Plant phenology is changing because of recent global warming, and this change may precipitate changes in animal distribution (e.g., pests), alter the synchronization between species, and have feedback effects on the climate system through the alteration of biogeochemical and physical processes of vegetated land surface. Here, ground observations (leaf unfolding/first leaf separation of six deciduous tree species) and satellite-derived start-of-growing season (SOS) are used to assess how the timing of leafing/SOS in Western European deciduous forest responded to climate variability between 2001 and 2011 and evaluate the reliability of satellite SOS estimates in tracking the response of forest leafing to climate variability in this area. Satellite SOS estimates are derived from the Normalized Difference Vegetation Index (NDVI) time series of the Moderate Resolution Imaging Spectroradiometer (MODIS). Temporal trends in the SOS are quantified using linear regression, expressing SOS as a function of time. We demonstrated that the growing season was starting earlier between 2001 and 2011 for the majority of temperate deciduous forests in Western Europe, possibly influenced by regional spring warming effects experienced during the same period. A significant shift of up to 3 weeks to early leafing was found in both ground observations and satellite SOS estimates. We also show that the magnitude and trajectory of shifts in satellite SOS estimates are well comparable to that of in situ observations, hence highlighting the importance of satellite imagery in monitoring leaf phenology under a changing climate.
References
[1]
Menzel, A.; Fabian, P. Growing season extended in Europe. Nature 1999, 397, 659.
[2]
Badeck, F.-W.; Bondeau, A.; B？ttcher, K.; Doktor, D.; Lucht, W.; Schaber, J.; Sitch, S. Responses of spring phenology to climate change. New Phytol 2004, 162, 295–309.
[3]
Menzel, A.; Sparks, T.H.; Estrella, N.; Koch, E.; Aaasa, A.; Ahas, R.; Alm-Kübler, K.; Bissolli, P.; Braslavska, O.; Briede, A.; et al. European phenological response to climate change matches the warming pattern. Glob. Chang. Biol 2006, 12, 1969–1976.
[4]
Linkosalo, T.; Hakkinen, R.; Hanninen, H. Models of the spring phenology of boreal and temperate trees: Is there something missing? Tree Physiol 2006, 26, 1165–1172.
[5]
Pe？uelas, J.; Rutishauser, T.; Filella, I. Phenology feedbacks on climate change. Science 2009, 324, 887.
[6]
Steltzer, H.; Post, E. Seasons and life cycles. Science 2009, 324, 886–887.
[7]
Polgar, A.C.; Primack, B.R. Leaf-out phenology of temperate woody plants: From trees to ecosystems. New Phytol 2011, 191, 926–941.
[8]
Pe？uelas, J.; Filella, I. Responses to a warming world. Science 2001, 294, 793–795.
[9]
Post, E.; Pedersen, C.; Wilmers, C.C.; Forchhammer, M.C. Warming, plant phenology and the spatial dimension of trophic mismatch for large herbivores. Proc. R. Soc. B Biol. Sci 2008, 275, 2005–2013.
[10]
Van Vliet, A.J.H.; de Groot, R.S.; Bellens, Y.; Braun, P.; Bruegger, R.; Bruns, E.; Clevers, J.; Estreguil, C.; Flechsig, M.; Jeanneret, F.; et al. The European phenology network. Int. J. Biometeorol 2003, 47, 202–212.
[11]
Mayer, A. Phenology and citizen science. BioScience 2010, 60, 172–175.
[12]
White, M.A.; Thornton, P.E.; Running, S.W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob. Biogeochem. Cy 1997, 11, 217–234.
[13]
Roerink, J.G.; Menenti, M.; Verhoef, W. Reconstructing cloudfree NDVI composites using Fourier analysis of time series. Int. J. Remote Sens 2000, 21, 1911–1917.
[14]
Zhang, X.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.F.; Gao, F.; Reed, C.B.; Huete, A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ 2003, 84, 471–475.
[15]
de Beurs, M.K.; Henebry, M.G. Land surface phenology and temperature variation in the International Geosphere-Biosphere Program high-latitude transects. Glob. Chang. Biol 2005, 11, 779–790.
[16]
White, A.M.; Nemani, R.R. Real-time monitoring and short-term forecasting of land surface phenology. Remote Sens. Environ 2006, 104, 43–49.
[17]
de Jong, R.; de Bruin, S.; de Wit, A.; Schaepman, M.E.; Dent, D.L. Analysis of greening and browning trends from global NDVI time-series. Remote Sens. Environ 2011, 115, 692–702.
[18]
Liang, L.; Schwartz, M. Landscape phenology: An integrative approach to seasonal vegetation dynamics. Landsc. Ecol 2009, 24, 465–472.
[19]
White, M.A.; de Beurs, K.M.; Didan, K.; Inouye, D.W.; Richardson, A.D.; Jensen, O.P.; John O’keefe, J.; Zhang, G.; Nemani, R.R.; van Leeuwen, W.J.D.; et al. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob. Chang. Biol 2009, 15, 2335–2359.
[20]
Kross, A.; Fernandes, R.; Seaquist, J.; Beaubien, E. The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests. Remote Sens. Environ 2011, 115, 1564–1575.
[21]
Myneni, R.B.; Keeling, C.D.; Tucker, C.J.; Asrar, G.; Nemani, R.R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 1997, 386, 698–702.
[22]
Zhang, X.; Friedl, A.M.; Schaaf, C.B. Sensitivity of vegetation phenology detection to the temporal resolution of satellite data. Int. J. Remote Sens 2009, 30, 2061–2074.
[23]
Verbesselt, J.; Hyndman, R.; Newnham, G.; Culvenor, D. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens. Environ 2010, 114, 2970–2980.
[24]
Jeong, S.J.; Ho, C.H.; Gim, H.J.; Brown, M.E. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob. Chang. Biol 2011, 17, 2385–2399.
[25]
Zeng, H.; Jia, G.; Epstein, H. Recent changes in phenology over the northern high latitudes detected from multi-satellite data. Environ. Res. Lett 2011, doi:10.1088/1748-9326/6/4/045508.
[26]
Doktor, D.; Bondeau, A.; Koslowski, D.; Badeck, F.-W. Influence of heterogeneous landscapes on computed green-up dates based on daily AVHRR NDVI observations. Remote Sens. Environ 2009, 113, 2618–2632.
[27]
Wu, X.; Liu, H. Consistent shifts in spring vegetation green-up date across temperate biomes in China, 1982–2006. Glob. Chang. Biol 2013, 19, 870–880.
[28]
Cong, N.; Wang, T.; Nan, H.; Ma, Y.; Wang, X.; Myneni, B.R.; Piao, S. Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: A multimethods analysis. Glob. Chang. Biol 2013, 19, 881–891.
[29]
Luo, X.; Chen, X.; Xu, L.; Myneni, R.; Zhu, Z. Assessing performance of NDVI and NDVI3g in monitoring leaf unfolding dates of the deciduous broadleaf forest in Northern China. Remote Sens 2013, 5, 845–861.
[30]
Van Leeuwen, W.J.D.; Hartfield, K.; Miranda, M.; Meza, F.J. Trends and ENSO/AAO driven variability in NDVI derived productivity and phenology alongside the Andes mountains. Remote Sens 2013, 5, 1177–1203.
[31]
Heumann, B.W.; Seaquist, J.W.; Eklundh, L.; J？nsson, P. AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982–2005. Remote Sens. Environ 2007, 108, 385–392.
[32]
Stockli, R.; Vidale, L.P. European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. Int. J. Remote Sens 2004, 25, 3303–3330.
[33]
Zheng, X.; Friedl, M.; Schaaf, C.B. Global vegetation phenology from MODIS: Evaluation of global patterns and comparison with in situ measurements. J. Geophys. Res 2006, 111, G04017.
[34]
Intergovernmental Panel on Climate Change (IPCC). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L., Eds.; Cambridge University Press: Cambridge, UK/New York, NY, USA, 2007.
[35]
The European Topic Centre on Land Use and Spatial Information. Available online: http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/clc-2006-v13-250m (accessed on 13 February 2013).
[36]
Pan European Phenological Database. Available online: http://www.pep725.eu (accessed on 21 February 2013).
[37]
Brus, D.J.; Hengeveld, G.M.; Walvoort, D.J.J.; Goedhart, P.W.; Heidema, A.H.; Nabuurs, G.J.; Gunia, K. Statistical mapping of tree species over Europe. Eur. J. For. Res 2011, 131, 145–157.
[38]
Haylock, M.R.; Hofstra, N.; Klein Tank, A.M.G.; Klok, E.J.; Jones, P.D.; New, M. A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res.: Atmos 2008, doi:10.1029/2008JD010201.
[39]
Home European Climate Assessment & Dataset Project. Available online: http://eca.knmi.nl (accessed on 8 July 2012).
[40]
Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Greenbelt, MD, USA, 10 December 1973; pp. 3010–3017.
[41]
Prince, S.D.; Tucker, C.J. Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation. Int. J. Remote Sens 1986, 7, 1555–1570.
[42]
Chen, J.; J？nsson, P.; Tamura, M.; Gu, Z.H.; Matsushita, B.; Eklundh, L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ 2004, 91, 332–344.
[43]
J？nsson, P.; Eklundh, L. TIMESAT—A program for analyzing time-series of satellite sensor data. Comput. Geosci 2004, 30, 833–845.
[44]
Spearman, C. The proof and measurement of association between two things. Am. J. Psychol 1904, 15, 72–101.
[45]
Hansen, J.; Ruedy, R.; Sato, M.; Lo, K. Global surface temperature change. Rev. Geophys 2010, 48, RG4004.
[46]
Chatterjee, S.; Hadi, S.A. Influential observations, high leverage points, and outliers in linear regression. Stat. Sci 1986, 1, 379–393.
[47]
Hirsch, R.M.; Slack, J.R.; Smith, R.A. Techniques of trend analysis for monthly water quality data. Water Resourc. Res 1982, 18, 107–107.
[48]
de Beurs, K.M.; Henebry, G.M. A statistical framework for the analysis of long image time series. Int. J. Remote Sens 2005, 26, 1551–1573.
[49]
Morin, X.; Roy, J.; Sonié, L.; Chuine, I. Changes in leaf phenology of three European oak species in response to experimental climate change. New Phytol 2010, 186, 900–910.
[50]
Menzel, A. Phenology: Its importance to the global change community. Clim. Chang 2002, 54, 379–385.
[51]
Dose, V.; Menzel, A. Bayesian correlation between temperature and blossom onset data. Glob. Chang. Biol 2006, 12, 1451–1459.
[52]
Brown, M.E.; de Beurs, K.; Vrieling, A. The response of African land surface phenology to large scale climate oscillations. Remote Sens. Environ 2010, 114, 2286–2296.
[53]
O’Connor, B.; Dwyer, E.; Cawkwell, F.; Eklundh, L. Spatio-temporal patterns in vegetation start of season across the island of Ireland using the MERIS Global Vegetation Index. ISPRS J. Photogramm. Remote Sens 2012, 68, 79–94.
[54]
Gutiérrez-Jurado, A.H.; Vivoni, R.E. Ecogeomorphic expressions of an aspect-controlled semiarid basin: II. Topographic and vegetation controls on solar irradiance. Ecohydrology 2013, 6, 24–37.
[55]
Kattel, D.B.; Yao, T. Recent temperature trends at mountain stations on the southern slope of the central Himalayas. J. Earth Syst. Sci 2013, 122, 215–227.
[56]
Moser, L.; Fonti, P.; Büntgen, U.; Esper, J.; Luterbacher, J.; Franzen, J.; Frank, D. Timing and duration of European larch growing season along altitudinal gradients in the Swiss Alps. Tree Physiol 2009, 30, 225–233.
