Project Team

Principle Investigator:

Andrew D. Richardson, Harvard University

Co PI's

Mark Friedl, Boston University

Steve Frolking, University of New Hampshire

Data manager

Tom Milliman, University of New Hampshire

Students and postdocs

Koen Hufkens, postdoc, Harvard University

Margaret Kosmala, postdoc, Harvard University

Min Chen, postdoc, Harvard University

Don Aubrecht, postdoc, Harvard University

Eli Melaas, postdoc, Boston University

Josh Gray, postdoc, Boston University

Steve Klosterman, PhD student, Harvard University

Collaborators

Sandra Henderson, NEON

Jake Weltzin, USA-NPN

Past Contributors

Michael Toomey, former postdoc, Harvard University

Trevor Keenan, former postdoc, Harvard University

Oliver Sonnentag, former postdoc, Harvard University

Bobby Braswell, research faculty, University of New Hampshire (now @ Applied GeoSolutions)

Motivation

Reducing uncertainties about the role of terrestrial ecosystems in the global carbon [C] cycle requires better understanding of the spatial and temporal variation in biologically-mediated sources and sinks of C. Particularly in temperate and boreal forest ecosystems, phenological events such as spring leaf emergence and autumn senescence exert strong control on primary productivity and are therefore critical to ecosystem C cycling. Phenology also influences hydrologic processes, as leaf-out is accompanied by an increase in evapotranspiration; nutrient cycling processes, as senescence results in fresh litter (nutrient) inputs to the forest floor; and feedbacks to the climate system, as the amount and condition of foliage present affects surface energy balance, albedo, and surface roughness. Phenology also influences ecological interactions among individuals (e.g., competition) and across trophic levels (e.g., herbivory).

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.

The PhenoCam Network

We initiated the PhenoCam Network in order to provide automated, near-surface remote sensing of canopy phenology across the northeastern United States and adjacent Canada. This work was funded by two grants (2007-2009, 2009-2011) from the Northeastern States Research Cooperative. Following pilot work at Bartlett Experimental Forest we installed high-resolution digital cameras (webcams) at more than a dozen established research sites distributed throughout this region. Images from these cameras are uploaded to the PhenoCam server every half hour, and we then use simple analysis techniques to extract quantitative color information from each picture. Canopy greenness indices thus provide information about the amount of foliage present, and its color. In this way, image analysis of archived digital camera images provides an objective means by which canopy phenology can be monitored and quantified, at relatively low cost and with minimal personnel expenses, without the need for a human observer.

At many of the PhenoCam network sites, cooperating researchers are conducting ongoing measurements of surface-atmosphere exchange of carbon and water using the eddy covariance method, and these flux data are being used to evaluate the implications of seasonal changes in canopy state for ecosystem function. We are actively seeking to establish new collaborations with existing AmeriFlux sites.

Unlike conventional remote sensing, near-surface remote sensing provides imagery that is continuous in time, free of contamination by clouds, and not requiring correction for atmospheric effects. A key problem with satellite remote sensing (e.g. MODIS imagery) is the coarse spatial resolution; digital camera imagery, on the other hand, offers the opportunity to either integrate the phenological signal across the whole canopy, or to identify individual tree crowns and conduct separate analyses for different species. At the same time, the PhenoCam network provides information needed to link what is actually happening on the ground and what is observed by airborne and satellite sensors. Thus data from this project will contribute to efforts in which remote sensing is used to scale from intensively monitored sites to more extensive spatial domains.

Funding from the National Science Foundation’s Macrosystems Biology Program (award EF-1065074) has enabled the expansion of the network, a focus on broader science questions, and improvements to our online data visualization and delivery tools. Our goal is to function as a continental-scale phenological observatory, spanning as wide a range of biogeoclimatic zones and vegetation types as possible (Figure 1). New cameras have been installed at research sites from Alaska to Panama, Hawaii to Georgia, and Arizona to Wisconsin. More than 80 cameras, deployed following our standardized protocol, are currently uploading half-hourly imagery to the PhenoCam server.

Figure 1. Vegetation canopy greenness, as quantified by the green chromatic coordinate (GCC) using PhenoCam imagery, in relation to seasonal patterns of monthly precipitation (blue bars) and air temperature (red bars). Green lines correspond to GCC for trees, and brown to GCC for grasses, in camera field of view. The seasonal patterns, and interannual variability, vary among sites in relation to climate drivers and vegetation type.

Research Questions

The primary objective of the PhenoCam project is to use automated, near-surface remote sensing to provide continuous, real-time monitoring of vegetation phenology across a range of ecosystems and climate zones.

The science questions motivating the project are as follows:

1. How do photoperiod, temperature, and precipitation govern phenological transitions in different vegetation types?
2. How will phenology respond to climate change, and what are the associated uncertainties?
3. How will these phenological shifts impact ecosystem processes, and climate system feedbacks, related to carbon and water?

About the data

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 minute) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

Citing the data

Use of data should be acknowledged using the following citation:

Richardson, A.D. et al. (2018) Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data, 5, 180028.

When extending datasets past 2015, the temporal extend of the data, using the phenocamr R package we ask to cite both the original dataset and the methods paper:

Hufkens K. et al. (2018) An integrated phenology modelling framework in R: Phenology modelling with phenor. Methods in Ecology & Evolution, 9: 1-10.

License agreement

All data is in the public domain under a Creative Commons license and free to use. Please respect this work by citing the above papers.

Acknowledgements

The development of PhenoCam has been funded by the Northeastern States Research Cooperative, NSF’s Macrosystems Biology program (awards EF-1065029 and EF-1702697), and DOE’s Regional and Global Climate Modeling program (award DE-SC0016011). We acknowledge additional support from the US National Park Service Inventory and Monitoring Program and the USA National Phenology Network (grant number G10AP00129 from the United States Geological Survey), and from the USA National Phenology Network and North Central Climate Science Center (cooperative agreement number G16AC00224 from the United States Geological Survey). Additional funding, through the National Science Foundation’s LTER program, has supported research at Harvard Forest (DEB-1237491) and Bartlett Experimental Forest (DEB-1114804). We also thank the USDA Forest Service Air Resource Management program and the National Park Service Air Resources program for contributing their camera imagery to the PhenoCam archive.