Abstract
Accurate measurements of the associated vegetation phenological dynamics are crucial for understanding the relationship between climate change and terrestrial ecosystems. However, at present, most vegetation phenological calculations are based on a single algorithm or method. Because of the spatial, temporal, and ecological complexity of the vegetation growth processes, a single algorithm or method for monitoring all these processes has been indicated to be elusive. Therefore, in this study, from the perspective of plant growth characteristics, we established a method to remotely determine the start of the growth season (SOG) and the end of the growth season (EOG), in which the maximum relative change rate of the normalized difference vegetation index (NDVI) corresponds to the SOG, and the next minimum absolute change rate of the NDVI corresponds to the EOG. Taking the Three-River Headwaters Region in 2000–2013 as an example, we ascertained the spatiotemporal and vertical characteristics of its vegetation phenological changes. Then, in contrast to the actual air temperature data, observed data and other related studies, we found that the SOG and EOG calculated by the proposed method is closer to the time corresponding to the air temperature, and the trends of the SOG and EOG calculated by the proposed method are in good agreement with other relevant studies. Meantime, the error of the SOG between the calculated and observed in this study is smaller than that in other studies.
Similar content being viewed by others
References
An R, Wang HL, Feng XZ, et al. (2017) Monitoring rangeland degradation using a novel local NPP scaling based scheme over the “Three-River Headwaters” region, hinterland of the Qinghai-Tibetan Plateau. Quaternary International 444: 97–114. https://doi.org/10.1016/j.quaint.2016.07.050
Bao G, Bao YH, Sanjjava A, et al. (2015) NDVI-indicated long-term vegetation dynamics in Mongolia and their response to climate change at biome scale. International Journal of Climatology 35(14): 4293–4306. https://doi.org/10.1002/joc.4286
Balzter H, Gerard F, George C, et al. (2007) Coupling of vegetation growing season anomalies and fire activity with hemispheric and regional-scale climate patterns in central and East Siberia. Journal of Climate 20: 3713–3729. https://doi.org/10.1175/JCLI4226
Beaubien EG, Freeland HJ (2000) Spring phenology trends in Alberta, Canada: links to ocean temperature. International Journal of Biometeorology 44(2): 53–59. https://doi.org/10.1007/s004840000050
Bradley BA, Jacob RW, Hermance JF, et al. (2007) A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sensing of Environment 106(2): 137–145. https://doi.org/10.1016/j.rse.2006.08.002
Brandt LA, Butler PR, Handler SD, et al. (2017) Integrating science and management to assess forest ecosystem vulnerability to climate change. Journal of Forestry 115(3): 212–221. https://doi.org/10.5849/jof.15-147
Chang Q, Wang SY, Sun YX, et al. (2014) The remote sensing monitoring model of the typical vegetation phenology in the Qinghai-Tibetan Plateau. Journal of Geo-Information Science 16: 815–823. https://doi.org/10.3724/SPJ.1047.2014.00815
Chen J, Jönsson P, Tamura M, et al. (2004) A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment 91(3–4): 332–344. https://doi.org/10.1016/j.rse.2004.03.014
Cheng M, Jin JX, Zhang JM, et al. (2018) Effect of climate change on vegetation phenology of different land-cover types on the Tibetan Plateau. International Journal of Remote Sensing 39(2): 470–487. https://doi.org/10.1080/01431161.2017.1387308
Cleland E, Chuine I, Menzel A, et al. (2007) Shifting plant phenology in response to global change. Trends in Ecology & Evolution 22(7): 357–365. https://doi.org/10.1016/j.tree.2007.04.003
Cong N, Piao SL, Chen AP, et al. (2012) Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis. Agricultural and Forest Meteorology 165: 104–113. https://doi.org/10.1016/j.agrformet.2012.06.009
Davis CL, Hoffman MT, Roberts W (2017) Long-term trends in vegetation phenology and productivity over Namaqualand using the GIMMS AVHRR NDVI3g data from 1982 to 2011. South African Journal of Botany 111: 76–85. https://doi.org/10.1016/j.sajb.2017.03.007
de Beurs KM, Henebry GM (2005) A statistical framework for the analysis of long image time series. International Journal of Remote Sensing 26(8): 1551–1573. https://doi.org/10.1080/01431160512331326657
de Beurs KM, Henebry GM (2004) Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment 89(4): 497–509. https://doi.org/10.1016/j.rse.2003.11.006
de Beurs, KM, de Henebry GM (2010) Spatio-temporal statistical methods for modelling land surface phenology. In: Phenological Research. Springer, Dordrecht. pp 177–208. https://doi.org/10.1007/978-90-481-3335-2_9
Delbart N, Toan TL, Kergoat L, et al. (2006) Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982–2004). Remote Sensing of Environment 101(1):52–62. https://doi.org/10.1016/j.rse.2005.11.012
Ding MJ, Li LH, Zhang YL, et al. (2015) Start of vegetation growing season on the Tibetan Plateau inferred from multiple methods based on GIMMS and SPOT NDVI data. Journal of Geographical Sciences 25(2): 131–148. https://doi.org/10.1007/s11442-015-1158-y
Ding MJ, Zhang YL, Sun XM, et al. (2013) Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan Plateau from 1999 to 2009. Chinese Science Bulletin 58(3): 396–405. https://doi.org/10.1007/s11434-012-5407-5
Elmore AJ, Guinn SM, Minsley BJ, et al. (2012) Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Global Change Biology 18(2): 656–674. https://doi.org/10.1111/j.1365-2486.2011.02521.x
Feng L, Guo S, Zhu LJ, et al. (2017) Urban vegetation phenology analysis using high spatio-temporal NDVI time series. Urban Forestry & Urban Greening 25: 43–57. https://doi.org/10.1016/j.ufug.2017.05.001
Fisher JI, Mustard JF, Vadeboncoeur MA (2006) Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sensing of Environment 100: 265–279. https://doi.org/10.1016/j.rse.2005.10.022
Gamon JA, Huemmrich KF, Peddle DR, et al. (2004) Remote sensing in BOREAS: lessons learned. Remote Sensing of Environment 89(2): 139–162. https://doi.org/10.1016/j.rse.2003.08.017
Güsewell S, Furrer R, Gehrig R, et al. (2017) Changes in temperature sensitivity of spring phenology with recent climate warming in Switzerland are related to shifts of the preseason. Global Change Biology 23: 5189–5202. https://doi.org/10.1111/gcb.13781
Hall-Beyer M (2003) Comparison of single-year and multiyear NDVI time series principal components in cold temperate biomes. IEEE Transactions on Geoscience & Remote Sensing 41: 2568–2574. https://doi.org/10.1109/TGRS.2003.817274
Herrmann SM, Anyamba A, Tucker CJ (2017) Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Global Environmental Change 15(4):394–404. https://doi.org/10.1016/j.gloenvcha.2005.08.004
Hmimina G, Dufrêne E, Pontailler JY, et al. (2013) Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements. Remote Sensing of Environment 132: 145–158. https://doi.org/10.1016/j.rse.2013.01.010
IPCC (2014) Climate Change 2014 — Impacts, Adaptation and Vulnerability: Part A: Global and Sectoral Aspects: Volume 1, Global and Sectoral Aspects: Working Group to the IPCC Fifth Assessment Report (1 edition). Cambridge University Press, New York, NY.
Jakubauskas M, Legates DR, Kastens J (2001) Harmonic analysis of time-series AVHRR NDVI data. Photogrammetric Engineering & Remote Sensing 67: 461–470. https://doi.org/0099-1112/01/6704-461
Jeong SJ, Ho CH, Gim HJ, et al. (2011) Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Global Change Biology 17: 2385–2399. https://doi.org/10.1111/j.1365-2486.2011.02397.x
Jones HG (2014) Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology (3 edition). Cambridge University Press, Cambridge, New York.
Jönsson P, Eklundh L (2004) TIMESAT—a program for analyzing time-series of satellite sensor data. Computers & Geosciences 30: 833–845. https://doi.org/10.1016/jxageo.2004.05.006
Julien Y, Sobrino JA (2009) Global land surface phenology trends from GIMMS database. International Journal of Remote Sensing 30: 3495–3513. https://doi.org/10.1080/01431160802562255
Julien Y, Sobrino JA (2010) Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sensing of Environment 114: 618–625. https://doi.org/10.1016/j.rse.2009.11.001
Kafaki SB, Mataji A, Hashemi SA (2009) Monitoring growing season length of deciduous broad leaf forest derived from satellite data in Iran. American Journal of Environmental Sciences 5(5): 647–652. https://doi.org/10.3844/ajessp.2009.647.652
Lee R, Yu F, Price KP (2002) Evaluating vegetation phenological patterns in Inner Mongolia using NDVI time-series analysis. International Journal of Remote Sensing 23: 2505–2512. https://doi.org/10.1080/01431160110106087
Li X, Wang Z, Zhao J, et al. (2017) Altitudinal variations in the sensitivity of alpine meadow productivity to temperature and precipitation changes along the southern slope of Nyainqentanglha Mountains. Acta Ecologica Sinica 37: 5591–5601. https://doi.org/10.5846/stxb201606141147
Liang TG, Yang SX, Feng QS, et al. (2016) Multi-factor modeling of above-ground biomass in alpine grassland: A case study in the Three-River Headwaters Region, China. Remote Sensing of Environment 186: 164–172. https://doi.org/10.1016/j.rse.2016.08.014
Liu XF, Zhu XF, Zhu WQ, et al. (2014) Changes in spring phenology in the three-Rivers headwater region from 1999 to 2013. Remote Sensing 6(9): 9130–9144. https://doi.org/10.3390/rs6099130
Lyapustin A, Wang Y, Xiong X, et al. (2014) Scientific impact of MODIS C5 calibration degradation and C6+ improvements. Atmospheric Measurement Techniques 7(12): 4353–4365. https://doi.org/10.5194/amt-7-4353-2014
Melaas EK, Friedl MA, Richardson AD (2016) Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States. Global Change Biology 22: 792–805. https://doi.org/10.1111/gcb.13122
Melaas EK, Friedl MA, Zhu Z (2013) Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM + data. Remote Sensing of Environment 132:176–185. https://doi.org/10.1016/j.rse.2013.01.011
Menzel A (2002) Phenology: Its importance to the global change community. Climatic Change 54(4): 379–385. https://doi.org/10.1023/A:1016125215496
Moody A, Johnson DM (2001) Land-surface phenologies from AVHRR using the discrete Fourier transform. Remote Sensing of Environment 75(3): 305–323. https://doi.org/10.1016/s0034-4257(00)00175-9
Mupangwa W, Walker S, Twomlow S (2011) Start, end and dry spells of the growing season in semi-arid southern Zimbabwe. Journal of Arid Environments 75: 1097–1104. https://doi.org/10.1016/j.jaridenv.2011.05.011
Nayak RK, Mishra N, Dadhwal VK, et al. (2016) Assessing the consistency between AVHRR and MODIS NDVI datasets for estimating terrestrial net primary productivity over India. Journal of Earth System Science 125: 1189–1204. https://doi.org/10.1007/s12040-016-0723-9
O’Neill BC, Oppenheimer M, Warren R, et al. (2017) IPCC reasons for concern regarding climate change risks. Nature Climate Change 7(1): 28–37. https://doi.org/10.1038/nclimate3179
Piao SL, Cui MD, Chen AP, et al. (2011) Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agricultural and Forest Meteorology 151(12): 1599–1608. https://doi.org/10.1016/j.agrformet.2011.06.016
Piao SL, Fang JY, Zhou LM, et al. (2006) Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology 12(4): 672–685. https://doi.org/10.1111/j.1365-2486.2006.01123.x
Prentice IC, Cramer W, Harrison SP, et al. (1992) Special Paper: A Global Biome Model Based on Plant Physiology and Dominance, Soil Properties and Climate. Journal of Biogeography 19(2): 117–134. https://doi.org/10.2307/2845499
Rafique R, Zhao F, de Jong R, et al. (2016) Global and Regional Variability and Change in Terrestrial Ecosystems Net Primary Production and NDVI: A Model-Data Comparison. Remote Sensing 8(3): 177–193. https://doi.org/10.3390/rs8030177
Reed BC, Brown JF, VanderZee D, et al. (1994) Measuring phenological variability from satellite imagery. Journal of Vegetation Science 5: 703–714. https://doi.org/10.2307/3235884
Roerink GJ, Menenti M, Verhoef W (2000) Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing 21(9): 1911–1917. https://doi.org/10.1080/014311600209814
Schloss AL, Kicklighter DW, Kaduk J, et al. (1999) Comparing global models of terrestrial net primary productivity (NPP): comparison of NPP to climate and the Normalized Difference Vegetation Index (NDVI). Global Change Biology 5: 25–34. https://doi.org/10.1046/j.1365-2486.1999.00004.x
Shen MG, Piao SL, Chen XQ, et al. (2016) Strong impacts of daily minimum temperature on the green-up date and summer greenness of the Tibetan Plateau. Global Change Biology 22(9): 3057–3066. https://doi.org/10.1111/gcb.13301
Soudani K, Hmimina G, Delpierre N, 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. https://doi.org/10.1016/j.rse.2012.03.012
Studer S, Stöckli R, Appenzeller C, et al. (2007) A comparative study of satellite and ground-based phenology. International Journal of Biometeorology 51(5): 405–414. https://doi.org/10.1007/s00484-006-0080-5
Suepa T, Qi JG, Lawawirojwong S, et al. (2016) Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon Southeast Asia. Environmental Research 147: 621–629. https://doi.org/10.1016/j.envres.2016.02.005
Taiz L, Zeiger E, Møller IM, et al. (2014) Plant Physiology and Development(6 edition). Sinauer Associates is an imprint of Oxford University Press.
Tarnavsky E, Garrigues S, Brown ME (2008) Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sensing of Environment 112(2): 535–549. https://doi.org/10.1016/j.rse.2007.05.008
Tateishi R, Ebata M (2004) Analysis of phenological change patterns using 1982–2000 Advanced Very High Resolution Radiometer (AVHRR) data. International Journal of Remote Sensing 25(12): 2287–2300. https://doi.org/10.1080/01431160310001618455
Tucker CJ, Slayback DA, Pinzon JE, et al. (2001) Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. International Journal of Biometeorology 45(4): 184–190. https://doi.org/10.1007/s00484-001-0109-8
Ulsig L, Nichol CJ, Huemmrich KF, et al. (2017) Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series. Remote Sensing 9(1): 49–57. https://doi.org/10.3390/rs9010049
Villegas D, Aparicio N, Blanco R, et al. (2001) Biomass Accumulation and Main Stem Elongation of Durum Wheat Grown under Mediterranean Conditions. Annals of Botany 88(4): 617–627. https://doi.org/10.1006/anbo.2001.1512
Viovy N, Arino O, Belward AS (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8): 1585–1590. https://doi.org/10.1080/01431169208904212
Vrieling A, Meroni M, Darvishzadeh R, et al. (2018) Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island. Remote Sensing of Environment 215(15): 517–529. https://doi.org/10.1016/j.rse.2018.03.014
Wang D, Morton D, Masek J, et al. (2012) Impact of sensor degradation on the MODIS NDVI time series. Remote Sensing of Environment 119(3): 55–61. https://doi.org/10.1016/j.rse.2011.12.001
Wang H, Liu D, Lin H, et al. (2015) NDVI and vegetation phenology dynamics under the influence of sunshine duration on the Tibetan plateau. International Journal of Climatology 35(5):687–698. https://doi.org/10.1002/joc.4013
Wang Z (2006) Plant physiology. Science and Technology Literature Press.
Westerling AL, Hidalgo HG, Cayan DR, et al. (2006) Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity. Science 313:940–943. https://doi.org/10.1126/science.1128834
White MA, Nemani RR (2006) Real-time monitoring and short-term forecasting of land surface phenology. Remote Sensing of Environment 104(1): 43–49. https://doi.org/10.1016/j.rse.2006.04.014
Wylie BK, Johnson DA, Laca E, et al. (2003) Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush-steppe ecosystem. Remote Sensing of Environment 85: 243–255. https://doi.org/10.1016/S0034-4257(03)00004-X
Xu X, Liu J, Shao Q, et al. (2008) The dynamic changes of ecosystem spatial pattern and structure in the Three-River Headwaters region in Qinghai Province during recent 30 years. Geographical Research 27(4): 829–839. https://doi.org/10.11821/yj2008040011
Yang B, He M, Shishov V, et al. (2017) A new perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data. Processing of the National Academy of Science of the United States of America 114(27):6966–6971. https://doi.org/10.1073/pnas.1616608114
You X, Meng J, Zhang M, et al. (2013) Remote Sensing Based Detection of Crop Phenology for Agricultural Zones in China Using a New Threshold Method. Remote Sensing 5:3190–3211. https://doi.org/10.3390/rs5073190
Yu F, Price KP, Ellis J, et al. (2003) Response of seasonal vegetation development to climatic variations in eastern central Asia. Remote Sensing of Environment 87: 42–54. https://doi.org/10.1016/S0034-4257(03)00144-5
Yu S, Xia JJ, Yan ZW, et al. (2018) Changing spring phenology dates in the Three-Rivers Headwater Region of the Tibetan Plateau during 1960–2013. Advances in Atmospheric Sciences 35(1): 116–126. https://doi.org/10.1007/s00376-017-6296-y
Zeng L, Wardlow BD, Wang R, et al. (2016) A hybrid approach for detecting corn and soybean phenology with time-series MODIS data. Remote Sensing of Environment 181: 237–250. https://doi.org/10.1016/j.rse.2016.03.039
Zhang X, Friedl MA, Schaaf CB, et al. (2003) Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84: 471–475. https://doi.org/10.1016/S0034-4257(02)00135-9
Zhao X (2009) Alpine meadow ecosystem and global change. Science Press.
Zhou HK, Yao BQ, Xu WX, et al. (2014) Field evidence for earlier leaf-out dates in alpine grassland on the eastern Tibetan Plateau from 1990 to 2006. Biology Letters 10(8): 1565–1579. https://doi.org/10.1098/rsbl.2014.0291
Zhou JH, Cai WT, Qin Y, et al. (2016) Alpine vegetation phenology dynamic over 16 years and its covariation with climate in a semi-arid region of China. Science of the Total Environment 572:119–128. https://doi.org/10.1016/j.scitotenv.2016.07.206
Zhou L, Tucker CJ, Kaufmann RK, et al. (2001) Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research Atmospheres 106(D17): 20069–20083. https://doi.org/10.1029/2000JD000115
Zhu Z, Woodcock CE, Olofsson P (2012) Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sensing of Environment 122(3): 75–91. https://doi.org/10.1016/j.rse.2011.10.030
Acknowledgements
This work is supported by National Natural Science Foundation of China (Grant No. 41801099).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chen, Tt., Yi, Gh., Zhang, Tb. et al. A method for determining vegetation growth process using remote sensing data: A case study in the Three-River Headwaters Region, China. J. Mt. Sci. 16, 2001–2014 (2019). https://doi.org/10.1007/s11629-018-4982-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-018-4982-6