地理学报(英文版)

Journal of Geographical Sciences ›› 2011, Vol. 21 ›› Issue (1): 135-149.doi: 10.1007/s11442-011-0834-9

• Ecology and Environment • Previous Articles     Next Articles

Analysis of vegetation response to rainfall with satellite images in Dongting Lake

JIANG Weiguo1,2, HOU Peng1,3, ZHU Xiaohua4, CAO Guangzhen5, LIU Xiaoman3,4, CAO Ruyin1   

  1. 1. State Key Laboratory of Earth Process and Resource Ecology, Beijing Normal University, Beijing 100875, China|
    2. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing100875, China|
    3. Satellite Environment Center, MEP, Beijing 100094|China|
    4. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China|
    5. Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 100081, China
  • Received:2010-08-05 Revised:2010-09-20 Online:2011-02-15 Published:2011-01-13
  • Supported by:

    National Natural Science Foundation of China, No.40701172; No.40671122; The International Program for Cooperation in Science and Technology, No.2007DFA20640; National Science and Technology Supporting Item, No.2008BAC34B01; The Beijing Municipal Science and Technology Plan, No.D08040600580801

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Abstract:

We analyzed the Normalized Difference Vegetation Index (NDVI) from satellite images and precipitation data from meteorological stations from 1998 to 2007 in the Dongting Lake wetland watershed to better understand the eco-hydrological effect of atmospheric precipitation and its relationship with vegetation. First, we analyzed its general spatio-temporal distribution using its mean, standard deviation and linear trend. Then, we used the Empirical Orthogonal Functions (EOF) method to decompose the NDVI and precipitation data into spatial and temporal modes. We selected four leading modes based on North and Scree test rules and analyzed the synchronous seasonal and inter-annual variability between the vegetation index and precipitation, distinguishing time-lagged correlations between EOF modes with the correlative degree analysis method. According to our detailed analyses, the vegetation index and precipitation exhibit a prominent correlation in spatial distribution and seasonal variation. At the 90% confidence level, the time lag is around 110 to 140 days, which matches well with the seasonal variation.

Key words: eco-hydrology, precipitation, vegetation, remote sensing, wetland, Dongting Lake