Journal of Geographical Sciences

, Volume 21, Issue 1, pp 135–149

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

Authors

  • Weiguo Jiang
    • State Key Laboratory of Earth Process and Resource EcologyBeijing Normal University
    • Academy of Disaster Reduction and Emergency ManagementBeijing Normal University
    • State Key Laboratory of Earth Process and Resource EcologyBeijing Normal University
    • Satellite Environment CenterMEP
  • Xiaohua Zhu
    • Institute of Geographic Sciences and Natural Resources ResearchCAS
  • Guangzhen Cao
    • Key Laboratory of Radiometric Calibration and Validation for Environmental SatellitesChina Meteorological Administration
  • Xiaoman Liu
    • Satellite Environment CenterMEP
    • Institute of Geographic Sciences and Natural Resources ResearchCAS
  • Ruyin Cao
    • State Key Laboratory of Earth Process and Resource EcologyBeijing Normal University
Article

DOI: 10.1007/s11442-011-0834-9

Cite this article as:
Jiang, W., Hou, P., Zhu, X. et al. J. Geogr. Sci. (2011) 21: 135. doi:10.1007/s11442-011-0834-9

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.

Keywords

eco-hydrologyprecipitationvegetationremote sensingwetlandDongting Lake

Copyright information

© Science in China Press and Springer-Verlag Berlin Heidelberg 2011