, ndvi = 'NDVI', qa = 'SummaryQA', good = c(0, 1))
filter_winter(ndvi, probs = 0.025, limits = c(60L, 300L), doy = 'DayOfYear', id = 'id')
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.