High-Resolution Phenological Data

  • Mark D. SchwartzEmail author
  • Liang Liang


Measurements of visual plant phenology at both high-spatial and high-temporal resolutions have many applications, but are especially useful for bridging the gap between ground-based phenological measurements and moderate-resolution satellite-derived measures of phenology. Results have demonstrated that satellite-derived phenology does present a reasonable representation of spring growth in a northern mixed forest environment (Wisconsin, USA), given the known temporal limitations. Other applications of high-resolution phenological data, including measurements during the autumn season are under development.


Normalize Difference Vegetation Index Plant Phenology Phenological Pattern Minimum Noise Fraction Field Protocol 
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.


  1. Defries RS, Hansen MC, Townshend JRG (2000) Global continuous fields of vegetation characteristics: a linear mixture model applied to multi-year 8 km AVHRR data. Int J Remote Sens 21(6–7):1389–1414CrossRefGoogle Scholar
  2. Liang L, Schwartz MD (2009) Landscape phenology: an integrative approach to seasonal vegetation dynamics. Landsc Ecol 24(4):465–472CrossRefGoogle Scholar
  3. Liang L, Schwartz MD, Fei S (2011) Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sens Environ 115:143–157CrossRefGoogle Scholar
  4. Liang L, Schwartz MD, Fei S (2012) Photographic assessment of temperate forest understory phenology in relation to springtime meteorological drivers. Int J Biometeorol 56(2):343–355PubMedCrossRefGoogle Scholar
  5. Lu DS, Batistella M, Moran E (2004) Multitemporal spectral mixture analysis for Amazonian land-cover change detection. Can J Remote Sens 30(1):87–100CrossRefGoogle Scholar
  6. Morellato LPC, Camargo MGG, D’Eça Neves FF, Luize BG, Mantovani A, Hudson IL (2010) The influence of sampling method, sample size, and frequency of observations on plant phenological patterns and interpretation in tropical forest trees. In: Hudson IL, Keatley MR (eds) Phenological research. Springer, DordrechtGoogle Scholar
  7. Roberts D, Gardner M, Church R, Ustin S, Scheer G, Green R (1998) Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sens Environ 65(3):267–279CrossRefGoogle Scholar
  8. Schwartz MD, Hanes JM, Liang L (2013) Comparing carbon flux and high-resolution spring phenological measurements in a northern mixed forest. Agric For Meteorol 169:136–147CrossRefGoogle Scholar
  9. West N, Wein R (1971) A plant phenological index technique. Bioscience 21(3):116–117CrossRefGoogle Scholar
  10. White MA, Hoffman F, Hargrove WW, Nemani RR (2005) A global framework for monitoring phenological responses to climate change. Geophys Res Lett 32:L04705CrossRefGoogle Scholar
  11. Wu J (1999) Hierarchy and scacling: extrapolating information along a scaling ladder. Can J Remote Sens 25(4):367–380Google Scholar
  12. Wu J, Loucks OL (1995) From balance of nature to hierarchical patch dynamics: a paradigm shift in ecology. Q Rev Biol 70(4):439–466CrossRefGoogle Scholar
  13. Wu CS, Murray AT (2003) Estimating impervious surface distribution by spectral mixture analysis. Remote Sens Environ 84(4):493–505CrossRefGoogle Scholar
  14. Wu J, Jones KB, Li H, Loucks OL (2006) Scaling and uncertainty analysis in ecology: methods and applications. Springer, DordrechtCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2013

Authors and Affiliations

  1. 1.Department of GeographyUniversity of Wisconsin-MilwaukeeMilwaukeeUSA
  2. 2.Department of GeographyUniversity of KentuckyLexingtonUSA

Personalised recommendations