Development, maintenance, and processing of a large and ever-expanding digital image archive to support phenological research
Ecological data are being collected in ways that would have be unimaginable just 25 years ago. Rugged and automated sensors, wireless networking, and data logging and storage technologies now permit the collection and real-time dissemination of high-quality, high-frequency measurements. The new challenge is, “How do we handle so much data?”
In 2005, we mounted a networked digital camera at the top of the AmeriFlux tower in the Bartlett Experimental Forest, New Hampshire, and began archiving images each day. We soon found that these images could be quantitatively analyzed to yield information about the phenology of the deciduous forest canopy, in terms of a canopy “greenness” index. This led to the development of the PhenoCam network, which monitors vegetation phenology across the major climatic zones and vegetation types of North America. At present, over 80 “core site” cameras, deployed using a standardized protocol and inexpensive camera system, are uploading half-hourly imagery to the PhenoCam server every day. Imagery from almost as many “affiliate site” cameras is uploaded at least once a day. The multi-terabyte PhenoCam image archive now includes over 500 site-years of data.
Sharing data with the community is a priority. We make both raw image data and processed data products including measures of canopy greenness, calculated from each image, freely available through the PhenoCam web page (http://phenocam.unh.edu/). A growing roster of registered site users is evidence of the success of this endeavor.
In this talk we will discuss both the opportunities and challenges presented by PhenoCam.
On one hand, this is a unique data set with which we can investigate science questions that include:
1. How do environmental factors regulate phenological transitions in different vegetation types?
2. How will phenology respond to climate change?
3. How will future phenological shifts impact ecosystem services and climate system feedbacks?
On the other hand, organizing and documenting the data archive is non-trivial. Despite standardized protocols and processing, data from each site must be vetted on a case-by-case basis. Power outages and human error cause data gaps, and shifts in camera field of view complicate analyses. Finally, going from seasonality in canopy greenness to biologically relevant events like leaf-out, senescence, or abscission requires visual interpretation of the images rather than automated processing. To address these challenges, we are initiating a crowdsourcing project, in which we will engage motivated citizen scientists to assist with the interpretation, analysis, and annotation of the PhenoCam dataset.