Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems. Design Type(s) observation design • time series design Measurement Type(s) vegetation layer Technology Type(s) digital imaging Factor Type(s) spatiotemporal_interval Sample Characteristic(s) North America • terrestrial biome Design Type(s) observation design • time series design Measurement Type(s) vegetation layer Technology Type(s) digital imaging Factor Type(s) spatiotemporal_interval Sample Characteristic(s) North America • terrestrial biome Machine-accessible metadata file describing the reported data (ISA-Tab format)

• Publication year

  2018

• Venue

  Scientific Data

• Publication date

  2018-03-13

• Fields of study

  Medicine, Environmental Science

• Identifiers
  DOI 10.1038/sdata.2018.28PMID 29533393PMCID 5848786
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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