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### 20世纪中国耕地格网化数据分区重建

1. 1. 中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室,北京 100101
2. 中国科学院大学,北京100049
3. 中国科学院地理科学与资源研究所,北京 100101
• 出版日期:2014-11-25 发布日期:2014-09-30
• 作者简介:

作者简介：冯永恒(1991-),男,河南南阳人,硕士生,主要研究方向为土地利用/覆被变化,E-mail:fengyh.12s@igsnrr.ac.cn

• 基金资助:
国家自然科学基金项目(41371116);中国科学院战略性先导科技专项(XDA05110202)

### Separate reconstruction of Chinese cropland grid data in the 20th century

Yongheng FENG1,2(), Shihuang ZHANG1(), Fanneng HE3, Zhaoyuan ZHOU1,2

1. 1. Key Laboratory of Ecosystem Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
• Online:2014-11-25 Published:2014-09-30

Abstract:

Many studies have demonstrated that land use and cover change (LUCC) has played a key role in global environmental change. The contemporary land cover is a result of human land use in the history. In order to simulate the LUCC's influence in climate and ecosystem, it is important to have a historical LUCC dataset, especially high-resolution land cover dataset. However, in China, such national coverage dataset is still missing, and this has limited the national environmental change simulations. So there is an urgent need to develop an effective way to reconstruct historical cropland distribution with high-resolution grids. Considering the complexity of the natural environment in China, in this study we developed a separate reconstruction method. First, we divided China into four regions based on a qualitative analysis: the traditional cultivated region, the northeastern region, the northwestern region, and the Qinghai-Tibet Plateau. This division is mostly consistent with other recent studies except for the northwestern region, which differs slightly from common delineation. Second, in every region we examined the relationship between cropland distribution and various natural and human factors and built a reconstruction model. In the traditional cultivated region and the northeastern region, we found that elevation, slope, and population density were the main contributing factors to cropland distribution. In other regions, however, population density was the sole significant contributing factor. This model was then used to reconstruct the cropland distribution of China in 1913, 1933, 1950, 1970, 1990 and 2000 at a spatial resolution of 10 km×10 km. By comparing the reconstruction result with remote sensing data interpretation for 1990, we found that the reconstructed cropland distribution data are reliable not only at the county scale, but also at the grid scale. The comparison between the reconstructed change and the remote sensing data-derived change from 1990 to 2000 also supports this view, that is, the separate reconstruction method developed in this study is effective for capturing cropland change over time. The reconstructed dataset indicates the follows. (1) In the northeastern region, the cropland area slightly decreased at the beginning of the People's Republic of China in 1949; up to 1970, the cropland area had recovered and the modern distribution pattern formed; thereafter, the Sanjiang Plain was brought into agricultural development gradually. (2) In Xinjiang in western China, the first cropland development climax appeared in the republican period influenced by the agricultural policy; the second climax appeared between the 1950s and the 1970s, but most of the cropland was distributed in the area of the Tianshan Mountains. (3) Change in cropland distribution of the Qinghai-Tibet Plateau was not notable, but the area had increased much; the spatial distribution of cropland in the traditional cultivated region also did not change significantly, but the reclamation ratio has increased. In conclusion, cropland area in China had increased in the early 20th century and then decreased, and the inflection point was likely in the late 20th century. This trend occurred not only in cropland area, but also in reclamation ratio. However, the change varies in different regions and is more pronounced in the northeastern and northwestern regions.

• K298.6