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"))
}