• DocumentCode
    3671918
  • Title

    Spatiotemporal vegetation dynamic patterns in a subtropical humid region of China

  • Author

    Bingwen Qiu;Min Feng;Ming Zhong;Zhenghong Tang;Chongcheng Chen

  • Author_Institution
    Spatial Information Research Centre of Fujian Province, Fuzhou University, Fuzhou 350002, Fujian, China
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    93
  • Lastpage
    98
  • Abstract
    This paper aims to improve our knowledge of the complex vegetation-climate relationship in subtropical humid region in the context of global warming, by taking into considerations of spatio-temporal variation of both vegetation and climate change. A multi-resolution analysis (MRA) based on the wavelet transform (WT) is applied to examine the vegetation growth and its relationship with climate factors based on 250m 16-day composites MODIS vegetation EVI dataset in subtropical humid region of China over the period 2001-2010. A general greening up (68%) was observed over the period 2001-2010, as well as rather local negative trends. A trend toward global warming was also observed for the whole study region, whereas no obvious trend of precipitation is examined in most areas. Temperature generally has a positive influence on vegetation; with only very few negative EVI-temperature coefficients observed on the south portion principally due to changes in land use, land degradation, and cloud noise. However, nearly equally positive and negative EVI-rainfall relationship is observed on the inter-annual level, with negative coefficients principally observed in the northwest portion with abundant precipitation. Very strong positive relationship is observed between both EVI-temperature and EVI-precipitation at seasonal level. It is revealed that spatio-temporal variation of both vegetation and climate should be taken into considerations when analyzing the long-term effects of global climate change. Interactions between vegetation dynamics and climate variability must be studied through spatially and temporally explicit multi-scale analysis to investigate the influence of long-term climate change on the vegetation growth.
  • Keywords
    "Vegetation","Meteorology","Market research","Time series analysis","Vegetation mapping","Wavelet transforms"
  • Publisher
    ieee
  • Conference_Titel
    Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2015 2nd IEEE International Conference on
  • Print_ISBN
    978-1-4799-7748-2
  • Type

    conf

  • DOI
    10.1109/ICSDM.2015.7298032
  • Filename
    7298032