Literature DB >> 29533393

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery.

Andrew D Richardson1,2,3, Koen Hufkens1, Tom Milliman4, Donald M Aubrecht1, Min Chen1, Josh M Gray5,6, Miriam R Johnston1, Trevor F Keenan1,7, Stephen T Klosterman1, Margaret Kosmala1, Eli K Melaas5, Mark A Friedl5, Steve Frolking4.   

Abstract

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.

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Mesh:

Year:  2018        PMID: 29533393      PMCID: PMC5848786          DOI: 10.1038/sdata.2018.28

Source DB:  PubMed          Journal:  Sci Data        ISSN: 2052-4463            Impact factor:   8.501


  10 in total

1.  Influence of spring and autumn phenological transitions on forest ecosystem productivity.

Authors:  Andrew D Richardson; T Andy Black; Philippe Ciais; Nicolas Delbart; Mark A Friedl; Nadine Gobron; David Y Hollinger; Werner L Kutsch; Bernard Longdoz; Sebastiaan Luyssaert; Mirco Migliavacca; Leonardo Montagnani; J William Munger; Eddy Moors; Shilong Piao; Corinna Rebmann; Markus Reichstein; Nobuko Saigusa; Enrico Tomelleri; Rodrigo Vargas; Andrej Varlagin
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-10-12       Impact factor: 6.237

2.  Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis.

Authors:  Michael Toomey; Mark A Friedl; Steve Frolking; Koen Hufkens; Stephen Klosterman; Oliver Sonnentag; Dennis D Baldocchi; Carl J Bernacchi; Sebastien C Biraud; Gil Bohrer; Edward Brzostek; Sean P Burns; Carole Coursolle; David Y Hollinger; Hank A Margolis; Harry Mccaughey; Russell K Monson; J William Munger; Stephen Pallardy; Richard P Phillips; Margaret S Torn; Sonia Wharton; Marcelo Zeri; Andrew D And; Andrew D Richardson
Journal:  Ecol Appl       Date:  2015-01       Impact factor: 4.657

3.  Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States.

Authors:  Eli K Melaas; Mark A Friedl; Andrew D Richardson
Journal:  Glob Chang Biol       Date:  2016-01-06       Impact factor: 10.863

4.  Ecology. Phenology feedbacks on climate change.

Authors:  Josep Peñuelas; This Rutishauser; Iolanda Filella
Journal:  Science       Date:  2009-05-15       Impact factor: 47.728

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

Authors:  Andrew D Richardson; Bobby H Braswell; David Y Hollinger; Julian P Jenkins; Scott V Ollinger
Journal:  Ecol Appl       Date:  2009-09       Impact factor: 4.657

6.  Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework.

Authors:  James M Omernik; Glenn E Griffith
Journal:  Environ Manage       Date:  2014-09-16       Impact factor: 3.266

7.  Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment.

Authors:  B Darby; E Felts; O Sonnentag; M A Friedl; K Hufkens; J O'Keef; S Klosterman; J W Munger; M Toome; A D Richardson
Journal:  Ecol Appl       Date:  2014       Impact factor: 4.657

8.  Climate change and shifts in spring phenology of three horticultural woody perennials in northeastern USA.

Authors:  David W Wolfe; Mark D Schwartz; Alan N Lakso; Yuka Otsuki; Robert M Pool; Nelson J Shaulis
Journal:  Int J Biometeorol       Date:  2004-12-09       Impact factor: 3.787

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

Authors:  Andrew D Richardson; Julian P Jenkins; Bobby H Braswell; David Y Hollinger; Scott V Ollinger; Marie-Louise Smith
Journal:  Oecologia       Date:  2007-03-07       Impact factor: 3.298

10.  Standardized phenology monitoring methods to track plant and animal activity for science and resource management applications.

Authors:  Ellen G Denny; Katharine L Gerst; Abraham J Miller-Rushing; Geraldine L Tierney; Theresa M Crimmins; Carolyn A F Enquist; Patricia Guertin; Alyssa H Rosemartin; Mark D Schwartz; Kathryn A Thomas; Jake F Weltzin
Journal:  Int J Biometeorol       Date:  2014-01-25       Impact factor: 3.787

  10 in total
  18 in total

1.  Later springs green-up faster: the relation between onset and completion of green-up in deciduous forests of North America.

Authors:  Stephen Klosterman; Koen Hufkens; Andrew D Richardson
Journal:  Int J Biometeorol       Date:  2018-05-31       Impact factor: 3.787

2.  Detecting the onset of autumn leaf senescence in deciduous forest trees of the temperate zone.

Authors:  Bertold Mariën; Manuela Balzarolo; Inge Dox; Sebastien Leys; Marchand J Lorène; Charly Geron; Miguel Portillo-Estrada; Hamada AbdElgawad; Han Asard; Matteo Campioli
Journal:  New Phytol       Date:  2019-07-23       Impact factor: 10.151

3.  Comparison of large-scale citizen science data and long-term study data for phenology modeling.

Authors:  Shawn D Taylor; Joan M Meiners; Kristina Riemer; Michael C Orr; Ethan P White
Journal:  Ecology       Date:  2018-12-24       Impact factor: 5.499

4.  FluoSpec 2-An Automated Field Spectroscopy System to Monitor Canopy Solar-Induced Fluorescence.

Authors:  Xi Yang; Hanyu Shi; Atticus Stovall; Kaiyu Guan; Guofang Miao; Yongguang Zhang; Yao Zhang; Xiangming Xiao; Youngryel Ryu; Jung-Eun Lee
Journal:  Sensors (Basel)       Date:  2018-06-28       Impact factor: 3.847

5.  RadialPheno: A tool for near-surface phenology analysis through radial layouts.

Authors:  Greice C Mariano; Bruna Alberton; Leonor Patrícia C Morellato; Ricardo da S Torres
Journal:  Appl Plant Sci       Date:  2019-06-05       Impact factor: 1.936

6.  Testing Hopkins' Bioclimatic Law with PhenoCam data.

Authors:  Andrew D Richardson; Koen Hufkens; Xiaolu Li; Toby R Ault
Journal:  Appl Plant Sci       Date:  2019-03-18       Impact factor: 1.936

7.  Comparison of Landsat and Land-Based Phenology Camera Normalized Difference Vegetation Index (NDVI) for Dominant Plant Communities in the Great Basin.

Authors:  Keirith A Snyder; Justin L Huntington; Bryce L Wehan; Charles G Morton; Tamzen K Stringham
Journal:  Sensors (Basel)       Date:  2019-03-06       Impact factor: 3.576

8.  Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing.

Authors:  Andrew D Richardson; Koen Hufkens; Tom Milliman; Steve Frolking
Journal:  Sci Rep       Date:  2018-04-09       Impact factor: 4.996

9.  The Plant Phenology Ontology: A New Informatics Resource for Large-Scale Integration of Plant Phenology Data.

Authors:  Brian J Stucky; Rob Guralnick; John Deck; Ellen G Denny; Kjell Bolmgren; Ramona Walls
Journal:  Front Plant Sci       Date:  2018-05-01       Impact factor: 5.753

10.  PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits.

Authors:  Helge Aasen; Norbert Kirchgessner; Achim Walter; Frank Liebisch
Journal:  Front Plant Sci       Date:  2020-06-18       Impact factor: 6.627

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