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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.
Afiliación
  • Seyednasrollah B; Northern Arizona University, School of Informatics, Computing, and Cyber Systems, Flagstaff, AZ, 86011, USA. bijan.s.nasr@gmail.com.
  • Young AM; Northern Arizona University, Center for Ecosystem Science and Society, Flagstaff, AZ, 86011, USA. bijan.s.nasr@gmail.com.
  • Hufkens K; Harvard University, Department of Organismic and Evolutionary Biology, Cambridge, MA, 02138, USA. bijan.s.nasr@gmail.com.
  • Milliman T; Northern Arizona University, School of Informatics, Computing, and Cyber Systems, Flagstaff, AZ, 86011, USA.
  • Friedl MA; Northern Arizona University, Center for Ecosystem Science and Society, Flagstaff, AZ, 86011, USA.
  • Frolking S; Faculty of Bioscience Engineering, Ghent University, Ghent, 9000, Belgium.
  • Richardson AD; INRA, UMR ISPA, Villenave d'Ornon, France.
Sci Data ; 6(1): 222, 2019 10 22.
Article en En | MEDLINE | ID: mdl-31641140
ABSTRACT
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 CO2 and H2O flux data, and to study climate change impacts on the terrestrial biosphere.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Data Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Data Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos