Plant phenology is one of the most salient and sensitive indicators of terrestrial ecosystem's response to climate change. Understanding its spatiotemporal change is significantly important for understanding both land surface processes and carbon cycle and predicting changes in the terrestrial ecosystem. MODIS MOD09A1, with the spatial resolution of 500 m × 500 m and at an 8-day temporal interval, was used in this study to investigate the change in forest phenology in the Qinling zone of central China in 2001?2010. First, we used the day of year (DOY) of MOD09A1 to improve the temporal precision of EVI; we then combined the maximum ratio and the threshold method for phenology data extraction [start of growth season (SOG), end of growth season (EOG), and length of growth season (LOG)] in the Qinling zone. Results of this study show that: Accompanying the deterioration in heat and water conditions from low altitude to high altitude and southeast to northwest, SOG delayed, EOG advanced, and LOG shortened gradually. SOG and EOG mainly occurred on the 81th?120th and 270th?311th days respectively. LOG was mainly between 150 and 230 days. The phenology of forest in Qinling zone is closely related to altitude, with every 100 m rising in altitude, SOG, EOG, and LOG gradualy delayed 2 days, advanced 1.9 days, and shortened 3.9 days, respectively. From 2001 to 2010, early SOG, late EOG, and extended LOG mainly occurred in medium altitude. SOG, EOG, and LOG gradually delayed, advanced, and shortened respectively in some areas that are lowered than 1,000 m above sea level. Interannual changes at high altitude were more complicated than that at low altitude, and SOG advanced, EOG advanced, and LOG shortened above 2000 m. The reasons for these changes remain unclear. The findings quantified the differences of forest phenology with the change in elevation and revealed the spatiotemporal variations in forest phenology from 2001 to 2010. This article provides a reference for the evaluation and protection of ecological environment in the Qinling zone. In future study, reasons for the above mentioned differences in forest phenology need to be explored.
夏浩铭,李爱农,赵伟,边金虎,雷光斌. 2001-2010年秦岭森林物候时空变化遥感监测[J]. 地理科学进展, 2015, 34(10): 1297-1305.
XIA Haoming,LI Ainong,ZHAO Wei,BIAN Jinhu,LEI Guangbin. Spatiotemporal variations of forest phenology in the Qinling zone based on remote sensing monitoring, 2001-2010. PROGRESS IN GEOGRAPHY, 2015, 34(10): 1297-1305.
[Hou X H, Niu Z, Gao S, et al.2013. Monitoring vegetation phenology in farming-pastoral zone using SPOT-VGT NDVI data[J]. Transactions of the Chinese Society of Agricultural Engineering, 29(1): 142-150.]
[Jiang C, Mu X M, Ma W Y, et al.2015. Spatial and temporal variation of absolute humidity and its relationship with potential evaporation in the northern and southern regions of Qinling Mountains[J]. Acta Ecologica Sinica, 35(2): 378-388.]
[Zhou Q, Bian J J, Zheng J Y.2011. Variation of air temperature and thermal resources in northern and southern regions of the Qinling Mountains from 1951 to 2009[J]. Acta Geographica Sinica, 66(9): 1211-1218.]
[Zhu X Q, Liu K, Qin Y M.2006. GIS-based study of vegetation-environment gradient relationship in Qinling Mountain[J]. Journal of Soil and Water Conservation, 20(5): 192-196.]
竺可桢, 宛敏渭. 1973. 物候学[M]. 北京: 科学出版社. [Zhu K Z, Wan M W. 1973. Phenology[M]. Beijing, China: Science Press.]
Duchemin B, Goubier J, Courrier G.1999. Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data[J]. Remote Sensing of Environment, 67(1): 68-82.
Eleonora R, Akihoko K, Ketut W, et al.2001. NDVI-derived length of the growth period estimations for different vegetation types in Monsoon Asia[J]. IECI Chapter Japan Series, 3: 106-109.
Høgda K A, Tømmervik H, Karlsen S R.2013. Trends in the start of the growing season in fennoscandia 1982-2011[J]. Remote Sensing, 5(9): 4304-4318.
Huete A, Didan K, Miura T, et al.2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment, 83(1-2): 195-213.
Jin C, Xiao X M, Merbold L, et al.2013. Phenology and gross primary production of two dominant savanna woodland ecosystems in Southern Africa[J]. Remote Sensing of Environment, 135: 189-201.
Jonsson P, Eklundh L.2002. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1824-1832.
Moulin S, Kergoat L, Viovy N, et al.1997. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements[J]. Journal of Climate, 10(6): 1154-1170.
Myneni R B, Keeling C D, Tucker C J, et al.1997. Increased plant growth in the northern high latitudes from 1981 to 1991[J]. Nature, 386: 698-702.
Peñuelas J, Filella I.2001. Responses to a warming world[J]. Science, 294: 793-795.
Piao S L, Fang J Y, Zhou L M, et al.2006. Variations in satellite-derived phenology in China's temperate vegetation[J]. Global Change Biology, 12(4): 672-685.
Roerink G J, Menenti M, Verhoef W.2000. Reconstructing cloudfree NDVI composites using Fourier analysis of time series[J]. International Journal of Remote Sensing, 21(9): 1911-1917.
Schwartz M D.2003. Phenology: an integrative environmental science[M]. Kluwer, Netherlands: Springer.
White M A, de Beurs K M, Didan K, et al.2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006[J]. Global Change Biology, 15(10): 2335-2359.
White M A, Hoffman F, Hargrove W W, et al.2005. A global framework for monitoring phenological responses to climate change[J]. Geophysical Research Letters, 32(4): L04705.