## 2 Study Area and Methods
### Methods
#### 2.2.6 Statistical Analysis and Data Source  
Changes in the provision of ecosystem services from 2000 to 2015 are the result of changes in biophysical factors and socioeconomic development. Here, a broad set of biophysical and socioeconomic variables were considered. The main biophysical variables are the initial level of ecosystem composition and quality, and the changes of ecosystem composition and quality. The main socioeconomic variables are total human population, agricultural population, agricultural output value, grain yield, and livestock inventories, which can reflect the human activity influences on land-use changes or grassland degradation.  
A correlation analysis and multiple general linear model (GLM) regression were performed in order to identify the relationships between four types of ecosystem services and the above socioeconomic impact factors. All the statistical analyses were conducted using the software Stata (version 13.0; StataCorp, College Station, TX, USA).  
The data for this study were obtained from the different sources listed below.  
1. Ecosystem classification images for the years of 2000 and 2015 were obtained from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. The images are environmental and disaster monitoring and forecasting of small satellite constellation (HJ-1A/B) and Landsat OLI (resolution 90 m) images. The images were classified using object-oriented multi-scale segmentation and decision tree procedures [32]. The ecosystems were categorized into forest, shrub, grassland, wetland, farmland, urban, and barren land.
2. Data on vegetation cover for the years of 2000 and 2015, in the form of moderate resolution imaging spectroradiometer (MODIS) data (250 m resolution), were also obtained from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences.
3. The Digital Elevation Model (DEM) was from the U.S. Geological Survey (USGS) with the resolution of 90 m [37].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ecological Risk Grade** & \
\begin{tabular}{c} **Thresholds for Water Erosion** \\ **(t-km\({}^{-2}\cdot\)yr\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Thresholds for Wind Erosion** \\ **(t-km\({}^{-2}\cdot\)yr\({}^{-1}\))** \\ \end{tabular} \\ \hline No & [0, 500] & [0, 200] \\ Slight & (500, 1000] & (200, 400] \\ Medium & (1000, 1500] & (400, 600] \\ High & (1500, 2000] & (600, 800] \\ Extremely high & \textgreater{}2000\) & \textgreater{}800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thresholds for ecological risk warning of ecosystem services.  
Soil maps (1:1,000,000) and data on associated soil attributes (e.g., the mass percentages of clay, silt, sand, and soil organic matter) were from the Second National Soil Survey of China [38].
* Average annual rainfall erosivity data were obtained from Beijing Normal University. Monthly average rainfall, wind speed, and temperature data were obtained from the Chinese Ecosystem Research Network (CERN) ([[http://www.chern.org.cn/](http://www.chern.org.cn/)]([http://www.chern.org.cn/](http://www.chern.org.cn/))) and the Institute of Geographical Sciences and Natural Resource Research in China. Spatial interpolation was conducted on the meteorological station data to get weather raster data.
* County-level socio-economic and agriculture data were from the Agricultural Information Institute of the Chinese Academy of Agricultural Sciences. They contained crop yield (e.g., annual grain yield of rice, wheat, maize, and oil from oilseed crops) and the total population in each county in the years of 2000 and 2015 [38].