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)

Arguments

site

the site name, as mentioned on the PhenoCam web page expressed as a regular expression ("harvard$" == exact match)

veg_type

vegetation type (DB, EN, ... default = ALL)

frequency

frequency of the time series product (1, 3, "roistats")

roi_id

the id of the ROI to download (default = ALL)

outlier_detection

TRUE or FALSE, detect outliers

smooth

smooth data (logical, default is TRUE)

contract

contract 3-day data (logical, default is TRUE)

daymet

TRUE or FALSE, merges the daymet data

trim_daymet

TRUE or FALSE, trims data to match PhenoCam data

trim

year (numeric) to which to constrain the output (default = NULL)

phenophase

logical, calculate transition dates (default = FALSE)

out_dir

output directory where to store downloaded data (default = tempdir())

internal

allow for the data element to be returned to the workspace

Value

Downloaded files in out_dir of requested time series products, as well as derived phenophase estimates based upon these time series.

Examples

# 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")
#> Downloading: harvard_DB_1000_3day.csv
#> -- Flagging outliers!
#> -- Smoothing time series!
# read phenocam data into phenocamr data structure df <- read_phenocam(file.path(tempdir(),"harvard_DB_1000_3day.csv"))