The PhenoCam Network: Evolution and lessons learned

Andrew D. RichardsonDepartment of Organismic and Evolutionary Biology

Harvard University

I acknowledge the contributions of my PhenoCam collaborators to this work

Outline

• Motivation: Climate change, phenology and climate system feedbacks

• Evolution and growth of the PhenoCam network

• Online data archiving, display, and delivery • Challenges

Phenology “is perhaps the simplest process in which to track changes in the ecology of species in response to climate change”

– IPCC Fourth Assessment Report (2007)

Phenology and climate system feedbacks

Phenology

Richardson et al. AFM (2013)

Phenology and climate system feedbacks

Phenology

Richardson et al. AFM (2013)

Foliage development and senescence

Physiological activityof canopy

Phenology and climate system feedbacks

Phenology

Richardson et al. AFM (2013)

Foliage development and senescence

Physiological activityof canopy

PhotosynthesisCO2 fluxes

VOC emissions

EvapotranspirationH2O fluxes

AlbedoBowen ratioEnergy fluxes

Phenology and climate system feedbacks

Phenology

AtmosphericStructure/composition

Richardson et al. AFM (2013)

Foliage development and senescence

Physiological activityof canopy

PhotosynthesisCO2 fluxes

VOC emissions

EvapotranspirationH2O fluxes

AlbedoBowen ratioEnergy fluxes

Phenology and climate system feedbacks

Phenology

AtmosphericStructure/composition

Richardson et al. AFM (2013)

Foliage development and senescence

Physiological activityof canopy

Weather

PhotosynthesisCO2 fluxes

VOC emissions

EvapotranspirationH2O fluxes

AlbedoBowen ratioEnergy fluxes

Phenology and climate system feedbacks

Phenology

AtmosphericStructure/composition

Richardson et al. AFM (2013)

Foliage development and senescence

Physiological activityof canopy

WeatherClimate

PhotosynthesisCO2 fluxes

VOC emissions

EvapotranspirationH2O fluxes

AlbedoBowen ratioEnergy fluxes

Richardson et al. (2013) in Schwartz (ed.)

Quantitative analysis of camera imagery

RGB Color Model

RGB Triplet

Richardson et al. (2013) in Schwartz (ed.)

Quantitative analysis of camera imagery

RGB Color Model

RGB Triplet

Richardson et al. (2013) in Schwartz (ed.)

Quantitative analysis of camera imagery

RGB Color Model

Canopy “Greenness”

RGB Triplet

Richardson et al. (2013) in Schwartz (ed.)

Quantitative analysis of camera imagery

RGB Color Model

Cano

py “

Gre

enne

ss”

Canopy “Greenness”

RGB Triplet

WINTER SPRING SUMMER EARLY AUTUMN LATE AUTUMN

WINTER SPRING SUMMER EARLY AUTUMN LATE AUTUMN

Our conclusion…“Given the widespread popularity of webcams, and the fact that they are already ubiquitous in our landscape … images from such cameras could offer a novel opportunity to provide data that would complement [national phenology monitoring efforts], at relatively low cost. This … would provide chances for public outreach by the earth systems science community.”

2009: 12 Core PhenoCam sites

• Focus on forested research sites in northeastern US and adjacent Canada

• Sites span 10° latitude and 10° MAT across a range of forest types

• 7 sites measuring surface-atmosphere CO2/H2O exchange with eddy covariance, as well as complete meteorological data

• Observer records at several sites• Unique opportunities for

outreach/public engagement

2013: 80 Core PhenoCam sites

…also 75+ “affiliated” cameras covering most ecoregions of North America (incl. Alaska and Hawaii)

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Challenges

• Data volume (≈ 2 TB) — manageable if processing can be automated, but archive ever-increasing in size (approx. 5000 new images per day)

Challenges

• Data volume (≈ 2 TB) — manageable if processing can be automated, but archive ever-increasing in size (approx. 5000 new images per day)

• Biological interpretation – a lot of work because human input (“expert judgment”) is required; seasonality of greenness means different things in different ecosystems; flowering difficult to identify

Challenges

• Data volume (≈ 2 TB) — manageable if processing can be automated, but archive ever-increasing in size (approx. 5000 new images per day)

• Biological interpretation – a lot of work because human input (“expert judgment”) is required; seasonality of greenness means different things in different ecosystems; flowering difficult to identify

• Consistency – FOV shifts are a hassle because correction can’t yet be automated (yet)

Challenges

• Data volume (≈ 2 TB) — manageable if processing can be automated, but archive ever-increasing in size (approx. 5000 new images per day)

• Biological interpretation – a lot of work because human input (“expert judgment”) is required; seasonality of greenness means different things in different ecosystems; flowering difficult to identify

• Consistency – FOV shifts are a hassle because correction can’t yet be automated (yet)

• Representativeness – constrained by infrastructure and local partners; (sub-) tropical ecosystems very under-represented

Challenges

• Standardization – common configuration facilitated by new install tool; complete standardization difficult: need a good, inexpensive reference panel; camera calibration?

Challenges

https://bitbucket.org/khufkens/phenocam-installation-tool

• Standardization – common configuration facilitated by new install tool; complete standardization difficult: need a good, inexpensive reference panel; camera calibration?

• Metadata – lacking in past; new approach now uploads .meta file with each camera image (camera settings, exposure, etc.)

Challenges

https://bitbucket.org/khufkens/phenocam-installation-tool

Wrap-up

• The PhenoCam network uses networked digital cameras, and a common configuration and deployment protocol, to track vegetation phenology at research sites across North America

Wrap-up

• The PhenoCam network uses networked digital cameras, and a common configuration and deployment protocol, to track vegetation phenology at research sites across North America

• We have more than 500 years of data, making this a truly unique dataset

Wrap-up

• The PhenoCam network uses networked digital cameras, and a common configuration and deployment protocol, to track vegetation phenology at research sites across North America

• We have more than 500 years of data, making this a truly unique dataset

• Data and imagery are made publicly available through the PhenoCam web page

Wrap-up

• The PhenoCam network uses networked digital cameras, and a common configuration and deployment protocol, to track vegetation phenology at research sites across North America

• We have more than 500 years of data, making this a truly unique dataset

• Data and imagery are made publicly available through the PhenoCam web page

• There are significant challenges associated with managing and analyzing this volume of image data

Wrap-up

• The PhenoCam network uses networked digital cameras, and a common configuration and deployment protocol, to track vegetation phenology at research sites across North America

• We have more than 500 years of data, making this a truly unique dataset

• Data and imagery are made publicly available through the PhenoCam web page

• There are significant challenges associated with managing and analyzing this volume of image data

• We are always open to new collaborators joining the network, and leveraging the cyberinfrastructure we have developed

Wrap-up

Thank you.The PhenoCam Network has been funded by the Northeast States Research Cooperative,

the USA NPS Monitoring Program in partnership with USA-NPN through USGS, and the National Science Foundation’s MacroSystems Biology Program.