Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

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

doi:10.1038/sdata.2018.28

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

Journal Article · Tue Mar 13 00:00:00 EDT 2018 · Scientific Data

DOI:https://doi.org/10.1038/sdata.2018.28· OSTI ID:1433125

Richardson, Andrew D. ^[1]; Hufkens, Koen ^[2]; Milliman, Tom ^[3]; Aubrecht, Donald M. ^[2]; Chen, Min ^[2]; Gray, Josh M. ^[4]; Johnston, Miriam R. ^[2]; Keenan, Trevor F. ^[5]; Klosterman, Stephen T. ^[2]; Kosmala, Margaret ^[2]; Melaas, Eli K. ^[6]; Friedl, Mark A. ^[6]; Frolking, Steve ^[3]

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
Harvard Univ., Cambridge, MA (United States). Dept. of Organismic and Evolutionary Biology
Univ. of New Hampshire, Durham, NH (United States). Earth Systems Research Center
Boston Univ., MA (United States). Dept. of Earth and Environment; North Carolina State Univ., Raleigh, NC (United States). Dept. of Forestry and Environmental Resources
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
Boston Univ., MA (United States). Dept. of Earth and Environment

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.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Science Foundation (NSF)

Grant/Contract Number:: AC02-05CH11231; SC0016011; G10AP00129; AC05-76RL01830

OSTI ID:: 1433125

Alternate ID(s):: OSTI ID: 1773102

Report Number(s):: PNNL-SA-133983; ark:/13030/qt46v7n34w

Journal Information:: Scientific Data, Vol. 5; ISSN 2052-4463

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 240 works

Citation information provided by
Web of Science

References (67)

Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis Toomey, Michael; Friedl, Mark A.; Frolking, Steve Ecological Applications, Vol. 25, Issue 1 https://doi.org/10.1890/14-0005.1	journal	January 2015
Climate change and shifts in spring phenology of three horticultural woody perennials in northeastern USA Wolfe, David W.; Schwartz, Mark D.; Lakso, Alan N. International Journal of Biometeorology, Vol. 49, Issue 5 https://doi.org/10.1007/s00484-004-0248-9	journal	December 2004
Phenopix: A R package for image-based vegetation phenology Filippa, Gianluca; Cremonese, Edoardo; Migliavacca, Mirco Agricultural and Forest Meteorology, Vol. 220 https://doi.org/10.1016/j.agrformet.2016.01.006	journal	April 2016
Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future Baldocchi, Dennis D. Global Change Biology, Vol. 9, Issue 4 https://doi.org/10.1046/j.1365-2486.2003.00629.x	journal	April 2003
Influence of spring and autumn phenological transitions on forest ecosystem productivity Richardson, Andrew D.; Andy Black, T.; Ciais, Philippe Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 365, Issue 1555 https://doi.org/10.1098/rstb.2010.0102	journal	October 2010
PhenoCam Dataset v1.0: Vegetation Phenology from Digital Camera Imagery, 2000-2015 Richardson, A. D.; Hufkens, K.; Milliman, T. ORNL Distributed Active Archive Center https://doi.org/10.3334/ORNLDAAC/1511	collection	January 2017
Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States Melaas, Eli K.; Friedl, Mark A.; Richardson, Andrew D. Global Change Biology, Vol. 22, Issue 2 https://doi.org/10.1111/gcb.13122	journal	January 2016
Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology Hufkens, Koen; Friedl, Mark; Sonnentag, Oliver Remote Sensing of Environment, Vol. 117 https://doi.org/10.1016/j.rse.2011.10.006	journal	February 2012
Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing Kosmala, Margaret; Crall, Alycia; Cheng, Rebecca Remote Sensing, Vol. 8, Issue 9 https://doi.org/10.3390/rs8090726	journal	September 2016
Growing season changes in the last century Linderholm, Hans W. Agricultural and Forest Meteorology, Vol. 137, Issue 1-2 https://doi.org/10.1016/j.agrformet.2006.03.006	journal	March 2006
Analysis of the phenology of global vegetation using meteorological satellite data Justice, C. O.; Townshend, J. R. G.; Holben, B. N. International Journal of Remote Sensing, Vol. 6, Issue 8 https://doi.org/10.1080/01431168508948281	journal	August 1985
Interpreting canopy development and physiology using a European phenology camera network at flux sites Wingate, L.; Ogée, J.; Cremonese, E. Biogeosciences, Vol. 12, Issue 20 https://doi.org/10.5194/bg-12-5995-2015	journal	January 2015
Beyond leaf color: Comparing camera-based phenological metrics with leaf biochemical, biophysical, and spectral properties throughout the growing season of a temperate deciduous forest: Seasonality of leaf properties Yang, Xi; Tang, Jianwu; Mustard, John F. Journal of Geophysical Research: Biogeosciences, Vol. 119, Issue 3 https://doi.org/10.1002/2013JG002460	journal	March 2014
Very high resolution interpolated climate surfaces for global land areas Hijmans, Robert J.; Cameron, Susan E.; Parra, Juan L. International Journal of Climatology, Vol. 25, Issue 15 https://doi.org/10.1002/joc.1276	journal	January 2005
Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat Melaas, Eli K.; Sulla-Menashe, Damien; Gray, Josh M. Remote Sensing of Environment, Vol. 186 https://doi.org/10.1016/j.rse.2016.09.014	journal	December 2016
Monitoring vegetation phenology using an infrared-enabled security camera Petach, Anika R.; Toomey, Michael; Aubrecht, Donald M. Agricultural and Forest Meteorology, Vol. 195-196 https://doi.org/10.1016/j.agrformet.2014.05.008	journal	September 2014
Limitations to winter and spring photosynthesis of a Rocky Mountain subalpine forest Bowling, David R.; Logan, Barry A.; Hufkens, Koen Agricultural and Forest Meteorology, Vol. 252 https://doi.org/10.1016/j.agrformet.2018.01.025	journal	April 2018
Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics Shuai, Yanmin; Schaaf, Crystal; Zhang, Xiaoyang International Journal of Remote Sensing, Vol. 34, Issue 16 https://doi.org/10.1080/01431161.2013.803169	journal	May 2013
Generating surfaces of daily meteorological variables over large regions of complex terrain Thornton, Peter E.; Running, Steven W.; White, Michael A. Journal of Hydrology, Vol. 190, Issue 3-4 https://doi.org/10.1016/S0022-1694(96)03128-9	journal	March 1997
MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets Friedl, Mark A.; Sulla-Menashe, Damien; Tan, Bin Remote Sensing of Environment, Vol. 114, Issue 1 https://doi.org/10.1016/j.rse.2009.08.016	journal	January 2010
The relationship between carbon dioxide uptake and canopy colour from two camera systems in a deciduous forest in southern England Mizunuma, Toshie; Wilkinson, Matthew; L. Eaton, Edward Functional Ecology, Vol. 27, Issue 1 https://doi.org/10.1111/1365-2435.12026	journal	December 2012
Cross-site evaluation of eddy covariance GPP and RE decomposition techniques Desai, Ankur R.; Richardson, Andrew D.; Moffat, Antje M. Agricultural and Forest Meteorology, Vol. 148, Issue 6-7 https://doi.org/10.1016/j.agrformet.2007.11.012	journal	June 2008
Divide and conquer: atomizing and parallelizing a task in a mobile crowdsourcing platform Amato, Ariel; Sappa, Angel D.; Fornés, Alicia Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia - CrowdMM '13 https://doi.org/10.1145/2506364.2506371	conference	January 2013
World Map of the Köppen-Geiger climate classification updated [World Map of the Köppen-Geiger climate classification updated] Kottek, Markus; Grieser, Jürgen; Beck, Christoph Meteorologische Zeitschrift, Vol. 15, Issue 3 https://doi.org/10.1127/0941-2948/2006/0130	journal	July 2006
Monitoring vegetation phenology using MODIS Zhang, Xiaoyang; Friedl, Mark A.; Schaaf, Crystal B. Remote Sensing of Environment, Vol. 84, Issue 3 https://doi.org/10.1016/S0034-4257(02)00135-9	journal	March 2003
Phenology Feedbacks on Climate Change Penuelas, J.; Rutishauser, T.; Filella, I. Science, Vol. 324, Issue 5929 https://doi.org/10.1126/science.1173004	journal	May 2009
Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis Richardson, Andrew D.; Anderson, Ryan S.; Arain, M. Altaf Global Change Biology, Vol. 18, Issue 2 https://doi.org/10.1111/j.1365-2486.2011.02562.x	journal	November 2011
Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves Baumann, Matthias; Ozdogan, Mutlu; Richardson, Andrew D. International Journal of Applied Earth Observation and Geoinformation, Vol. 54 https://doi.org/10.1016/j.jag.2016.09.005	journal	February 2017
Productivity of North American grasslands is increased under future climate scenarios despite rising aridity Hufkens, Koen; Keenan, Trevor F.; Flanagan, Lawrence B. Nature Climate Change, Vol. 6, Issue 7 https://doi.org/10.1038/nclimate2942	journal	February 2016
Optimal Detection of Changepoints With a Linear Computational Cost Killick, R.; Fearnhead, P.; Eckley, I. A. Journal of the American Statistical Association, Vol. 107, Issue 500 https://doi.org/10.1080/01621459.2012.737745	journal	September 2012
Terrestrial Ecoregions of the World: A New Map of Life on Earth Olson, David M.; Dinerstein, Eric; Wikramanayake, Eric D. BioScience, Vol. 51, Issue 11 https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2	journal	January 2001
Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN) Nasahara, Kenlo Nishida; Nagai, Shin Ecological Research, Vol. 30, Issue 2 https://doi.org/10.1007/s11284-014-1239-x	journal	January 2015
Ecoregions of the Conterminous United States: Evolution of a Hierarchical Spatial Framework Omernik, James M.; Griffith, Glenn E. Environmental Management, Vol. 54, Issue 6 https://doi.org/10.1007/s00267-014-0364-1	journal	September 2014
Use of digital cameras for phenological observations Ide, Reiko; Oguma, Hiroyuki Ecological Informatics, Vol. 5, Issue 5 https://doi.org/10.1016/j.ecoinf.2010.07.002	journal	September 2010
Phenology of a northern hardwood forest canopy Richardson, Andrew D.; Bailey, Amey Schenck; Denny, Ellen G. Global Change Biology, Vol. 12, Issue 7 https://doi.org/10.1111/j.1365-2486.2006.01164.x	journal	July 2006
Near-surface remote sensing of spatial and temporal variation in canopy phenology Richardson, Andrew D.; Braswell, Bobby H.; Hollinger, David Y. Ecological Applications, Vol. 19, Issue 6 https://doi.org/10.1890/08-2022.1	journal	September 2009
Digital repeat photography for phenological research in forest ecosystems Sonnentag, Oliver; Hufkens, Koen; Teshera-Sterne, Cory Agricultural and Forest Meteorology, Vol. 152 https://doi.org/10.1016/j.agrformet.2011.09.009	journal	January 2012
Assessing the use of camera-based indices for characterizing canopy phenology in relation to gross primary production in a deciduous broad-leaved and an evergreen coniferous forest in Japan Saitoh, Taku M.; Nagai, Shin; Saigusa, Nobuko Ecological Informatics, Vol. 11 https://doi.org/10.1016/j.ecoinf.2012.05.001	journal	September 2012
On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model Migliavacca, M.; Sonnentag, O.; Keenan, T. F. Biogeosciences, Vol. 9, Issue 6 https://doi.org/10.5194/bg-9-2063-2012	journal	January 2012
Use of digital webcam images to track spring green-up in a deciduous broadleaf forest Richardson, Andrew D.; Jenkins, Julian P.; Braswell, Bobby H. Oecologia, Vol. 152, Issue 2 https://doi.org/10.1007/s00442-006-0657-z	journal	March 2007
Net carbon uptake has increased through warming-induced changes in temperate forest phenology Keenan, Trevor F.; Gray, Josh; Friedl, Mark A. Nature Climate Change, Vol. 4, Issue 7 https://doi.org/10.1038/nclimate2253	journal	June 2014
Recovering high dynamic range radiance maps from photographs Debevec, Paul E.; Malik, Jitendra Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97 https://doi.org/10.1145/258734.258884	conference	January 1997
Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests Elmore, Andrew J.; Guinn, Steven M.; Minsley, Burke J. Global Change Biology, Vol. 18, Issue 2 https://doi.org/10.1111/j.1365-2486.2011.02521.x	journal	September 2011
Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006 White, Michael A.; de BEURS, Kirsten M.; Didan, Kamel Global Change Biology, Vol. 15, Issue 10 https://doi.org/10.1111/j.1365-2486.2009.01910.x	journal	October 2009
Using phenocams to monitor our changing Earth: toward a global phenocam network Brown, Tim B.; Hultine, Kevin R.; Steltzer, Heidi Frontiers in Ecology and the Environment, Vol. 14, Issue 2 https://doi.org/10.1002/fee.1222	journal	March 2016
Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion Hurvich, Clifford M.; Simonoff, Jeffrey S.; Tsai, Chih-Ling Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 60, Issue 2 https://doi.org/10.1111/1467-9868.00125	journal	January 1998
Standardized phenology monitoring methods to track plant and animal activity for science and resource management applications Denny, Ellen G.; Gerst, Katharine L.; Miller-Rushing, Abraham J. International Journal of Biometeorology, Vol. 58, Issue 4 https://doi.org/10.1007/s00484-014-0789-5	journal	January 2014
Testing the performance of a novel spectral reflectance sensor, built with light emitting diodes (LEDs), to monitor ecosystem metabolism, structure and function Ryu, Youngryel; Baldocchi, Dennis D.; Verfaillie, Joseph Agricultural and Forest Meteorology, Vol. 150, Issue 12 https://doi.org/10.1016/j.agrformet.2010.08.009	journal	December 2010
Growing season extended in Europe Menzel, Annette; Fabian, Peter Nature, Vol. 397, Issue 6721 https://doi.org/10.1038/17709	journal	February 1999
Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake Migliavacca, Mirco; Galvagno, Marta; Cremonese, Edoardo Agricultural and Forest Meteorology, Vol. 151, Issue 10 https://doi.org/10.1016/j.agrformet.2011.05.012	journal	October 2011
Climate change, phenology, and phenological control of vegetation feedbacks to the climate system Richardson, Andrew D.; Keenan, Trevor F.; Migliavacca, Mirco Agricultural and Forest Meteorology, Vol. 169 https://doi.org/10.1016/j.agrformet.2012.09.012	journal	February 2013
Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3 Thornton, P. E.; Thornton, M. M.; Mayer, B. W. ORNL Distributed Active Archive Center https://doi.org/10.3334/ORNLDAAC/1328	collection	January 2017
Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment Keenan, T. F.; Darby, B.; Felts, E. Ecological Applications, Vol. 24, Issue 6 https://doi.org/10.1890/13-0652.1	journal	September 2014
Ecological impacts of a widespread frost event following early spring leaf-out Hufkens, Koen; Friedl, Mark A.; Keenan, Trevor F. Global Change Biology, Vol. 18, Issue 7 https://doi.org/10.1111/j.1365-2486.2012.02712.x	journal	May 2012
The global network of outdoor webcams: properties and applications Jacobs, Nathan; Burgin, Walker; Fridrich, Nick Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '09 https://doi.org/10.1145/1653771.1653789	conference	January 2009
PhenoCam Dataset v1.0: Digital Camera Imagery from the PhenoCam Network, 2000-2015 Milliman, T.; Hufkens, K.; Richardson, A. D. ORNL Distributed Active Archive Center https://doi.org/10.3334/ORNLDAAC/1560	collection	January 2018
Macroscale prediction of autumn leaf coloration throughout the continental United States: Scaling and predicting autumn phenology Jeong, Su-Jong; Medvigy, David Global Ecology and Biogeography, Vol. 23, Issue 11 https://doi.org/10.1111/geb.12206	journal	July 2014
Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery Klosterman, S. T.; Hufkens, K.; Gray, J. M. Biogeosciences, Vol. 11, Issue 16 https://doi.org/10.5194/bg-11-4305-2014	journal	January 2014
Recovering high dynamic range radiance maps from photographs Debevec, Paul E.; Malik, Jitendra ACM SIGGRAPH 2008 classes on - SIGGRAPH '08 https://doi.org/10.1145/1401132.1401174	conference	January 2008
Divide and conquer: neuroevolution for multiclass classification McDonnell, Tyler; Andoni, Sari; Bonab, Elmira GECCO '18: Genetic and Evolutionary Computation Conference, Proceedings of the Genetic and Evolutionary Computation Conference https://doi.org/10.1145/3205455.3205476	conference	July 2018
Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006 White, M. A.; De Beurs, K. M.; Didan, K. Wiley-Blackwell https://doi.org/10.5167/uzh-19473	text	January 2009
Divide and conquer Mushtaq, Najum Bulletin of the Atomic Scientists, Vol. 63, Issue 1 https://doi.org/10.2968/063001005	journal	January 2007
Interpreting canopy development and physiology using a European phenology camera network at flux sites Wingate, Lisa; Ogée, Jérôme; Cremonese, Edoardo ETH Zurich https://doi.org/10.3929/ethz-b-000106268	text	January 2015
Divide and conquer Duncan, Hamish BMJ, Vol. 336, Issue 7649 https://doi.org/10.1136/bmj.39535.556273.ce	journal	April 2008
Optimal detection of changepoints with a linear computational cost Killick, R.; Fearnhead, P.; Eckley, I. A. arXiv https://doi.org/10.48550/arxiv.1101.1438	text	January 2011
Phenology Feedbacks on Climate Change Peñuelas, Josep; Rutishauser, This; Filella, Iollanda American Association for the Advancement of Science https://doi.org/10.7892/boris.36392	text	January 2009
Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery Klosterman, S. T.; Hufkens, K.; Gray, J. M. Biogeosciences Discussions, Vol. 11, Issue 2 https://doi.org/10.5194/bgd-11-2305-2014	journal	January 2014

Cited By (44)

Testing Hopkins’ Bioclimatic Law with PhenoCam data Richardson, Andrew D.; Hufkens, Koen; Li, Xiaolu Applications in Plant Sciences, Vol. 7, Issue 3 https://doi.org/10.1002/aps3.1228	journal	March 2019
RadialPheno: A tool for near‐surface phenology analysis through radial layouts Mariano, Greice C.; Alberton, Bruna; Morellato, Leonor Patrícia C. Applications in Plant Sciences, Vol. 7, Issue 6 https://doi.org/10.1002/aps3.1253	journal	June 2019
Seasonal differences in relationships between changes in spring phenology and dynamics of carbon cycle in grasslands Xie, Zhiying; Zhu, Wenquan; Qiao, Kun Ecosphere, Vol. 10, Issue 5 https://doi.org/10.1002/ecs2.2733	journal	May 2019
Comparison of large‐scale citizen science data and long‐term study data for phenology modeling Taylor, Shawn D.; Meiners, Joan M.; Riemer, Kristina Ecology, Vol. 100, Issue 2 https://doi.org/10.1002/ecy.2568	journal	December 2018
Using by‐catch data from wildlife surveys to quantify climatic parameters and timing of phenology for plants and animals using camera traps Hofmeester, Tim R.; Young, Sherry; Juthberg, Sonya Remote Sensing in Ecology and Conservation, Vol. 6, Issue 2 https://doi.org/10.1002/rse2.136	journal	November 2019
Later springs green-up faster: the relation between onset and completion of green-up in deciduous forests of North America Klosterman, Stephen; Hufkens, Koen; Richardson, Andrew D. International Journal of Biometeorology, Vol. 62, Issue 9 https://doi.org/10.1007/s00484-018-1564-9	journal	May 2018
“Green pointillism”: detecting the within-population variability of budburst in temperate deciduous trees with phenological cameras Delpierre, Nicolas; Soudani, Kamel; Berveiller, Daniel International Journal of Biometeorology, Vol. 64, Issue 4 https://doi.org/10.1007/s00484-019-01855-2	journal	January 2020
Global Variability of Simulated and Observed Vegetation Growing Season Peano, D.; Materia, S.; Collalti, A. Journal of Geophysical Research: Biogeosciences, Vol. 124, Issue 11 https://doi.org/10.1029/2018jg004881	journal	November 2019
Complexity revealed in the greening of the Arctic Myers-Smith, Isla H.; Kerby, Jeffrey T.; Phoenix, Gareth K. Nature Climate Change, Vol. 10, Issue 2 https://doi.org/10.1038/s41558-019-0688-1	journal	January 2020
Six years of ecosystem-atmosphere greenhouse gas fluxes measured in a sub-boreal forest Richardson, Andrew D.; Hollinger, David Y.; Shoemaker, Julie K. Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0119-1	journal	July 2019
Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset Seyednasrollah, Bijan; Young, Adam M.; Hufkens, Koen Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0229-9	journal	October 2019
Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing Richardson, Andrew D.; Hufkens, Koen; Milliman, Tom Scientific Reports, Vol. 8, Issue 1 https://doi.org/10.1038/s41598-018-23804-6	journal	April 2018
Comparison of Grassland Phenology Derived from MODIS Satellite and PhenoCam Near-Surface Remote Sensing in North America Cui, Tengfei; Martz, Lawrence; Lamb, Eric G. Canadian Journal of Remote Sensing, Vol. 45, Issue 5 https://doi.org/10.1080/07038992.2019.1674643	journal	September 2019
Non-uniform seasonal warming regulates vegetation greening and atmospheric CO ₂ amplification over northern lands Li, Zhao; Xia, Jianyang; Ahlström, Anders Environmental Research Letters, Vol. 13, Issue 12 https://doi.org/10.1088/1748-9326/aae9ad	journal	November 2018
An integrated phenology modelling framework in r Hufkens, Koen; Basler, David; Milliman, Tom Methods in Ecology and Evolution, Vol. 9, Issue 5 https://doi.org/10.1111/2041-210x.12970	journal	February 2018
Detecting changes in understorey and canopy vegetation cycles in West Central Alberta using a fusion of Landsat and MODIS McClelland, Cameron J. R.; Coops, Nicholas C.; Berman, Ethan E. Applied Vegetation Science, Vol. 23, Issue 2 https://doi.org/10.1111/avsc.12466	journal	November 2019
Plant phenology and global climate change: Current progresses and challenges Piao, Shilong; Liu, Qiang; Chen, Anping Global Change Biology, Vol. 25, Issue 6 https://doi.org/10.1111/gcb.14619	journal	April 2019
Local snow melt and temperature—but not regional sea ice—explain variation in spring phenology in coastal Arctic tundra Assmann, Jakob J.; Myers‐Smith, Isla H.; Phillimore, Albert B. Global Change Biology https://doi.org/10.1111/gcb.14639	journal	April 2019
Cryptic phenology in plants: Case studies, implications, and recommendations Albert, Loren P.; Restrepo‐Coupe, Natalia; Smith, Marielle N. Global Change Biology, Vol. 25, Issue 11 https://doi.org/10.1111/gcb.14759	journal	August 2019
Characteristics of free air carbon dioxide enrichment of a northern temperate mature forest Hart, Kris M.; Curioni, Giulio; Blaen, Phillip Global Change Biology, Vol. 26, Issue 2 https://doi.org/10.1111/gcb.14786	journal	September 2019
Refining the role of phenology in regulating gross ecosystem productivity across European peatlands Koebsch, Franziska; Sonnentag, Oliver; Järveoja, Järvi Global Change Biology https://doi.org/10.1111/gcb.14905	journal	December 2019
Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery Richardson, Andrew D. New Phytologist, Vol. 222, Issue 4 https://doi.org/10.1111/nph.15591	journal	December 2018
Growth and opportunities in networked synthesis through AmeriFlux Keenan, Trevor F.; Moore, David J. P.; Desai, Ankur New Phytologist, Vol. 222, Issue 4 https://doi.org/10.1111/nph.15835	journal	May 2019
Effects of climate change on alpine plants and their pollinators Inouye, David W. Annals of the New York Academy of Sciences, Vol. 1469, Issue 1 https://doi.org/10.1111/nyas.14104	journal	April 2019
Integrating camera imagery, crowdsourcing, and deep learning to improve high-frequency automated monitoring of snow at continental-to-global scales Kosmala, Margaret; Hufkens, Koen; Richardson, Andrew D. PLOS ONE, Vol. 13, Issue 12 https://doi.org/10.1371/journal.pone.0209649	journal	December 2018
The Plant Phenology Ontology: A New Informatics Resource for Large-Scale Integration of Plant Phenology Data Stucky, Brian J.; Guralnick, Rob; Deck, John Frontiers in Plant Science, Vol. 9 https://doi.org/10.3389/fpls.2018.00517	journal	May 2018
Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard Lu, Xiaoliang; Liu, Zhunqiao; Zhou, Yuyu Remote Sensing, Vol. 10, Issue 6 https://doi.org/10.3390/rs10060932	journal	June 2018
Improving the Performance of 3-D Radiative Transfer Model FLIGHT to Simulate Optical Properties of a Tree-Grass Ecosystem Melendo-Vega, José; Martín, M.; Pacheco-Labrador, Javier Remote Sensing, Vol. 10, Issue 12 https://doi.org/10.3390/rs10122061	journal	December 2018
A Comparison of the Signal from Diverse Optical Sensors for Monitoring Alpine Grassland Dynamics Rossi, Mattia; Niedrist, Georg; Asam, Sarah Remote Sensing, Vol. 11, Issue 3 https://doi.org/10.3390/rs11030296	journal	February 2019
Measuring Vegetation Phenology with Near-Surface Remote Sensing in a Temperate Deciduous Forest: Effects of Sensor Type and Deployment Liu, Fan; Wang, Xingchang; Wang, Chuankuan Remote Sensing, Vol. 11, Issue 9 https://doi.org/10.3390/rs11091063	journal	May 2019
Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology Sousa, Daniel; Small, Christopher; Spalton, Andrew Remote Sensing, Vol. 11, Issue 10 https://doi.org/10.3390/rs11101203	journal	May 2019
Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform Remote Sensing, Vol. 11, Issue 13 https://doi.org/10.3390/rs11131514	journal	June 2019
The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation Klouček, Tomáš; Komárek, Jan; Surový, Peter Remote Sensing, Vol. 11, Issue 13 https://doi.org/10.3390/rs11131561	journal	July 2019
Influence of Landscape Heterogeneity and Spatial Resolution in Multi-Temporal In Situ and MODIS NDVI Data Proxies for Seasonal GPP Dynamics Balzarolo, Manuela; Peñuelas, Josep; Veroustraete, Frank Remote Sensing, Vol. 11, Issue 14 https://doi.org/10.3390/rs11141656	journal	July 2019
Asymmetric Behavior of Vegetation Seasonal Growth and the Climatic Cause: Evidence from Long-Term NDVI Dataset in Northeast China Zhou, Yuke Remote Sensing, Vol. 11, Issue 18 https://doi.org/10.3390/rs11182107	journal	September 2019
Leafing Patterns and Drivers across Seasonally Dry Tropical Communities Alberton, Bruna; da Silva Torres, Ricardo; Sanna Freire Silva, Thiago Remote Sensing, Vol. 11, Issue 19 https://doi.org/10.3390/rs11192267	journal	September 2019
N : P stoichiometry and habitat effects on Mediterranean savanna seasonal root dynamics Nair, Richard K. F.; Morris, Kendalynn A.; Hertel, Martin Biogeosciences, Vol. 16, Issue 9 https://doi.org/10.5194/bg-16-1883-2019	journal	January 2019
A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States Li, Xuecao; Zhou, Yuyu; Meng, Lin Earth System Science Data, Vol. 11, Issue 2 https://doi.org/10.5194/essd-11-881-2019	journal	January 2019
Ecosystem warming extends vegetation activity but heightens vulnerability to cold temperatures Richardson, Andrew D.; Hufkens, Koen; Milliman, Thomas Nature, Vol. 560, Issue 7718, p. 368-371 https://doi.org/10.1038/s41586-018-0399-1	journal	August 2018
Detecting the onset of autumn leaf senescence in deciduous forest trees of the temperate zone Mariën, Bertold; Balzarolo, Manuela; Dox, Inge New Phytologist, Vol. 224, Issue 1 https://doi.org/10.1111/nph.15991	journal	June 2019
Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology Sousa, Daniel John; Small, Christopher; Spalton, Andrew Columbia University https://doi.org/10.7916/d8-p5zf-ws16	text	January 2019
Comparison Of Large-Scale Citizen Science Data And Long-Term Study Data For Phenology Modeling Taylor, Shawn D.; Meiners, Joan M.; Riemer, Kristina Zenodo https://doi.org/10.5281/zenodo.1256705	text	January 2018
FluoSpec 2—An Automated Field Spectroscopy System to Monitor Canopy Solar-Induced Fluorescence Yang, Xi; Shi, Hanyu; Stovall, Atticus Sensors, Vol. 18, Issue 7 https://doi.org/10.3390/s18072063	journal	June 2018
Comparison of Landsat and Land-Based Phenology Camera Normalized Difference Vegetation Index (NDVI) for Dominant Plant Communities in the Great Basin Snyder, Keirith; Huntington, Justin; Wehan, Bryce Sensors, Vol. 19, Issue 5 https://doi.org/10.3390/s19051139	journal	March 2019

Similar Records

Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset

Journal Article · Tue Oct 22 00:00:00 EDT 2019 · Scientific Data · OSTI ID:1433125

Seyednasrollah, Bijan; Young, Adam M.; Hufkens, Koen; +4 more

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

Journal Article · Mon Apr 09 00:00:00 EDT 2018 · Scientific Reports · OSTI ID:1433125

Richardson, Andrew D.; Hufkens, Koen; Milliman, Tom; +1 more

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2022

Dataset · Sun Jan 01 00:00:00 EST 2023 · OSTI ID:1433125

Richardson, Andrew; Schädel, Christina; Blais, Jacob; +4 more

Related Subjects

54 ENVIRONMENTAL SCIENCES

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

Citation Formats

References (67)

Cited By (44)

Similar Records

Related Subjects