Richardson phenocam ACEAS 2014
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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
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Outline
• Motivation: Climate change, phenology and climate system feedbacks
• Evolution and growth of the PhenoCam network
• Online data archiving, display, and delivery • Challenges
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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)
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Phenology and climate system feedbacks
Phenology
Richardson et al. AFM (2013)
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Phenology and climate system feedbacks
Phenology
Richardson et al. AFM (2013)
Foliage development and senescence
Physiological activityof canopy
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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
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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
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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
![Page 9: Richardson phenocam ACEAS 2014](https://reader038.fdocuments.net/reader038/viewer/2022103115/557c35f2d8b42aad418b53b6/html5/thumbnails/9.jpg)
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
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Richardson et al. (2013) in Schwartz (ed.)
Quantitative analysis of camera imagery
RGB Color Model
RGB Triplet
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Richardson et al. (2013) in Schwartz (ed.)
Quantitative analysis of camera imagery
RGB Color Model
RGB Triplet
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Richardson et al. (2013) in Schwartz (ed.)
Quantitative analysis of camera imagery
RGB Color Model
Canopy “Greenness”
RGB Triplet
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Richardson et al. (2013) in Schwartz (ed.)
Quantitative analysis of camera imagery
RGB Color Model
Cano
py “
Gre
enne
ss”
Canopy “Greenness”
RGB Triplet
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WINTER SPRING SUMMER EARLY AUTUMN LATE AUTUMN
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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.”
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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
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2013: 80 Core PhenoCam sites
…also 75+ “affiliated” cameras covering most ecoregions of North America (incl. Alaska and Hawaii)
http:
//ph
enoc
am.u
nh.e
du
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Challenges
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• Data volume (≈ 2 TB) — manageable if processing can be automated, but archive ever-increasing in size (approx. 5000 new images per day)
Challenges
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• 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
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• 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
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• 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
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• 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
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• 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
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Wrap-up
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• 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
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• 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
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• 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
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• 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
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• 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
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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.