PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018

Seyednasrollah, Bijan; Young, Adam M.; Hufkens, Koen; Milliman, Tom; Friedl,; Frolking, Steve; Richardson, A D; Abraha, Michael; Allen, D. W.; Apple, M. E.; Arain,; Baker, Joanne; Baldocchi, Dennis; Bernacchi, Carl J.; Bhattacharjee, Joy; Blanken, Peter D.; Bosch, David D.; Boughton, Raoul K.; Boughton, Elizabeth H.; Brown, R.F.; Browning, Dawn M.; Brunsell, Nathaniel A.; Burns, S.P.; Cavagna, M.; Chu, Hsin; Clark, Patrick E.; Conrad, B.J.; Cremonese, Edoardo; Debinski, Diane M.; Desai, A.R.; Díaz-Delgado, Ricardo; Duchesne, Luc; Dunn, A.L.; Eissenstat, David M.; El‐Madany, Tarek S.; Ellum, D.S.S.; Ernest, Siwa; Esposito, Alessandro; Fenstermaker, L.; Flanagan, Lawrence B.; Forsythe, Brandon; Gallagher, Julia; Gianelle, Damiano; Griffis, Timothy J.; Groffman, Peter M.; Gu, Lingjia; Guillemot, Joannès; Halpin, M.; Hanson, P. J.; Hemming, Deborah; Hove, Alisa A.; Humphreys, Elyn; Jaimes-Hernandez, A.; Jaradat, A.A.; Johnson, James A.; Keel, E.; Kelly, Vince; Kirchner, Julian; Kirchner, P. B.; Knapp, Martín; Krassovski, Misha; Langvall, Ola; Lanthier, G.; Maire, Guerric le; Magliulo, Enzo; Martin, Taali; McNeil, Brenden E.; Meyer, G.A.; Migliavacca, M.; Mohanty, B.P.; Moore, Courtney E.; Mudd, R. G.; Munger, J. William; Murrell, Z.E.; Nesic, Zoran; Neufeld, Howard S.; O’Halloran, T. L.; Oechel, W. C.; Oishi, Ayame; Oswald, W. Wyatt; Perkins, Timothy D.; Reba, M. L.; Rundquist, Brad; Runkle, Benjamin R. K.; Russell, Eric S.; Sadler, E. J.; Saha, Aniruddha; Saliendra, Nicanor Z.; Schmalbeck, L.; Schwartz,; Scott, Russell L.; Smith, Eric; Sonnentag, Oliver; Stoy, Paul C.; Strachan, Scotty; Suvočarev, K.; Thom, Jonathan E.; Thomas, Ryan W.; Berg, A.K. Van Den; Vargas, Rodrigo; Verfaillie, Joseph; Vogel, C. S.; Walker, Jessica J.; Webb, Nathan D.; Wetzel, Patricia J.; Weyers, Sheila; Whipple, A.V.; Whitham, Thomas G.; Wohlfahrt, Georg; Wood, J.D.; Wolf, Skylar L.; Yang, Jian; Yang, Xiuling; Yenni, Glenda M.; Zhang, Y.; Zhang, Q.; Zona, Donatella

doi:10.3334/ornldaac/1674

Cited by 9 publications

(9 citation statements)

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“…The PhenoCam Network uses digital repeat photography to track seasonal changes in canopy color at over 600 sites across North America and around the world (Richardson, 2019). Over 100 sites in the network include evergreen conifer vegetation, and the most recent curated PhenoCam data release (v.2; Seyednasrollah et al, 2019b) includes 265 site-years of data for evergreen conifer sites. The PhenoCam processing workflow is fully described in previous publications (Richardson et al, 2018a;Seyednasrollah et al, 2019a); we summarize the key steps here.…”

Section: Phenocam Datamentioning

confidence: 99%

“…directly modeling SOS and EOS transition dates, e.g. Melaas et al, 2016;Richardson et al, 2019b), whereby day-to-day changes in greenness are driven by the preceding day's maximum (spring) or minimum (autumn) air temperature. The model was inspired by formulations previously presented to simulate the seasonality of developmental processes (Hänninen & Kramer, 2007;Hänninen, 2016), and seasonal upregulation and downregulation of photosynthetic capacity (Richardson et al, 2010b) of winter-dormant conifers.…”

Section: Modeling the Seasonal Trajectory Of Canopy Colormentioning

confidence: 99%

“…All PhenoCam data used here (Fig. 1) are publicly available from the PhenoCam dataset v.2.0 (Seyednasrollah et al, 2019a) which is available for download from the ORNL DAAC (Seyednasrollah et al, 2019b).…”

Section: Phenocam Datamentioning

confidence: 99%

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Seasonal variation in the canopy color of temperate evergreen conifer forests

et al. 2020

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 Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated.  Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, towerbased CO 2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics.  We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color.  These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.

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Section: Phenocam Datamentioning

confidence: 99%

Section: Modeling the Seasonal Trajectory Of Canopy Colormentioning

confidence: 99%

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Seasonal variation in the canopy color of temperate evergreen conifer forests

et al. 2020

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“…Direct impacts of this event are captured in several NEON data products, with sensor measurements recording until power was no longer available and data collection resuming immediately upon its restoration 2 weeks later. These include meteorological data products like mean barometric pressure (NEON, 2022a), which dropped precipitously before the hurricane (Figure 2A), and automated Phenology images (Seyednasrollah et al, 2019b). Imagery from before and after the hurricane not only confirm the immediate effects of significant winds on the plant community, with notable canopy defoliation following the event (Figure 2B), but also enable the study of longer-term impacts and resilience.…”

Section: Assessing Environmental Resilience In the Face Of Natural Di...mentioning

confidence: 98%

Multiple Dimensions of Resilience: How NEON Supports Ecology and the Research Community in the Face of Compounding Disasters

Robinson

Barnett²,

Jones

et al. 2022

Front. Environ. Sci.

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Quantifying the resilience of ecological communities to increasingly frequent and severe environmental disturbance, such as natural disasters, requires long-term and continuous observations and a research community that is itself resilient. Investigators must have reliable access to data, a variety of resources to facilitate response to perturbation, and mechanisms for rapid and efficient return to function and/or adaptation to post-disaster conditions. There are always challenges to meeting these requirements, which may be compounded by multiple, co-occurring incidents. For example, travel restrictions resulting from the COVID-19 pandemic hindered preparations for, and responses to, environmental disasters that are the hallmarks of resilient research communities. During its initial years of data collection, a diversity of disturbances—earthquakes, wildfires, droughts, hurricanes and floods—have impacted sites at which the National Ecological Observatory Network (NEON) intends to measure organisms and environment for at least 30 years. These events strain both the natural and human communities associated with the Observatory, and additional stressors like public health crises only add to the burden. Here, we provide a case-study of how NEON has demonstrated not only internal resilience in the face of the public health crisis of COVID-19, but has also enhanced the resilience of ecological research communities associated with the network and provided crucial information for quantifying the impacts of and responses to disturbance events on natural systems—their ecological resilience. The key components discussed are: 1) NEON’s infrastructure and resources to support its core internal community, to adapt to rapidly changing situations, and to quickly resume operations following disruption, thus enabling the recovery of information flow crucial for data continuity; 2) how NEON data, tools, and materials are foundational in supporting the continuation of research programs in the face of challenges like those of COVID-19, thus enhancing the resilience of the greater ecological research community; and 3) the importance of diverse and consistent data for defining baseline and post-disaster conditions that are required to quantify the effects of natural disasters on ecosystem patterns and processes.

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“…Observations are sent to a central server every half hour for processing and archival (Seyednasrollah et al, 2019). These images are then masked by region of interest (ROI) for dominant land cover vegetation components.…”

Section: Phenology Observationsmentioning

confidence: 99%

Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems

et al. 2022

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Long‐running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem‐Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US‐WCr and US‐Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US‐Los and US‐ALQ), and a very tall (400 m) landscape‐level tower (US‐PFa). Together, they provided over 70 site‐years of observations. The 19‐tower Chequamegon Heterogenous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors 2019 campaign centered around US‐PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long‐term sites, but cross‐site coherence in interannual NEE in the earlier part of the record became weaker with time as non‐climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less‐insulated soils delayed start of spring green‐up. Higher CO2 increased maximum net assimilation parameters but not total gross primary productivity. Stand‐scale sites were larger net sinks than the landscape tower. Clustered, long‐term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.

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PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018

Cited by 9 publications

References 0 publications

Seasonal variation in the canopy color of temperate evergreen conifer forests

Seasonal variation in the canopy color of temperate evergreen conifer forests

Multiple Dimensions of Resilience: How NEON Supports Ecology and the Research Community in the Face of Compounding Disasters

Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems

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