Near-surface remote sensing of spatial and temporal variation in canopy phenology.

Academic Article


  • There is a need to document how plant phenology is responding to global change factors, particularly warming trends. "Near-surface" remote sensing, using radiometric instruments or imaging sensors, has great potential to improve phenological monitoring because automated observations can be made at high temporal frequency. Here we build on previous work and show how inexpensive, networked digital cameras ("webcams") can be used to document spatial and temporal variation in the spring and autumn phenology of forest canopies. We use two years of imagery from a deciduous, northern hardwood site, and one year of imagery from a coniferous, boreal transition site. A quantitative signal is obtained by splitting images into separate red, green, and blue color channels and calculating the relative brightness of each channel for "regions of interest" within each image. We put the observed phenological signal in context by relating it to seasonal patterns of gross primary productivity, inferred from eddy covariance measurements of surface-atmosphere CO2 exchange. We show that spring increases, and autumn decreases, in canopy greenness can be detected in both deciduous and coniferous stands. In deciduous stands, an autumn red peak is also observed. The timing and rate of spring development and autumn senescence varies across the canopy, with greater variability in autumn than spring. Interannual variation in phenology can be detected both visually and quantitatively; delayed spring onset in 2007 compared to 2006 is related to a prolonged cold spell from day 85 to day 110. This work lays the foundation for regional- to continental-scale camera-based monitoring of phenology at network observatory sites, e.g., National Ecological Observatory Network (NEON) or AmeriFlux.
  • Authors

  • Richardson, Andrew D
  • Braswell, Bobby H
  • Hollinger, David Y
  • Jenkins, Julian P
  • Ollinger, Scott
  • Status

    Publication Date

  • September 2009
  • Published In


  • Climate
  • Ecosystem
  • Maine
  • New Hampshire
  • Photography
  • Seasons
  • Trees
  • Digital Object Identifier (doi)

    Pubmed Id

  • 19769091
  • Start Page

  • 1417
  • End Page

  • 1428
  • Volume

  • 19
  • Issue

  • 6