5 Theme: Autumn phenology
What: Terrestrial phenology defined by daily greenness and fall colors (redness) of plants
Where: 8 deciduous broadleaf forest NEON sites in the continental U.S.
When: Daily forecasts for 35-days in the future from July 15 - December 31, 2021. Forecast submissions are accepted daily, and later submissions after the July 15 start are permissible.
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
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 about the analogous spring phenology forecast on January 27, 2021. You can find a recording from that session HERE.
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 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.sr.unh.edu/.
This is an open ecological forecasting challenge to forecast spring green-up of the common greenness index (gcc), as measured by digital cameras at various deciduous broadleaf NEON sites. The primary forecast will be for daily mean gcc (specifically the 90% quantile called the gcc_90, which has been shown to be more robust). In additional, there is a second (optional) forecast for daily mean rcc_90 (90% quantile of the red chromatic coordinate), which we are using as an index of fall colors. The sites include Harvard Forest (HARV), Bartlett Experimental Forest (BART), Smithsonian Conservation Biology Institute, (SCBI), Steigerwaldt Land Services (STEI), The University of Kansas Field Station, KS (UKFS), Great Smoky Mountains National Park (GRSM), Dead Lake (DELA), and National Grassland (CLBJ).
NOAA Global Ensemble Forecast System weather forecasts for each NEON site is provided for teams to use: https://data.ecoforecast.org/minio/drivers/noaa/
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.
5.3 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 neon4cast-phenology GitHub repository.
5.3.1 Green chromatic coordinate (gcc)
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)
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.
5.3.2 Red chromatic coordinate (rcc)
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)
While gcc is primarily a metric of vegetation greeness, 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 photosythetic activity; translocation of nutrients) and abscission (actual leaf fall). Forecasting two indices helps us disentangle our ability to predict these two processes.
5.3.3 Focal sites
|Site Name||Site (and PhenoCam) ID||NEON Domain||Latitude||Longitude||Dominant Species|
|Harvard Forest, MA||NEON.D01.HARV.DP1.00033||D01: Northeast||42.537||-72.173||Quercus rubra, Acer rubrum, Aralia nudicaulis|
|Bartlett Experimental Forest, NH||NEON.D01.BART.DP1.00033||D01: Northeast||44.0639||-71.2874||Liriodendron tulipifera, Microstegium vimineum, Juglans nigra|
|Steigerwaldt Land Services, WI||NEON.D05.STEI.DP1.00033||D05: Great Lakes||45.509||-89.586||Populus tremuloides, Abies balsamea, Acer rubrum|
|The University of Kansas Field Station, KS||NEON.D06.UKFS.DP1.00033||D06: Prairie Peninsula||39.040||-95.192||Symphoricarpos orbiculatus, Celtis occidentalis, Carya ovata|
|Great Smoky Mountains National Park, TN||NEON.D07.GRSM.DP1.00033||D07: Appalachians and Cumberland Plateau||35.689||-83.502||Liriodendron tulipifera, Acer rubrum, Acer pensylvanicum|
|Dead Lake, AL||NEON.D08.DELA.DP1.00033||D08: Ozarks Complex||32.542||-87.804||Celtis laevigata, Ligustrum sinense, Liquidambar styraciflua|
|National Grassland, TX||NEON.D11.CLBJ.DP1.00033||D11: Southern Plains||33.401||-97.570||Quercus marilandica, Schizachyrium scoparium|
5.3.4 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. Important among these is the green chromatic coordinate (GCC), calculated as GCC = GDN / (RDN + GDN + BDN) and the red chromatic coordinate (RCC), calculated as RCC = RDN / (RDN + GDN + BDN).
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 “DB_1000” for 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 “DB_1000” 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.
5.3.5 Target file
Here is the format of the target file
::read_csv("https://data.ecoforecast.org/targets/phenology/phenology-targets.csv.gz", guess_max = 1e6)readr
## # A tibble: 14,168 x 6 ## time siteID gcc_90 rcc_90 gcc_sd rcc_sd ## <date> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 2016-12-13 HARV 0.329 0.375 0.00157 0.00388 ## 2 2016-12-14 HARV 0.328 0.411 0.00157 0.00388 ## 3 2016-12-15 HARV 0.330 0.401 0.00157 0.00388 ## 4 2016-12-16 HARV 0.329 0.398 0.00157 0.00388 ## 5 2016-12-17 HARV 0.332 0.351 0.00157 0.00388 ## 6 2016-12-18 HARV 0.332 0.353 0.00157 0.00388 ## 7 2016-12-19 HARV 0.329 0.399 0.00157 0.00388 ## 8 2016-12-20 HARV 0.330 0.405 0.00157 0.00388 ## 9 2016-12-21 HARV 0.329 0.412 0.00157 0.00388 ## 10 2016-12-22 HARV 0.329 0.412 0.00157 0.00388 ## # … with 14,158 more rows
The target file has the following columns:
siteID: NEON site code (e.g., BART)
gcc_90: 90th percentile GCC for the ROI
gcc_sd: standard deviation of the 90th percentile (GCC)
rcc_90: 90th percentile RCC for the ROI
rcc_sd: standard deviation of the 90th percentile (RCC)
Forecasts for a minimum of 35 days can be submitted daily by 6 pm ET for the period of July 15th through December 15th, 2021. Forecast should be submitted starting July 15th by 6 pm ET. A minimum of 35 days in the future must be forecasted for each submission. For example, the first submitted forecast should be for at least July 15th – August 19th, but it could be for the full autumn. New forecasts can be submitted daily as new weather forecasts and observations (e.g., PhenoCam) become available. Processed PhenoCam data will be available daily by 11:59 pm ET for each day. Teams are allowed to start submitting forecasts after February 1st, but only forecasts of future days (when submitted) will be allowed. Late forecasts might be allowed under extenuating circumstances related to computer failure or processing delays on our end. Forecasts do not have to be submitted daily and can be longer than 35 days.
5.5 Design team
Kathryn Wheeler, Boston University
Michael Dietze, Boston University
Kathy Gerst, National Phenology Network
Chris Jones, NC State University
Andrew Richardson, Northern Arizona University
Bijan Seyednasrollah, Northern Arizona University, PhenoCam Network
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/).
The forecasting challenge was developed in collaboration with the USA National Phenology Network: https://www.usanpn.org/usa-national-phenology-network.
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