process_phenocam: Function to post-process PhenoCam time series

In khufkens/phenocamr: Facilitates 'PhenoCam' Data Access and Time Series Post-Processing

Function to post-process PhenoCam time series

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,
  ...
)

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

## 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)

khufkens/phenocamr documentation built on Feb. 4, 2024, 5:22 a.m.