Articles | Volume 12, issue 20
Biogeosciences, 12, 5995–6015, 2015

Special issue: EUROSPEC – spectral sampling tools for vegetation biophysical...

Biogeosciences, 12, 5995–6015, 2015

Research article 21 Oct 2015

Research article | 21 Oct 2015

Interpreting canopy development and physiology using a European phenology camera network at flux sites

L. Wingate1, J. Ogée1, E. Cremonese2, G. Filippa2, T. Mizunuma3, M. Migliavacca4, C. Moisy1, M. Wilkinson5, C. Moureaux6, G. Wohlfahrt7,29, A. Hammerle7, L. Hörtnagl15,7, C. Gimeno8, A. Porcar-Castell9, M. Galvagno2, T. Nakaji10, J. Morison5, O. Kolle4, A. Knohl11, W. Kutsch12, P. Kolari9, E. Nikinmaa9, A. Ibrom13, B. Gielen14, W. Eugster15, M. Balzarolo16,14, D. Papale16, K. Klumpp17, B. Köstner18, T. Grünwald18, R. Joffre19, J.-M. Ourcival19, M. Hellstrom20, A. Lindroth20, C. George21, B. Longdoz22, B. Genty23,24, J. Levula9, B. Heinesch6, M. Sprintsin25, D. Yakir26, T. Manise6, D. Guyon1, H. Ahrends15,27, A. Plaza-Aguilar28, J. H. Guan4, and J. Grace3 L. Wingate et al.
  • 1INRA, UMR ISPA 1391, 33140 Villenave d'Ornon, France
  • 2Environmental Protection Agency of Aosta Valley, Climate Change Unit, ARPA Valle d'Aosta, Italy
  • 3School of GeoSciences, University of Edinburgh, Edinburgh, EH9 3JN, UK
  • 4Max Planck Institute for Biogeochemistry, Jena, Germany
  • 5Forest Research, Alice Holt, Farnham, GU10 4LH, UK
  • 6Unité de Physique des Biosystemes, Gembloux Agro-Bio Tech, Université of Liège, 5030 Gembloux, Belgium
  • 7University of Innsbruck, Institute of Ecology, Innsbruck, Austria
  • 8Centro de Estudios Ambientales del Mediterráneo, Paterna, Spain
  • 9Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014, Helsinki, Finland
  • 10University of Hokkaido, Regional Resource Management Research, Hokkaido, Japan
  • 11Georg-August University of Göttingen, Faculty of Forest Sciences and Forest Ecology, 37077 Göttingen, Germany
  • 12Johann Heinrich von Thünen-Institut (vTI) Institut für Agrarrelevante Klimaforschung, 38116, Braunschweig, Germany
  • 13Risø National Laboratory for Sustainable Energy, Risø DTU, 4000 Roskilde, Denmark
  • 14Department of Biology/Centre of Excellence PLECO, University of Antwerp, Antwerp, Belgium
  • 15ETH Zurich, Institute of Agricultural Sciences, 8092 Zurich, Switzerland
  • 16Department of Forest Environment and Resources, University of Tuscia, Viterbo, Italy
  • 17INRA, Grassland Ecosystem Research Unit, UR874, 63100 Clermont Ferrand, France
  • 18Chair of Meterorology, Technische Universität Dresden, Tharandt, Germany
  • 19CNRS, CEFE (UMR5175), Montpellier, France
  • 20Department of Physical Geography and Ecosystem Science, Lund University, 22362 Lund, Sweden
  • 21Centre for Ecology and Hydrology, Wallingford, Oxford, UK
  • 22INRA, UMR EEF (UMR1137) Nancy, France
  • 23CEA, IBEB, SVBME, Laboratoire d'Ecophysiologie Moléculaire des Plantes, 13108, Saint-Paul-lez-Durance, France
  • 24CNRS, UMR Biologie Végétale et Microbiologie Environnementales (UMR7265), 13108 Saint-Paul-lez-Durance, France
  • 25Forest Management and GIS Department, Jewish National Fund-Keren Kayemet LeIsrael, Eshtaol, M.P. Shimshon, 99775, Israel
  • 26Weizmann Institute for Science, Rehovot, Israel
  • 27Institute for Geophysics and Meteorology, University of Cologne, 50674 Cologne, Germany
  • 28University of Cambridge, Plant Sciences, Cambridge, UK
  • 29European Academy of Bolzano, 39100 Bolzano, Italy

Abstract. Plant phenological development is orchestrated through subtle changes in photoperiod, temperature, soil moisture and nutrient availability. Presently, the exact timing of plant development stages and their response to climate and management practices are crudely represented in land surface models. As visual observations of phenology are laborious, there is a need to supplement long-term observations with automated techniques such as those provided by digital repeat photography at high temporal and spatial resolution. We present the first synthesis from a growing observational network of digital cameras installed on towers across Europe above deciduous and evergreen forests, grasslands and croplands, where vegetation and atmosphere CO2 fluxes are measured continuously. Using colour indices from digital images and using piecewise regression analysis of time series, we explored whether key changes in canopy phenology could be detected automatically across different land use types in the network. The piecewise regression approach could capture the start and end of the growing season, in addition to identifying striking changes in colour signals caused by flowering and management practices such as mowing. Exploring the dates of green-up and senescence of deciduous forests extracted by the piecewise regression approach against dates estimated from visual observations, we found that these phenological events could be detected adequately (RMSE < 8 and 11 days for leaf out and leaf fall, respectively). We also investigated whether the seasonal patterns of red, green and blue colour fractions derived from digital images could be modelled mechanistically using the PROSAIL model parameterised with information of seasonal changes in canopy leaf area and leaf chlorophyll and carotenoid concentrations. From a model sensitivity analysis we found that variations in colour fractions, and in particular the late spring `green hump' observed repeatedly in deciduous broadleaf canopies across the network, are essentially dominated by changes in the respective pigment concentrations. Using the model we were able to explain why this spring maximum in green signal is often observed out of phase with the maximum period of canopy photosynthesis in ecosystems across Europe. Coupling such quasi-continuous digital records of canopy colours with co-located CO2 flux measurements will improve our understanding of how changes in growing season length are likely to shape the capacity of European ecosystems to sequester CO2 in the future.

Short summary
The timing of plant development stages and their response to climate and management were investigated using a network of digital cameras installed across different European ecosystems. Using the relative red, green and blue content of images we showed that the green signal could be used to estimate the length of the growing season in broadleaf forests. We also developed a model that predicted the seasonal variations of camera RGB signals and how they relate to leaf pigment content and area well.
Final-revised paper