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Title: Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

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. Lastly, 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.

Authors:
 [1];  [2];  [3];  [2];  [2];  [4];  [2];  [5];  [2];  [2];  [6];  [6];  [3]
  1. Harvard Univ., Cambridge, MA (United States). Dept. of Organismic and Evolutionary Biology; Northern Arizona Univ., Flagstaff, AZ (United States). School of Informatics, Computing and Cyber Systems; Northern Arizona Univ., Flagstaff, AZ (United States). Center for Ecosystem Science and Society
  2. Harvard Univ., Cambridge, MA (United States). Dept. of Organismic and Evolutionary Biology
  3. Univ. of New Hampshire, Durham, NH (United States). Earth Systems Research Center
  4. Boston Univ., MA (United States). Dept. of Earth and Environment; North Carolina State Univ., Raleigh, NC (United States). Dept. of Forestry and Environmental Resources
  5. Harvard Univ., Cambridge, MA (United States). Dept. of Organismic and Evolutionary Biology; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth and Environmental Sciences
  6. Boston Univ., MA (United States). Dept. of Earth and Environment
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF)
OSTI Identifier:
1433125
Grant/Contract Number:  
AC02-05CH11231; SC0016011; G10AP00129
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Scientific Data
Additional Journal Information:
Journal Volume: 5; Journal ID: ISSN 2052-4463
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Richardson, Andrew D., Hufkens, Koen, Milliman, Tom, Aubrecht, Donald M., Chen, Min, Gray, Josh M., Johnston, Miriam R., Keenan, Trevor F., Klosterman, Stephen T., Kosmala, Margaret, Melaas, Eli K., Friedl, Mark A., and Frolking, Steve. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. United States: N. p., 2018. Web. doi:10.1038/sdata.2018.28.
Richardson, Andrew D., Hufkens, Koen, Milliman, Tom, Aubrecht, Donald M., Chen, Min, Gray, Josh M., Johnston, Miriam R., Keenan, Trevor F., Klosterman, Stephen T., Kosmala, Margaret, Melaas, Eli K., Friedl, Mark A., & Frolking, Steve. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. United States. doi:10.1038/sdata.2018.28.
Richardson, Andrew D., Hufkens, Koen, Milliman, Tom, Aubrecht, Donald M., Chen, Min, Gray, Josh M., Johnston, Miriam R., Keenan, Trevor F., Klosterman, Stephen T., Kosmala, Margaret, Melaas, Eli K., Friedl, Mark A., and Frolking, Steve. Tue . "Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery". United States. doi:10.1038/sdata.2018.28. https://www.osti.gov/servlets/purl/1433125.
@article{osti_1433125,
title = {Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery},
author = {Richardson, Andrew D. and Hufkens, Koen and Milliman, Tom and Aubrecht, Donald M. and Chen, Min and Gray, Josh M. and Johnston, Miriam R. and Keenan, Trevor F. and Klosterman, Stephen T. and Kosmala, Margaret and Melaas, Eli K. and Friedl, Mark A. and Frolking, Steve},
abstractNote = {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. Lastly, 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.},
doi = {10.1038/sdata.2018.28},
journal = {Scientific Data},
number = ,
volume = 5,
place = {United States},
year = {Tue Mar 13 00:00:00 EDT 2018},
month = {Tue Mar 13 00:00:00 EDT 2018}
}

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Cited by: 6 works
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