Climatic Change

, Volume 119, Issue 3–4, pp 747–760 | Cite as

Fluctuation of farming-pastoral ecotone in association with changing East Asia monsoon climate

  • Wei Lu
  • Gensuo Jia


As a monsoon climate dominated region, East Asia has a high rate of climate variation. Previous studies demonstrated that the East Asian monsoon had weakened since the end of 1970’s; however, contrary to the climatic trend, a common scenario of advancing farming-pastoral ecotone (FPE) has been proposed. The objective of this study is to analyze land surface changes in association with monsoon climate variability over past 25 years in East Asia. A combination of intensive ground survey of vegetation and land use, meteorological data, and remote sensing are used to quantify the relationship between vegetation and climate and to analyze the FPE fluctuations associated with changing climate. Field precipitation data from 1981 to 2005, are used to represent climate variations and to delineate the FPE boundary. NDVI data are used to evaluate greenness-precipitation linkages by vegetation type and to create land cover maps depicting spatial pattern fluctuations of the FPE. This study demonstrates that: (1) There was no persistent northwest shifting trend of either the FPE boundary or vegetation cover during last 25 years. (2) Time integrated NDVI (TI-NDVI) varies with precipitation, and the maximum or minimum NDVI may be only sensitive to precipitation for areas with mean annual precipitation lower than approximately 200 mm. (3) A significant relationship exists between NDVI and precipitation variations for areas with mean annual precipitation greater than approximately 300 mm, especially the ecotone with a ΔNDVI of 0.122 ± 0.032. (4) The “advances” of FPE closely mimic fluctuations of precipitation in East Asia.


Support Vector Machine Land Cover Support Vector Machine Classifier East Asia Monsoon Advanced Very High Resolution Radiometer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was supported by the National Basic Research Program of China (Grant No. 2009CB723904 and 2012CB956202). We thank NASA GIMMS team for providing the most updated AVHRR GIMMS dataset.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.TEA, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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