View source: R/process_phenocam.r
process_phenocam | R Documentation |
Wrapper around other more basic funtions, in order to generate phenocam data products.
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, ... )
file |
1 or 3-day PhenoCam time series file path |
outlier_detection |
TRUE or FALSE, detect outliers |
smooth |
smooth data (logical, default is |
contract |
contract 3-day data upon output
(logical, default is |
expand |
expand 3-day data upon input
(logical, default is |
truncate |
year (numeric) to which to constrain the output |
phenophase |
logical, calculate transition dates (default = |
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 |
Downloaded files in out_dir of requested time series products, as well as derived phenophase estimates based upon these time series.
## 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)
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