Exploring PhenoCam metadata
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 zero to several Regions of Interest (ROIs) per vegetation type. The phenocamapi package is an interface to interact with the PhenoCam server to extract those data and process them in an R environment.
To explore the PhenoCam data, we’ll use several packages for this tutorial.
library(data.table)
library(phenocamapi)
#> Loading required package: rjson
#> Loading required package: RCurl
#> Loading required package: bitops
library(jpeg)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.
# obtaining the phenocam site metadata from the server as data.table
phenos <- get_phenos()
# checking out the first few sites
head(phenos$site)
#> [1] "acadia" "aguatibiaeast" "aguatibianorth" "ahwahnee"
#> [5] "alleypond" "alligatorriver"
# checking out the columns
colnames(phenos)
#> [1] "site" "lat"
#> [3] "lon" "elev"
#> [5] "active" "utc_offset"
#> [7] "date_first" "date_last"
#> [9] "infrared" "contact1"
#> [11] "contact2" "site_description"
#> [13] "site_type" "group"
#> [15] "camera_description" "camera_orientation"
#> [17] "flux_data" "flux_networks"
#> [19] "flux_sitenames" "dominant_species"
#> [21] "primary_veg_type" "secondary_veg_type"
#> [23] "site_meteorology" "MAT_site"
#> [25] "MAP_site" "MAT_daymet"
#> [27] "MAP_daymet" "MAT_worldclim"
#> [29] "MAP_worldclim" "koeppen_geiger"
#> [31] "ecoregion" "landcover_igbp"
#> [33] "dataset_version1" "site_acknowledgements"
#> [35] "modified" "flux_networks_name"
#> [37] "flux_networks_url" "flux_networks_description"Now we have a better idea of the types of metadata that are available for the Phenocams.
Remove null values
We may want to explore some of the patterns in the metadata before we jump into specific locations.
Let’s look at Mean Annual Precipiation (MAP) and Mean Annual Temperature (MAT) across the differnet field site and classify those by the primary vegetation type (primary_veg_type) for each site. We can find out what the abbreviations for the vegetation types mean from the following table:
| Abbreviation | Description | |
|---|---|---|
| AG | agriculture | |
| DB | deciduous broadleaf | |
| DN | deciduous needleleaf | |
| EB | evergreen broadleaf | |
| EN | evergreen needleleaf | |
| GR | grassland | |
| MX | mixed vegetation (generally EN/DN, DB/EN, or DB/EB) | |
| SH | shrubs | |
| TN | tundra (includes sedges, lichens, mosses, etc.) | |
| WT | wetland | |
| NV | non-vegetated | |
| RF | reference panel | |
| XX | unspecified |
To do this we’d first want to remove the sites where there is not data and then plot the data.
# removing the sites with unkown MAT and MAP values
phenos <- phenos[!((MAT_worldclim == -9999)|(MAP_worldclim == -9999))]
# extracting the PhenoCam climate space based on the WorldClim dataset
# and plotting the sites across the climate space different vegetation type as different symbols and colors
phenos[primary_veg_type=='DB', plot(MAT_worldclim, MAP_worldclim, pch = 19, col = 'green', xlim = c(-5, 27), ylim = c(0, 4000))]
#> NULL
phenos[primary_veg_type=='DN', points(MAT_worldclim, MAP_worldclim, pch = 1, col = 'darkgreen')]
#> NULL
phenos[primary_veg_type=='EN', points(MAT_worldclim, MAP_worldclim, pch = 17, col = 'brown')]
#> NULL
phenos[primary_veg_type=='EB', points(MAT_worldclim, MAP_worldclim, pch = 25, col = 'orange')]
#> NULL
phenos[primary_veg_type=='AG', points(MAT_worldclim, MAP_worldclim, pch = 12, col = 'yellow')]
#> NULL
phenos[primary_veg_type=='SH', points(MAT_worldclim, MAP_worldclim, pch = 23, col = 'red')]
#> NULL
legend('topleft', legend = c('DB','DN', 'EN','EB','AG', 'SH'),
pch = c(19, 1, 17, 25, 12, 23),
col = c('green', 'darkgreen', 'brown', 'orange', 'yellow', 'red' ))
Filtering using attributes
Alternatively, we may want to only include Phenocams with certain attributes in our datasets. For example, we may be interested only in sites with a co-located flux tower. For this, we’d want to filter for those with a flux tower using the flux_sitenames attribute in the metadata.
# store sites with flux_data available and the FLUX site name is specified
phenofluxsites <- phenos[flux_data==TRUE&!is.na(flux_sitenames)&flux_sitenames!='',
.(PhenoCam=site, Flux=flux_sitenames)] # return as table
#and specify which variables to retain
phenofluxsites <- phenofluxsites[Flux!='']
# see the first few rows
head(phenofluxsites)
#> PhenoCam Flux
#> 1: alligatorriver US-NC4
#> 2: arscolesnorth LTAR
#> 3: arscolessouth LTAR
#> 4: arsgreatbasinltar098 US-Rws
#> 5: arsgreatbasinltar177 US-Rms
#> 6: arsmorris1 UnaffiliatedWe could further identify which of those Phenocams with a fluxtower and in decidious broadleaf forests (primary_veg_type=='DB').
