Wrapper around other more basic funtions, in order to generate phenocam data products.
process_phenocam(
file,
outlier_detection = TRUE,
smooth = TRUE,
contract = FALSE,
expand = TRUE,
truncate,
phenophase = TRUE,
snow_flag = FALSE,
penalty = 0.5,
out_dir = tempdir(),
internal = FALSE,
...
)Arguments
- file
1 or 3-day PhenoCam time series file path
- outlier_detection
TRUE or FALSE, detect outliers
- smooth
smooth data (logical, default is
TRUE)- contract
contract 3-day data upon output (logical, default is
TRUE)- expand
expand 3-day data upon input (logical, default is
TRUE)- truncate
year (numeric) to which to constrain the output
- phenophase
logical, calculate transition dates (default =
FALSE)- snow_flag
integrate snow flags?
- penalty
how sensitive is the change point algorithm, lower is more sensitve (< 1, default = 0.5)
- out_dir
output directory where to store downloaded data (default = tempdir())
- internal
allow for the data element to be returned to the workspace
- ...
additional parameters to be forwarded to the phenophases() function, used internally in the routine
Value
Downloaded files in out_dir of requested time series products, as well as derived phenophase estimates based upon these time series.
Examples
if (FALSE) {
# download the first ROI time series for the Harvard PhenoCam site
# at an aggregation frequency of 3-days.
download_phenocam(site = "harvard$",
veg_type = "DB",
roi_id = "1000",
frequency = "3")
# read phenocam data into phenocamr data structure
df <- process_phenocam(file.path(tempdir(),"harvard_DB_1000_3day.csv"))
}