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网格化历史耕地数据集能为历史时期耕地变化研究提供更精确的支持,并且为全球环境气候变化研究模型模拟提供驱动数据。本文综合考虑了中国历代土地利用开发的特点及自然人文因子对耕地的影响,设计了一套对中国耕地先分区再分层分配的网格化方法。基于国内3个主流区域耕地数据研究成果,采用上述方法建立了1820年(清仁宗嘉庆二十五年)和1936年(民国二十五年)中国10 km×10 km分辨率的耕地数据集,并绘制了分布图。本文还利用国内具有代表性的区域数据集对重建结果进行对比验证。结果表明,该方法可以保证耕地数量的权威性,并且建立具有区域性的高精度历史耕地数据集。
Gridded historic farmland datasets provide more accurate support for the study of arable land change in historic times and provide driving data for modeling global climate change research. In this paper, we consider the characteristics of land use and development in ancient China and the impact of natural and human factors on cultivated land. We design a set of gridding methods for re-stratification and distribution of arable land in China. Based on the research results of cultivated land data in the three mainstream regions in China, arable land datasets with a resolution of 10 km × 10 km in 1820 (25 years of Qingzhong Zong Jiaqing) and 1936 (25 years of Republic of China) were established by the above methods. And draw the distribution. This article also uses the representative regional data set to verify the reconstructed results. The results show that this method can guarantee the authority of cultivated land quantity and establish a high-precision historical cultivated land dataset with regional characteristics.