Species-and Community-Scale Simulation of the Phenology of a Temperate Forest in Changbai Mountain Based on Digital Camera Images

  • 1 Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 Institute of Applied Ecology, CAS, Shenyang 110016, China

Received date: 2013-02-20

  Revised date: 2013-05-20

  Online published: 2013-12-20

Supported by

this study was supported by “Strategic Priority Research Program” of the Chinese Academy of Sciences (Grant No. XDA05050600), National Natural Science Foundation of China (Grant No. 41071251), and National Program on Key Basic Research Project (973 Program, No.2010CB833504).


Vegetation phenology is an important parameter in models of global vegetation and land surfaces, as the accuracy of these simulations depends strongly on the description of the canopy status. Temperate forests represent one of the major types of vegetation at mid-high latitudes in the Northern Hemisphere and act as a globally important carbon sink. Thus, a better understanding of the phenological variables of temperate forests will improve the accuracy of vegetation models and estimates of regional carbon fluxes. In this work, we explored the possibility of using digital camera images to monitor phenology at species and community scales of a temperate forest in northeastern China, and used the greenness index derived from these digital images to optimize phenological model parameters. The results show that at the species scale, the onset dates of phenological phases (Korean pine, Mongolian oak) derived from the images are close to those from field observations (error < 3d). At the community scale the time series data accurately reflected the observed canopy status (R2=0.9) simulated using the phenological model, especially in autumn. The phenological model was derived from simple meteorological data and environmental factors optimized using the greenness index. These simulations provide a powerful means of analyzing environmental factors that control the phenology of temperate forests. The results indicate that digital images can be used to obtain accurate phenological data and provide reference data to validate remote-sensing phenological data. In addition, we propose a new method to accurately track phenological phases in land-surface models and reduce uncertainty in spatial carbon flux simulations.

Cite this article

ZHOU Lei, HE Honglin, SUN Xiaomin, ZHANG Li, YU Guirui, REN Xiaoli, WANG Jiayin, ZHANG Junhui . Species-and Community-Scale Simulation of the Phenology of a Temperate Forest in Changbai Mountain Based on Digital Camera Images[J]. Journal of Resources and Ecology, 2013 , 4(4) : 317 -326 . DOI: 10.5814/j.issn.1674-764x.2013.04.004


Adamsen F J, P J Pinter, E M Barnes, et al. 1999. Measuring wheat senescence with a digital camera. Crop Science, 39(3): 719-724.

Ahrends H E, R Brugger, R Stockli, et al. 2008. Quantitative phenological observations of a mixed beech forest in northern Switzerland with digital photography. Journal of Geophysical Research-Biogeosciences, 113(G4): 1-11.

Ahrends H E, S Etzold, W L Kutsch, et al. 2009. Tree phenology and carbon dioxide fluxes: use of digital photography for process-based interpretation the ecosystem scale. Climate Research, 39(3): 261-274.

Arora V K, G J Boer. 2005. A parameterization of leaf phenology for the terrestrial ecosystem component of climate models. Global Change Biology, 11(1): 39-59.

Augspurger C K, J M Cheeseman, C F Salk. 2005. Light gains and physiological capacity of understorey woody plants during phenological avoidance of canopy shade. Functional Ecology, 19(4): 537-546.

Borchert R, G Rivera. 2001. Photoperiodic control of seasonal development and dormancy in tropical stem-succulent trees. Tree Physiology, 21(4): 213-221.

Choler P, W Sea, P Briggs, et al. 2010. A simple ecohydrological model captures essentials of seasonal leaf dynamics in semi-arid tropical grasslands. Biogeosciences, 7(3): 907-920.

Coops N C, T Hilker, C W Bater, et al. 2012.Linking ground-based to satellite-derived phenological metrics in support of habitat assessment. Remote Sensing Letters, 3(3): 191-200.

Fisher J, J Mustard, M Vadeboncoeur. 2006. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sensing of Environment, 100(2): 265-279.

Gan S S, R M Amasino. 1997. Making sense of senescence -Molecular genetic regulation and manipulation of leaf senescence. Plant Physiology, 113(2): 313-319.

Ganguly S, M A Friedl, Tan B, et al. 2010. Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product. Remote Sensing of Environment, 114(8): 1805-1816.

Graham E A, Yuen E M, G F Robertson, et al. 2009. Budburst and leaf area expansion measured with a novel mobilecamera system and simple color thresholding. Environmental and Experimental Botany, 65: 238-244.

GuL H, M P Wilfred, D B Dennis, et al. 2003. Phenology of vegetation photosystem. In: Schwartz M D (ed.). Phenology: An Integrative Environmental Science, New York: Springer, 35-58.

Guan D X, Wu J B, Yu G R, et al. 2005. Meteorological control on CO2 flux above broad-leaved Korean pine mixed forest in Changbai Mountains. Science in China Series D-Earth Sciences, 48:116-122.

Heide O M. 1974. Growth and dormancy in Norway spruce ecotypes (Picea abies). 1. Interatction of photoperiod and temperature. Physiologia Plantarum, 30: 1-12.

Heumann B W, J W Seaquist, L Eklundh, et al. 2007. AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005. Remote Sensing of Environment, 108(4): 385-392.

Huete A, K Didan, T Miura, et al. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83: 195-213.

Imaizumi T, H G Tran, T E Swartz, et al. 2003. FKF1 is essential for photoperiodic-specific light signalling in Arabidopsis. Nature, 426(6964): 302-306.

Ingvarsson P K, M V Garcia, D Hall, et al. 2006. Clinal variation in phyB2, a candidate gene for day-length-induced growth cessation and bud set, across a latitudinal gradient in European aspen (Populus tremula). Genetics, 172(3): 1845-1853.

Janssen P H M, P S C Heuberger. 1995. Calibration of Process-Oriented Models. Ecological Modeling, 83: 55-66.

Jolly W M, R Nemani, S W Running. 2005. A generalized, bioclimatic index to predict foliar phenology in response to climate. Global Change Biology, 11(4): 619-632.

Körner C. 1999. Alpine plant life. Berlin: Springer. 338.

Keller F, C Korner. 2003. The role of photoperiodism in alpine plant development. Arctic Antarctic and Alpine Research, 35(3): 361-368.

Keskitalo J, G Bergquist, P Gardestrom, et al. 2005. A cellular timetable of autumn senescence. Plant Physiology, 139(4): 1635-1648.

Knorr W, T Kaminski, M Scholze, et al. 2010. Carbon cycle data assimilation with a generic phenology model. Journal of Geophysical Research-Biogeosciences, 115(G4): 1-16.

Korner C, D Basler. 2010. Phenology under global warming. Science, 327(5972): 1461-1462.

Koski V, J Selkainaho. 1982. Experiments on the joint effect of heat sum and photoperiod on seedlings of Betula pendula. Communicationes Instituti Forestalis Fenniae, 105, 1-34.

Kucharik C J, C C Barford, M E Maayar, et al. 2006. A multiyear evaluation of a Dynamic Global Vegetation Model at three AmeriFlux forest sites: Vegetation structure, phenology, soil temperature, and CO2 and H2O vapor exchange. Ecological Modelling, 196: 1-31.

Kurc S A, L M Benton. 2010. Digital image-derived greenness links deep soil moisture to carbon uptake in a creosote bush-dominated shrubland. Journal of Arid Environments, 74(5): 585-594.

Li M, Wu Z, Du H, et al. 2011. Growing-season Trends Determined from SPOT NDVI in Changbai Mountains, China, 1999-2008. Scientia Geographica Sinica, 31(10): 1242-1248. (in Chinese)

Lim P. 2003. Molecular genetics of leaf senescence in Arabidopsis. Trends in Plant Science, 8(6): 272-278.

Linderholm H W. 2006. Growing season changes in the last century. Agricultural and Forest Meteorology, 137: 1-14.

Linkosalo T, T R Carter, R Hakkinen, et al. 2000. Predicting spring phenology and frost damage risk of Betula spp. under climatic warming: a comparison of two models. Tree Physiology, 20(17): 1175-1182.

Menzel A, P Fabian. 1999. Growing season extended in Europe. Nature, 397(6721): 659-659.

Menzel A, T H Sparks, N Estrella, et al. 2006. European phenological response to climate change matches the warming pattern. Global Change Biology, 12(10): 1969-1976.

Migliavacca M, M Meroni, L Busetto, et al. 2009. Modeling gross primary production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model. Sensors, 9(2): 922-942.

Migliavacca M, M Galvagno, E Cremonese, et al. 2011. Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake. Agricultural and Forest Meteorology, 151:1325-1337.

Mizunuma T, Koyanagi T, Mencuccini M, et al. 2011. The comparison of several colour indices for the photographic recording of canopy phenology of Fagus crenata Blume in eastern Japan. Plant Ecology & Diversity, 4(1): 67-77.

Moulin S, L Kergoat, N Viovy, et al. 1997. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. Journal of Climate, 10(6): 1154-1170.

Nagai S, Nasahara K N, Muraoka H, et al. 2010. Field experiments to test the use of the normalized-difference vegetation index for phenology detection. Agricultural and Forest Meteorology, 150(2): 152-160.

Nasholm T, A Ekblad, A Nordin, et al. 1998. Boreal forest plants take up organic nitrogen. Nature, 392(6679): 914-916.

Noodén L D, J J Guiamet, I John. 1997. Senescence mechanisms. Physiologia Plantarum, 101(4): 746-753.

Parmesan C. 2006. Ecological and evolutionary responses to recent climate change. Annual Review of Ecology Evolution and Systematics, 37: 637-669.

Richardson A D, B H Braswell, D Y Hollinger, et al. 2009.Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecological Applications, 19(6): 1417-1428.

Richardson A D, J P Jenkins, B H Braswell, et al. 2007.Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia, 152(2): 323-334.

Ryu S-R, Chen J, A Noormets, et al. 2008. Comparisons between PnET-Day and eddy covariance based gross ecosystem production in two Northern Wisconsin forests. Agricultural and Forest Meteorology, 148(2): 247-256.

Saitoh T M, Nagai S, Saigusa N, et al. 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.

Schwartz M D. 1998.Green-wave phenology. Nature, 394(6696): 839-840.

Schwartz M D, Reiter B E. 2000.Changes in North American spring. International Journal of Climatology, 20(8): 929-932.

Schwartz M D, B C Reed. 1999. Surface phenology and satellite sensor-derived onset of greenness: an initial comparison. International Journal of Remote Sensing, 20(17): 3451-3457.

Smit-Spinks B, B T Swanson, A H Markhart. 1985. The effect of photoperiod and thermoperiod on cold acclimation and growth of Pinus sylvestris. Canadian Journal of Forest Research, 15: 453-460.

Snow D W. 1965. A possible selective factor in the evolution of fruiting seasons. Oikos, 15: 274-281.

Sonnentag O, K Hufkensc, C Teshera-Sternea, et al. 2012. Digital repeat photography for phenological research in forest ecosystems. Agricultural and Forest Meteorology, 152: 159-177.

Soudani K, G Hmimina, N Delpierre, et al. 2012.Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote Sensing of Environment, 123: 234-245.

Stockli R, T Rutishauser, D Dragoni, et al. 2008. Remote sensing data assimilation for a prognostic phenology model. Journal of Geophysical Research-Biogeosciences, 113(G4): 1-19.

Studer S, R Stockli, C Appenzeller, et al. 2007. A comparative study of satellite and ground-based phenology. International Journal of Biometeorology, 51(5): 405-414.

Tong Q X, Zhang B, Zheng L F. 2006.Hyperspectral remote sensing. Beijing: Higher Education Press, 19-30. (in Chinese)

Valverde F, A Mouradov, W Soppe, et al. 2004. Photoreceptor regulation of CONSTANS protein in photoperiodic flowering. Science, 303(5660):1003-1006. Vegis A. 1964. Dormancy in higher plants. Annual Review of Plant Physiology, 15:185-222.

Yu X F, Zhuang D. 2006. Monitoring Forest Phenophases of Northeast China based on MODIS NDVI Data. Resources Science, 28(4): 111-117. (in Chinese)

Zhang J, Hu Y, Xiao X, et al. 2009. Satellite-based estimation of evapotranspiration of an old-growth temperate mixed forest. Agricultural and Forest Meteorology, 149: 976-984.

Zhang J, Yu G, Han S, et al. 2006. Seasonal and annual variation of CO2 flux above a broadleaved Korean pine mixed forest. Science in China Series D: Earth Sciences, 49(Supp.II): 63-73.

Zhou L, He H L, Sun X M, et al. 2012a. Using digital repeat photography to model winter wheat phenology and photosynthetic CO2 uptake. Acta Ecological Sinica, 32(16): 5146-5153. (in Chinese)

Zhou L, He H L, Zhang L, et al. 2012b. Simulations of phenology in alpine grassland communities in Damxung, Xizang, based on digital camera images. Chinese Journal of Plant Ecology, 36(11): 1125-1135. (in Chinese)

Zhu K Zh, Wei M W. 1973. Phenology. Beijing: Science Press. (in Chinese)

Zhu W Q, Tian H Q, Xu X F, et al. 2012. Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982-2006. Global Ecology and Biogeography, 21(2): 260-271.