Emerging opportunities and challenges in phenology: A review

Ecosphere

Volumn 7, Issue 8, 2016, Pages

  Tang, Jianwu ^a   Körner, Christian ^b   Muraoka, Hiroyuki ^c   Piao, Shilong ^d,e   Shen, Miaogen ^e   Thackeray, Stephen J ^f   Yang, Xi ^g  

^a ECOSYSTEMS CENTER   (United States)

^b UNIVERSITY OF BASEL   (Switzerland)

^c GIFU UNIVERSITY   (Japan)

^d PEKING UNIVERSITY   (China)

^e CAS CENTER FOR EXCELLENCE IN TIBETAN PLATEAU EARTH SCIENCES   (China)

^f CENTRE FOR ECOLOGY AND HYDROLOGY   (United Kingdom)

^g BROWN UNIVERSITY   (United States)

Author keywords

Cameras; Greenness; ILTER; International LTER; Modeling; Phenology; Scale; Special feature

Indexed keywords

EID: 84984890580     PISSN: None     EISSN: 21508925     Source Type: Journal    
DOI: 10.1002/ecs2.1436     Document Type: Article

References (120)

1. Allstadt, A. J., S. J. Vavrus, P. J. Heglund, A. M. Pidgeon, W. E. Thogmartin, and V. C. Radeloff. 2015. Spring plant phenology and false springs in the conterminous US during the 21st century. Environmental Research Letters 10:104008. http://dx.doi.org/10.1088/1748-9326/10/10/104008.
2. Anderson, M. C. 1964. Studies of the woodland light climate. 1. The photographic computation of light conditions. Journal of Ecology 52:27-41.
3. Asner, G. P., and R. E. Martin. 2008. Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sensing of Environment 112:3958-3970.
4. Asner, G. P., R. E. Martin, R. Tupayachi, C. B. Anderson, F. Sinca, L. Carranza-Jimenez, and P. Martinez. 2014. Amazonian functional diversity from forest canopy chemical assembly. Proceedings of the National Academy of Sciences USA 111:5604-5609.
5. Baker, N. R. 2008. Chlorophyll fluorescence: A probe of photosynthesis in vivo. Annual Review of Plant Biology 59:89-113.
6. Basler, D. 2016. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe. Agricultural and Forest Meteorology 217:10-21.
7. Basler, D., and C. Körner. 2012. Photoperiod sensitivity of bud burst in 14 temperate forest tree species. Agricultural and Forest Meteorology 165:73-81.
8. Botta, A., N. Viovy, P. Ciais, P. Friedlingstein, and P. Monfray. 2000. A global prognostic scheme of leaf onset using satellite data. Global Change Biology 6:709-725.
9. Cao, R. Y., J. Chen, M. G. Shen, and Y. H. Tang. 2015. An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data. Agricultural and Forest Meteorology 200:9-20.
10. Chazdon, R. L., and C. B. Field. 1987. Photographic estimation of photosynthetically active radiation - evaluation of a computerized technique. Oecologia 73:525-532.
11. Chew, Y. H., A. M. Wilczek, M. Williams, S. M. Welch, J. Schmitt, and K. J. Halliday. 2012. An augmented Arabidopsis phenology model reveals seasonal temperature control of flowering time. New Phytologist 194:654-665.
12. Chuine, I. 2000. A unified model for budburst of trees. Journal of Theoretical Biology 207:337-347.
13. Chuine, I., K. Kramer, and H. Hänninen. 2003. Plant development models. Pages 217-235 in M. Schwartz, editor. Phenology: An integrative environmental science. Springer, Dordrecht, The Netherlands.
14. Chung, H., H. Muraoka, M. Nakamura, S. Han, O. Muller, and Y. Son. 2013. Experimental warming studies on tree species and forest ecosystems: A literature review. Journal of Plant Research 126:447-460.
15. Cleland, E. E., I. Chuine, A. Menzel, H. A. Mooney, and M. D. Schwartz. 2007. Shifting plant phenology in response to global change. Trends in Ecology & Evolution 22:357-365.
16. Cong, N., S. L. Piao, A. P. Chen, X. H. Wang, X. Lin, S. P. Chen, S. J. Han, G. S. Zhou, and X. P. Zhang. 2012. Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis. Agricultural and Forest Meteorology 165:104-113.
17. Cook, B. I., E. M. Wolkovich, and C. Parmesan. 2012. Divergent responses to spring and winter warming drive community level flowering trends. Proceedings of the National Academy of Sciences USA 109:9000-9005.
18. Craine, J. M., E. M. Wolkovich, E. G. Towne, and S. W. Kembel. 2012. Flowering phenology as a functional trait in a tallgrass prairie. New Phytologist 193:673- 682.
19. Daumard, F., S. Champagne, A. Fournier, Y. Goulas, A. Ounis, J. F. Hanocq, and I. Moya. 2010. A field platform for continuous measurement of canopy fluorescence. IEEE Transactions on Geoscience and Remote Sensing 48:3358-3368.
20. Defila, C., and B. Clot. 2001. Phytophenological trends in Switzerland. International Journal of Biometeorology 45:203-207.
21. Fila, G., M. Gardiman, P. Belvini, F. Meggio, and A. Pitacco. 2014. A comparison of different modelling solutions for studying grapevine phenology under present and future climate scenarios. Agricultural and Forest Meteorology 195:192-205.
22. Fisher, J. I., and J. F. Mustard. 2007. Cross-scalar satellite phenology from ground, Landsat, and MODIS data. Remote Sensing of Environment 109:261-273.
23. Fu, Y. S. H., M. Campioli, M. Van Oijen, G. Deckmyn, and I. A. Janssens. 2012. Bayesian comparison of six different temperature-based budburst models for four temperate tree species. Ecological Modelling 230:92-100.
24. Gitelson, A. A., Y. Gritz, and M. N. Merzlyak. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 160:271-282.
25. Guanter, L., C. Frankenberg, A. Dudhia, P. E. Lewis, J. G omez-Dans, A. Kuze, H. Suto, and R. G. Grainger. 2012. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GO SAT space measurements. Remote Sensing of Environment 121:236-251.
26. Guanter, L., M. Rossini, R. Colombo, M. Meroni, C. Frankenberg, J. E. Lee, and J. Joiner. 2013. Using field spectroscopy to assess the potential of statistical approaches for the retrieval of sun-induced chlorophyll fluorescence from ground and space. Remote Sensing of Environment 133:52-61.
27. Hanninen, H., and K. Kramer. 2007. A framework for modelling the annual cycle of trees in boreal and temperate regions. Silva Fennica 41:167-205.
28. Heffernan, J. B., et al. 2014. Macrosystems ecology: understanding ecological patterns and processes at continental scales. Frontiers in Ecology and the Environment 12:5-14.
29. Hilker, T., et al. 2010. Remote sensing of photosynthetic light-use efficiency across two forested biomes: spatial scaling. Remote Sensing of Environment 114:2863-2874.
30. Hirose, T. 2005. Development of the Monsi-Saeki theory on canopy structure and function. Annals of Botany 95:483-494.
31. Homolova, L., Z. Maenovsky, J. G. P. W. Clevers, G. G arcia-Santos, and M. E. Schaeprnan. 2013. Review of optical-based remote sensing for plant trait mapping. Ecological Complexity 15:1-16.
32. Horvath, D. 2010. Bud dormancy and growth. Pages 53-70 in E. C. Pua and M. R. Davey, editors. Plant developmental biology: biotechnological perspectives. Springer, Berlin, Germany.
33. Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83:195-213.
34. Ide, R., and H. Oguma. 2010. Use of digital cameras for phenological observations. Ecological Informatics 5:339-347.
35. Janick, J. 2005. The origin of fruits, fruit growing and fruit breeding. Plant Breeding Reviews 25:255-320.
36. Jeong, S.J., D. Medvigy, E. Shevliakova, and S. Malyshev. 2013. Predicting changes in temperate forest budburst using continental-scale observations and models. Geophysical Research Letters 40:359-364.
37. Joiner, J., L. Guanter, R. Lindstrot, M. Voigt, A. P. Vasilkov, E. M. Middleton, K. F. Huemmrich, Y. Yoshida, and C. Frankenberg. 2013. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral- resolution near-infrared satellite measurements: methodology, simulations, and application to GO ME-2. Atmospheric Measurement Techniques 6:2803-2823.
38. Joiner, J., Y. Yoshida, A. P. Vasilkov, Y. Yoshida, L. A. Corp, and E. M. Middleton. 2011. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences 8:637-651.
39. Jonckheere, I., S. Fleck, K. Nackaerts, B. Muys, P. Coppin, M. Weiss, and F. Baret. 2004. Review of methods for in situ leaf area index determination-Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology 121:19-35.
40. Keenan, T. F., et al. 2012. Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO2 exchange. Global Change Biology 18:1971-1987.
41. Keenan, T. F., et al. 2014. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nature Climate Change 4:598-604.
42. Knorr, W., T. Kaminski, M. Scholze, N. Gobron, B. Pinty, R. Giering, and P. P. Mathieu. 2010. Carbon cycle data assimilation with a generic phenology model. Journal of Geophysical Research-Biogeosciences 115:G04017. http://dx.doi.org/10.1029/2009JG001119.
43. Körner, C., and D. Basler. 2010. Phenology under global warming. Science 327:1461-1462.
44. Körner, C., D. Basler, G. Hoch, C. Kollas, A. Lenz, C. F. Randin, Y. Vitasse, and N. E. Zimmermann. 2016. Where, why and how? Explaining the low temperature range limits of temperate tree species. Journal of Ecology 104:1076-1088.
45. Krinner, G., N. Viovy, N. de Noblet-Ducoudre, J. Ogee, J. Polcher, P. Friedlingstein, P. Ciais, S. Sitch, and I. C. Prentice. 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles 19:GB1015. http://dx.doi.org/10.1029/2003GB002199.
46. Lafont, S., Y. Zhao, J. C. Calvet, P. Peylin, P. Ciais, F. Maignan, and M. Weiss. 2012. Modelling LAI, surface water and carbon fluxes at high-resolution over France: comparison of ISBA-A- gs and ORCHIDEE. Biogeosciences 9:439-456.
47. Larcher, W. 2006. Altitudinal variation in flowering time of lilac (Syringa vulgaris L.) in the Alps in relation to temperatures. Sitzungsber Abt. 212:3-18.
48. Lee, J. E., et al. 2013. Forest productivity and water stress in Amazonia: observations from GO SAT chlorophyll fluorescence. Proceedings of Royal Society B 280:20130171.
49. Lenz, A., Y. Vitasse, G. Hoch, and C. Körner. 2014. Growth and carbon relations of temperate deciduous tree species at their upper elevation range limit. Journal of Ecology 102:1537-1548.
50. Liu, Z., H. Hu, H. Yu, X. Yang, H. Yang, C. Ruan, Y. Wang, and J. Tang. 2015. Relationship between leaf physiologic traits and canopy color indices during the leaf expansion period in an oak forest. Ecosphere 6:259.
51. Markwell, J., J. C. Osterman, and J. L. Mitchell. 1995. Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynthesis Research 46:467-472.
52. Meroni, M., M. Rossini, L. Guanter, L. Alonso, U. Rascher, R. Colombo, and J. Moreno. 2009. Remote sensing of solar-induced chlorophyllfluorescence: review of methods and applications. Remote Sensing of Environment 113:2037-2051.
53. Migliavacca, M., O. Sonnentag, T. F. Keenan, A. Cescatti, J. O'Keefe, and A. D. Richardson. 2012. On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model. Biogeosciences 9:2063-2083.
54. Miller-Rushing, A. J., R. B. Primack, D. Primack, and S. Mukunda. 2006. Photographs and herbarium specimens as tools to document phenological changes in response to global warming. American Journal of Botany 93:1667-1674.
55. Monsi, M., and T. Saeki. 2005. On the factor light in plant communities and its importance for matter production. Annals of Botany 95:549-567.
56. Morisette, J. T., et al. 2009. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Frontiers in Ecology and the Environment 7:253-260.
57. Motohka, T., K. N. Nasahara, H. Oguma, and S. Tsuchida. 2010. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sensing 2:2369-2387.
58. Muraoka, H., and H. Koizumi. 2009. Satellite Ecology (SATECO) - linking ecology, remote sensing and micrometeorology, from plot to regional scale, for the study of ecosystem structure and function. Journal of Plant Research 122:3-20.
59. Muraoka, H., R. Ishii, S. Nagai, R. Suzuki, T. Motohka, H. Noda, M. Hirota, K. N. Nasahara, H. Oguma, and K. Muramatsu. 2013a. Linking remote sensing and in situ ecosystem/biodiversity observations by "Satellite Ecology". Pages 277-308 in S. E. A. Nakano, editor. The biodiversity observation network in the Asia-Pacific region: Toward further development of monitoring. Ecological Research Monographs. Springer, New York, New York, USA.
60. Muraoka, H., and H. Koizumi. 2005. Photosynthetic and structural characteristics of canopy and shrub trees in a cool-temperate deciduous broadleaved forest: implication to the ecosystem carbon gain. Agricultural and Forest Meteorology 134:39-59.
61. Muraoka, H., H. M. Noda, S. Nagai, T. Motohka, T. M. Saitoh, K. N. Nasahara, and N. Saigusa. 2013b. Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest. Journal of Plant Ecology 6:393-407.
62. Muraoka, H., N. Saigusa, K. N. Nasahara, H. Noda, J. Yoshino, T. M. Saitoh, S. Nagai, S. Murayama, and H. Koizumi. 2010. Effects of seasonal and interannual variations in leaf photosynthesis and canopy leaf area index on gross primary production of a cool-temperate deciduous broadleaf forest in Takayama, Japan. Journal of Plant Research 123:563-576.
63. Nagai, S., T. Maeda, M. Gamo, H. Muraoka, R. Suzuki, and K. N. Nasahara. 2011. Using digital camera images to detect canopy condition of deciduous broad-leaved trees. Plant Ecology & Diversity 4:79-89.
64. Nagai, S., S. Yoshitake, T. Inoue, R. Suzuki, H. Muraoka, K. N. Nasahara, and T. M. Saitoh. 2014. Year-to- year blooming phenology observation using time-lapse digital camera images. Journal of Agricultural Meteorology 70:163-170.
65. Nakaji, T., R. Ide, H. Oguma, N. Saigusa, and Y. Fujinuma. 2007. Utility of spectral vegetation index for estimation of gross CO2 flux under varied sky conditions. Remote Sensing of Environment 109:274- 284.
66. Nakaji, T., R. Ide, K. Takagi, Y. Kosugi, S. Ohkubo, K. N. Nasahara, N. Saigusa, and H. Oguma. 2008. Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Japan. Agricultural and Forest Meteorology 148:776-787.
67. Nasahara, K. N., and S. Nagai. 2015. Review: Development of an in situ observation network for terrestrial ecological remote sensing: The Phenological Eyes Network (PEN). Ecological Research 30:211- 223.
68. Nijland, W., R. de Jong, S. M. de Jong, M. A. Wulder, C. W. Bater, and N. C. Coops. 2014. Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agricultural and Forest Meteorology 184:98-106.
69. Noda, H. M., H. Muraoka, K. N. Nasahara, N. Saigusa, S. Murayama, and H. Koizumi. 2015. Phenology of leaf morphological, photosynthetic, and nitrogen use characteristics of canopy trees in a cool-temperate deciduous broadleaf forest at Takayama, central Japan. Ecological Research 30:247-266.
70. Olsson, C., K. Bolmgren, J. Lindstrom, and A. M. Jonsson. 2013. Performance of tree phenology models along a bioclimatic gradient in Sweden. Ecological Modelling 266:103-117.
71. Pau, S., E. M. Wolkovich, B. I. Cook, T. J. Davies, N. J. B. Kraft, K. Bolmgren, J. L. Betancourt, and E. E. Cleland. 2011. Predicting phenology by integrating ecology, evolution and climate science. Global Change Biology 17:3633-3643.
72. Pearcy, R. W. 1989. Radiation and light measurements. Pages 97-116 in R. W. Pearcy, J. Ehleringer, H. A. Money, and P. W. Rundel, editors. Plant physiological ecology: field methods and instrumentation. Chapman & Hall, Boca Raton, Florida, USA.
73. Penuelas, J., T. Rutishauser, and I. Filella. 2009. Phenology feedbacks on climate change. Science 324: 887-888.
74. Pianka, E. R. 1970. On r-and K-selection. American Naturalist 104:592-597.
75. Piao, S. L., et al. 2008. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451:49-52.
76. Piao, S. L., et al. 2015. Leaf onset in the northern hemisphere triggered by daytime temperature. Nature Communications 6:6911. http://dx.doi.org/10.1038/ncomms7911.
77. Piao, S. L., J. Y. Fang, L. M. Zhou, Q. H. Guo, M. Henderson, W. Ji, Y. Li, and S. Tao. 2003. Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. Journal of Geophysical Research-Atmospheres 108:4401. http://dx.doi.org/10.1029/2002JD002848.
78. Piao, S. L., P. Friedlingstein, P. Ciais, N. Viovy, and J. Demarty. 2007. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Global Biogeochemical Cycles 21:GB3018. http://dx.doi.org/10.1029/2006GB002888.
79. Picard, G., S. Quegan, N. Delbart, M. R. Lomas, T. Le Toan, and F. I. Woodward. 2005. Bud-burst modelling in Siberia and its impact on quantifying the carbon budget. Global Change Biology 11:2164- 2176.
80. Polgar, C. A., and R. B. Primack. 2011. Leaf-out phenology of temperate woody plants: from trees to ecosystems. New Phytologist 191:926-941.
81. Porra, R. J., W. A. Thompson, and P. E. Kriedemann. 1989. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophyll a and b extracted with four different solvents: verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochimica et Biophysica Acta 975: 384-394.
82. Potithep, S., S. Nagai, K. N. Nasahara, H. Muraoka, and R. Suzuki. 2013. Two separate periods of the LAI-VIs relationships using in situ measurements in a deciduous broadleaf forest. Agricultural and Forest Meteorology 169:148-155.
83. Richardson, A. D., A. S. Bailey, E. G. Denny, C. W. Martin, and J. O'Keefe. 2006. Phenology of a northern hardwood forest canopy. Global Change Biology 12:1174-1188.
84. Richardson, A. D., B. H. Braswell, D. Y. Hollinger, J. P. Jenkins, and S. V. Ollinger. 2009. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecological Applications 19:1417-1428.
85. Richardson, A. D., et al. 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society B 365:3227-3246.
86. Richardson, A. D., et al. 2012. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program site synthesis. Global Change Biology 18:566-584.
87. Richardson, A. D., J. P. Jenkins, B. H. Braswell, D. Y. Hollinger, S. V. Ollinger, and M. L. Smith. 2007. Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152:323- 334.
88. Richardson, A. D., T. F. Keenan, M. Migliavacca, Y. Ryu, O. Sonnentag, and M. Toomey. 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology 169:156-173.
89. Rollinson, C. R., and M. W. Kaye. 2012. Experimental warming alters spring phenology of certain plant functional groups in an early successional forest community. Global Change Biology 18:1108-1116.
90. Saitoh, T. M., S. Nagai, N. Saigusa, H. Kobayashi, R. Suzuki, K. N. Nasahara, and H. Muraoka. 2012. Assessing the use of camera-based indices for characterizing canopy phenology in relation to gross primary production in a deciduous broad-leaved and an evergreen coniferous forest in Japan. Ecological Informatics 11:45-54.
91. Saitoh, T. M., S. Nagai, J. Yoshino, H. Kondo, I. Tamagawa, and H. Muraoka. 2015. Effects of canopy phenology on deciduous overstory and evergreen understory carbon budgets in a cool-temperate forest ecosystem under ongoing climate change. Ecological Research 30:267-277.
92. Saleska, S., K. Didan, A. Huete, and H. R. Da Rocha. 2007. Amazon forests green-up during 2005 drought. Science 318:612.
93. Satake, A., T. Kawagoe, Y. Saburi, Y. Chiba, G. Sakurai, and H. Kudoh. 2013. Forecasting flowering phenology under climate warming by modelling the regulatory dynamics of flowering-time genes. Nature Communications 4:2303. http://dx.doi.org/10.1038/ncomms3303.
94. Schaber, J., and F. W. Badeck. 2003. Physiology-based phenology models for forest tree species in Germany. International Journal of Biometeorology 47:193-201.
95. Schwartz, M. D., and J. M. Hanes. 2010. Continental-scale phenology: Warming and chilling. International Journal of Climatology 30:1595-1598.
96. Shen, M. G., et al. 2014a. Earlier-season vegetation has greater temperature sensitivity of spring phenology in Northern Hemisphere. PLoS ONE 9:e88178. http://dx.doi.org/10.1371/journal.pone.0088178.
97. Shen, M. G., S. L. Piao, N. Cong, G. X. Zhang, and I. A. Jassens. 2015. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Global Change Biology 21:3647-3656.
98. Shen, M. G., Y. H. Tang, A. R. Desai, C. Gough, and J. Chen. 2014b. Can EVI-derived land-surface phenology be used as a surrogate for phenologyof canopy photosynthesis? International Journal of Remote Sensing 35:1162-1174.
99. Sims, D. A., et al. 2006. On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. Journal of Geophysical Research-Biogeosciences 111:G04015. http://dx.doi.org/10.1029/2006JG000162.
100. Sonnentag, O., K. Hufkens, C. Teshera-Sterne, A. M. Young, M. Friedl, B. H. Braswell, T. Milliman, J. O 'Keefe, and A. D. Richardson. 2012. Digital repeat photography for phenological research in forest ecosystems. Agricultural and Forest Meteorology 152:159-177.
101. Stagakis, S., N. Markos, O. Sykioti, and A. Kyparissis. 2014. Tracking seasonal changes of leaf and canopy light use efficiency in a Phlomis fruticosa Mediterranean ecosystem using field measurements and multi-angular satellite hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing 97:138-151.
102. Steltzer, H., and E. Post. 2009. Seasons and life cycles. Science 324:886-887.
103. Tang, J., P. V. Bolstad, A. R. Desai, J. G. Martin, B. D. Cook, K. J. Davis, and E. V. Carey. 2008. Ecosystem respiration and its components in an old-growth northern forest. Agricultural and Forest Meteorology 148:171-185.
104. Verhoef, W. 1984. Light-scattering by leaf layers with application to canopy reflectance modeling: The sail model. Remote Sensing of Environment 16:125-141.
105. Visser, M. E., and L. J. M. Holleman. 2001. Warmer springs disrupt the synchrony of oak and winter moth phenology. Proceedings of the Royal Society B 268:289-294.
106. Vitasse, Y. 2013. Ontogenic changes rather than difference in temperature cause understory trees to leaf out earlier. New Phytologist 198:149-155.
107. Vitasse, Y., S. Delzon, E. Dufrene, J. Y. Pontailler, J. M. Louvet, A. Kremer, and R. Michalet. 2009. Leaf phenology sensitivity to temperature in European trees: Do within-species populations exhibit similar responses? Agricultural and Forest Meteorology 149:735-744.
108. Vitasse, Y., C. Francois, N. Delpierre, E. Dufrene, A. Kremer, I. Chuine, and S. Delzon. 2011. Assessing the effects of climate change on the phenology of European temperate trees. Agricultural and Forest Meteorology 151:969-980.
109. Vitasse, Y., A. Lenz, G. Hoch, and C. Körner. 2014a. Earlier leaf-out rather than difference in freezing resistance puts juvenile trees at greater risk of damage than adult trees. Journal of Ecology 102:981-988.
110. Vitasse, Y., A. Lenz, and C. Körner. 2014b. The interaction between freezing tolerance and phenology in temperate deciduous trees. Frontiers in Plant Science. http://dx.doi.org/10.3389/fpls.2014.00541.
111. Walther, G. R., E. Post, P. Convey, A. Menzel, C. Parmesan, T. J. C. Beebee, J. M. Fromentin, O. Hoegh-G uldberg, and F. Bairlein. 2002. Ecological responses to recent climate change. Nature 416:389-395.
112. Wilczek, A. M., et al. 2009. Effects of genetic perturbation on seasonal life history plasticity. Science 323:930-934.
113. Wingate, L., et al. 2015. Interpreting canopy development and physiology using the EUROPhen camera network at flux sites. Biogeosciences Discussion 12:7979-8034.
114. Wolkovich, E. M., B. I. Cook, and T. J. Davies. 2014. Progress towards an interdisciplinary science of plant phenology: building predictions across space, time and species diversity. New Phytologist 201: 1156-1162.
115. Xiao, X. M., Q. Y. Zhang, B. Braswell, S. Urbanski, S. Boles, S. Wofsy, M. Berrien, and D. Ojima. 2004. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment 91:256-270.
116. Xu, L., et al. 2013. Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change 3:581-586.
117. Yang, X., J. F. Mustard, J. W. Tang, and H. Xu. 2012. Regional-scale phenology modeling based on meteorological records and remote sensing observations. Journal of Geophysical Research-Biogeosciences 117:G03029. http://dx.doi.org/10.1029/2012JG001977.
118. Yang, X., J. Tang, and J. Mustard. 2014. Beyond leaf color: comparing camera-based phenological metrics with leaf biochemical, biophysical and spectral properties throughout the growing season of a temperate deciduous forest. Journal of Geophysical Research-Biogeosciences 119:181-191.
119. Yang, X., J. W. Tang, J. F. Mustard, J. E. Lee, M. Rossini, J. Joiner, J. W. Munger, A. Kornfeld, and A. D. Richardson. 2015. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophysical Research Letters 42:2977-2987.
120. Zhang, X. Y., M. A. Friedl, C. B. Schaaf, A. H. Strahler, J. C. F. Hodges, F. Gao, B. C. Reed, and A. Huete. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84:471-475.