This is a wrapper around most of all the other functions. It downloads a time series and extract relevant phenological transition dates or phenophases.
download_phenocam(
site = "harvard$",
veg_type = NULL,
frequency = "3",
roi_id = NULL,
outlier_detection = TRUE,
smooth = TRUE,
contract = FALSE,
daymet = FALSE,
trim_daymet = TRUE,
trim = NULL,
phenophase = FALSE,
out_dir = tempdir(),
internal = FALSE
)
the site name, as mentioned on the PhenoCam web page expressed as a regular expression ("harvard$" == exact match)
vegetation type (DB, EN, ... default = ALL)
frequency of the time series product (1, 3, "roistats")
the id of the ROI to download (default = ALL)
TRUE or FALSE, detect outliers
smooth data (logical, default is TRUE
)
contract 3-day data (logical, default is TRUE
)
TRUE or FALSE, merges the daymet data
TRUE or FALSE, trims data to match PhenoCam data
year (numeric) to which to constrain the output (default = NULL
)
logical, calculate transition dates (default = FALSE
)
output directory where to store downloaded data (default = tempdir())
allow for the data element to be returned to the workspace
Downloaded files in out_dir of requested time series products, as well as derived phenophase estimates based upon these time series.
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 <- read_phenocam(file.path(tempdir(),"harvard_DB_1000_3day.csv"))
}