knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
This R package is an effort to simplify data wrangling steps for fusion of PhenoCam time-series and other datasets.
Each PhenoCam site has specific metadata including but not limited to how a site is set up and where it is located, what vegetation type is visible from the camera, and its climate regime. Each PhenoCam may have none to several ROI's per vegetation type.
phenocamapi is an interface to interact with the PhenoCam server to extract those data and process them in an R environment.
I begin with several examples for extracting Phenocam data directly from the server:
- Exploring the PhenoCam metadata
- Downloading PhenoCam time-series data
- Merging with other time-series
Exploriong the PhenoCam metadata
We can obtain an up-to-date
data.frame of the metadata of the entire PhenoCam network using the
get_phenos() function. The returning value would be a
data.table in order to simplify further data exploration.
library(data.table) library(phenocamapi) # obtaining the metadata as data.table phenos <- get_phenos()
Filtering the dataset using attributes
For example we can just list the sites that have flux tower:
# store sites with flux_data available phenofluxsites <- phenos[flux_data==TRUE&!is.na(flux_sitenames), .(PhenoCam=site, Flux=flux_sitenames)] # see the first few rows head(phenofluxsites) #list deciduous broadleaf sites with flux tower DB.flux <- phenos[flux_data==TRUE&primary_veg_type=='DB', site] # see the first few rows head(DB.flux)
Downloading PhenoCam time-series data
PhenoCam time-series are extracted time-series data obtained from ROI's for a given site.
To download the phenological time-series from the PhenoCam, we need to know the sitename, vegetation type and ROI ID. This information can be obtained from the PhenoCam website or using the
# obtaining the list of all the available ROI's on the PhenoCam server rois <- get_rois() head(rois$roi_name) colnames(rois) # list all the ROI's for dukehw rois[site=='dukehw',]
get_pheno_ts() function can download a time-series and return the result as a
data.table. For example, to obtain the time-series for DB_1000 from the dukehw PhenoCam site, we can run the following code:
# to obtain the DB 1000 from dukehw dukehw_DB_1000 <- get_pheno_ts(site = 'dukehw', vegType = 'DB', roiID = 1000, type = '3day') colnames(dukehw_DB_1000) dukehw_DB_1000[,date:=as.Date(date)] dukehw_DB_1000[,plot(date, gcc_90, col = 'green', type = 'b')] mtext('Duke Forest, Hardwood', font = 2)
Merge with other time-series such as flux data
In a fully programmatic settings you can load the PhenoCam dataset, find the related flux data, load the flux data and merge everything together as follows:
phenots <- get_pheno_ts(site = 'oregonMP', vegType = 'EN', roiID = 1000) colnames(phenots) fluxfile <- system.file('fluxnetrepo/FLX_US-Me2/FLX_US-Me2_FULLSET_DD.csv', package = 'phenocamapi') fluxts <- read.csv(fluxfile, skip = 0) fluxts[fluxts==-9999] <- NA fluxts <- as.data.table(fluxts) fluxts[,datetime:=as.POSIXct(as.character(TIMESTAMP), format='%Y%m%d')] fluxts[,YYYYMMDD:=as.character(as.Date(datetime))] fluxts[,YEAR:=year(datetime)] fluxts[,DOY:=yday(datetime)] head(fluxts[, .(TIMESTAMP, TA_F)])