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1998-2016年全球LUCC研究进展与热点分析 被引量:8

Researches Progress and Hotspots Analysis of Global LUCC Research During 1998 to 2016
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摘要 以Web of Science为数据源,运用文献计量方法与可视化分析工具,从文献时间分布、研究机构、科学领域分布、文献作者和研究热点等维度,对1998-2016年全球土地利用/覆盖变化(LUCC)研究进展进行数据挖掘.研究结果表明:目前LUCC研究日益成熟,文献逐年稳定增长,发展前景依然广阔;机构和作者合作网络还相对松散,LUCC全球合作有待进一步加强;LUCC研究学科交叉性强,综合集成了环境科学、生态学、地理学等多个自然与社会学科;LUCC研究热点众多,气候变化、生态系统、地表景观、城镇化、森林砍伐等热点受到高度关注;综合性研究是未来LUCC的发展趋势,体现在多学科、多时空、多尺度、多方法的交叉.未来LUCC要在数据标准化、大尺度遥感图像融合、历史地图资料不准确性与当代资料整合,以及LUCC全球分类体系和LUCC成果应用等方面加强研究. Using the Web of Science database as data source,the literature measurement method and visual analysis tool have been used to analyze the research situation of LUCC(land-use and land-cover change)during 1998 to 2016 in the literature time distribution,research institutes,scientific field distribution,literature authors and research hot-spots,etc..The results show that:at present,the LUCC research is becoming more and more mature,but the literature is still growing steadily and the prospect is broad;the cooperative network of institutions and authors is relatively loose,suggesting that the global cooperation of LUCC needs to be further strengthened;the study of LUCC is a highly interdisciplinary research,covering environmental science,ecology,geography and many other disciplines.LUCC has many research hot-spots,including climate change,ecosystem,surface landscape,urbanization,deforestation and so on;synthesis is the future development trend of LUCC,embodied in multidisciplinary,multi-temporal scale,multi-level,theory and methods and so on.Next,data standardization,large-scale remote sensing image pixel mixing,inaccurate processing and integration of historical map data,establishment of LUCC global classification system and results application should be strengthened in LUCC.
出处 《华侨大学学报(自然科学版)》 北大核心 2017年第5期591-601,共11页 Journal of Huaqiao University(Natural Science)
基金 国家自然科学基金资助项目(41001054) 教育部人文社会科学基金资助项目(13YJCZH016 13YJA840009 14YJA630053)
关键词 土地利用/覆盖变化 文献计量 可视化分析 数据挖掘 land-use and land-cover change bibliometrics visual analysis data mining
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