Assimilating phenology datasets automatically across ICOS ecosystem stations

Abstract 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.

[1]  O. Sonnentag,et al.  Climate change, phenology, and phenological control of vegetation feedbacks to the climate system , 2013 .

[2]  T. A. Black,et al.  Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: a synthesis of FLUXNET data , 2005, International journal of biometeorology.

[3]  Hideki Kobayashi,et al.  Utilization of ground-based digital photography for the evaluation of seasonal changes in the aboveground green biomass and foliage phenology in a grassland ecosystem , 2015, Ecol. Informatics.

[4]  Mark A. Friedl,et al.  Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery , 2014 .

[5]  I. Wing,et al.  Net carbon uptake has increased through warming-induced changes in temperate forest phenology , 2014 .

[6]  John A. Silander,et al.  Deciduous forest responses to temperature, precipitation, and drought imply complex climate change impacts , 2015, Proceedings of the National Academy of Sciences.

[7]  Y. Xue,et al.  Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis , 2012 .

[8]  A. Skidmore,et al.  Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island , 2018, Remote Sensing of Environment.

[9]  David Morin,et al.  Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem , 2017, Remote. Sens..

[10]  G. Meyer,et al.  Color indices for weed identification under various soil, residue, and lighting conditions , 1994 .

[11]  Craig E. Tweedie,et al.  Climate and nutrient effects on Arctic wetland plant phenology observed from phenocams , 2018 .

[12]  Kenlo Nishida Nasahara,et al.  Using digital camera images to detect canopy condition of deciduous broad-leaved trees , 2011 .

[13]  Oliver Sus,et al.  A linked carbon cycle and crop developmental model: Description and evaluation against measurements of carbon fluxes and carbon stocks at several European agricultural sites , 2010 .

[14]  E. Nikinmaa,et al.  Interpreting canopy development and physiology using a European phenology camera network at flux sites , 2015 .

[15]  L. Hutley,et al.  Tree-grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna , 2016 .

[16]  M. Migliavacca,et al.  Heat wave hinders green wave: The impact of climate extreme on the phenology of a mountain grassland , 2017 .

[17]  Min Chen,et al.  A new seasonal‐deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios , 2016, Global change biology.

[18]  Dara Entekhabi,et al.  Regionally strong feedbacks between the atmosphere and terrestrial biosphere. , 2017, Nature geoscience.

[19]  Takeshi Motohka,et al.  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 (Special issue: Remote sensing and GIS research group) , 2011 .

[20]  Andrew D Richardson,et al.  Near-surface remote sensing of spatial and temporal variation in canopy phenology. , 2009, Ecological applications : a publication of the Ecological Society of America.

[21]  Tom Milliman,et al.  Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing , 2018, Scientific Reports.

[22]  A. Richardson,et al.  Productivity of North American grasslands is increased under future climate scenarios despite rising aridity , 2016 .

[23]  L. Morrison,et al.  Observer error in sampling a rare plant population , 2016 .

[24]  D. Baldocchi,et al.  Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales , 2017 .

[25]  Andrew D. Richardson,et al.  Monitoring vegetation phenology using an infrared-enabled security camera , 2014 .

[26]  A. Gonsamo,et al.  The match and mismatch between photosynthesis and land surface phenology of deciduous forests , 2015 .

[27]  Peter D. Blanken,et al.  Limitations to winter and spring photosynthesis of a Rocky Mountain subalpine forest , 2018 .

[28]  M. Rossini,et al.  Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake , 2011 .

[29]  P. Ciais,et al.  Influence of spring and autumn phenological transitions on forest ecosystem productivity , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[30]  D. Gianelle,et al.  Trade-offs between global warming and day length on the start of the carbon uptake period in seasonally cold ecosystems , 2013, Geophysical research letters.

[31]  J. Lundquist,et al.  Evaluating observational methods to quantify snow duration under diverse forest canopies , 2014 .

[32]  Andrew D Richardson,et al.  Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States , 2016, Global change biology.

[33]  Jean-Michel Friedt,et al.  Monitoring seasonal snow dynamics using ground based high resolution photography ( Austre Lovenbreen , Svalbard , 79 N ) , 2012 .

[34]  Oliver Sonnentag,et al.  Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis. , 2015, Ecological applications : a publication of the Ecological Society of America.

[35]  M. Migliavacca,et al.  Phenopix: A R package for image-based vegetation phenology , 2016 .

[36]  Thomas Hilker,et al.  Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment , 2011, Environmental monitoring and assessment.

[37]  Takeshi Motohka,et al.  8 million phenological and sky images from 29 ecosystems from the Arctic to the tropics: the Phenological Eyes Network , 2018, Ecological Research.

[38]  Margaret Kosmala,et al.  Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery , 2018, Scientific Data.

[39]  H. Leith,et al.  Phenology and Seasonality Modeling , 1975 .

[40]  Margaret Kosmala,et al.  Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing , 2016, Remote. Sens..

[41]  O. Sonnentag,et al.  NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types , 2018 .

[42]  Liang Liang,et al.  Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest , 2011 .

[43]  Nicolas Delpierre,et al.  Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models , 2016, Annals of Forest Science.

[44]  Mark A. Friedl,et al.  Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology , 2012 .

[45]  Mark A. Friedl,et al.  Digital repeat photography for phenological research in forest ecosystems , 2012 .

[46]  Maurizio Mencuccini,et al.  Sensitivity of colour indices for discriminating leaf colours from digital photographs , 2014 .

[47]  Bjorn J. M. Robroek,et al.  Snow cover manipulation effects on microbial community structure and soil chemistry in a mountain bog , 2013, Plant and Soil.

[48]  P. Ciais,et al.  Net carbon dioxide losses of northern ecosystems in response to autumn warming , 2008, Nature.

[49]  Bruno D. Borges,et al.  Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation , 2017 .

[50]  Keirith A. Snyder,et al.  Extracting Plant Phenology Metrics in a Great Basin Watershed: Methods and Considerations for Quantifying Phenophases in a Cold Desert , 2016, Sensors.

[51]  Jacqueline de Chazal,et al.  Climate change 2007 : impacts, adaptation and vulnerability : Working Group II contribution to the Fourth Assessment Report of the IPCC Intergovernmental Panel on Climate Change , 2014 .

[52]  Tom Milliman,et al.  An integrated phenology modelling framework in r , 2018 .

[53]  Reiko Ide,et al.  A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems , 2013, Ecol. Informatics.

[54]  S. Nagai,et al.  Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN) , 2015, Ecological Research.