Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

Near-Surface Sensor-Derived Phenology

  • Chapter
  • First Online:
Phenology: An Integrative Environmental Science

Abstract

“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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • 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:G04004

    Article  Google Scholar 

  • 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 39:261–274

    Article  Google Scholar 

  • 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 2:990–1013

    Article  Google Scholar 

  • 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 11:7954–7981

    Google Scholar 

  • 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 180:1–13

    Article  Google Scholar 

  • 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 39:231–247

    Article  CAS  Google Scholar 

  • Booth DT, Cox SE (2008) Image-based monitoring to measure ecological change in rangeland. Front Ecol Environ 6:185–190

    Article  Google Scholar 

  • 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 71–96

    Google Scholar 

  • Crimmins MA, Crimmins TM (2008) Monitoring plant phenology using digital repeat photography. Environ Manag 41:949–958

    Article  Google Scholar 

  • Doughty CE, Goulden ML (2008) Seasonal patterns of tropical forest leaf area index and CO2 exchange. J Geophys Res-Biogeosci 113:G00B06

    Article  Google Scholar 

  • Eklundh L, Jin HX, Schubert P, Guzinski R, Heliasz M (2011) An optical sensor network for vegetation phenology monitoring and satellite data calibration. Sensors 11:7678–7709

    Article  Google Scholar 

  • 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 18:656–674

    Article  Google Scholar 

  • 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 103:312–323

    Article  Google Scholar 

  • Gamon JA, Penuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41:35–44

    Article  Google Scholar 

  • 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 112:492–501

    Article  Google Scholar 

  • 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 103:246–254

    Article  Google Scholar 

  • 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 150:489–496

    Article  Google Scholar 

  • 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 151:1741–1752

    Article  Google Scholar 

  • 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 167:751–758

    Article  CAS  Google Scholar 

  • 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 65:238–244

    Article  Google Scholar 

  • 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 16:3014–3023

    Google Scholar 

  • Hague T, Tillett ND, Wheeler H (2006) Automated crop and weed monitoring in widely spaced cereals. Precis Agric 7:21–32

    Article  Google Scholar 

  • 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 38:2165–2175

    Article  Google Scholar 

  • 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 56:72–84

    Article  Google Scholar 

  • Hilker T, Gitelson A, Coops NC, Hall FG, Black TA (2011) Tracking plant physiological properties from multi-angular tower-based remote sensing. Oecologia 165:865–876

    Article  Google Scholar 

  • Huemmrich KF, Black TA, Jarvis PG, McCaughey JH, Hall FG (1999) High temporal resolution NDVI phenology from micrometeorological radiation sensors. J Geophys Res-Atmos 104:27935–27944

    Article  Google Scholar 

  • 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 117:307–321

    Article  Google Scholar 

  • Ide R, Oguma H (2010) Use of digital cameras for phenological observations. Ecol Inform 5:339–347

    Article  Google Scholar 

  • 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 112:156–172

    Article  Google Scholar 

  • 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 111–120

    Google Scholar 

  • 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 143:64–79

    Article  Google Scholar 

  • Kurc SA, Benton LM (2010) Digital image-derived greenness links deep soil moisture to carbon uptake in a creosotebush-dominated shrubland. J Arid Environ 74:585–594

    Article  Google Scholar 

  • 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 103:236–245

    Article  Google Scholar 

  • 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 4:408–413

    Article  Google Scholar 

  • 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 151:1325–1337

    Article  Google Scholar 

  • 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 4:67–77

    Article  Google Scholar 

  • 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 150:152–160

    Article  Google Scholar 

  • 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 4:79–89

    Article  Google Scholar 

  • 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 121:21–29

    Article  CAS  Google Scholar 

  • 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 152:323–334

    Article  Google Scholar 

  • 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 19:1417–1428

    Article  Google Scholar 

  • 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 18:566–584

    Article  Google Scholar 

  • Rocha AV, Shaver GR (2009) Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agr For Meteorol 149:1560–1563

    Article  Google Scholar 

  • 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 150:1597–1606

    Article  Google Scholar 

  • 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 154–155:113–126

    Article  Google Scholar 

  • 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 12:293–306

    Article  CAS  Google Scholar 

  • 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 14:30–46

    Article  Google Scholar 

  • Slaughter DC, Giles DK, Downey D (2008) Autonomous robotic weed control systems: a review. Comput Electron Agric 61:63–78

    Article  Google Scholar 

  • 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 151:916–926

    Article  Google Scholar 

  • 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 152:159–177

    Article  Google Scholar 

  • Sparks TH, Menzel A (2002) Observed changes in seasons: an overview. Int J Climatol 22:1715–1725

    Article  Google Scholar 

  • Steltzer H, Welker JM (2006) Modeling the effect of photosynthetic vegetation properties on the NDVI-LAI relationship. Ecology 87:2765–2772

    Article  Google Scholar 

  • Tittebrand A, Spank U, Bernhofer C (2009) Comparison of satellite- and ground-based NDVI above different land-use types. Theor Appl Climatol 98:171–186

    Article  Google Scholar 

  • 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 9:383–395

    Article  Google Scholar 

  • Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO (2004) Using imaging spectroscopy to study ecosystem processes and properties. Biosci 54:523–534

    Article  Google Scholar 

  • Verhoeven GJJ (2010) It’s all about the format – unleashing the power of RAW aerial photography. Int J Remote Sens 31:2009–2042

    Article  Google Scholar 

  • 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 93:225–237

    Article  Google Scholar 

  • 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 15:2335–2359

    Article  Google Scholar 

  • Wilson TB, Meyers TP (2007) Determining vegetation indices from solar and photosynthetically active radiation fluxes. Agr For Meteorol 144:160–179

    Article  Google Scholar 

  • Woebbecke DM, Meyer GE, Vonbargen K, Mortensen DA (1995) Color indexes for weed identification under various soil, residue, and lighting conditions. Trans ASAE 38:259–269

    Google Scholar 

  • 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:G04017

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA

    Andrew D. Richardson, Stephen Klosterman & Michael Toomey

Authors
  1. Andrew D. Richardson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen Klosterman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Toomey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew D. Richardson .

Editor information

Editors and Affiliations

  1. Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA

    Mark D. Schwartz

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Richardson, A.D., Klosterman, S., Toomey, M. (2013). Near-Surface Sensor-Derived Phenology. In: Schwartz, M. (eds) Phenology: An Integrative Environmental Science. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6925-0_22

Download citation

Publish with us

Policies and ethics

Search

Navigation