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PhenoCam:
A continental-scale phenological observatory
Andrew Richardson
Harvard University
Harvard Forest, Massachusetts (1 hour west of Boston/Cambridge)
Personal Background
• Grew up in Toronto, Canada
• Undergraduate degree in Economics, master‘s degree in Forestry, PhD
in Forest Ecophysiology
• Currently Associate Professor in the Department of Organismic and
Evolutionary Biology, Harvard University
• I have a 3-y old daughter whose nickname is „Pingo“
Bartlett Experimental Forest, New Hampshire (3 hour north of Boston/Cambridge)
“Pingo”
Research Areas/Interests
• Overarching question: How does global change affect the
structure and function of terrestrial ecosystems, and feedbacks
between these ecosystems and the larger Earth system?
• Or: How do trees and forests “work,“ how are these processes
affected by environment, and how do forests in turn affect the
environment at micro-to-macro scales?
• Three main areas of research:
• Ecosystem carbon cycling – tower-based measurements of
ecosystem-atmosphere fluxes of carbon, water and energy; part of
global network „FLUXNET“
• Carbon allocation in trees – studies on individual organisms to
understand the transport, storage, and use of photosynthetic
products, and the implications for growth and resilience to stress
• Vegetation phenology – leaf-to-canopy studies of the seasonality of
vegetation activity, how it is influenced by environmental factors,
and how it regulates feedbacks of ecosystems to weather and
climate; leader of „PhenoCam“ network
• Model-data integration as a unifying theme, used for scaling
up (or down) and extrapolation in time and space
Trees are amazing organisms
Links to „Computer Science meets Ecology“
• PI of the PhenoCam project, which uses imagery from networked
digital cameras to track vegetation phenology
• The network has almost 400 cameras, most of which have been
configured and deployed following a common protocol
• Images are recorded every 30 minutes, 4 am to 10 pm, 365 days per
year
• The archive consists of approximately 15 million images, requiring
about 6 TB of storage. These numbers are growing rapidly.
• Image processing is straightforward but challenges include identifying
and accounting for field of view shifts, minimizing the influence of
changes in lighting, and flagging „bad“ weather events like rain and
snow which obscure the vegetation of interest.
• We have a lot to learn from CS and CV about how we might do some of
this processing more effectively