Environmental Earth Sciences

, Volume 72, Issue 3, pp 663–676 | Cite as

Characterizing land condition variability in Northern China from 1982 to 2011

  • Youzhi An
  • Wei Gao
  • Zhiqiang GaoEmail author
Original Article


For the last three decades, Northern China has been considered as one of the most sensitive areas regarding global environmental change. The integration of AVHRR GIMMS and MODIS NDVI data (1982–2011), of which for the overlapping period of 2000–2006 show good consistency, were used for characterizing land condition variability. The trends of standardized annually ΣNDVI, temperature, precipitation and PDSI were obtained using a linear regression model. The results showed that Northern China has a general increase in greenness for the period 1982–2011 (a = 0.05). Also, annually ΣNDVI is significantly correlated with temperature and precipitation data at the regional scale (p < 0.05), implying that temperature and precipitation are the dominant limiting factors for vegetation growth. Since the greening is not uniform, factors other than temperature and precipitation may contribute to greening in some areas, while the grassland and cropland ecosystem are becoming increasingly vulnerable to drought. The results of trend analysis indicate that greenness seems to be evident in most of the study areas.


NDVI Land degradation Linear trend analysis Climate change 



This paper was supported by Key Research Program of the Chinese Academy of Sciences (Grant No. KZZD-EW-14), National Natural Science Foundation of China (41071278 and 41171334), Ecological Innovation and Breeding Project (Y254021031, 355031061), USDA NIFA Project (2010-34263-21075). The authors thank GIMMS (the NASA Global Inventory Modeling and Mapping Studies) group and NASA/MODIS Land Discipline Group for sharing the GIMMS NDVI and MODIS LAND data. We would like to the comments from the anonymous reviewers, which helped to improve this manuscript.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Key Laboratory of Geographic Information Science, Ministry of EducationEast China Normal University, Joint Laboratory for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE, CASShanghaiChina
  2. 2.Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsUSA
  3. 3.Yantai Institute of Coastal Zone ResearchChinese Academy of ScienceYantaiChina

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