JOURNAL OF NATURAL RESOURCES ›› 2015, Vol. 30 ›› Issue (3): 409-422.doi: 10.11849/zrzyxb.2015.03.005

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Application of Partial Least Squares (PLS) Regression Method in Attribution of Vegetation Change in Eastern China

HOU Mei-ting1, HU Wei2, QIAO Hai-long3, LI Wei-guang4, YAN Xiao-dong5   

  1. 1. China Meteorological Administration Training Centre, Beijing 100081, China;
    2. Department of Soil Science, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada;
    3. Institute of Agricultural Sciences in the Coastal Area in Jiangsu, Yancheng 224002, China;
    4. Hainan Institute of Meteorological Science, Haikou 570273, China;
    5. Beijing Normal University, Beijing 100875, China
  • Received:2014-03-04 Revised:2014-10-20 Online:2015-03-20 Published:2015-03-23


Vegetation change is generally caused by the combined effects of various climate variables, which is further complicated by the impacts of human activities. Assessing the importance of each explanatory variable is critical for the study of vegetation change attribution. The responses of vegetation to temperature and precipitation in eastern China have been widely explored in previous studies. However, less attention has been paid to the influence of other climate variables in vegetation change. In this study, we introduced a statistical method called partial least squares (PLS) to investigate the relative importance of different climate variables. ThePLS regression, combining features of principal components analysis (PCA) and multiple regression, overcomes the multicollinearity problem which arises when two or more explanatory variables in a multiple regression model are highly correlated. Using GIMMS NDVI products and PLS method, we first investigated the relative effects of different climate variables (temperature, precipitation, sunrise, relative humidity, wind) on vegetation change in eastern China from the period 1982 to 2006. Then, the relative contribution of anthropogenic factors on the vegetation change was quantified in the region of Jiangsu Province where vegetation shows distinctive changes. The results indicated that: 1) there were distinct north -south differences among interannual variations of monthly NDVI in eastern China in the period of 1982-2006. A significant increase of NDVI was found in December through May in some areas north to the Huaihe River, while the drop of NDVI occurred in June through October in some areas south to the Huaihe River; 2) in the areas with significantly increased NDVI, the greatest contributor was temperature and it had the most significant effect on the increase of NDVI. In particular, the temperature rise could play a dominant role in driving the increase of NDVI in the Huang-Huai-Hai Plain in late winter and early spring (February-March). The decrease in NDVI, by contrast, might not be attributed to climate factors in many areas. However, it should be noted that there was no obvious change in NDVI trends in many parts of eastern China compared with the areas suffering significant NDVI change; 3) Jiangsu Province was mainly characterized by a significant decline of NDVI in June from 1982 to 2006. However, such large regional concentration of NDVI change was not observed in other months and regions. Statistical analysis showed that the agricultural structural adjustment played a key role in controlling the NDVI change in June in Jiangsu Province. The decline of NDVI in June was mainly attributed to the decrease in sown area of cotton across a large spatial extent.

Key words: Partial Least Squares (PLS) Regression, vegetation change, eastern China

CLC Number: 

  • Q948.1