6  Theme: Phenology

What: Terrestrial phenology defined by daily greenness and redness of plants

Where: 47 sites in total - 15 deciduous broadleaf forest, 11 evergreen needleleaf, 9 grassland, 5 tundra, 3 agriculture, 2 evergreen broadleaf (tropical) and 2 shrubland NEON sites across the U.S. and Puerto Rico

When: Daily forecasts for at least 30-days in the future. Forecast submissions are accepted daily. The only requirement is that submissions are predictions of the future at the time the forecast is submitted.

Why: Phenology has been identified as one of the primary ecological fingerprints of global climate change.

Who: Open to any individual or team that registers

How: REGISTER your team and submit forecast. If you registered for the Round 1 (2021) and are using the same team and method then you do not need to re-register.

The video below is an overview of the Phenology Challenge that was recorded for the 2021 Early Career Annual Meeting

We held a Q&A session on January 27, 2021. You can find a recording from that session HERE.

6.1 Overview

Phenology has been shown to be a robust integrator of the effects of year-to-year climate variability and longer-term climate change on natural systems (e.g., recent warming trends). Experimental studies have shown how other global change factors (e.g., elevated CO2 and N deposition) can also influence phenology. There is a need to better document biological responses to a changing world, and improved phenological monitoring at scales from individual organisms to ecosystems, regions, and continents will contribute to achieving this goal.

Phenology researchers often use digital cameras (such as those that are part of the PhenoCam Network) that take regular repeated images of plant canopies to monitor changes in greenness and redness throughout the year. The PhenoCam Network is a cooperative continental-scale phenological observatory that uses digital repeat photography to track vegetation phenology in a diverse range of ecosystems across North America and around the World. Imagery and data are made publicly available in near-real-time through the PhenoCam webpage: http://phenocam.nau.edu/.

6.2 Challenge

This is an open ecological forecasting challenge to forecast spring green-up of the greenness (gcc) and redness (rcc) indices, as measured by digital cameras at various NEON sites. The forecasts will be forecasts of daily mean gcc and rcc (specifically the 90% quantile called the gcc_90 and rcc_90) for a region of interests with each site’s digital photograph.

Teams must provide access (minimum of URL, but ideally a script) to any additional data they wish to use to all teams in the challenge. Teams of various career stages and disciplines are encouraged to submit forecasts.

6.3 Data: Targets

The challenge uses the following NEON data products:

DP1.00033.001: Phenology images

A file with previously released NEON data that has been processed into “targets” is provided below. The same processing will be applied to new data that are used for forecast evaluation. The processing script is available on the here.

6.3.1 Green chromatic coordinate (gcc)

Definition

The ratio of the green digital number to the sum of the red, green, blue digital numbers from a digital camera. gcc_90 is the 90th percentile of the gcc within a set of pixel called a region of interest (ROI)

Motivation

Quantitative metrics of vegetation color extracted from PhenoCam imagery provide data that are consistent with ground observations of phenology and as well as other conventional vegetation indices across ecosystems.

6.3.2 Red chromatic coordinate (rcc)

Definition

The ratio of the red digital number to the sum of the red, green, blue digital numbers from a digital camera. rcc_90 is the 90th percentile of the rcc within a set of pixel called a region of interest (ROI)

Motivation

While gcc is primarily a metric of vegetation greenness, rcc is more a metric of fall color. Adding rcc to the autumn forecast challenge has two motivations. First, from an end-user’s perspective the timing of peak fall coloration has aesthetic value, which translates into economic for tourism. Second, from the ecological perspective, autumn phenology involves two distinct (but coupled) processes, senescence (loss of leaf chlorophyll and photosynthetic activity; translocation of nutrients) and abscission (actual leaf fall). Forecasting two indices helps us disentangle our ability to predict these two processes.

6.3.3 Focal sites

Information on the sites can be found here:

site_data <- readr::read_csv("https://raw.githubusercontent.com/eco4cast/neon4cast-targets/main/NEON_Field_Site_Metadata_20220412.csv") |> 
  dplyr::filter(phenology == 1)

with full site table at the end of this page.

The distribution of sites across ecosystems types is:

Vegetation type Count
deciduous broadleaf 15
evergreen needleleaf 11
grassland 9
tundra 5
agriculture 3
evergreen broadleaf 2
shrubland 2

6.3.4 Target data calculation

Digital cameras mounted above forests are pointed at the forest canopy. Images are collected every half hour.

The images are a set of pixels values in red, green, and blue color channels (RGB). A pixel value is an 8-bit digital number (DN). Because internal processing (including exposure control) and external factors affecting scene illumination (weather and atmospheric effects) both influence the retrieved RGB signature, we calculate a number of vegetation indices that are effective at suppressing this unwanted variation and maximizing the underlying phenological signal. Most important among these is the green chromatic coordinate (GCC), calculated as GCC = GDN / (RDN + GDN + BDN). The red chromatic coordinate (GCC) is calculated in a similar way.

For additional details, see Richardson et al. (2018) Scientific Data, and Richardson (2019) New Phytologist.

PhenoCam data are processed and posted daily and the low latency of the PhenoCam data allows for a unique opportunity to evaluate forecasts in real-time.

Each image has a defined “region of interest’ (ROI). An ROI is a set of pixels that isolates particular features in the image (i.e., a set of deciduous trees in a mixed forest). The ROI of the below top-of-canopy PhenoCams will be used to assess the forecasts’ accuracy. The mid-day (noon) mean GCC and GCC standard deviation for the ROI will be used for evaluation.

All data in the supplied file is available to build and evaluate models before submitting a forecast to challenge. Once new data becomes avialable, the data are appended to the existing file. Within the challenge scoring, only the new data are used to evaluate previously submitted forecasts.

6.3.5 Target file

Here is the format of the target file

readr::read_csv("https://data.ecoforecast.org/neon4cast-targets/phenology/phenology-targets.csv.gz", guess_max = 1e6) |> 
  na.omit()
# A tibble: 174,778 × 4
   datetime   site_id variable observation
   <date>     <chr>   <chr>          <dbl>
 1 2017-05-30 ABBY    gcc_90         0.417
 2 2017-05-31 ABBY    gcc_90         0.416
 3 2017-06-01 ABBY    gcc_90         0.418
 4 2017-06-02 ABBY    gcc_90         0.415
 5 2017-06-03 ABBY    gcc_90         0.422
 6 2017-06-04 ABBY    gcc_90         0.417
 7 2017-06-05 ABBY    gcc_90         0.409
 8 2017-06-06 ABBY    gcc_90         0.416
 9 2017-06-07 ABBY    gcc_90         0.426
10 2017-06-08 ABBY    gcc_90         0.430
# … with 174,768 more rows

The target file has the following columns:

  • datetime: date of observation
  • site_id: NEON site code
  • variable: variable (gcc_90 or rcc_90)
  • observation: daily value

6.4 Timeline

Forecasts for a minimum of 30 days can be submitted daily by 12:59 pm UTC any day. A minimum of 35 days in the future must be forecasted for each submission. For example, a forecast submitted on March 1 should be for at least March 1st – March 30th, but it could be for the full spring. New forecasts can be submitted daily as new weather forecasts and observations (e.g., PhenoCam) become available (see Appendix B). The key is that submissions are predictions of the future.

Daily submissions are allowed and encouraged as new observations and weather forecasts become available, therefore the automation of forecast generation may be ideal. There are many ways to automate scripts that are written to download observations and meteorology drivers, generate forecasts, and submit forecasts. Two tools that many have used are cron jobs (see the R package cronR) that execute tasks at user specifics times and github actions. See more in Chapter 12

Cron jobs work on unix and mac systems. An example of a script that executes a cron job using R can be found here.

6.5 Submissions

The required names for forecasted variables: gcc_90, and rcc_90.

The required time unit: date in YYYY-MM-DD format

Instructions for submitting forecasts are found in Chapter 2

6.6 Meterological inputs for modeling

Information about forecasted meteorology that is available for you to use when generating your forecasts can be found in Chapter 9

6.7 Useful functions

Functions for validating, evaluating and submitting forecasts can be found in Chapter 10

Functions for downloading and working with the meteorology forecasts can be be found Chapter 9

6.8 Null models

Two null models are automatically generated each day. The persistence null model use the most recent measurement of gcc_90 or rcc_90 and predicts that the values will be constant in the future. The climatology null model futures that the gcc_90 or rcc_90 will be equal to the historical mean of that day of year.

Code for the persistence null model can be found here

Code for the climatology null model can be found here ## FAQ

Answers to frequency asks questions can be found in Chapter 12

6.9 Design team

Min Chen, University of Wisconsin, Madison
Michael Dietze, Boston University
Kathy Gerst, National Phenology Network
Chris Jones, NC State University David LeBauer, University of Arizona
Dabasmita Pal, Michigan State University
Andrew Richardson, Northern Arizona University
Arun Ross, Michigan State University
Bijan Seyednasrollah, Northern Arizona University, PhenoCam Network
Quinn Thomas, Virginia Tech
Kathryn Wheeler, Boston University
Luke Zachmann, Conservation Science Partners
Kai Zhu, University of California - Santa Cruz

6.10 Partners

The challenge is hosted by the Ecological Forecasting Initiative (EFI; https://ecoforecast.org/) and its U.S. National Science Foundation sponsored Research Coordination Network (EFI-RCN; https://ecoforecast.org/rcn/).

Data used in the challenge are collected by the National Ecological Observatory Network (NEON; https://www.neonscience.org/) and hosted by the Phenocam Network (http://phenocam.sr.unh.edu/).

The forecasting challenge was developed in collaboration with the USA National Phenology Network: https://www.usanpn.org/usa-national-phenology-network.

6.11 Site table

siteID site name Phenocam vegetation type Phenocam code Phenocam ROI NEON site URL
KONA Konza Prairie Agroecosystem NEON agriculture NEON.D06.KONA.DP1.00033 AG_1000 https://www.neonscience.org/field-sites/kona
LAJA Lajas Experimental Station NEON agriculture NEON.D04.LAJA.DP1.00033 AG_1000 https://www.neonscience.org/field-sites/laja
STER North Sterling NEON agriculture NEON.D10.STER.DP1.00033 AG_1000 https://www.neonscience.org/field-sites/ster
BART Bartlett Experimental Forest NEON deciduous broadleaf NEON.D01.BART.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/bart
BLAN Blandy Experimental Farm NEON deciduous broadleaf NEON.D02.BLAN.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/blan
CLBJ Lyndon B. Johnson National Grassland NEON deciduous broadleaf NEON.D11.CLBJ.DP1.00033 DB_2000 https://www.neonscience.org/field-sites/clbj
DELA Dead Lake NEON deciduous broadleaf NEON.D08.DELA.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/dela
GRSM Great Smoky Mountains National Park NEON deciduous broadleaf NEON.D07.GRSM.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/grsm
HARV Harvard Forest & Quabbin Watershed NEON deciduous broadleaf NEON.D01.HARV.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/harv
LENO Lenoir Landing NEON deciduous broadleaf NEON.D08.LENO.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/leno
MLBS Mountain Lake Biological Station NEON deciduous broadleaf NEON.D07.MLBS.DP1.00033 DB_2000 https://www.neonscience.org/field-sites/mlbs
ORNL Oak Ridge NEON deciduous broadleaf NEON.D07.ORNL.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/ornl
SCBI Smithsonian Conservation Biology Institute NEON deciduous broadleaf NEON.D02.SCBI.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/scbi
SERC Smithsonian Environmental Research Center NEON deciduous broadleaf NEON.D02.SERC.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/serc
STEI Steigerwaldt-Chequamegon NEON deciduous broadleaf NEON.D05.STEI.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/stei
TREE Treehaven NEON deciduous broadleaf NEON.D05.TREE.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/tree
UKFS University of Kansas Field Station NEON deciduous broadleaf NEON.D06.UKFS.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/ukfs
UNDE University of Notre Dame Environmental Research Center NEON deciduous broadleaf NEON.D05.UNDE.DP1.00033 DB_1000 https://www.neonscience.org/field-sites/unde
GUAN Guanica Forest NEON evergreen broadleaf NEON.D04.GUAN.DP1.00033 EB_1000 https://www.neonscience.org/field-sites/guan
PUUM Pu’u Maka’ala Natural Area Reserve NEON evergreen broadleaf NEON.D20.PUUM.DP1.00033 EB_1000 https://www.neonscience.org/field-sites/puum
ABBY Abby Road NEON evergreen needleleaf NEON.D16.ABBY.DP1.00033 EN_1000 https://www.neonscience.org/field-sites/abby
BONA Caribou-Poker Creeks Research Watershed NEON evergreen needleleaf NEON.D19.BONA.DP1.00033 EN_1000 https://www.neonscience.org/field-sites/bona
JERC The Jones Center At Ichauway NEON evergreen needleleaf NEON.D03.JERC.DP1.00033 EN_2000 https://www.neonscience.org/field-sites/jerc
OSBS Ordway-Swisher Biological Station NEON evergreen needleleaf NEON.D03.OSBS.DP1.00033 EN_1001 https://www.neonscience.org/field-sites/osbs
RMNP Rocky Mountains NEON evergreen needleleaf NEON.D10.RMNP.DP1.00033 EN_1000 https://www.neonscience.org/field-sites/rmnp
SJER San Joaquin Experimental Range NEON evergreen needleleaf NEON.D17.SJER.DP1.00033 EN_2000 https://www.neonscience.org/field-sites/sjer
SOAP Soaproot Saddle NEON evergreen needleleaf NEON.D17.SOAP.DP1.00033 EN_1001 https://www.neonscience.org/field-sites/soap
TALL Talladega National Forest NEON evergreen needleleaf NEON.D08.TALL.DP1.00033 EN_1000 https://www.neonscience.org/field-sites/tall
TEAK Lower Teakettle NEON evergreen needleleaf NEON.D17.TEAK.DP1.00033 EN_1000 https://www.neonscience.org/field-sites/teak
WREF Wind River Experimental Forest NEON evergreen needleleaf NEON.D16.WREF.DP1.00033 EN_1000 https://www.neonscience.org/field-sites/wref
YELL Yellowstone National Park NEON evergreen needleleaf NEON.D12.YELL.DP1.00033 EN_1000 https://www.neonscience.org/field-sites/yell
CPER Central Plains Experimental Range NEON grassland NEON.D10.CPER.DP1.00033 GR_1000 https://www.neonscience.org/field-sites/cper
DCFS Dakota Coteau Field Site NEON grassland NEON.D09.DCFS.DP1.00033 GR_1000 https://www.neonscience.org/field-sites/dcfs
DSNY Disney Wilderness Preserve NEON grassland NEON.D03.DSNY.DP1.00033 GR_1000 https://www.neonscience.org/field-sites/dsny
JORN Jornada Experimental Range NEON grassland NEON.D14.JORN.DP1.00033 GR_1000 https://www.neonscience.org/field-sites/jorn
KONZ Konza Prairie Biological Station NEON grassland NEON.D06.KONZ.DP1.00033 GR_1000 https://www.neonscience.org/field-sites/konz
MOAB Moab NEON grassland NEON.D13.MOAB.DP1.00033 GR_1000 https://www.neonscience.org/field-sites/moab
NOGP Northern Great Plains Research Laboratory NEON grassland NEON.D09.NOGP.DP1.00033 GR_1000 https://www.neonscience.org/field-sites/nogp
OAES Marvin Klemme Range Research Station NEON grassland NEON.D11.OAES.DP1.00033 GR_1000 https://www.neonscience.org/field-sites/oaes
WOOD Chase Lake National Wildlife Refuge NEON grassland NEON.D09.WOOD.DP1.00033 GR_1000 https://www.neonscience.org/field-sites/wood
ONAQ Onaqui NEON shrubland NEON.D15.ONAQ.DP1.00033 SH_1000 https://www.neonscience.org/field-sites/onaq
SRER Santa Rita Experimental Range NEON shrubland NEON.D14.SRER.DP1.00033 SH_1000 https://www.neonscience.org/field-sites/srer
BARR Utqiaġvik NEON tundra NEON.D18.BARR.DP1.00033 TN_1000 https://www.neonscience.org/field-sites/barr
DEJU Delta Junction NEON tundra NEON.D19.DEJU.DP1.00033 EN_1000 https://www.neonscience.org/field-sites/deju
HEAL Healy NEON tundra NEON.D19.HEAL.DP1.00033 TN_1000 https://www.neonscience.org/field-sites/heal
NIWO Niwot Ridge NEON tundra NEON.D13.NIWO.DP1.00033 TN_1000 https://www.neonscience.org/field-sites/niwo
TOOL Toolik Field Station NEON tundra NEON.D18.TOOL.DP1.00033 TN_1000 https://www.neonscience.org/field-sites/tool

6.12 References

Richardson, A., Hufkens, K., Milliman, T. et al. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Sci Data 5, 180028 (2018). https://doi.org/10.1038/sdata.2018.28

Richardson, A.D. (2019), Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery. New Phytol, 222: 1742-1750. https://doi.org/10.1111/nph.15591