基于分层分区法的中国历史耕地数据的网格化重建

doi:10.11821/dlxb201607005

地理学报 ›› 2016, Vol. 71 ›› Issue (7): 1144-1156.doi: 10.11821/dlxb201607005

基于分层分区法的中国历史耕地数据的网格化重建

魏希文¹(), 缪丽娟², 江源¹, 崔雪锋⁴()

1. 北京师范大学资源学院,北京 100875
2. 南京信息工程大学地理与遥感学院,南京 210044
3. 中国科学院大气物理研究所季风系统研究中心,北京 100029
4. 北京师范大学系统科学学院,北京 100875

收稿日期:2015-12-07 修回日期:2016-02-28 出版日期:2016-07-25 发布日期:2016-07-25
作者简介:
作者简介：魏希文(1990-), 男, 湖南长沙人, 硕士, 研究方向为历史土地利用重建。E-mail:weixiwen@mail.bnu.edu.cn
基金资助:
国家基础研究发展计划(973) (2011CB952001);国家自然科学基金项目(41271542)

Reconstruction of the historical crop land data in China using the partition and layering-based gridded method

Xiwen WEI¹(), Lijuan MIAO², Yuan JIANG¹, Reshmita NATH³, Xuefeng CUI⁴()

1. College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
2. School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China
3. Center for Monsoon System Research, Institute of Atmospheric Physics, CAS, Beijing 100029, China
4. School of System Science, Beijing Normal University, Beijing 100875, China

Received:2015-12-07 Revised:2016-02-28 Online:2016-07-25 Published:2016-07-25
Supported by:
National Basic Research Program of China (973 Program), No.2011CB952001;National Natural Science Foundation of China, No.41271542]

摘要/Abstract

摘要：

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。

关键词: 中国, 历史耕地, 分区分层, 网格化重建

Abstract:

Reconstruction of the historical cropland database could provide an accurate support for the cropland researches on historical periods. Moreover, it will also act as the driving data for the global climate models. In China, there are several ways to rebuild the historical cropland on the regional scale, concerning the traditional cultivation area and the northeastern China. But there are few studies on the national scale, which is primarily due to lacking of accurate historical cropland inventory data. Therefore, it is necessary to simulate the climate and ecosystem scenarios in a wider spatial scale. In the next step, an improvement in the research on reconstruction of the historical cropland database is an efficient and quick method to meet the demand. The present paper has adopted a different approach, which divides the entire landmass of China into cropland area and livestock area according to the climate conditions and cultural diversities of the country. Several representative historical cropland databases are used as the background of our study to ensure the authenticity of the outcomes. Considering the historical land use exploitation, and the impact of the natural and anthropogenic factors on the cropland dynamics, we have developed a partition and layering-based gridded reconstruction method. This is an empirical method for spatial allocation of the historical cropland inventory data in the individual grid cells. Then we use this method to rebuild the historical cropland database maps for China during 1820 (the 25th year in the reign of Emperor Renzong Jiaqing in the Qing Dynasty) and 1936 (the 25th year of the Republic of China), at a resolution of 10 km×10 km. A precision validation is applied on the rebuilt maps by comparing them with other existing regional databases. The results show that this method could ensure the authenticity of the cropland data, with higher accuracy in the regional cropland reconstruction.

Key words: China, historical cropland, partition and layering, gridded method

魏希文, 缪丽娟, 江源, 崔雪锋. 基于分层分区法的中国历史耕地数据的网格化重建[J]. 地理学报, 2016, 71(7): 1144-1156.

Xiwen WEI, Lijuan MIAO, Yuan JIANG, Reshmita NATH, Xuefeng CUI. Reconstruction of the historical crop land data in China using the partition and layering-based gridded method[J]. Acta Geographica Sinica, 2016, 71(7): 1144-1156.

图/表 3

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基于分层分区法的中国历史耕地数据的网格化重建

Reconstruction of the historical crop land data in China using the partition and layering-based gridded method

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