site_data <- readr::read_csv("https://raw.githubusercontent.com/eco4cast/neon4cast-targets/main/NEON_Field_Site_Metadata_20220412.csv") |>
dplyr::filter(phenology == 1)4 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 are accepted at any time. 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.
4.1 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/.
4.2 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.
4.3 Data: Targets
The challenge uses the following NEON data products:
DP1.00033.001: Phenology images
4.3.1 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.
4.3.2 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.
4.3.3 Focal sites
Information on the sites can be found here:
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 |
4.3.4 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 available, the data are appended to the existing file. Within the challenge scoring, only the new data are used to evaluate previously submitted forecasts.
4.4 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 |
4.5 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