process_phenocam: Function to post-process PhenoCam time series

In bnasr/phenocamrCS: Facilitates 'PhenoCam' Data Access and Time Series Post-Processing

Description

Wrapper around other more basic funtions, in order to generate phenocam data products.

Usage

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,
  span = NULL,
  plot_optim = 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
internal	allow for the data element to be returned to the workspace
span	fixed span, NULL by default
plot_optim	whether to plot optimal (default = tempdir())
...	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

## Not run: 
# 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"))
                  

## End(Not run)

bnasr/phenocamrCS documentation built on June 5, 2020, 3:52 a.m.