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

AbstractMonitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO2 and H2O flux data, and to study climate change impacts on the terrestrial biosphere.Measurement(s)plant phenological trait • plant greennessTechnology Type(s)digital cameraSample Characteristic - OrganismEmbryophytaSample Characteristic - Environmentterrestrial biomeSample Characteristic - LocationEarth (planet) Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9897089

[1]  Koen Hufkens,et al.  Testing Hopkins’ Bioclimatic Law with PhenoCam data , 2019, Applications in plant sciences.

[2]  Mirco Migliavacca,et al.  Assimilating phenology datasets automatically across ICOS ecosystem stations , 2018, International Agrophysics.

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

[4]  Bijan Seyednasrollah,et al.  Data extraction from digital repeat photography using xROI: An interactive framework to facilitate the process , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[5]  Mirco Migliavacca,et al.  On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model , 2012 .

[6]  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.

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

[8]  T. A. Black,et al.  PhenoCam Dataset v1.0: Digital Camera Imagery from the PhenoCam Network, 2000-2015 , 2017 .

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

[10]  Margaret Kosmala,et al.  PhenoCam Dataset v1.0: Vegetation Phenology from Digital Camera Imagery, 2000-2015 , 2017 .

[11]  D. Hollinger,et al.  Use of digital webcam images to track spring green-up in a deciduous broadleaf forest , 2007, Oecologia.

[12]  K. E. Moore,et al.  Climatic Consequences of Leaf Presence in the Eastern United States , 2001 .

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

[14]  Jeffrey T. Morisette,et al.  Cross-scale phenological data integration to benefit resource management and monitoring , 2017 .

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

[16]  Margaret Kosmala,et al.  Volunteer recruitment and retention in online citizen science projects using marketing strategies: lessons from Season Spotter , 2017 .

[17]  Andrew D. Richardson,et al.  Predicting Climate Change Impacts on the Amount and Duration of Autumn Colors in a New England Forest , 2013, PloS one.

[18]  R. C. Macridis.  A review , 1963 .

[19]  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.

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

[21]  Andrew D. Richardson,et al.  Near-Surface Sensor-Derived Phenology , 2013 .

[22]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[23]  A. Huete,et al.  Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography , 2016 .

[24]  M. Shen,et al.  Emerging opportunities and challenges in phenology: a review , 2016 .

[25]  Mark A. Friedl,et al.  Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography , 2018 .

[26]  Damien Sulla-Menashe,et al.  Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat , 2016 .

[27]  Peter Lesica,et al.  Precipitation and temperature are associated with advanced flowering phenology in a semi-arid grassland , 2010 .

[28]  A. Huete,et al.  Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography , 2016 .

[29]  R. Q. Thomas,et al.  PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018 , 2019 .

[30]  H. Lieth.  Phenology and Seasonality Modeling , 1974, Ecological Studies.

[31]  M. D. Schwartz,et al.  From Caprio's lilacs to the USA National Phenology Network , 2012 .

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

[33]  Mark D. Schwartz,et al.  DETECTING ENERGY-BALANCE MODIFICATIONS AT THE ONSET OF SPRING , 2001 .

[34]  Nathaniel A. Brunsell,et al.  Warm spring reduced carbon cycle impact of the 2012 US summer drought , 2016, Proceedings of the National Academy of Sciences.

[35]  M. Friedl,et al.  Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment. , 2014, Ecological applications : a publication of the Ecological Society of America.

[36]  J. Domec,et al.  Spatiotemporal sensitivity of thermal stress for monitoring canopy hydrological stress in near real-time , 2019, Agricultural and Forest Meteorology.

[37]  S. Hainsworth,et al.  A CRITICAL ASSESSMENT , 2014 .

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

[39]  Margaret Kosmala,et al.  An interactive toolkit to extract phenological time series data from digital repeat photography , 2017 .

[40]  Bruna Alberton,et al.  Linking plant phenology to conservation biology , 2016 .

[41]  R. Q. Thomas,et al.  PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018 , 2019 .

[42]  Andrew D Richardson,et al.  Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery. , 2018, The New phytologist.

[43]  E. S. Melnikov,et al.  The Circumpolar Arctic vegetation map , 2005 .

[44]  Joel A. Granados,et al.  Using phenocams to monitor our changing Earth: toward a global phenocam network , 2016 .

[45]  James M. Omernik,et al.  Ecoregions of the Conterminous United States: Evolution of a Hierarchical Spatial Framework , 2014, Environmental Management.

[46]  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.

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

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