The vector map of China is obtained from the Resource and Environment Data Cloud Platform (http://www.resdc.cn/Default.aspx). This map contains the administrative divisions at city scale, which is highly controlled for precision and quality. The vector map of eastern China is extracted from the vector map of China at the city level, and it can be used as a mask for the extraction of NDVI, the raster map of topographic factors (e.g., elevation, aspect, and slope), the raster map of socio-economic factors (e.g., GDP, urban areas, and population density), as well as the raster map of climate factors (e.g., precipitation and temperature) at different levels.
The raster map of land use types, population density, and GDP is generated with a 5- year interval with a spatial resolution of 1 km provided by Resource and Environment Data Cloud Platform (http://www.resdc.cn/Default.aspx). These raster maps are available for every five years. Thus, the raster map of land use types, population density,
and GDP in 2000 and 2015 is applied in this study to detect the spatial interaction between annual NDVI and the topographic factors from 2001 to 2016. All of the raster datasets cover the whole of China. The land use types, population density, and GDP can be extracted by the vector map of eastern China.
Table 3-3. The land use types Land Use Type Description
Farmland Paddy field and dry land
Forest Land Dense forest land (with forest land), shrubland, sparse forest land, and other forest lands
Grassland High-cover grassland, medium-cover grassland, and low-cover grassland
Water Body Wetlands, canals, lakes, reservoirs, glaciers, permanent snow, and beaches
Built-up Land Urban areas, rural residential areas, as well as industrial and mining areas
Desert Sandy land, Gobi, saline-alkali land, and alpine desert Barren Land Barren land and bare rock gravel
4 Methodology
The dynamic change of vegetation cover and the spatiotemporal pattern of vegetation growth in response to its driving forces are complicated because the climate system varies from regional to global scales, from different topographical conditions, and from frequency of disturbances derived from human activities (Buermann et al., 2014, Gamon et al., 2013, Nemani et al., 2003, Zhang et al., 2013b, Buyantuyev and Wu, 2009, Liu et al., 2008b, Liu and Diamond, 2008). This study proposes a practical framework for monitoring the spatiotemporal vegetation cover change, estimating the vegetation stability, and predicting the future variation trend of vegetation cover in eastern China, and further presents a series of mathematical methods to analyze the spatiotemporal dynamics of NDVI and the relationships between NDVI and its driving factors (e.g., climate factors, topographic factors, and socio-economic factors). In this study, MODIS NDVI was used as a representation of vegetation productivity, which indicates the biomass of the vegetation during the study period in eastern China.
The framework consists of four sections. A set of mathematical methods and analysis modules are applied to achieve the research objectives of this study. Section 4.1 exhibits the quantification of spatiotemporal pattern of vegetation cover change. Regarding the MODIS NDVI data, MVC method, geographical mean value calculation, linear regression analysis, stability analysis, and R/S analysis were adopted to display the distribution pattern, changing trend, future changing trend, and fluctuation degree on spatiotemporal scales.
Section 4.2 exhibits the pattern of NDVI in response to climate change both on spatial and temporal scales. In this section, the linear regression analysis was applied to analyze the changing trend of the climate factors, and Pearson’s correlation analysis was adopted to determine the correlation coefficients between NDVI and the climate factors both on annual and seasonal scales. Furthermore, t-test was employed to test the significance level of the correlation coefficients. Moreover, the lag time for maximum NDVI response to climate variation was investigated, and the spatial characteristics of the lag time for maximum NDVI response to climate variation were displayed both on annual and seasonal scales.
Section 4.3 exhibits the spatial pattern of NDVI in response to topographic variation. The surface analysis module was utilized to acquire the raster data of the elevation, aspect, and slope. The raster data of elevation, aspect, and slope were further used to overlap the map of the annual NDVI, changing slope of the annual NDVI, as well as the CV of the annual NDVI.
Section 4.4 reveals the interplay between the annual NDVI and socio-economic development. Pearson’s correlation analysis was applied to explore the strength of the relationships between the annual NDVI and the socio-economic factors. In this section, we further analyzed the spatial coupling features of NDVI variation referring to changes in socio-economic factors. Buffer analysis and overlay analysis were employed to detect the spatial pattern of the annual NDVI responding to socio-economic development, urban expansion, and population growth in eastern China for the study period.
With the support of Geographic Information System (GIS) and RS technologies, the spatiotemporal variation of vegetation cover and its driving forces are analyzed. The flow charts of data processing and analysis are as follows:
Figure 4-2. The workflow for calculation of the relationship between NDVI and
climate factors
Figure 4-3. The workflow for investigation of interaction between NDVI and topographic factors
4.1 Quantification of spatiotemporal pattern of vegetation cover change