Using phenocams to monitor our changing earth: Toward a global phenocam network

Tim B. Brown, Kevin R. Hultine, Heidi Steltzer, Ellen G. Denny, Michael W. Denslow, Joel Granados, Sandra Henderson, David Moore, Shin Nagai, Michael Sanclements, Arturo Sánchez-Azofeifa, Oliver Sonnentag, David Tazik, Andrew D. Richardson

Research output: Contribution to journalReview articlepeer-review

207 Scopus citations

Abstract

Rapid changes to the biosphere are altering ecological processes worldwide. Developing informed policies for mitigating the impacts of environmental change requires an exponential increase in the quantity, diversity, and resolution of field-collected data, which, in turn, necessitates greater reliance on innovative technologies to monitor ecological processes across local to global scales. Automated digital time-lapse cameras - "phenocams" - can monitor vegetation status and environmental changes over long periods of time. Phenocams are ideal for documenting changes in phenology, snow cover, fire frequency, and other disturbance events. However, effective monitoring of global environmental change with phenocams requires adoption of data standards. New continental-scale ecological research networks, such as the US National Ecological Observatory Network (NEON) and the European Union's Integrated Carbon Observation System (ICOS), can serve as templates for developing rigorous data standards and extending the utility of phenocam data through standardized ground-truthing. Open-source tools for analysis, visualization, and collaboration will make phenocam data more widely usable.

Original languageEnglish (US)
Pages (from-to)84-93
Number of pages10
JournalFrontiers in Ecology and the Environment
Volume14
Issue number2
DOIs
StatePublished - Mar 1 2016
Externally publishedYes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology

Fingerprint

Dive into the research topics of 'Using phenocams to monitor our changing earth: Toward a global phenocam network'. Together they form a unique fingerprint.

Cite this