# Agricultural Evolution: Process, Pattern and Water Resource Effect
## 2 Materials and Methods
### Data Preprocessing  
According to our previous work [21; 43; 44], land-use maps in the West Songnen Plain for four periods, 1990, 2000, 2010, and 2015, were developed. Land-use maps in 1990, 2000 and 2010 were  
Figure 1: Location map of the West Songnen Plain which includes 14 counties.  
obtained from China's land-use data in previous publications. Additionally, we updated the data to 2015 according to the same methodology published in previous papers. Landsat Thematic Mapper (TM) images were applied to map land-use patterns in 1990, 2000, and 2010 while Landsat-8 Operational Land Imager (OLI) images were used to update the land-use map in 2015. The resolution of both TM and OLI images is 30 m.  
ERDAS and ArcGIS software were applied to process data in our study. Landsat TM/OLI images obtained from June to October were chosen to identify different land-use types because plants in the West Songnen Plain grow vigorously during this period. The standard false color composition which combine bands 4 (Red), 3 (Green), 2 (Blue) for TM images and 5 (Red), 4 (Green), 3 (Blue) for OLI images, respectively, was applied to produce the original images for interpretation. The standard coordinate and projection system, the Beijing 1954 Krasovsky_Albers projection, was used to keep consistency of all spatial data. The primary method to acquire land-use patterns in different periods is visual interpretation which digitizes remote-sensing images after geometric rectification and ortho-rectification [21]. Geometric rectification of related remote-sensing images was done based on ground control points, with the Root Mean Squared (RMS) Error less than 1.5 pixels (45 m). In this study, the smallest mapping unit and the minimum edge are larger than 6 \(\times\) 6 pixels (3.24 ha) and 4 pixels (120 m), respectively. Additionally, land-use vector outlines were drawn through comparing images between two years to identify real land-use changes in order to decrease the errors resulting from post-classification comparison.  
Standard quality control was used to ensure the accuracy of the land-use data. Interpreting samples were produced by field surveys before interpretation of remote-sensing images. Then, interpreters were trained uniformly to recognize remote-sensing image characteristics of various land-use categories. After interpretation, the accuracy was mainly checked by verification points generated by substantial field survey photos and Google Earth images. In addition, we selected the verification points randomly at a ratio of 10%. Unmanned aerial vehicle (UAV) were also used to carry out field surveys in 2015. UAV can fly to places that are difficult for humans to reach such as marshy wetlands. Therefore, it is very helpful for field surveys. UAV photos have a resolution of 5-6 m when our UAV flies to 200 m. The overall accuracy of the six first-level classes was larger than 94.3%, and that of the 25 land-use sub-classes was larger than 91.2% [21]. We used the same first-level classification system as [PERSON] et al. [21]. The difference is that we separated paddy and dry farmland to analyze cultivated land changes. Considering the wide distribution of marshy wetlands in the study area, we separated marshy wetland from unused land to analyze land-use changes.