Use of digital webcam images to track spring green-up in a deciduous broadleaf forest

Understanding relationships between canopy structure and the seasonal dynamics of photosynthetic uptake of CO2 by forest canopies requires improved knowledge of canopy phenology at eddy covariance flux tower sites. We investigated whether digital webcam images could be used to monitor the trajectory of spring green-up in a deciduous northern hardwood forest. A standard, commercially available webcam was mounted at the top of the eddy covariance tower at the Bartlett AmeriFlux site. Images were collected each day around midday. Red, green, and blue color channel brightness data for a 640 × 100-pixel region-of-interest were extracted from each image. We evaluated the green-up signal extracted from webcam images against changes in the fraction of incident photosynthetically active radiation that is absorbed by the canopy (fAPAR), a broadband normalized difference vegetation index (NDVI), and the light-saturated rate of canopy photosynthesis (Amax), inferred from eddy flux measurements. The relative brightness of the green channel (green %) was relatively stable through the winter months. A steady rising trend in green % began around day 120 and continued through day 160, at which point a stable plateau was reached. The relative brightness of the blue channel (blue %) also responded to spring green-up, although there was more day-to-day variation in the signal because blue % was more sensitive to changes in the quality (spectral distribution) of incident radiation. Seasonal changes in blue % were most similar to those in fAPAR and broadband NDVI, whereas changes in green % proceeded more slowly, and were drawn out over a longer period of time. Changes in Amax lagged green-up by at least a week. We conclude that webcams offer an inexpensive means by which phenological changes in the canopy state can be quantified. A network of cameras could offer a novel opportunity to implement a regional or national phenology monitoring program.

[1]  D. M. Gates,et al.  Spectral Distribution of Solar Radiation at the Earth's Surface. , 1966, Science.

[2]  H. Lieth.  Modeling the Primary Productivity of the World , 1975 .

[3]  Ramakrishna R. Nemani,et al.  Regional hydrologic and carbon balance responses of forests resulting from potential climate change , 1991 .

[4]  M. S. Moran,et al.  Normalization of sun/view angle effects using spectral albedo-based vegetation indices , 1995 .

[5]  K. E. Moore,et al.  Seasonal Variation in Radiative and Turbulent Exchange at a Deciduous Forest in Central Massachusetts , 1996 .

[6]  J. William Munger,et al.  Measurements of carbon sequestration by long‐term eddy covariance: methods and a critical evaluation of accuracy , 1996 .

[7]  K. E. Moore,et al.  Detecting leaf area and surface resistance during transition seasons , 1997 .

[8]  S. Running,et al.  A continental phenology model for monitoring vegetation responses to interannual climatic variability , 1997 .

[9]  J. Gamon,et al.  The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels , 1997, Oecologia.

[10]  S. Bassow,et al.  HOW ENVIRONMENTAL CONDITIONS AFFECT CANOPY LEAF‐LEVEL PHOTOSYNTHESIS IN FOUR DECIDUOUS TREE SPECIES , 1998 .

[11]  S. Running,et al.  The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest , 1999, International journal of biometeorology.

[12]  E. Davidson,et al.  Seasonal patterns and environmental control of carbon dioxide and water vapour exchange in an ecotonal boreal forest , 1999 .

[13]  Karl Fred Huemmrich,et al.  High temporal resolution NDVI phenology from micrometeorological radiation sensors , 1999 .

[14]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[15]  Dennis D. Baldocchi,et al.  Leaf age affects the seasonal pattern of photosynthetic capacity and net ecosystem exchange of carbon in a deciduous forest , 2001 .

[16]  A. Richardson,et al.  Spectral reflectance of Picea rubens (Pinaceae) and Abies balsamea (Pinaceae) needles along an elevational gradient, Mt. Moosilauke, New Hampshire, USA. , 2001, American journal of botany.

[17]  J. Schaber.  Phenology in Germany in the 20th century : methods, analyses and models , 2002 .

[18]  J. Peñuelas,et al.  Changed plant and animal life cycles from 1952 to 2000 in the Mediterranean region , 2002 .

[19]  A. Fitter,et al.  Rapid Changes in Flowering Time in British Plants , 2002, Science.

[20]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[21]  Mark D. Schwartz,et al.  Assessing satellite‐derived start‐of‐season measures in the conterminous USA , 2002 .

[22]  Steve Frolking,et al.  Detecting and predicting spatial and interannual patterns of temperate forest springtime phenology in the eastern U.S. , 2002 .

[23]  I. C. Prentice,et al.  Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect , 2002, Science.

[24]  Annette Menzel,et al.  Observed changes in seasons: an overview , 2002 .

[25]  S. T. Gower,et al.  A cross‐biome comparison of daily light use efficiency for gross primary production , 2003 .

[26]  Gordon M. Heisler,et al.  Changes in ultraviolet-B and visible optical properties and absorbing pigment concentrations in pecan leaves during a growing season , 2003 .

[27]  Tiina Markkanen,et al.  Air temperature triggers the recovery of evergreen boreal forest photosynthesis in spring , 2003 .

[28]  J. Morison,et al.  Seasonal changes in the photosynthetic capacity of canopy oak (Quercus robur) leaves: the impact of slow development on annual carbon uptake , 2003, International journal of biometeorology.

[29]  C. Tucker,et al.  Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999 , 2003, Science.

[30]  Peter B Reich,et al.  Predicting leaf area index from scaling principles: corroboration and consequences. , 2003, Tree physiology.

[31]  Pascal Yiou,et al.  Historical phenology: Grape ripening as a past climate indicator , 2004, Nature.

[32]  Markus Reichstein,et al.  Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland , 2004 .

[33]  Sean C. Thomas,et al.  The worldwide leaf economics spectrum , 2004, Nature.

[34]  J. Schaber,et al.  Responses of spring phenology to climate change , 2004 .

[35]  E. Davidson,et al.  Spatial and temporal variability in forest–atmosphere CO2 exchange , 2004 .

[36]  T. A. Black,et al.  A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest , 2005 .

[37]  T. A. Black,et al.  Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: a synthesis of FLUXNET data , 2005, International journal of biometeorology.

[38]  D. Hollinger,et al.  Uncertainty in eddy covariance measurements and its application to physiological models. , 2005, Tree physiology.

[39]  Michael D. Dettinger,et al.  Implementing a U.S. national phenology network , 2005 .

[40]  Xiangming Xiao,et al.  Spatial analysis of growing season length control over net ecosystem exchange , 2005 .

[41]  J. Mustard,et al.  Green leaf phenology at Landsat resolution: Scaling from the field to the satellite , 2006 .

[42]  K. Davis,et al.  A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes , 2006 .

[43]  Ranga B. Myneni,et al.  Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS , 2006 .

[44]  Nicholas C. Coops,et al.  MODIS spectral signals at a flux tower site: Relationships with high-resolution data, and CO2 flux and light use efficiency measurements , 2006 .

[45]  Hans Peter Schmid,et al.  Local-scale heterogeneity of photosynthetically active radiation (PAR), absorbed PAR and net radiation as a function of topography, sky conditions and leaf area index , 2006 .

[46]  J. O'keefe,et al.  Phenology of a northern hardwood forest canopy , 2006 .

[47]  D. Hollinger,et al.  Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements , 2007 .