Near-Surface Sensor-Derived Phenology

  • Andrew D. RichardsonEmail author
  • Stephen Klosterman
  • Michael Toomey


“Near-surface” remote sensing provides a novel approach to phenological monitoring. Optical sensors mounted in relatively close proximity (typically 50 m or less) to the land surface can be used to quantify, at high temporal frequency, changes in the spectral properties of the surface associated with vegetation development and senescence. The scale of these measurements—intermediate between individual organisms and satellite pixels—is unique and advantageous for a variety of applications. In this chapter, we review and discuss a variety of approaches to near-surface remote sensing of phenology, including methods based on broad- and narrow-band radiometric sensors, and using commercially available digital cameras as inexpensive imaging sensors.


Normalize Difference Vegetation Index Imaging Sensor Hyperspectral Imaging Enhance Vegetation Index Photochemical Reflectance Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Oliver Sonnentag and Youngryel Ryu for assistance with processing the data used in Fig. 22.1, and Koen Hufkens for providing the code used to generate the time series shown in Figs. 22.2 and 22.4. A.D.R. acknowledges support from the National Science Foundation, through the Macrosystems Biology program, award EF-1065029; the Northeastern States Research Cooperative; and the US Geological Survey Status and Trends Program, the US National Park Service Inventory and Monitoring Program, and the USA National Phenology Network through grant number G10AP00129 from the United States Geological Survey. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or USGS.


  1. Ahrends HE, Brugger R, Stockli R, Schenk J, Michna P, Jeanneret F, Wanner H, Eugster W (2008) Quantitative phenological observations of a mixed beech forest in northern Switzerland with digital photography. J Geophys Res-Biogeosci 113:G04004CrossRefGoogle Scholar
  2. Ahrends HE, Etzold S, Kutsch WL, Stoeckli R, Bruegger R, Jeanneret F, Wanner H, Buchmann N, Eugster W (2009) Tree phenology and carbon dioxide fluxes: use of digital photography at for process-based interpretation the ecosystem scale. Clim Res 39:261–274CrossRefGoogle Scholar
  3. Baghzouz M, Devitt DA, Fenstermaker LF, Young MH (2010) Monitoring vegetation phenological cycles in two different semi-arid environmental settings using a ground-based NDVI system: a potential approach to improve satellite data interpretation. Remote Sens 2:990–1013CrossRefGoogle Scholar
  4. Balzarolo M, Anderson K, Nichol C, Rossini M, Vescovo L, Arriga N, Wohlfahrt G, Calvet JC, Carrara A, Cerasoli S, Cogliati S, Daumard F, Eklundh L, Elbers JA, Evrendilek F, Handcock RN, Kaduk J, Klumpp K, Longdoz B, Matteucci G, Meroni M, Montagnani L, Ourcival JM, Sanchez-Canete EP, Pontailler JY, Juszczak R, Scholes B, Martin MP (2011) Ground-based optical measurements at European flux sites: a review of methods, instruments and current controversies. Sensors 11:7954–7981Google Scholar
  5. Bater CW, Coops NC, Wulder MA, Hilker T, Nielsen SE, McDermid G, Stenhouse GB (2011a) Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment. Environ Monit Assess 180:1–13CrossRefGoogle Scholar
  6. Bater CW, Coops NC, Wulder MA, Nielsen SE, McDermid G, Stenhouse GB (2011b) Design and installation of a camera network across an elevation gradient for habitat assessment. Instrum Sci Technol 39:231–247CrossRefGoogle Scholar
  7. Booth DT, Cox SE (2008) Image-based monitoring to measure ecological change in rangeland. Front Ecol Environ 6:185–190CrossRefGoogle Scholar
  8. Brown TB, Zimmermann C, Panneton W, Noah N, Borevitz J (2012) High-resolution, time-lapse imaging for ecosystem-scale phenotyping in the field. In: Normanly J (ed) Methods in molecular biology. Springer, New York, pp 71–96Google Scholar
  9. Crimmins MA, Crimmins TM (2008) Monitoring plant phenology using digital repeat photography. Environ Manag 41:949–958CrossRefGoogle Scholar
  10. Doughty CE, Goulden ML (2008) Seasonal patterns of tropical forest leaf area index and CO2 exchange. J Geophys Res-Biogeosci 113:G00B06CrossRefGoogle Scholar
  11. Eklundh L, Jin HX, Schubert P, Guzinski R, Heliasz M (2011) An optical sensor network for vegetation phenology monitoring and satellite data calibration. Sensors 11:7678–7709CrossRefGoogle Scholar
  12. Elmore AJ, Guinn SM, Minsley BJ, Richardson AD (2012) Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob Change Biol 18:656–674CrossRefGoogle Scholar
  13. Fuentes DA, Gamon JA, Cheng YF, Claudio HC, Qiu HL, Mao ZY, Sims DA, Rahman AF, Oechel W, Luo HY (2006) Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS. Remote Sens Environ 103:312–323CrossRefGoogle Scholar
  14. Gamon JA, Penuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41:35–44CrossRefGoogle Scholar
  15. Gamon JA, Serrano L, Surfus JS (1997) The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112:492–501CrossRefGoogle Scholar
  16. Gamon JA, Cheng YF, Claudio H, MacKinney L, Sims DA (2006) A mobile tram system for systematic sampling of ecosystem optical properties. Remote Sens Environ 103:246–254CrossRefGoogle Scholar
  17. Garrity SR, Vierling LA, Bickford K (2010) A simple filtered photodiode instrument for continuous measurement of narrowband NDVI and PRI over vegetated canopies. Agr For Meteorol 150:489–496CrossRefGoogle Scholar
  18. Garrity SR, Bohrer G, Maurer KD, Mueller KL, Vogel CS, Curtis PS (2011) A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange. Agr For Meteorol 151:1741–1752CrossRefGoogle Scholar
  19. Graham EA, Hamilton MP, Mishler BD, Rundel PW, Hansen MH (2006) Use of a networked digital camera to estimate net CO2 uptake of a desiccation-tolerant moss. Int J Plant Sci 167:751–758CrossRefGoogle Scholar
  20. Graham EA, Yuen EM, Robertson GF, Kaiser WJ, Hamilton MP, Rundel PW (2009) Budburst and leaf area expansion measured with a novel mobile camera system and simple color thresholding. Environ Exp Bot 65:238–244CrossRefGoogle Scholar
  21. Graham EA, Riordan EC, Yuen EM, Estrin D, Rundel PW (2010) Public Internet-connected cameras used as a cross-continental ground-based plant phenology monitoring system. Glob Change Biol 16:3014–3023Google Scholar
  22. Hague T, Tillett ND, Wheeler H (2006) Automated crop and weed monitoring in widely spaced cereals. Precis Agric 7:21–32CrossRefGoogle Scholar
  23. Higgins SI, Delgado-Cartay MD, February EC, Combrink HJ (2011) Is there a temporal niche separation in the leaf phenology of savanna trees and grasses? J Biogeogr 38:2165–2175CrossRefGoogle Scholar
  24. Hilker T, Coops NC, Nesic Z, Wulder MA, Black AT (2007) Instrumentation and approach for unattended year round tower based measurements of spectral reflectance. Comput Electron Agric 56:72–84CrossRefGoogle Scholar
  25. Hilker T, Gitelson A, Coops NC, Hall FG, Black TA (2011) Tracking plant physiological properties from multi-angular tower-based remote sensing. Oecologia 165:865–876CrossRefGoogle Scholar
  26. Huemmrich KF, Black TA, Jarvis PG, McCaughey JH, Hall FG (1999) High temporal resolution NDVI phenology from micrometeorological radiation sensors. J Geophys Res-Atmos 104:27935–27944CrossRefGoogle Scholar
  27. Hufkens K, Friedl M, Sonnentag O, Braswell BH, Milliman T, Richardson AD (2012) Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens Environ 117:307–321CrossRefGoogle Scholar
  28. Ide R, Oguma H (2010) Use of digital cameras for phenological observations. Ecol Inform 5:339–347CrossRefGoogle Scholar
  29. Inoue Y, Penuelas J, Miyata A, Mano M (2008) Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice. Rem Sens Environ 112:156–172CrossRefGoogle Scholar
  30. Jacobs N, Burgin W, Fridrich N, Abrams A, Miskell K, Braswell BH, Richardson AD, Pless R (2009) The global network of outdoor webcams: properties and applications. In: Proceedings ACM GIS ’09, November 4–6, 2009 Seattle, WA, pp 111–120Google Scholar
  31. Jenkins JP, Richardson AD, Braswell BH, Ollinger SV, Hollinger DY, Smith ML (2007) Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements. Agr For Meteorol 143:64–79CrossRefGoogle Scholar
  32. Kurc SA, Benton LM (2010) Digital image-derived greenness links deep soil moisture to carbon uptake in a creosotebush-dominated shrubland. J Arid Environ 74:585–594CrossRefGoogle Scholar
  33. Leuning R, Hughes D, Daniel P, Coops NC, Newnham G (2006) A multi-angle spectrometer for automatic measurement of plant canopy reflectance spectra. Remote Sens Environ 103:236–245CrossRefGoogle Scholar
  34. Luscier JD, Thompson WL, Wilson JM, Gorham BE, Dragut LD (2006) Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots. Front Ecol Environ 4:408–413CrossRefGoogle Scholar
  35. 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, di Celia UM, Pari E, Siniscalco C, Richardson AD (2011) Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake. Agr For Meteorol 151:1325–1337CrossRefGoogle Scholar
  36. Mizunuma T, Koyanagi T, Mencuccini M, Nasahara KN, Wingate L, Grace J (2011) The comparison of several colour indices for the photographic recording of canopy phenology of Fagus crenata Blume in eastern Japan. Plant Ecol Divers 4:67–77CrossRefGoogle Scholar
  37. Nagai S, Nasahara KN, Muraoka H, Akiyama T, Tsuchida S (2010) Field experiments to test the use of the normalized-difference vegetation index for phenology detection. Agr For Meteorol 150:152–160CrossRefGoogle Scholar
  38. Nagai S, Maeda T, Gamo M, Muraoka H, Suzuki R, Nasahara KN (2011) Using digital camera images to detect canopy condition of deciduous broad-leaved trees. Plant Ecol Divers 4:79–89CrossRefGoogle Scholar
  39. Nagy Z, Pinter K, Czobel S, Balogh J, Horvath L, Foti S, Barcza Z, Weidinger T, Csintalan Z, Dinh NQ, Grosz B, Tuba Z (2007) The carbon budget of semi-arid grassland in a wet and a dry year in Hungary. Agr Ecosyst Environ 121:21–29CrossRefGoogle Scholar
  40. Richardson AD, Jenkins JP, Braswell BH, Hollinger DY, Ollinger SV, Smith ML (2007) Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152:323–334CrossRefGoogle Scholar
  41. Richardson AD, Braswell BH, Hollinger DY, Jenkins JP, Ollinger SV (2009) Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol Appl 19:1417–1428CrossRefGoogle Scholar
  42. Richardson AD, Anderson RS, Arain MA, Barr AG, Bohrer G, Chen G, Chen JM, Ciais P, Davis KJ, Desai AR, Dietze MC, Dragoni D, Garrity SR, Gough CM, Grant R, Hollinger DY, Margolis HA, McCaughey H, Migliavacca M, Monson RK, Munger JW, Poulter B, Raczka BM, Ricciuto DM, Sahoo AK, Schaefer K, Tian H, Vargas R, Verbeeck H, Xiao J, Xue Y (2012) Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program site synthesis. Glob Change Biol 18:566–584CrossRefGoogle Scholar
  43. Rocha AV, Shaver GR (2009) Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agr For Meteorol 149:1560–1563CrossRefGoogle Scholar
  44. Ryu Y, Baldocchi DD, Verfaillie J, Ma S, Falk M, Ruiz-Mercado I, Hehn T, Sonnentag O (2010) Testing the performance of a novel spectral reflectance sensor, built with light emitting diodes (LEDs), to monitor ecosystem metabolism, structure and function. Agr For Meteorol 150:1597–1606CrossRefGoogle Scholar
  45. Sakamoto T, Gitelson AA, Nguy-Robertson AL, Arkebauer TJ, Wardlow BD, Suyker AE, Verma SB, Shibayama M (2012) An alternative method using digital cameras for continuous monitoring of crop status. Agr For Meteorol 154–155:113–126CrossRefGoogle Scholar
  46. Shibayama M, Sakamoto T, Takada E, Inoue A, Morita K, Takahashi W, Kimura A (2009) Continuous monitoring of visible and near-infrared band reflectance from a rice paddy for determining nitrogen uptake using digital cameras. Plant Prod Sci 12:293–306CrossRefGoogle Scholar
  47. Shibayama M, Sakamoto T, Takada E, Inoue A, Morita K, Takahashi W, Kimura A (2011) Estimating paddy rice leaf area index with fixed point continuous observation of near infrared reflectance using a calibrated digital camera. Plant Prod Sci 14:30–46CrossRefGoogle Scholar
  48. Slaughter DC, Giles DK, Downey D (2008) Autonomous robotic weed control systems: a review. Comput Electron Agric 61:63–78CrossRefGoogle Scholar
  49. Sonnentag O, Detto M, Vargas R, Ryu Y, Runkle BRK, Kelly M, Baldocchi DD (2011) Tracking the structural and functional development of a perennial pepperweed (Lepidium latifolium L.) infestation using a multi-year archive of webcam imagery and eddy covariance measurements. Agr For Meteorol 151:916–926CrossRefGoogle Scholar
  50. Sonnentag O, Hufkens K, Teshera-Sterne C, Young AM, Friedl M, Braswell BH, Milliman T, O’Keefe J, Richardson AD (2012) Digital repeat photography for phenological research in forest ecosystems. Agr For Meteorol 152:159–177CrossRefGoogle Scholar
  51. Sparks TH, Menzel A (2002) Observed changes in seasons: an overview. Int J Climatol 22:1715–1725CrossRefGoogle Scholar
  52. Steltzer H, Welker JM (2006) Modeling the effect of photosynthetic vegetation properties on the NDVI-LAI relationship. Ecology 87:2765–2772CrossRefGoogle Scholar
  53. Tittebrand A, Spank U, Bernhofer C (2009) Comparison of satellite- and ground-based NDVI above different land-use types. Theor Appl Climatol 98:171–186CrossRefGoogle Scholar
  54. Turner DP, Urbanski S, Bremer D, Wofsy SC, Meyers T, Gower ST, Gregory M (2003) A cross-biome comparison of daily light use efficiency for gross primary production. Glob Change Biol 9:383–395CrossRefGoogle Scholar
  55. Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO (2004) Using imaging spectroscopy to study ecosystem processes and properties. Biosci 54:523–534CrossRefGoogle Scholar
  56. Verhoeven GJJ (2010) It’s all about the format – unleashing the power of RAW aerial photography. Int J Remote Sens 31:2009–2042CrossRefGoogle Scholar
  57. Wang Q, Tenhunen J, Dinh NQ, Reichstein M, Vesala T, Keronen P (2004) Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland. Rem Sens Environ 93:225–237CrossRefGoogle Scholar
  58. White MA, de Beurs KM, Didan K, Inouye DW, Richardson AD, Jensen OP, O’Keefe J, Zhang G, Nemani RR, van Leeuwen WJD, Brown JF, de Wit A, Schaepman M, Lin XM, Dettinger M, Bailey AS, Kimball J, Schwartz MD, Baldocchi DD, Lee JT, Lauenroth WK (2009) Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob Change Biol 15:2335–2359CrossRefGoogle Scholar
  59. Wilson TB, Meyers TP (2007) Determining vegetation indices from solar and photosynthetically active radiation fluxes. Agr For Meteorol 144:160–179CrossRefGoogle Scholar
  60. Woebbecke DM, Meyer GE, Vonbargen K, Mortensen DA (1995) Color indexes for weed identification under various soil, residue, and lighting conditions. Trans ASAE 38:259–269Google Scholar
  61. Zhang XY, Friedl MA, Schaaf CB (2006) Global vegetation phenology from moderate resolution imaging spectroradiometer (MODIS): evaluation of global patterns and comparison with in situ measurements. J Geophys Res-Biogeosci 111:G04017CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2013

Authors and Affiliations

  • Andrew D. Richardson
    • 1
    Email author
  • Stephen Klosterman
    • 1
  • Michael Toomey
    • 1
  1. 1.Department of Organismic and Evolutionary BiologyHarvard UniversityCambridgeUSA

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