Spatio-temporal patterns of satellite-derived grassland vegetation phenology from 1998 to 2012 in Inner Mongolia, China
详细信息    查看全文
  • 作者:Zongyao Sha ; Jialin Zhong ; Yongfei Bai ; Xicheng Tan ; Jonathan Li
  • 关键词:phenological timing ; degradation ; harmonic analysis ; human activity ; climate ; restoration
  • 刊名:Journal of Arid Land
  • 出版年:2016
  • 出版时间:June 2016
  • 年:2016
  • 卷:8
  • 期:3
  • 页码:462-477
  • 全文大小:1,243 KB
  • 参考文献:Atkinson P M, Jeganathan C, Dash J, et al. 2012. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment, 123: 400–417.CrossRef
    Bradley B A, Jacob R W, Hermance J F, 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.CrossRef
    Cao R Y, Chen J, Shen M G, et al. 2015. An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data. Agricultural and Forest Meteorology, 200: 9–20.CrossRef
    Cong N, Wang T, Nan H J, et al. 2013. Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Global Change Biology, 19(3): 881–891.CrossRef
    Ding M J, Zhang Y L, Sun X M, 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.CrossRef
    Fabricante I, Oesterheld M, Paruelo J M. 2009. Annual and seasonal variation of NDVI explained by current and previous precipitation across Northern Patagonia. Journal of Arid Environments, 73(8): 745–753.CrossRef
    Fensholt R, Rasmussen K, Nielsen T T, et al. 2009. Evaluation of earth observation based long term vegetation trends-intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sensing of Environment, 113(9): 1886–1898.CrossRef
    Hmimina G, Dufrêne E, Pontailler J Y, 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.CrossRef
    Hong Y, Nix H A, Hutchinson M F, et al. 2005. Spatial interpolation of monthly mean climate data for China. International Journal of Climatology, 25(10): 1369–1379.CrossRef
    Horion S, Cornet Y, Erpicum M, et al. 2013. Studying interactions between climate variability and vegetation dynamic using a phenology based approach. International Journal of Applied Earth Observation and Geoinformation, 20: 20–32.CrossRef
    Jakubauskas M E, Legates D R, Kastens J H. 2002. Crop identification using harmonic analysis of time-series AVHRR NDVI data. Computers and Electronics in Agriculture, 37(1–3): 127–139.CrossRef
    Jeganathan C, Dash J, Atkinson P M. 2014. Remotely sensed trends in the phenology of northern high latitude terrestrial vegetation, controlling for land cover change and vegetation type. Remote Sensing of Environment, 143: 154–170.CrossRef
    Julien Y, Sobrino J A. 2010. Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sensing of Environment, 114(3): 618–625.CrossRef
    Lambers H, Chapin III F S, Pons T S. 2008. Plant Physiological Ecology (2nd ed.). New York: Springer.CrossRef
    Lee R, Yu F, Price K P, et al. 2002. Evaluating vegetation phenological patterns in Inner Mongolia using NDVI time-series analysis. International Journal of Remote Sensing, 23(12): 2505–2512.CrossRef
    Li S, Xie Y C. 2013. Investigating coupled impacts of climate change and socioeconomic transformation on desertification by using multitemporal Landsat images: a case study in central Xilingol, China. IEEE Geoscience and Remote Sensing Letters, 10(5): 1244–1248.CrossRef
    Li Y L, Cui J Y, Zhang T H, et al. 2009. Effectiveness of sand-fixing measures on desert land restoration in Kerqin Sandy Land, northern China. Ecological Engineering, 35(1): 118–127.CrossRef
    Liu H, Tian F, Hu H C, et al. 2013. Soil moisture controls on patterns of grass green-up in inner Mongolia: an index based approach. Hydrology and Earth System Sciences, 17(2): 805–815.CrossRef
    Ma T, Zhou C H. 2012. Climate-associated changes in spring plant phenology in China. International Journal of Biometeorology, 56(2): 269–275.CrossRef
    Menzel A. 2000. Trends in phenological phases in Europe between 1951 and 1996. International Journal of Biometeorology, 44(2): 76–81.CrossRef
    Miao L J, Luan Y B, Luo X Z, et al. 2013. Analysis of the phenology in the Mongolian Plateau by inter-comparison of global vegetation datasets. Remote Sensing, 5(10): 5193–5208.CrossRef
    Mu S J, Chen Y Z, Li J L, et al. 2013a. Grassland dynamics in response to climate change and human activities in Inner Mongolia, China between 1985 and 2009. The Rangeland Journal, 35(3): 315–329.CrossRef
    Mu S J, Zhou S X, Chen Y Z, et al. 2013b. Assessing the impact of restoration-induced land conversion and management alternatives on net primary productivity in Inner Mongolian grassland, China. Global and Planetary Change, 108: 29–41.CrossRef
    Myneni R B, Keeling C D, Tucker C J, et al. 1997. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386(6626): 698–702.CrossRef
    Pan Z K, Huang J F, Zhou Q B, et al. 2015. Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data. International Journal of Applied Earth Observation and Geoinformation, 34: 188–197.CrossRef
    Piao S L, Fang J Y, Zhou L M, et al. 2006a. Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology, 12(4): 672–685.CrossRef
    Piao S L, Mohammat A, Fang J Y, et al. 2006b. NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Global Environmental Change, 16(4): 340–348.CrossRef
    Piao S L, Ciais P, Friedlingstein P, et al. 2008. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451(7174): 49–52.CrossRef
    Pokrovsky I, Pokrovsky O, Roujean J L. 2003. Development of an operational procedure to estimate surface albedo from the SEVIRI/MSG observing system by using POLDER BRDF measurements: I. Data quality control and accumulation of information corresponding to the IGBP land cover classes. Remote Sensing of Environment, 87(2–3): 198–214.CrossRef
    Price D T, McKenney D W, Nalder I A, et al. 2000. A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agricultural and Forest Meteorology, 101(2–3): 81–94.CrossRef
    Richardson A D, Anderson R S, Arain M A, et al. 2012. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American carbon program site synthesis. Global Change Biology, 18(2): 566–584.CrossRef
    Rigge M, Smart A, Wylie B, et al. 2013. Linking phenology and biomass productivity in South Dakota Mixed-Grass Prairie. Rangeland Ecology & Management, 66(5): 579–587.CrossRef
    Roerink G J, Menenti M, Verhoef W. 2000. Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9): 1911–1917.CrossRef
    Schaber J, Badeck F W. 2003. Physiology-based phenology models for forest tree species in Germany. International Journal of Biometeorology, 47(4): 193–201.CrossRef
    Shen M G, Tang Y H, Chen J, et al. 2011. Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology, 151(12): 1711–1722.CrossRef
    Shinoda M, Ito S, Nachinshonhor G U, et al. 2007. Phenology of mongolian grasslands and moisture conditions. Journal of the Meteorological Society of Japan, 85(3): 359–367.CrossRef
    Soudani K, Maire G I, Dufrêne E, et al. 2008. Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote Sensing of Environment, 112(5): 2643–2655.CrossRef
    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.CrossRef
    Tarnavsky E, Garrigues S, Brown M E. 2008. Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sensing of Environment, 112(2): 535–549.CrossRef
    Tucker C J, Sellers P J. 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing, 7(11): 1395–1416.CrossRef
    Vancutsem C, Pekel J F, Evrard C, et al. 2009. Mapping and characterizing the vegetation types of the Democratic Republic of Congo using SPOT VEGETATION time series. International Journal of Applied Earth Observation and Geoinformation, 11(1): 62–76.CrossRef
    Verbesselt J, Somers B, van Aardt J, et al. 2006. Monitoring herbaceous biomass and water content with SPOT VEGETATION time-series to improve fire risk assessment in savanna ecosystems. Remote Sensing of Environment, 101(3): 399–414.CrossRef
    Walther G R, Post E, Convey P, et al. 2002. Ecological responses to recent climate change. Nature, 416(6879): 389–395.CrossRef
    Wang Q, Tenhunen J, Dinh N Q, et al. 2004. Similarities in ground-and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland. Remote Sensing of Environment, 93(1–2): 225–237.CrossRef
    Wang X H, Piao S L, Ciais P, et al. 2010. Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006. Proceedings of the National Academy of Sciences of the United States of America, 108(4): 1240–1245.CrossRef
    Wei H Y, Heilman P, Qi J G, et al. 2012. Assessing phenological change in China from 1982 to 2006 using AVHRR imagery. Frontiers of Earth Science, 6(3): 227–236.CrossRef
    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. Global Change Biology, 15(10): 2335–2359.CrossRef
    Wu X C, Liu H Y. 2013. Consistent shifts in spring vegetation green-up date across temperate biomes in China, 1982–2006. Global Change Biology, 19(3): 870–880.CrossRef
    Xie Y C, Sha Z Y, Yu M. 2008. Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology, 1(1): 9–23.CrossRef
    Xin Q C, Broich M, Zhu P, et al. 2015. Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics. Remote Sensing of Environment, 161: 63–77.CrossRef
    Xu L, Myneni R B, Chapin III F S, et al. 2013. Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, 3(6): 581–586.
    Yu F F, Price K P, Ellis J, et al. 2003. Response of seasonal vegetation development to climatic variations in eastern central Asia. Remote Sensing of Environment, 87(1): 42–54.CrossRef
    Zhang X Y, Friedl M A, Schaaf C B, et al. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471–475.CrossRef
    Zhen J Z. 2013. Exploring the impact of eco-migration project, Ordos, Inner Mongolia, China. MSc Thesis. Sweden: Uppsala University.
  • 作者单位:Zongyao Sha (1) (2)
    Jialin Zhong (1)
    Yongfei Bai (3)
    Xicheng Tan (1)
    Jonathan Li (2)

    1. International Software School, Wuhan University, Wuhan, 430079, China
    2. Department of Geography & Environmental Management, University of Waterloo, Waterloo, N2L 3G1, Canada
    3. Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
  • 刊物主题:Physical Geography; Plant Ecology; Sustainable Development;
  • 出版者:Springer Berlin Heidelberg
文摘
Spatio-temporal variations of vegetation phenology, e.g. start of green-up season (SOS) and end of vegetation season (EOS), serve as important indicators of ecosystems. Routinely processed products from remotely sensed imagery, such as the normalized difference vegetation index (NDVI), can be used to map such variations. A remote sensing approach to tracing vegetation phenology was demonstrated here in application to the Inner Mongolia grassland, China. SOS and EOS mapping at regional and vegetation type (meadow steppe, typical steppe, desert steppe and steppe desert) levels using SPOT-VGT NDVI series allows new insights into the grassland ecosystem. The spatial and temporal variability of SOS and EOS during 1998–2012 was highlighted and presented, as were SOS and EOS responses to the monthly climatic fluctuations. Results indicated that SOS and EOS did not exhibit consistent shifts at either regional or vegetation type level; the one exception was the steppe desert, the least productive vegetation cover, which exhibited a progressive earlier SOS and later EOS. Monthly average temperature and precipitation in preseason (February, March and April) imposed most remarkable and negative effects on SOS (except for the non-significant impact of precipitation on that of the meadow steppe), while the climate impact on EOS was found to vary considerably between the vegetation types. Results showed that the spatio-temporal variability of the vegetation phenology of the meadow steppe, typical steppe and desert steppe could be reflected by the monthly thermal and hydrological factors but the progressive earlier SOS and later EOS of the highly degraded steppe desert might be accounted for by non-climate factors only, suggesting that the vegetation growing period in the highly degraded areas of the grassland could be extended possibly by human interventions.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700