Skip to main content
SpringerLink
Log in

Examining land surface phenology in the tropical moist forest eco-zone of South America

  • Original Paper
  • Published:
International Journal of Biometeorology Aims and scope Submit manuscript

Abstract

Using leaf area index (LAI) data from 1981 to 2014 in the tropical moist forest eco-zone of South America, we extracted start (SOS) and end (EOS) dates of the active growing season in forest and savanna at each pixel. Then, we detected spatiotemporal characteristics of SOS and EOS in the two vegetation types. Moreover, we analyzed relationships between interannual variations of SOS/EOS and climatic factors, and simulated SOS/EOS time series based on preceding mean air temperature and accumulated rainfall. Results show that mean SOS and EOS ranged from 260 to 330 day of year (DOY) and from 150 to 260 DOY across the study region, respectively. From 1981 to 2014, SOS advancement is more extensive than SOS delay, while EOS advancement and delay are similarly extensive. For most pixels of forest and savanna in tropical moist forest eco-zone, preceding rainfall correlates predominantly negatively with SOS but positively with EOS, while the relationship between preceding temperature and phenophases is location-specific. In addition, preceding rainfall is more extensive than preceding temperature in simulating SOS, while both preceding rainfall and temperature play an important role for simulating EOS. This study highlights the reliability of using LAI data for long-term phenological analysis in the tropical moist forest eco-zone.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Bajpai O, Kumar A, Mishra AK, Sahu N (2012) Phenological study of two dominant tree species in tropical moist deciduous forest from the northern India. Int J Bot 8(2):66–72

    Google Scholar 

  • Bajpai O, Pandey J, Chaudhary L (2017) Periodicity of different phenophases in selected trees from Himalayan Terai of India. Agrofor Syst 91(2):363–374

    Google Scholar 

  • Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF (2018) Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5:180214

    Google Scholar 

  • Bloomfield P (2004) Fourier analysis of time series: an introduction. John Wiley and Sons, Hoboken

  • Bobée C, Ottlé C, Maignan F, De Noblet-Ducoudré N, Maugis P, Lézine A-M, Ndiaye M (2012) Analysis of vegetation seasonality in Sahelian environments using MODIS LAI, in association with land cover and rainfall. J Arid Environ 84:38–50

    Google Scholar 

  • Borchert R (1980) Phenology and ecophysiology of tropical trees: Erythrina poeppigiana OF cook. Ecology 61(5):1065–1074

    Google Scholar 

  • Borchert R (1998) Responses of tropical trees to rainfall seasonality and its long-term changes. In: Potential Impacts of Climate Change on Tropical Forest Ecosystems. Springer, Dordrecht, pp 241–253

  • Brinck K, Fischer R, Groeneveld J, Lehmann S, De Paula MD, Pütz S, Sexton JO, Song D, Huth A (2017) High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat Commun 8:14855

    CAS  Google Scholar 

  • Bullock SH, Solis-Magallanes JA (1990) Phenology of canopy trees of a tropical deciduous forest in Mexico. Biotropica 22:22–35

    Google Scholar 

  • Chapman CA, Chapman LJ, Struhsaker TT, Zanne AE, Clark CJ, Poulsen JR (2005) A long-term evaluation of fruiting phenology: importance of climate change. J Trop Ecol 21(1):31–45

    Google Scholar 

  • Chen X, Xu L (2012) Phenological responses of Ulmus pumila (Siberian Elm) to climate change in the temperate zone of China. Int J Biometeorol 56(4):695–706

    Google Scholar 

  • Chen X, Zhang W, Ren S, Lang W, Liang B, Liu G (2017) Temporal coherence of phenological and climatic rhythmicity in Beijing. Int J Biometeorol 61(10):1733–1748

  • Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22(7):357–365

    Google Scholar 

  • Cong N, Piao S, Chen A, Wang X, Lin X, Chen S, Han S, Zhou G, Zhang X (2012) Spring vegetation green-up date in China inferred from SPOT NDVI data: a multiple model analysis. Agric For Meteorol 165:104–113

    Google Scholar 

  • De Bie S, Ketner P, Paasse M, Geerling C (1998) Woody plant phenology in the West Africa savanna. J Biogeogr 25(5):883–900

    Google Scholar 

  • FAO (1993) Forest Resources Assessment 1990: Tropical Countries. FAO Forestry Paper 112

  • FAO (2012) Global ecological zones for FAO forest reporting: 2010 update. Forest Resources Assessment Working Paper 179. Food and Agriculture Organization of the United Nations, Rome

  • FAO J, SDSU U (2009) The 2010 global forest resources assessment remote sensing survey: an outline of the objectives, data, methods and approach. Forest Resources Assessment Working Paper 155

  • Fisher JI, Mustard JF, Vadeboncoeur MA (2006) Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sens Environ 100(2):265–279

    Google Scholar 

  • Keeling CD, Chin J, Whorf T (1996) Increased activity of northern vegetation inferred from atmospheric CO2 measurements. Nature 382(6587):146–149

    CAS  Google Scholar 

  • Keenan TF, Gray J, Friedl MA, Toomey M, Bohrer G, Hollinger DY, Munger JW, O’Keefe J, Schmid HP, Wing IS, Yang B, Richardson AD (2014) Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat Clim Chang 4(7):598–604

    CAS  Google Scholar 

  • Keenan RJ, Reams GA, Achard F, de Freitas JV, Grainger A, Lindquist E (2015) Dynamics of global forest area: results from the FAO global forest resources assessment 2015. For Ecol Manag 352:9–20

    Google Scholar 

  • Kendall MG, Gibbons JD (1990) Rank correlation methods. Edward Arnold, London

    Google Scholar 

  • Kushwaha C, Singh K (2005) Diversity of leaf phenology in a tropical deciduous forest in India. J Trop Ecol 21(1):47–56

    Google Scholar 

  • 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):343–355

    Google Scholar 

  • Liang S, Zhang X, Xiao Z, Cheng J, Liu Q, Zhao X (2013) Global Land Surface Satellite (GLASS) products: algorithms, validation and analysis. Springer Science and Business Media, Berlin

  • Loveland T, Reed B, Brown J, Ohlen D, Zhu Z, Yang L, Merchant J (2000) Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int J Remote Sens 21(6–7):1303–1330

    Google Scholar 

  • Lüdeke MK, Ramage PH, Kohlmaier G (1996) The use of satellite NDVI data for the validation of global vegetation phenology models: application to the Frankfurt Biosphere Model. Ecol Model 91(1–3):255–270

    Google Scholar 

  • Ma X, Huete A, Yu Q, Coupe NR, Davies K, Broich M, Ratana P, Beringer J, Hutley LB, Cleverly J (2013) Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect. Remote Sens Environ 139:97–115

    Google Scholar 

  • Malhi Y, Farfán Amézquita F, Doughty CE, Silva-Espejo JE, Girardin CA, Metcalfe DB, Aragão LE, Huaraca-Quispe LP, Alzamora-Taype I, Eguiluz-Mora L (2014) The productivity, metabolism and carbon cycle of two lowland tropical forest plots in south-western Amazonia, Peru. Plant Ecol Divers 7(1–2):85–105

    Google Scholar 

  • Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, Alm-Kübler K, Bissolli P, Og B, Briede A (2006) European phenological response to climate change matches the warming pattern. Global change biology 12(10):1969–1976

    Google Scholar 

  • Myneni RB, Keeling C, Tucker CJ, Asrar G, Nemani RR (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386(6626):698

    CAS  Google Scholar 

  • Phillips OL, Malhi Y, Higuchi N, Laurance WF, Núnez PV, Vásquez RM, Laurance SG, Ferreira LV, Stern M, Brown S (1998) Changes in the carbon balance of tropical forests: evidence from long-term plots. Science 282(5388):439–442

    CAS  Google Scholar 

  • Piao S, Fang J, Zhou L, Ciais P, Zhu B (2006) Variations in satellite-derived phenology in China’s temperate vegetation. Glob Chang Biol 12(4):672–685

    Google Scholar 

  • Piao S, Ciais P, Friedlingstein P, Peylin P, Reichstein M, Luyssaert S, Margolis H, Fang J, Barr A, Chen A (2008) Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451(7174):49–52

    CAS  Google Scholar 

  • Piao S, Yin G, Tan J, Cheng L, Huang M, Li Y, Liu R, Mao J, Myneni RB, Peng S (2015) Detection and attribution of vegetation greening trend in China over the last 30 years. Glob Chang Biol 21(4):1601–1609

    Google Scholar 

  • Prasad VK, Badarinath K, Eaturu A (2007) Spatial patterns of vegetation phenology metrics and related climatic controls of eight contrasting forest types in India–analysis from remote sensing datasets. Theor Appl Climatol 89(1–2):95–107

    Google Scholar 

  • Rankine C, Sánchez-Azofeifa G, Guzmán JA, Espirito-Santo M, Sharp I (2017) Comparing MODIS and near-surface vegetation indexes for monitoring tropical dry forest phenology along a successional gradient using optical phenology towers. Environ Res Lett 12(10):105007

    Google Scholar 

  • Ren S, Chen X, Lang W, Schwartz MD (2018) Climatic controls of the spatial patterns of vegetation phenology in mid-latitude grasslands of the Northern Hemisphere. J Geophys Res Biogeosci 123(8):2323–2336

    Google Scholar 

  • Richardson AD, Bailey AS, Denny EG, MARTIN CW, O’KEEFE J (2006) Phenology of a northern hardwood forest canopy. Glob Chang Biol 12(7):1174–1188

    Google Scholar 

  • Richardson AD, Braswell BH, Hollinger DY, Jenkins JP, Ollinger SV (2009) Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol Appl 19(6):1417–1428

    Google Scholar 

  • Richardson AD, Black TA, Ciais P, Delbart N, Friedl MA, Gobron N, Hollinger DY, Kutsch WL, Longdoz B, Luyssaert S, Migliavacca M, Montagnani L, Munger JW, Moors E, Piao S, Rebmann C, Reichstein M, Saigusa N, Tomelleri E, Vargas R, Varlagin A (2010) Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos Trans R Soc Lond Ser B Biol Sci 365(1555):3227–3246

    Google Scholar 

  • Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013) Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol 169:156–173

    Google Scholar 

  • Saha S, BassiriRad H, Joseph G (2005) Phenology and water relations of tree sprouts and seedlings in a tropical deciduous forest of South India. Trees 19(3):322–325

    Google Scholar 

  • Savitzky A, Golay MJ (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36(8):1627–1639

    CAS  Google Scholar 

  • Schwartz MD, Ahas R, Aasa A (2006) Onset of spring starting earlier across the Northern Hemisphere. Glob Chang Biol 12(2):343–351

    Google Scholar 

  • Schwarzenberg-Czerny A (1989) On the advantage of using analysis of variance for period search. Mon Not R Astron Soc 241(2):153–165

    Google Scholar 

  • Tang H, Yu K, Geng X, Zhao Y, Jiang K, Liang S (2013) A time series method for cloud detection applied to MODIS surface reflectance images. Int J Digit Earth 6:157–171

    Google Scholar 

  • Verger A, Filella I, Baret F, Peñuelas J (2016) Vegetation baseline phenology from kilometric global LAI satellite products. Remote Sens Environ 178:1–14

    Google Scholar 

  • Weedon GP, Balsamo G, Bellouin N, Gomes S, Best MJ, Viterbo P (2014) The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour Res 50(9):7505–7514

    Google Scholar 

  • Williams R, Myers B, Muller W, Duff G, Eamus D (1997) Leaf phenology of woody species in a north Australian tropical savanna. Ecology 78(8):2542–2558

    Google Scholar 

  • Williams LJ, Bunyavejchewin S, Baker PJ (2008) Deciduousness in a seasonal tropical forest in western Thailand: interannual and intraspecific variation in timing, duration and environmental cues. Oecologia 155(3):571–582

    Google Scholar 

  • Wolfe DW, Schwartz MD, Lakso AN, Otsuki Y, Pool RM, Shaulis NJ (2005) Climate change and shifts in spring phenology of three horticultural woody perennials in northeastern USA. Int J Biometeorol 49(5):303–309

    Google Scholar 

  • Xiao Z, Liang S, Wang J, Chen P, Yin X, Zhang L, Song J (2014) Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans Geosci Remote Sens 52(1):209–223

    Google Scholar 

  • Yu H, Luedeling E, Xu J (2010) Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc Natl Acad Sci 107(51):22151–22156

    CAS  Google Scholar 

  • Yue S, Pilon P, Cavadias G (2002) Power of the Mann–Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J Hydrol 259(1–4):254–271

    Google Scholar 

  • Zhang X (2015) Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data. Remote Sens Environ 156:457–472

    Google Scholar 

  • Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JC, Gao F, Reed BC, Huete A (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84(3):471–475

    Google Scholar 

  • Zhang XY, Friedl MA, Schaaf CB, Strahler AH, Liu Z (2005) Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. J Geophys Res Atmos 110:(D12103)

  • Zhang XY, Friedl MA, Tan B, Goldberg MD, Yu Y (2012). Long-term detection of global vegetation phenology from satellite instruments. In: Phenology and climate change. InTech, London, pp 297–320

  • Zhao X, Liang S, Liu S, Yuan W, Xiao Z, Liu Q, Cheng J, Zhang X, Tang H, Zhang X (2013) The Global Land Surface Satellite (GLASS) remote sensing data processing system and products. Remote Sens 5(5):2436–2450

    Google Scholar 

  • Zhou L, Tucker CJ, Kaufmann RK, Slayback D, Shabanov NV, Myneni RB (2001) Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J Geophys Res Atmos 106(D17):20069–20083

    Google Scholar 

Download references

Funding

This research was funded by the National Natural Science Foundation of China under grant nos. 41771049 and 41471033, and the scholarship of the China Scholarship Council.

Author information

Authors and Affiliations

  1. College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing, 100871, People’s Republic of China

    Boyi Liang, Xiaoqiu Chen, Weiguang Lang & Guohua Liu

  2. Environmental Change Institute, School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK

    Boyi Liang, Yadvinder Malhi & Sami Rifai

Authors
  1. Boyi Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoqiu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weiguang Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guohua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yadvinder Malhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sami Rifai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoqiu Chen.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, B., Chen, X., Lang, W. et al. Examining land surface phenology in the tropical moist forest eco-zone of South America. Int J Biometeorol 64, 1911–1922 (2020). https://doi.org/10.1007/s00484-020-01978-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00484-020-01978-x

Keywords

Search

Navigation