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中国水土流失研究热点区的空间分布制图
引用本文:胡云锋,韩月琪,曹巍,张云芝.中国水土流失研究热点区的空间分布制图[J].生态学报,2019,39(16):5829-5835.
作者姓名:胡云锋  韩月琪  曹巍  张云芝
作者单位:中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室;中国科学院大学资源与环境学院
基金项目:国家重点研发计划(2016YFC0503701,2016YFB0501502);中国科学院先导专项A类(XDA19040301,XDA20010202)
摘    要:中国是世界上水土流失最为严重的国家之一。准确掌握既有水土流失研究的空间分布格局是一项基础性工作。以中国知网学术期刊数据库作为数据源,应用自然语言处理方法,对1980—2017年中国水土流失研究地区进行了地名信息提取及研究热点建模;继而应用RUSLE模型模拟,得到全国土壤侵蚀强度的空间分布;在上述研究基础上,对研究热点地区与侵蚀强度之间的空间耦合关系进行了对比分析。结果表明:(1)1980年以来,中国水土流失研究热点区主要分布在黄土高原及贵州高原,涉及陕西、宁夏、内蒙古、甘肃、贵州以及黑龙江等省区;中等及以上热度的县(区、市)共171个,占全国国土总面积的5.33%。(2)RUSLE模型模拟表明,严重的土壤侵蚀主要分布在黄土高原及云贵高原,涉及陕西、宁夏、甘肃、山西、贵州、云南、四川等省区;侵蚀模数大于20 t hm~(-2) a~(-1)的县(区、市)共251个,占全国国土总面积的7.04%。(3)研究热点地图与水土流失强度模型模拟地图之间存在空间差异。对特定空间耦合模式的分析有助于判断科研资源配置的合理性。

关 键 词:水力侵蚀  热点区域  空间分布  自然语言处理  大数据
收稿时间:2018/12/29 0:00:00
修稿时间:2019/6/18 0:00:00

Mapping the spatial distribution of water erosion research hot regions in China
HU Yunfeng,HAN Yueqi,CAO Wei and ZHANG Yunzhi.Mapping the spatial distribution of water erosion research hot regions in China[J].Acta Ecologica Sinica,2019,39(16):5829-5835.
Authors:HU Yunfeng  HAN Yueqi  CAO Wei and ZHANG Yunzhi
Institution:State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China,State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China,State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China and State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:China is one of the countries facing the most serious water erosion in the world. One key issue is to accurately map the distribution of existing researches about water erosion. The paper used the China Academic Journal Network Publishing Database as the data source and applied the natural language processing method to carry out place-name information extraction and research hotness modeling for mapping Chinese soil erosion and water conservation research hot regions during 1980-2017. The RUSLE (Revised Universal Soil Loss Equation) model was then applied for mapping Chinese water erosion intensity. Finally, the spatial relationship between the research hot regions and the distribution of water erosion intensity is compared and discussed. The results show that since 1980, the hot regions of water erosion research in China have mainly been distributed in the Loess Plateau and Guizhou Plateau, involving Shaanxi, Ningxia, Inner Mongolia, Gansu, Guizhou, and Heilongjiang provinces. There are 171 counties (districts or cities), accounting for 5.33% of the total land area, where the level of research hotness was judged to be moderate, or above moderate. The RUSLE model simulation indicated that severe water erosion is mainly distributed in the Loess Plateau and Yunnan-Guizhou Plateau, involving Shaanxi, Ningxia, Gansu, Shanxi, Guizhou, Yunnan, and Sichuan provinces. There are 251 counties (districts or cities), accounting for 7.04% of the total land area, with erosion rates greater than 20 t hm-2 a-1. There existing abvious spatial differences between the map of the research hot regions and the model simulation map of water erosion intensity. Analysis of the above spatial coupling model is helpful to assess the rationality of the allocation of scientific research resources.
Keywords:water erosion  hot region  spatial distribution  natural language processing  literature big data
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