process_phenocam: Function to post-process PhenoCam time series

Description Usage Arguments Value Examples

View source: R/process_phenocam.r

Description

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

Usage

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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

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## 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)

phenocamr documentation built on March 26, 2020, 7:59 p.m.