Assimilating phenology datasets automatically across ICOS ecosystem stations

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The presence or absence of leaves within plant canopies exert a strong influence on the carbon, water and energy balance of ecosystems. Identifying key changes in the timing of leaf elongation and senescence during the year can help to understand the sensitivity of different plant functional types to changes in temperature. When recorded over many years these data can provide information on the response of ecosystems to long-term changes in climate. The installation of digital cameras that take images at regular intervals of plant canopies across the Integrated Carbon Observation System ecosystem stations will provide a reliable and important record of variations in canopy state, colour and the timing of key phenological events. Here, we detail the procedure for the implementation of cameras on Integrated Carbon Observation System flux towers and how these images will help us understand the impact of leaf phenology and ecosystem function, distinguish changes in canopy structure from leaf physiology and at larger scales will assist in the validation of (future) remote sensing products. These data will help us improve the representation of phenological responses to climatic variability across Integrated Carbon Observation System stations and the terrestrial biosphere through the improvement of model algorithms and the provision of validation datasets.

Alberton B., Torres R. da S., Cancian L.F., Borges B.D., Almeida J., Mariano G.C., Santos J. dos, and Morellato L.P.C., 2017. Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation. Perspect. Ecol. Conserv., 15, 82-90.

Andresen C.G., Tweedie C.E., and Lougheed V.L., 2018. Climate and nutrient effects on Arctic wetland plant phenology observed from phenocams. Remote Sens. Environ., 205, 46-55.

Baldocchi D.D., Black T.A., Curtis P.S., Falge E., Fuentes J.D., Granier A., Gu L., Knohl A., Pilegaard K., Schmid H.P., Valentini R., Wilson K., Wofsy S., Xu L., and Yamamoto S., 2005. Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: a synthesis of FLUXNET data. Int. J. Biometeorol., 49, 377-387.

Bater C.W., Coops N.C., Wulder M.A., Hilker T., Nielsen S.E., McDermid G., and Stenhouse G.B., 2011. Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment. Environ. Monit. Assess. 180, 1-13.

Bernard É., Friedt J.M., Tolle F., Griselin M., Martin G., Laffly D., and Marlin C., 2013. Monitoring seasonal snow dynamics using ground based high resolution photography (Austre Lovénbreen, Svalbard, 79°N). ISPRS J. Photogramm. Remote Sens. 75, 92-100.

Bowling D.R., Logan B.A., Hufkens K., Aubrecht D.M., Richardson A.D., Burns S.P., Blanken P.D., and Eiriksson D., 2018. Limitations to winter photosynthesis in a Rocky Mountain subalpine forest. Agric. For. Meteorol. 252, 241-255.

Browning D.M., Karl J.W., Morin D., Richardson A.D., and Tweedie C.E., 2017. Phenocams bridge the gap between field and satellite observations in an arid grassland ecosystem. Remote Sens, 9.

Chen M., Melaas E.K., Gray J.M., Friedl M.A., and Richardson A.D., 2016. A new seasonal-deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios. Glob. Chang. Biol. 22, 3675-3688.

Cremonese E., Filippa G., Galvagno M., Siniscalco C., Oddi L., Morra di Cella U., and Migliavacca M., 2017. Heat wave hinders green wave: The impact of climate extreme on the phenology of a mountain grassland. Agric. For. Meteorol., 247, 320-330,

D’Odorico P., Gonsamo A., Gough C.M., Bohrer G., Morison J., Wilkinson M., Hanson P.J., Gianelle D., Fuentes J.D., and Buchmann N., 2015. The match and mismatch between photosynthesis and land surface phenology of deciduous forests. Agric. For. Meteorol., 214-215, 25-38,

Delpierre N., Vitasse Y., Chuine I., Guillemot J., Bazot S., Rutishauser T., and Rathgeber C.B.K., 2016. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models. Ann. For. Sci., 73, 5-25.

Dickerson-Lange S.E., Lutz J.A., Martin K.A., Raleigh M.S., Gersonde R., and Lundquist J.D., 2015. Evaluating observational methods to quantify snow duration under diverse forest canopies. Water Resour. Res., 51, 1203-1224,

Filippa G., Cremonese E., Migliavacca M. et al., 2018. NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types. Agricultural and Forest Meteorology, 249, 275-285.

Filippa G., Cremonese E., Migliavacca M., Galvagno M., Forkel M., Wingate L., Tomelleri E., Morra di Cella U., and Richardson A.D., 2016. Phenopix: A R package for image-based vegetation phenology. Agric. For. Meteorol., 220, 141-150,

Filippa G., Cremonese E., Migliavacca M., Galvagno M., Sonnentag O., Humphreys E., Hufkens K., Ryu Y., Verfaillie J., Morra di Cella U., and Richardson A.D., 2018. NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types. Agric. For. Meteorol.,

Green J.K., Konings A.G., Alemohammad S.H., Berry J., Entekhabi D., Kolassa J., Lee J.E., and Gentine P., 2017. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci., 10, 410-414.

Hufkens K., Basler D., Milliman T., Melaas E.K., and Richardson A.D., 2018. An integrated phenology modelling framework in R. Methods Ecol. Evol. 9, 1-10.

Hufkens K., Friedl M., Sonnentag O., Braswell B.H., Milliman T., and Richardson A.D., 2012. Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens. Environ., 117, 307-321.

Hufkens K., Keenan T.F., Flanagan L.B., Scott R.L., Bernacchi C.J., Joo E., Brunsell N.A., Verfaillie J., and Richardson A.D., 2016. Productivity of North American grasslands is increased under future climate scenarios despite rising aridity. Nat. Clim. Chang.,

Ide R., Nakaji T., Motohka T., and Oguma H., 2011. Advantages of visible-band spectral remote sensing at both satellite and near-surface scales for monitoring the seasonal dynamics of GPP in a Japanese larch forest. J. Agric. Meteorology, 67, 75-84.

Ide R. and Oguma H., 2013. A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems. Ecol. Inform., 16, 25-34.

Inoue T., Nagai S., Kobayashi H., and Koizumi H., 2015. Utilization of ground-based digital photography for the evaluation of seasonal changes in the aboveground green biomass and foliage phenology in a grassland ecosystem. Ecol. Inform., 25, 1-9,

Keenan T.F., Gray J., Friedl M.A., Toomey M., Bohrer G., Hollinger D.Y., Munger J.W., Keefe J.O., Schmid H.P., Wing I.S., Yang B., and Richardson A.D., 2014. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Chang., 1-7,

Klosterman S.T., Hufkens K., Gray J.M., Melaas E., Sonnentag O., Lavine I., Mitchell L., Norman R., Friedl M.A., and Richardson A.D., 2014. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences Discuss, 11, 2305-2342,

Kosmala M., Crall A., Cheng R., Hufkens K., Henderson S., and Richardson A., 2016. Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing. Remote Sensing, 8, 726.

Leith H., 1974. Phenology and seasonality modelling. Springer, Heidelberg.

Liang L., Schwartz M.D., and Fei S., 2011. Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sens. Environ., 115, 143-157,

Liu Y., Hill M.J., Zhang X., Wang Z., Richardson A.D., Hufkens K., Filippa G., Baldocchi D.D., Ma S., Verfaillie J., and Schaaf C.B., 2017. Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales. Agric. For. Meteorol., 237-238, 311-325,

Melaas E.K., Friedl M.A., and Richardson A.D., 2016. Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States. Glob. Chang. Biol., 22, 792-805.

Migliavacca M., Galvagno M., Cremonese E., Rossini M., Meroni M., Sonnentag O., Cogliati S., Manca G., Diotri F., Busetto L., Cescatti A., Colombo R., Fava F., Morra di Cella U., Pari E., Siniscalco C., and Richardson A.D., 2011. Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake. Agric. For. Meteorol., 151, 1325-1337,

Moore C.E., Beringer J., Evans B., Hutley L.B., and Tapper N.J., 2017. Tree-grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna. Biogeosci., 14, 111-129,

Morrison L.W. and Young C.C., 2016. Observer error in sampling a rare plant population. Plant Ecol. Divers., 9, 289-297,

Mizunuma T., Mencuccini M., Wingate L., Ogée J., Nichol C., and Grace J., 2014. Sensitivity of colour indices for discriminating leaf colours from digital photographs. Methods in Ecology and Evolution, 5, 1078-1085.

Nagai S., Akitsu T., Saitoh T.M. et al., 2018. 8 million phenological and sky images from 29 ecosystems from the Arctic to the tropics: the Phenological Eyes Network. Ecological Research, 33, 1-2.

Nagai S., Maeda T., Gamo M., Muraoka H., Suzuki R., and Nasahara K.N., 2011. Using digital camera images to detect canopy condition of deciduous broad-leaved trees. Plant Ecol. Divers., 4, 79-89,

Nasahara K.N. and Nagai S., 2015. Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN). Ecol. Res., 30, 211-223,

Petach A.R., Toomey M., Aubrecht D.M., and Richardson A.D., 2014. Monitoring vegetation phenology using an infrared-enabled security camera. Agric. For. Meteorol., 195-196, 143-151,

Piao S., Ciais P., Friedlingstein P., Peylin P., Reichstein M., Luyssaert S., Margolis H., Fang J., Barr A., Chen A., Grelle A., Hollinger D.Y., Laurila T., Lindroth A., Richardson A.D., and Vesala T., 2008. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451, 49-52,

Richardson A.D., Anderson R.S., AltafArain M., Barr A.G., Bohrer G., Chen G., Chen J.M., Ciais P., Davis K.J., Desai A.R., Dietze M.C., Dragoni D., Maayar M. El, Garrity S., Gough C.M., Grant R., Hollinger D.Y., Margolis H.A., McCaughey H., Migliavacca M., Monson R.K., William Munger J., Poulter B., Raczka B.M., Ricciuto D.M., Sahoo A.K., Schaefer K., Tian H., Vargas R., Verbeeck H., Xiao J., and Xue Y., 2011. Terrestrial biosphere models need better representation of vegetation phenology: Results from the North American Carbon Program. Glob. Chang. Biol., n/a-n/a.

Richardson A.D., Black T.A., Ciais P., Delbart N., Friedl M.A., Gobron N., Hollinger D.Y., Kutsch W.L., Longdoz B., Luyssaert S., Migliavacca M., Montagnani L., Munger J.W., Moors E., Piao S., Rebmann C., Reichstein M., Saigusa N., Tomelleri E., Vargas R., and Varlagin A., 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. Lond. B. Biol. Sci., 365, 3227-46.

Richardson A.D., Braswell B.H., Hollinger D.Y., Jenkins J.P., and Ollinger S.V, 2009. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol. Appl., 19, 1417-28.

Richardson A.D., Hufkens K., Milliman T., Aubrecht D.M., Chen M., Gray J.M., Johnston M.R., Keenan T.F., Klosterman S.T., Kosmala M., Melaas E.K., Friedl M.A., and Frolking S., 2018a. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Sci. Data.

Richardson A.D., Hufkens K., Milliman T., and Frolking S., 2018b. Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing. Sci. Rep.

Richardson A.D., Keenan T.F., Migliavacca M., Ryu Y., Sonnentag O., and Toomey M., 2013. Climate change, phenology, and phenological control of vegetation feed-backs to the climate system. Agric. For. Meteorol., 169, 156-173,

Robroek B.J.M., Heijboer A., Jassey V.E.J., Hefting M.M., Rouwenhorst T.G., Buttler A., and Bragazza L., 2013. Snow cover manipulation effects on microbial community structure and soil chemistry in a mountain bog. Plant Soil, 369, 151-164,

Rosenzweig C., Casassa G., Karoly Imeson A., Liu C., Menzel A., Rawlins S., Root T.L., Seguin B., and Tryjanowski P., 2007. Climate change 2007 : impacts, adaptation and vulnerability : Working Group II contribution to the Fourth Assessment Report of the IPCC Intergovernmental Panel on Climate Change (Eds Parry M.L., Canziani O.F., Palutikof J.P., van der Linden P.J., Hanson C.E.),

SanClements M. and Roberti J., 2016. Neon Sensor Command Control And Configuration (C3) Document: Phenology Camera / Snow Depth Camera.

Snyder K.A., Wehan B.L., Filippa G., Huntington J.L., Stringham T.K., and Snyder D.K., 2016. Extracting plant phenology metrics in a great basin watershed: Methods and considerations for quantifying phenophases in a cold desert. Sensors (Switzerland), 16,

Sonnentag O., Hufkens K., Teshera-Sterne C., Young A.M., Friedl M., Braswell B.H., Milliman T., O’Keefe J., and Richardson A.D., 2012. Digital repeat photography for phenological research in forest ecosystems. Agric. For. Meteorol., 152, 159-177,

Sus O., Williams M., Bernhofer C., Béziat P., Buchmann N., Ceschia E., Doherty R., Eugster W., Grünwald T., Kutsch W., Smith P., and Wattenbach M., 2010. A linked carbon cycle and crop developmental model: Description and evaluation against measurements of carbon fluxes and carbon stocks at several European agricultural sites. Agric. Ecosyst. Environ., 139, 402-418,

Toomey M., Friedl M.A., Frolking S., Hufkens K., Klosterman S., Sonnentag O., Baldocchi D.D., Bernacchi C.J., Biraud S.C., and Richardson A.D., 2015. Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis. Ecol. Appl., 25, 99-115.

Vrieling A., Meroni M., Darvishzadeh R., Skidmore A.K., Wang T., Zurita-Milla R., Oosterbeek K., O’Connor B., and Paganini M., 2018. Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island. Remote Sens. Environ., 1-13,

Wingate L., Ogeé J., Cremonese E., Filippa G., Mizunuma T., Migliavacca M., Moisy C., Wilkinson M., Moureaux C., Wohlfahrt G., Hammerle A., Hörtnagl L., Gimeno C., Porcar-Castell A., Galvagno M., Nakaji T., Morison J., Kolle O., Knohl A., Kutsch W., Kolari P., Nikinmaa E., Ibrom A., Gielen B., Eugster W., Balzarolo M., Papale D., Klumpp K., Köstner B., Grünwald T., Joffre R., Ourcival J.M., Hellstrom M., Lindroth A., George C., Longdoz B., Genty B., Levula J., Heinesch B., Sprintsin M., Yakir D., Manise T., Guyon D., Ahrends H., Plaza-Aguilar A., Guan J.H., and Grace J., 2015. Interpreting canopy development and physiology using a European phenology camera network at flux sites. Biogeosciences, 12, 5995-6015,

Woebbecke D., Meyer G., Von Bargen K., and Mortensen D., 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE, 38, 259-269.

Wohlfahrt G., Cremonese E., Hammerle A., Hörtnagl L., Galvagno M., Gianelle D., Marcolla B., and di Cella U.M., 2013. Trade-offs between global warming and day length on the start of the carbon uptake period in seasonally cold ecosystems. Geophys. Res. Lett., 40, n/a-n/a.

Xie Y., Wang X., and Silander J.A., 2015. Deciduous forest responses to temperature, precipitation, and drought imply complex climate change impacts. Proc. Natl. Acad. Sci., 112, 13585-13590,

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