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Remote Sensing-Based Determination of Conifer Needle Flushing Phenology over Boreal-Dominant Regions

  • Navdeep S. Sekhon
  • Quazi K. Hassan
  • Mohammad  M. Kamal
Chapter
Part of the Society of Earth Scientists Series book series (SESS)

Abstract

Coniferous needle flushing [CNF: defined as the date when the tips of at least 75 % fresh buds of white spruce (Picea glauca) and/or black spruce (Picea mariana) in the surrounding area have reached to a minimum of 2 cm new growth since the start of the growing season] is one of the critical phenological stage in particular to boreal forest. Here, our objective was to evaluate the performance of remotely sensed MODIS-data in determining CNF stage in the Canadian province of Alberta. We employed two predictors primarily using Moderate Resolution Imaging Spectroradiometer (MODIS) data, i.e. (1) accumulated growing degree days (AGDD) and (2) normalized difference water index (NDWI). For determining the thresholds for both of the predictors, we extracted temporal trends AGDD and NDWI during the period of ground-based CNF observations at the lookout tower sites. We found that individual thresholds of AGDD (i.e., 200 degree days) and NDWI (i.e., 0.525) were in better agreements (i.e., ~85 and ~72 % of the times for AGDD and NDWI respectively for ±2 periods of deviation) with the ground-based CNF observation periods. The combination of the two predictors revealed that their logical ‘OR’ combination produced the overall best agreements (i.e., on an average ~85 % of the times within ±2 periods of deviation).

Keywords

Accumulated growing degree days Normalized difference water index MODIS data White spruce (Picea glaucaBlack spruce (Picea mariana

Notes

Acknowledgements

This study was partially supported by an NSERC Discovery Grant provided to Dr. Hassan. The authors would like to acknowledge: (1) NASA for providing the MODIS data; and (2) Alberta Department of Sustainable Resource Development for providing ground-based CNF observation data.

References

  1. Agee JK, Wright CS, Williamson N et al (2002) Foliar moisture content of pacific northwest vegetation and its relation to wildland fire behaviour. Forest Ecol Manage 167:57–66CrossRefGoogle Scholar
  2. Akther MS, Hassan QK (2011) Remote sensing based estimates of surface wetness conditions and growing degree days over northern Alberta, Canada. Boreal Environ Res 16:407–416Google Scholar
  3. Carrao H, Goncalves P, Caetano M (2010) A nonlinear harmonic model for fitting satellite image time series: analysis and prediction of land cover dynamics. IEEE Trans Geosci Remote Sens 48:1919–1930CrossRefGoogle Scholar
  4. Cleland EE, Chiune I, Menzel A et al (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22:357–365CrossRefGoogle Scholar
  5. Delbart N, Kergoats L, Toan TL et al (2005) Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens Environ 97:26–38CrossRefGoogle Scholar
  6. Dowing DJ, Pettapiece WW (eds) (2006) Natural regions and subregions of Alberta. Natural Regions Committee, Government of Alberta, Alberta, Canada. Publication No. T/852Google Scholar
  7. Dufour B, Morin H (2010) Tracheid production phenology of Picea mariana and its relationship with climatic fluctuations and bud development using multivariate analysis. Tree Physiol 30:853–865CrossRefGoogle Scholar
  8. FFMT (Forest Fire Management Terms) (1999) Forest Protection Division: Alberta Land and Forest Service. http://www.srd.alberta.ca/Wildfire/WildfireOperations/documents/ForestFireManagementTerms-Glossary-1999.pdf. Accessed 20 Nov 2012
  9. Fisher JI, Mustard JF (2007) Cross-scalar satellite phenology from ground, Landsat, and MODIS data. Remote Sens Environ 109:261–273CrossRefGoogle Scholar
  10. Gamache I, Payette S (2004) Height growth response of tree line black spruce to recent climate warming across the forest-tundra of eastern Canada. J Ecol 92:835–845CrossRefGoogle Scholar
  11. Hannerz M (1999) Evaluation of temperature models for predicting bud burst in Norway spruce. Can J Forest Res 29:9–19CrossRefGoogle Scholar
  12. Hassan QK, Bourque CP-A, Meng F-R et al (2007) Spatial mapping of growing degree days: an application of MODIS-based surface temperatures and enhanced vegetation index. J Appl Remote Sens 1:013511:1–013511:12Google Scholar
  13. Hassan QK, Bourque CP-A (2009) Potential species distribution of balsam fir based on the integration of biophysical variables derived with remote sensing and process-based methods. Remote Sens 1:393–407CrossRefGoogle Scholar
  14. Hassan QK, Rahman KM (2013a) Applicability of remote sensing-based surface temperature regimes in determining deciduous phenology over boreal forest. J Plant Ecol 6:84–91CrossRefGoogle Scholar
  15. Hassan QK, Rahman KM (2013b) Remote sensing-based determination of understory grass greening over boreal forest. J Appl Remote Sens 7:073578:1–073578:10Google Scholar
  16. Jones GE, Cregg BM (2006) Screening exotic firs for the Midwestern United States: interspecific variation in adaptive traits. HortScience 41:323–328Google Scholar
  17. Leinonen I, Kramer K (2002) Applications of phenological models to predict the future carbon sequestration potential of boreal forests. Clim Change 5:99–113CrossRefGoogle Scholar
  18. Man R, Kayahara GJ, Dang QL, Rice JA (2009) A case of severe frost damage prior to budbreak in young conifers in Northeastern Ontario: consequence of climate change? Forest Chron 85:453–462Google Scholar
  19. Man R, Lu P (2010) Effects of thermal model and base temperature on estimates of thermal time to bud break in white spruce seedlings. Can J Forest Res 40:1815–1820CrossRefGoogle Scholar
  20. O’Reilly C, Parker WH (1982) Vegetative phenology in a clonal seed orchard of Picea glauca and Picea mariana in northwestern Ontario. Can J Forest Res 12:408–413CrossRefGoogle Scholar
  21. Reed BC, Schwartz MD, Xiao X (2009) Remote sensing phenology: status and the way forward. In: Noormets A (ed) Phenology of ecosystem processes. Springer Science+Business Media, New York, pp 231–246CrossRefGoogle Scholar
  22. Richardson AD, Hollinger DY, Dail DB et al (2009) Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol 29:321–331CrossRefGoogle Scholar
  23. Royce EB, Barbour MG (2001) Mediterranean climate effects. II. Conifer growth phenology across a Sierra Nevada ecotone. Am J Bot 88:919–932CrossRefGoogle Scholar
  24. Rossi S, Deslauriers A, Anfodillo T et al (2007) Evidence of threshold temperature for xylogenesis in conifers at high altitudes. Oecologia 152:1–12CrossRefGoogle Scholar
  25. Sekhon NS, Hassan QK, Sleep RW (2010) Evaluating potential of MODIS-based indices in determining “Snow Gone” stage over forest-dominant regions. Remote Sens 2:1348–1363CrossRefGoogle Scholar
  26. Tan B, Morisette JT, Wolfe RE et al (2011) An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data. IEEE J Sel Topics Appl Earth Observ Remote Sens 4:361–371CrossRefGoogle Scholar
  27. Tanja S, Berninger F, Vesala T et al (2003) Air temperature triggers the recovery of evergreen boreal forest photosynthesis in spring. Glob Change Biol 9:1410–1426CrossRefGoogle Scholar
  28. Wang Y, Zwiazek JJ (1999) Spring changes in water relations, gas exchange, and carbohydrates of white spruce (Picea glauca) seedlings. Can J Forest Res 29:332–338CrossRefGoogle Scholar
  29. Weilgolaski F-E (1999) Starting dates and basic temperatures in phenological observations of plants. Int J Biometeorol 42:158–168CrossRefGoogle Scholar
  30. Xiao X, Zhang J, Yan H et al (2009) Land surface phenology: convergence of satellite and CO2 eddy flux observations. In: Noormets A (ed) Phenology of ecosystem processes. Springer Science+Business Media, New York, pp 247–270CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Navdeep S. Sekhon
    • 1
  • Quazi K. Hassan
    • 1
  • Mohammad  M. Kamal
    • 2
  1. 1.Department of Geomatics Engineering, Schulich School of EngineeringUniversity of CalgaryCalgaryCanada
  2. 2.Department of Geography and PlanningUniversity of SaskatchewanSaskatoonCanada

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