PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018

Milliman, Tom; Seyednasrollah, Bijan; Young, Allen; Hufkens, Koen; Friedl,; Frolking, Steve; Richardson, A D; Abraha, Michael; Allen, David W.; Apple, M. E.; Arain,; Baker, Joanne; 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, Hongliang; Clark, Patrick E.; Conrad, B.J.; Cremonese, Edoardo; Debinski, Diane M.; Desai, A.R.; Dı́az-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, Leilei; Guillemot, Joannès; Halpin, M.; Hanson, P. J.; Hemming, Deborah; Hove, Alisa A.; Humphreys, Elyn; Jaimes-Hernandez, A.; Jaradat, A.A.; Johnson, Jeffrey P.; 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, Craig E.; Mudd, R. G.; Munger, J. William; Murrell, Z.E.; Nesic, Zoran; Neufeld, Howard S.; 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, Arijit; Saliendra, Nicanor Z.; Schmalbeck, L.; Schwartz,; Scott, R. K.; Smith, Eric; Sonnentag, Oliver; Stoy, Paul C.; Strachan, Scotty; Suvočarev, K.; Thom, Jonathan E.; Thomas, Ryan W.; Berg, A.K. Van Den; Vargas, Rodrigo; 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.; Yang, Jingjing; Yang, Xulei; Yenni, Glenda M.; Zhang, Y.; Zhang, Q.; Zona, Donatella; Baldocchi, Dennis; Verfaillie, Joseph

doi:10.3334/ornldaac/1689

Cited by 7 publications

(3 citation statements)

References 0 publications

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“…PhenoCam imagery, data, and data products for the SPRUCE project are publicly available in near real-time through https://phenocam.nau.edu/webcam/network/search/?group=spruce, and imagery has also been archived at ORNL (Milliman et al, 2019) with documentation in (Seyednasrollah et al, 2019). SPRUCE environmental data (Hanson et al, 2016) and snow presence data (Schädel et al, 2022) have been archived, with documentation, at ORNL.…”

Section: Data Availability Statementmentioning

confidence: 99%

Experimental Whole‐Ecosystem Warming Enables Novel Estimation of Snow Cover and Depth Sensitivities to Temperature, and Quantification of the Snow‐Albedo Feedback Effect

Richardson,

Schädel,

Westergaard‐Nielsen

et al. 2024

JGR Biogeosciences

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Climate change is reducing the amount, duration, and extent of snow across high‐latitude ecosystems. But, in landscapes where persistent winter snow cover develops, experimental platforms to specifically investigate interactions between warming and changes in snowpack, and impacts on ecosystem processes, have been lacking. We leveraged a whole‐ecosystem warming experiment in a boreal peatland forest to quantify how snow duration, depth, and fractional cover vary with warming of up to +9°C. We found that every snow‐related quantity we examined declined precipitously as the amount of warming increased. The importance of deep, continuous snow cover for moderating shallow soil temperature is highlighted by an increase in soil temperature variance and the frequency of short‐duration freeze‐thaw cycles in the warmer plots. We used a paired‐plot approach to estimate the magnitude of the snow‐albedo feedback effect, and demonstrate that albedo‐driven warming linked to reduced snow cover varies between December (+0.4°C increase in maximum air temperature) and March (+1.2°C increase) because of differences in insolation. Overall, results show that even modest future warming will have profound impacts on northern winters and cold‐season ecosystem processes. Plot‐level data from this warming experiment, and emergent relationships between warming and quantities related to snow cover and duration, could be of enormous value for testing and improving the representation of snow processes in simulation models, especially under future climate scenarios that are outside of the range of historically observed variability.

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Section: Data Availability Statementmentioning

confidence: 99%

Experimental Whole‐Ecosystem Warming Enables Novel Estimation of Snow Cover and Depth Sensitivities to Temperature, and Quantification of the Snow‐Albedo Feedback Effect

Richardson,

Schädel,

Westergaard‐Nielsen

et al. 2024

JGR Biogeosciences

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“…Data for the study sites were extracted from the PhenoCam Dataset V2.0 (Milliman et al, 2019;Seyednasrollah et al, 2019aSeyednasrollah et al, , 2019bSeyednasrollah et al, , 2019c whereby digital images were collected at 15-min intervals over a 24-h period. Canopy greenness time-series data were obtained from these images by delineating appropriate regions of interest (ROIs) and calculating the green chromatic coordinate (GCC) for each, using the following equation:…”

Section: Phenocam: Green Chromatic Coordinatementioning

confidence: 99%

Exploring discrepancies between in situ phenology and remotely derived phenometrics at NEON sites

Donnelly

Jones

et al. 2022

Ecosphere

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In recent decades, the use of satellite sensors, near-surface cameras, and other remote methods for monitoring vegetation phenology at landscape and higher scales has become increasingly common. These technologies provide a means to determine the timing of phenophases and growing season length at different spatial resolutions; coverage that is not attainable by human observers. However, in situ ground observations are required to validate remotely derived phenometrics. Despite increased knowledge and expertise there still remains the persistent challenge of reconciling ground observations at the individual plant level with remotely sensed (RS) phenometrics at landscape or larger scales. We compared the timing of in situ phenophase estimates (spring and autumn) with a range of corresponding remote sensing (moderate resolution imaging spectroradiometer [MODIS], visible infrared imaging radiometer suite [VIIRS], PhenoCam) phenometrics across five terrestrial sites in the United States' NEON (Harvard Forest [MA] [HARV], Onaqui [UT] [ONAQ], Abby Road [WA] [ABBY], Disney Wilderness Preserve [FL] [DSNY], and Ordway-Swisher Biological Station [FL] [OSBS]) focusing on the 3-year period from 2017 to 2019. Our main objective was to explore potential reasons for the observed discrepancies between in situ and RS phenometrics and to determine which technologies were better able to capture ground observations. Statistically significant relationships were strongest (p < 0.001) for spring phenophases, while the only RS phenometrics significantly correlated with in situ estimates of autumn phenophases were leaf fall (p < 0.01) and leaves (p < 0.000). In particular, root mean square error (RMSE) (mean bias error Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US government. The findings and conclusions in this paper are those of the authors and should not be construed to represent any official US Department of Agriculture, US Department of the Interior, or US government determination or policy.

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“…After fitting, the VGG16 model was used to classify approximately fifty-five thousand midday images from all agricultural PhenoCam sites , totalling approximately 170 siteyears [31]. These classified images were then put through a post-processing routine to produce a final classification for each day (Table 2).…”

Section: Post-processingmentioning

confidence: 99%

Classification of daily crop phenology in PhenoCams using deep learning and hidden markov models

Taylor

Browning

2021

Preprint

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Near surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images our workflow incorporates a hidden markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83-0.85, which improved to 0.86-0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local scale dataset of field states and phenological stages for agricultural research.

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PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018

Cited by 7 publications

References 0 publications

Experimental Whole‐Ecosystem Warming Enables Novel Estimation of Snow Cover and Depth Sensitivities to Temperature, and Quantification of the Snow‐Albedo Feedback Effect

Experimental Whole‐Ecosystem Warming Enables Novel Estimation of Snow Cover and Depth Sensitivities to Temperature, and Quantification of the Snow‐Albedo Feedback Effect

Exploring discrepancies between in situ phenology and remotely derived phenometrics at NEON sites

Classification of daily crop phenology in PhenoCams using deep learning and hidden markov models

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