Interpreting canopy development and physiology using a European phenology camera network at flux sites
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. A...
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Language: | English |
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Copernicus Publications
2015-10-01
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Series: | Biogeosciences |
Online Access: | http://www.biogeosciences.net/12/5995/2015/bg-12-5995-2015.pdf |
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language |
English |
format |
Article |
sources |
DOAJ |
author |
L. Wingate J. Ogée E. Cremonese G. Filippa T. Mizunuma M. Migliavacca C. Moisy M. Wilkinson C. Moureaux G. Wohlfahrt A. Hammerle L. Hörtnagl C. Gimeno A. Porcar-Castell M. Galvagno T. Nakaji J. Morison O. Kolle A. Knohl W. Kutsch P. Kolari E. Nikinmaa A. Ibrom B. Gielen W. Eugster M. Balzarolo D. Papale K. Klumpp B. Köstner T. Grünwald R. Joffre J.-M. Ourcival M. Hellstrom A. Lindroth C. George B. Longdoz B. Genty J. Levula B. Heinesch M. Sprintsin D. Yakir T. Manise D. Guyon H. Ahrends A. Plaza-Aguilar J. H. Guan J. Grace |
spellingShingle |
L. Wingate J. Ogée E. Cremonese G. Filippa T. Mizunuma M. Migliavacca C. Moisy M. Wilkinson C. Moureaux G. Wohlfahrt A. Hammerle L. Hörtnagl C. Gimeno A. Porcar-Castell M. Galvagno T. Nakaji J. Morison O. Kolle A. Knohl W. Kutsch P. Kolari E. Nikinmaa A. Ibrom B. Gielen W. Eugster M. Balzarolo D. Papale K. Klumpp B. Köstner T. Grünwald R. Joffre J.-M. Ourcival M. Hellstrom A. Lindroth C. George B. Longdoz B. Genty J. Levula B. Heinesch M. Sprintsin D. Yakir T. Manise D. Guyon H. Ahrends A. Plaza-Aguilar J. H. Guan J. Grace Interpreting canopy development and physiology using a European phenology camera network at flux sites Biogeosciences |
author_facet |
L. Wingate J. Ogée E. Cremonese G. Filippa T. Mizunuma M. Migliavacca C. Moisy M. Wilkinson C. Moureaux G. Wohlfahrt A. Hammerle L. Hörtnagl C. Gimeno A. Porcar-Castell M. Galvagno T. Nakaji J. Morison O. Kolle A. Knohl W. Kutsch P. Kolari E. Nikinmaa A. Ibrom B. Gielen W. Eugster M. Balzarolo D. Papale K. Klumpp B. Köstner T. Grünwald R. Joffre J.-M. Ourcival M. Hellstrom A. Lindroth C. George B. Longdoz B. Genty J. Levula B. Heinesch M. Sprintsin D. Yakir T. Manise D. Guyon H. Ahrends A. Plaza-Aguilar J. H. Guan J. Grace |
author_sort |
L. Wingate |
title |
Interpreting canopy development and physiology using a European phenology camera network at flux sites |
title_short |
Interpreting canopy development and physiology using a European phenology camera network at flux sites |
title_full |
Interpreting canopy development and physiology using a European phenology camera network at flux sites |
title_fullStr |
Interpreting canopy development and physiology using a European phenology camera network at flux sites |
title_full_unstemmed |
Interpreting canopy development and physiology using a European phenology camera network at flux sites |
title_sort |
interpreting canopy development and physiology using a european phenology camera network at flux sites |
publisher |
Copernicus Publications |
series |
Biogeosciences |
issn |
1726-4170 1726-4189 |
publishDate |
2015-10-01 |
description |
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 CO<sub>2</sub> 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 CO<sub>2</sub> flux measurements will improve our
understanding of how changes in growing season length are likely to shape
the capacity of European ecosystems to sequester CO<sub>2</sub> in the future. |
url |
http://www.biogeosciences.net/12/5995/2015/bg-12-5995-2015.pdf |
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doaj-aa21bea75205487fae64e11d3b839ef42020-11-25T00:29:54ZengCopernicus PublicationsBiogeosciences1726-41701726-41892015-10-0112205995601510.5194/bg-12-5995-2015Interpreting canopy development and physiology using a European phenology camera network at flux sitesL. Wingate0J. Ogée1E. Cremonese2G. Filippa3T. Mizunuma4M. Migliavacca5C. Moisy6M. Wilkinson7C. Moureaux8G. Wohlfahrt9A. Hammerle10L. Hörtnagl11C. Gimeno12A. Porcar-Castell13M. Galvagno14T. Nakaji15J. Morison16O. Kolle17A. Knohl18W. Kutsch19P. Kolari20E. Nikinmaa21A. Ibrom22B. Gielen23W. Eugster24M. Balzarolo25D. Papale26K. Klumpp27B. Köstner28T. Grünwald29R. Joffre30J.-M. Ourcival31M. Hellstrom32A. Lindroth33C. George34B. Longdoz35B. Genty36J. Levula37B. Heinesch38M. Sprintsin39D. Yakir40T. Manise41D. Guyon42H. Ahrends43A. Plaza-Aguilar44J. H. Guan45J. Grace46INRA, UMR ISPA 1391, 33140 Villenave d'Ornon, FranceINRA, UMR ISPA 1391, 33140 Villenave d'Ornon, FranceEnvironmental Protection Agency of Aosta Valley, Climate Change Unit, ARPA Valle d'Aosta, ItalyEnvironmental Protection Agency of Aosta Valley, Climate Change Unit, ARPA Valle d'Aosta, ItalySchool of GeoSciences, University of Edinburgh, Edinburgh, EH9 3JN, UKMax Planck Institute for Biogeochemistry, Jena, GermanyINRA, UMR ISPA 1391, 33140 Villenave d'Ornon, FranceForest Research, Alice Holt, Farnham, GU10 4LH, UKUnité de Physique des Biosystemes, Gembloux Agro-Bio Tech, Université of Liège, 5030 Gembloux, BelgiumUniversity of Innsbruck, Institute of Ecology, Innsbruck, AustriaUniversity of Innsbruck, Institute of Ecology, Innsbruck, AustriaUniversity of Innsbruck, Institute of Ecology, Innsbruck, AustriaCentro de Estudios Ambientales del Mediterráneo, Paterna, SpainDepartment of Forest Sciences, University of Helsinki, P.O. Box 27, 00014, Helsinki, FinlandEnvironmental Protection Agency of Aosta Valley, Climate Change Unit, ARPA Valle d'Aosta, ItalyUniversity of Hokkaido, Regional Resource Management Research, Hokkaido, JapanForest Research, Alice Holt, Farnham, GU10 4LH, UKMax Planck Institute for Biogeochemistry, Jena, GermanyGeorg-August University of Göttingen, Faculty of Forest Sciences and Forest Ecology, 37077 Göttingen, GermanyJohann Heinrich von Thünen-Institut (vTI) Institut für Agrarrelevante Klimaforschung, 38116, Braunschweig, GermanyDepartment of Forest Sciences, University of Helsinki, P.O. Box 27, 00014, Helsinki, FinlandDepartment of Forest Sciences, University of Helsinki, P.O. Box 27, 00014, Helsinki, FinlandRisø National Laboratory for Sustainable Energy, Risø DTU, 4000 Roskilde, DenmarkDepartment of Biology/Centre of Excellence PLECO, University of Antwerp, Antwerp, BelgiumETH Zurich, Institute of Agricultural Sciences, 8092 Zurich, SwitzerlandDepartment of Biology/Centre of Excellence PLECO, University of Antwerp, Antwerp, BelgiumDepartment of Forest Environment and Resources, University of Tuscia, Viterbo, ItalyINRA, Grassland Ecosystem Research Unit, UR874, 63100 Clermont Ferrand, FranceChair of Meterorology, Technische Universität Dresden, Tharandt, GermanyChair of Meterorology, Technische Universität Dresden, Tharandt, GermanyCNRS, CEFE (UMR5175), Montpellier, FranceCNRS, CEFE (UMR5175), Montpellier, FranceDepartment of Physical Geography and Ecosystem Science, Lund University, 22362 Lund, SwedenDepartment of Physical Geography and Ecosystem Science, Lund University, 22362 Lund, SwedenCentre for Ecology and Hydrology, Wallingford, Oxford, UKINRA, UMR EEF (UMR1137) Nancy, FranceCEA, IBEB, SVBME, Laboratoire d'Ecophysiologie Moléculaire des Plantes, 13108, Saint-Paul-lez-Durance, FranceDepartment of Forest Sciences, University of Helsinki, P.O. Box 27, 00014, Helsinki, FinlandUnité de Physique des Biosystemes, Gembloux Agro-Bio Tech, Université of Liège, 5030 Gembloux, BelgiumForest Management and GIS Department, Jewish National Fund-Keren Kayemet LeIsrael, Eshtaol, M.P. Shimshon, 99775, IsraelWeizmann Institute for Science, Rehovot, IsraelUnité de Physique des Biosystemes, Gembloux Agro-Bio Tech, Université of Liège, 5030 Gembloux, BelgiumINRA, UMR ISPA 1391, 33140 Villenave d'Ornon, FranceETH Zurich, Institute of Agricultural Sciences, 8092 Zurich, SwitzerlandUniversity of Cambridge, Plant Sciences, Cambridge, UKMax Planck Institute for Biogeochemistry, Jena, GermanySchool of GeoSciences, University of Edinburgh, Edinburgh, EH9 3JN, UKPlant 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 CO<sub>2</sub> 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 CO<sub>2</sub> flux measurements will improve our understanding of how changes in growing season length are likely to shape the capacity of European ecosystems to sequester CO<sub>2</sub> in the future.http://www.biogeosciences.net/12/5995/2015/bg-12-5995-2015.pdf |