Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset
Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant funct...
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ftpubmed:oai:pubmedcentral.nih.gov:6805894 2023-05-15T18:40:25+02:00 Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset Seyednasrollah, Bijan Young, Adam M. Hufkens, Koen Milliman, Tom Friedl, Mark A. Frolking, Steve Richardson, Andrew D. 2019-10-22 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805894/ http://www.ncbi.nlm.nih.gov/pubmed/31641140 https://doi.org/10.1038/s41597-019-0229-9 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805894/ http://www.ncbi.nlm.nih.gov/pubmed/31641140 http://dx.doi.org/10.1038/s41597-019-0229-9 © The Author(s) 2019 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 associated with this article. CC0 PDM CC-BY Data Descriptor Text 2019 ftpubmed https://doi.org/10.1038/s41597-019-0229-9 2019-11-03T01:52:34Z Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO(2) and H(2)O flux data, and to study climate change impacts on the terrestrial biosphere. Text Tundra PubMed Central (PMC) Scientific Data 6 1 |
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Data Descriptor Seyednasrollah, Bijan Young, Adam M. Hufkens, Koen Milliman, Tom Friedl, Mark A. Frolking, Steve Richardson, Andrew D. Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset |
topic_facet |
Data Descriptor |
description |
Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO(2) and H(2)O flux data, and to study climate change impacts on the terrestrial biosphere. |
format |
Text |
author |
Seyednasrollah, Bijan Young, Adam M. Hufkens, Koen Milliman, Tom Friedl, Mark A. Frolking, Steve Richardson, Andrew D. |
author_facet |
Seyednasrollah, Bijan Young, Adam M. Hufkens, Koen Milliman, Tom Friedl, Mark A. Frolking, Steve Richardson, Andrew D. |
author_sort |
Seyednasrollah, Bijan |
title |
Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset |
title_short |
Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset |
title_full |
Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset |
title_fullStr |
Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset |
title_full_unstemmed |
Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset |
title_sort |
tracking vegetation phenology across diverse biomes using version 2.0 of the phenocam dataset |
publisher |
Nature Publishing Group UK |
publishDate |
2019 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805894/ http://www.ncbi.nlm.nih.gov/pubmed/31641140 https://doi.org/10.1038/s41597-019-0229-9 |
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Tundra |
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Tundra |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805894/ http://www.ncbi.nlm.nih.gov/pubmed/31641140 http://dx.doi.org/10.1038/s41597-019-0229-9 |
op_rights |
© The Author(s) 2019 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 associated with this article. |
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CC0 PDM CC-BY |
op_doi |
https://doi.org/10.1038/s41597-019-0229-9 |
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