Public Internet‐connected cameras used as a cross‐continental ground‐based plant phenology monitoring system

Plant phenology is highly sensitive to changes in environmental conditions and can vary widely across landscapes. Current observation methods are either manual for small-scale, high precision measurements or by satellite remote sensing for large-scale, low spatial resolution measurement. The development of inexpensive approaches is necessary to advance large scale, high precision phenology monitoring. The use of publicly available, Internet-connected cameras, often associated with airports, national parks, and roadway conditions, for detecting and monitoring plant phenology at a continental scale can augment existing ground and satellite-based methodologies. We collected twice-daily images from over 1100 georeferenced public cameras across North America from February 2008 to 2009. Using a test subset of these cameras, we compared modeled spring 'green-up' with that from co-occurring remote sensing products. Although varying image exposure and color correction introduced noise to camera measurements, we were able to correlate spring green-up across North America with visual validation from images and detect a latitudinal trend. Public cameras had an equivalent or higher ability to detect spring compared with satellite-based data for corresponding locations, with fewer numbers of poor quality days, shorter continuous bad data days, and significantly lower errors of spring estimates. Manual image segmentation into deciduous, evergreen, and understory vegetation allowed detection of spring and fall onset for multiple vegetation types. Additional advantages of a public camera-based monitoring system include frequent image capture (subdaily) and the potential to detect quantitative responses to environmental changes in organisms, species, and communities. Public cameras represent a relatively untapped and freely available resource for supporting large-scale ecological and environmental monitoring.

[1]  William J. Kaiser,et al.  Budburst and leaf area expansion measured with a novel mobile camera system and simple color thresholding , 2009 .

[2]  Schwartz Detecting the onset of spring: a possible application of phenological models , 1990 .

[3]  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.

[4]  C. Tucker,et al.  Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 , 2001 .

[5]  Annette Menzel,et al.  Phenology: Its Importance to the Global Change Community , 2002 .

[6]  Mark D. Schwartz,et al.  Evolving plans for the USA National Phenology Network , 2007 .

[7]  John F. Mustard,et al.  Phenology model from surface meteorology does not capture satellite‐based greenup estimations , 2007 .

[8]  J. Mustard,et al.  Cross-scalar satellite phenology from ground, Landsat, and MODIS data , 2007 .

[9]  R. Stöckli,et al.  European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset , 2004 .

[10]  David C. Slaughter,et al.  Autonomous robotic weed control systems: A review , 2008 .

[11]  Bernard Panneton,et al.  Colour representation methods for segmentation of vegetation in photographs , 2009 .

[12]  D. Roberts,et al.  Design of an image analysis website for phenological and meteorological monitoring , 2010, Environ. Model. Softw..

[13]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[14]  C. Appenzeller,et al.  A comparative study of satellite and ground-based phenology , 2007, International journal of biometeorology.

[15]  C. Tucker,et al.  Northern hemisphere photosynthetic trends 1982–99 , 2003 .

[16]  O. Hoegh‐Guldberg,et al.  Ecological responses to recent climate change , 2002, Nature.

[17]  M. D. Schwartz,et al.  Climate change and shifts in spring phenology of three horticultural woody perennials in northeastern USA , 2005, International journal of biometeorology.

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

[19]  Jürgen Symanzik,et al.  On the use of the advanced very high resolution radiometer for development of prognostic land surface phenology models , 2007 .

[20]  Pim Martens,et al.  The European Phenology Network , 2003, International journal of biometeorology.

[21]  Tony J. Svejcar,et al.  A Visual Obstruction Technique for Photo Monitoring of Willow Clumps , 2005 .

[22]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[23]  Liangyun Liu,et al.  Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR) , 2004 .

[24]  H. Mooney,et al.  Shifting plant phenology in response to global change. , 2007, Trends in ecology & evolution.

[25]  W. Hargrove,et al.  Toward a national early warning system for forest disturbances using remotely sensed canopy phenology , 2009 .

[26]  G. Meyer,et al.  Verification of color vegetation indices for automated crop imaging applications , 2008 .

[27]  J. Mustard,et al.  Green leaf phenology at Landsat resolution: Scaling from the field to the satellite , 2006 .

[28]  C. Justice,et al.  Atmospheric correction of MODIS data in the visible to middle infrared: first results , 2002 .

[29]  R. Myneni,et al.  The interpretation of spectral vegetation indexes , 1995 .

[30]  Kevin P. Price,et al.  Julian dates and introduced temporal error in remote sensing vegetation phenology studies , 2008 .

[31]  B. Holben Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[32]  Jennifer N. Hird,et al.  Noise reduction of NDVI time series: An empirical comparison of selected techniques , 2009 .

[33]  Annette Menzel,et al.  Growing season extended in Europe , 1999, Nature.

[34]  Erwin Ulrich,et al.  Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data , 2008 .

[35]  Michael A. Crimmins,et al.  Monitoring Plant Phenology Using Digital Repeat Photography , 2008, Environmental management.

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

[37]  J. Abatzoglou,et al.  Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. , 2009 .

[38]  Deborah Estrin,et al.  New Approaches in Embedded Networked Sensing for Terrestrial Ecological Observatories , 2007 .

[39]  J. Cihlar,et al.  Multitemporal, multichannel AVHRR data sets for land biosphere studies—Artifacts and corrections , 1997 .

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

[41]  Hella Ellen Ahrends,et al.  Quantitative phenological observations of a mixed beech forest in northern Switzerland with digital photography , 2008 .

[42]  N. Delbart,et al.  Determination of phenological dates in boreal regions using normalized difference water index , 2005 .

[43]  N. Pettorelli,et al.  Using the satellite-derived NDVI to assess ecological responses to environmental change. , 2005, Trends in ecology & evolution.

[44]  J. Schaber,et al.  Responses of spring phenology to climate change , 2004 .

[45]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.