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      Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

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          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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          Most cited references54

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          Monitoring vegetation phenology using MODIS

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            Climate change, phenology, and phenological control of vegetation feedbacks to the climate system

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              Generating surfaces of daily meteorological variables over large regions of complex terrain

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                Author and article information

                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group
                2052-4463
                13 March 2018
                2018
                : 5
                : 180028
                Affiliations
                [1 ]Harvard University, Department of Organismic and Evolutionary Biology , Cambridge, MA 02138, USA
                [2 ]Northern Arizona University, School of Informatics, Computing and Cyber Systems , Flagstaff, AZ 86011, USA
                [3 ]Northern Arizona University, Center for Ecosystem Science and Society , Flagstaff, AZ 86011, USA
                [4 ]University of New Hampshire, Earth Systems Research Center , Durham, NH 03824, USA
                [5 ]Boston University, Department of Earth and Environment , Boston, MA 02215, USA
                [6 ]North Carolina State University, Department of Forestry and Environmental Resources , Raleigh, NC 27695, USA
                [7 ]Lawrence Berkeley National Laboratory, Earth and Environmental Sciences , Berkeley, CA 94720, USA
                Author notes
                [a ] A.D.R. (email: andrew.richardson@ 123456nau.edu ).
                []

                A.D.R. initiated the PhenoCam network, designed the observational protocol, proposed the format of the standardized data sets, and drafted the Data Descriptor. A.D.R., M.A.F., and S.F. oversaw project development. K.H., T.M., and A.D.R. led the image analysis and development of standardized data products for each site, with contributions from D.M.A., M.C., J.G., S.T.K., M.K. and E.M. T.M. maintained the image archive and coded the image analysis and processing routines. T.M. developed methods to identify camera field of view shifts. M.K. oversaw the crowdsourcing component. K.H. coded the outlier detection algorithm and phenophase transition date retrieval and associated uncertainty algorithm. A.D.R., D.M.A., T.F.K., S.T.K. and M.R.J. conducted analyses for the technical validation section. All authors provided feedback on the data processing routines and format of the standardized data sets, and all authors reviewed and approved of this Data Descriptor.

                Article
                sdata201828
                10.1038/sdata.2018.28
                5848786
                29533393
                d6ef55bb-388f-43d0-b5ac-7dd75e11e5f9
                Copyright © 2018, The Author(s)

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.

                History
                : 15 August 2017
                : 21 December 2017
                Categories
                Data Descriptor

                ecosystem ecology,phenology
                ecosystem ecology, phenology

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