Spatio-temporal Change Of Vegetation Cover In East China And Influence Of Artificial Factors

Vegetation is an important component of the terrestrial ecosystem, a centrum of materials cycle and energy flow and the vital resource for human development. Eastern China is selected as this study region with the longitude ranging from 113°E to 123°E and the latitude ranging from 21.5°N to 35.5°N. Based on SPOT/ VGT-NDVI images in different years, the aim of this research is to analyze spatio-temporal change of vegetation cover and influence of human factors on it.The total area of the vegetation in east China decreases gradually from 1998 to 2005, but the vigor of the vegetation in this area has an increasing trend. There is a positive correlation between vegetation decrease and economic development. The vegetation in northern region with low dense vegetation cover of Yangtze River has an obvious increasing trend especially in Fuyang city and Bozhou city of Anhui Province and the area around Poyang Lake. In the southern region of Yangtze River with high dense vegetation cover, vegetation has an obvious decreasing trend especially around Shanghai city, Suzhou city and Guangzhou city, the southeastern Zhejiang province, the southwestern Nanchang city, Xiamen-Zhangzhou-Quanzhou region in Fujian province and Changsha-Zhuzhou-Xiangtan region in Hunan province. The change of vegetation vigor differs among provinces in the study area. There is a distinct decreasing trend in Shanghai city, a slight decreasing trend in Fujian province, Zhejiang province and Jiangxi province and an increasing trend in Anhui province and Jiangsu province. The gravity centre of the vegetation in the study area always lies in Shangrao city of Anhui province and has been moving toward northwest since 1998.Then the change of the vegetation in any year from 1998 to 2005 is analyzed. Result shows that the Growth Season Length (GSL) of the vegetation in east China ranges from 240 to 320 days and the vegetation with longer GSL often distributes in low latitude region locating with the range of latitude from 28°N to 30°N. The earliest peak NDVI appears on the 18^th dekad (the third dekad of June), and the latest peak NDVI appears on the 28^th dekad (the first dekad of October). A descending sort of the Integrated NDVI (INDVI) in a year is Fujian province, Zhejiang province, Jiangxi province, Anhui province, Jiangsu province and Shanghai city. The vegetation in the region of low integrated NDVI increased obviously, whereas unobvious in high INDVI region. In order to extract the NDVI curves of a year of the vegetation, the vegetation in east China is classified into 18 types using the methods of Principal Components Analysis (PCA) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Classification accuracy is satisfying and kappa index is 0.82. Double-cropped crop and sub-tropical evergreen broadleaf forest have larger area than other types of vegetation. Each type of vegetation has its own NDVI curves of a year.There are most cities with high population density, quickly economic growth and obvious urban expansion in the study area. The vegetation distribution correlates negatively with Gross Domestic Product (GDP) per square kilometer, population density and GDP per square kilometer on construction land respectively. However, the situation of the correlation has evident heterogeneity in space. Strong negativecorrelation occurs in relatively developed areas, while positive correlation occurs around river, lake and coast. Analysis of the statistical data from 1998 to 2005 with the panel method shows that negative correlation between vegetation and urbanization has been increasing year by year. And logarithmic relationship is found between the vegetation cover and the urbanization. This research also shows that influence of urbanization on vegetation growth in a year is obvious according to multi-ring buffer sampling. In Yangtze River Delta, such 6 cities as Shanghai, Nanjing, Hangzhou, Suzhou, Wuxi and Changzhou, vegetation cover reduces especially within 10 kilometer far away from city center except Hangzhou city. Urbanization makes Start of Growing Season (SOS) earlier, makes End of Growing Season (EOS) later. So GSL is lengthened, but NDVI amplitude is declined more obviously. It's clear that GSL of the vegetation in urban is longer 5 days and the NDVI amplitude is smaller 0.21 than that in area which locates 8-10 kilometers away from city center by comparing the internal difference in the six cities with the average level of GSL and the NDVI amplitude of the six cities.At last Shanghai city is studied as a typical rapidly expanding urban area using Landsat TM images. The urban vegetation change trajectory from 1989 to 2003 is analyzed in this paper. The total vegetation area in Shanghai city appears continued downward trend particularly in the Pudong district. Stable vegetation trajectory in the three periods (1989, 1997 and 2003) occupies more half of total area of Shanghai city and stable non-vegetation trajectory in the three periods accounts for about twenty percent of total area of Shanghai city. Transformation from vegetation to non-vegetation in the first phase (1989 to 1997) usually takes place nearby the urban, whereas the same transformation in the second phase (1997 to 2003) often occurs in area far away from the urban. There is small area of transformation trajectory from non-vegetation to vegetation in two phases. Analysis with the Logistic regression model shows that the distance from road has the strongest influence on vegetation change trajectories than other 13 factors which in descending sort according to the influence degree is the distance from centers of districts, distance from edge between vegetation patch and non-vegetation (1997), distance from edge between vegetation patch and non-vegetation (1989), distance from business center, population density 1990, distance from river, distance from expressway, land use diversity within 100 meters, population density different between 2003 and 1990 and distance from the center point of Shanghai city. It is worth mentioning that the influence of distance from expressway on vegetation change trajectory is opposite compared with those of other 13 factors. That is to say that more near the expressway more vegetation distributes and the probability of transformation from vegetation to non-vegetation is less. The results also indicate regression models have a satisfying accuracy and binary logistic model is better than multinomial logistic model.