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High Resolution Land Cover datasets Integration and application Based on Landsat and GlobCover data from 1975 to 2010 in Siberia

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Chinese Geographical Science 2016年第4期26卷 429-438页

作者： LIU Tingxiang ZHANG Shuwen XU Xinliang BU Kun NING Jia CHANG Liping Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun 130012 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China

Land cover is recognized as one of the fundamental terrestrial datasets required in land system change and other ecosystem related researches across the globe. The regional differentiation and spatial-temporal variation of land cover has significant impact on regional natural environment and socio-economic sustainable development. Under this context, we reconstructed the history land cover data in Siberia to provide a comparable datasets to the land cover datasets in China and abroad. In this paper, the European Space Agency(ESA) Global Land Cover Map(GlobCover), Landsat Thematic Mapper(TM), Enhanced Thematic Mapper(ETM), Multispectral Scanner(MSS) images, Google Earth images and other additional data were used to produce the land cover datasets in 1975 and 2010 in Siberia. data evaluation show that the total user′s accuracy of land cover data in 2010 was 86.96%, which was higher than ESA GlobCover data in Siberia. The analysis on the land cover changes found that there were no big land cover changes in Siberia from 1975 to 2010 with only a few conversions between different natural forest types. The mainly changes are the conversion from deciduous needleleaf forest to deciduous broadleaf forest, deciduous needleleaf forest to mixed forest, savannas to deciduous needleleaf forest etc., indicating that the dominant driving factor of land cover changes in Siberia was natural element rather than human activities at some extent, which was very different from China. However, our purpose was not just to produce the land cover datasets at two time period or explore the driving factors of land cover changes in Siberia, we also paid attention on the significance and application of the datasets in various fields such as global climate change, geopolitics, cross-border cooperation and so on.

关键词： land cover change Landsat data integration data application Siberia

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China Argo project:progress in China Argo ocean observations and data applications

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Acta Oceanologica Sinica 2017年第6期36卷 1-11页

作者： LIU Zenghong WU Xiaofen XU Jianping LI Hong LU Shaolei SUN Chaohui CAO Minjie State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyState Oceanic AdministrationHangzhou 310012China Zhejiang Institute of Hydraulics and Estuary Hangzhou 310020China

This paper reviews the current achievements of the China Argo project. It considers aspects of both the construction of the Argo observing array, float technology, and the quality control and sharing of its data. The developments of associated data products and data applications for use in the fields of ocean, atmosphere, and climate research are discussed, particularly those related to tropical cyclones （typhoons）, ocean circulation, mesoscale eddies, turbulence, oceanic heat/salt storage and transportation, water masses, and operational oceanic/atmospheric/climatic forecasts and predictions. Finaliy, the challenges and opportunities involved in the long-term maintenance and sustained development of the China Argo ocean observation network are outlined. Discussion also focuses on the necessity for increasing the number of floats in the Indian Ocean and for expanding the regional Argo observation network in the South China Sea, together with the importance of promoting the use of Argo data by the maritime countries of Southeast Asia and India.

关键词： China Argo ocean observation float development Argo data data application

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BIG data:UNLEASHING INFORMATION

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Journal of Systems Science and Systems Engineering 2013年第2期22卷 127-151页

作者： James M.TIEN College of Engineering University of Miami

At present, it is projected that about 4 zettabytes （or 10^＊＊21 bytes） of digital data are being generated per year by everything from underground physics experiments to retail transactions to security cameras to global positioning systems. In the U. S., major research programs are being funded to deal with big data in all five sectors （i.e., services, manufacturing, construction, agriculture and mining） of the economy. Big data is a term applied to data sets whose size is beyond the ability of available tools to undertake their acquisition, access, analytics and/or application in a reasonable amount of time. Whereas Tien （2003） forewarned about the data rich, information poor （DRIP） problems that have been pervasive since the advent of large-scale data collections or warehouses, the DRIP conundrum has been somewhat mitigated by the Big data approach which has unleashed information in a manner that can support informed - yet, not necessarily defensible or valid - decisions or choices. Thus, by somewhat overcoming data quality issues with data quantity, data access restrictions with on-demand cloud computing, causative analysis with correlative data analytics, and model-driven with evidence-driven applications, appropriate actions can be undertaken with the obtained information. New acquisition, access, analytics and application technologies are being developed to further Big data as it is being employed to help resolve the 14 grand challenges （identified by the National Academy of Engineering in 2008）, underpin the 10 breakthrough technologies （compiled by the Massachusetts Institute of Technology in 2013） and support the Third Industrial Revolution of mass customization.

关键词： Big data data acquisition data access data analytics data application decision informatics products processes adaptive services digital manufacturing mass customization third industrial revolution

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互联网独角兽企业生态圈与数字经济环境的双向影响机制

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贸易经济 2021年第3期

作者：祁大伟宋立丰魏巍

构建商业生态系统逐渐成为现代企业的重要战略选择，然而，在数字经济情境下，近几年崛起的平台型互联网独角兽企业呈现出有别于传统商业生态系统的互联网生态圈模式。结合滴滴和美团生态圈构建案例，从数据生成、数据获取、数据分析、... 详细信息

构建商业生态系统逐渐成为现代企业的重要战略选择，然而，在数字经济情境下，近几年崛起的平台型互联网独角兽企业呈现出有别于传统商业生态系统的互联网生态圈模式。结合滴滴和美团生态圈构建案例，从数据生成、数据获取、数据分析、数据应用和数据辅助五个阶段总结了在数字经济环境下平台型互联网独角兽企业生态圈的构建动机和逻辑，并从动态视角探讨了独角兽企业的互联网生态圈与数字经济环境的交互和结果，在此基础上补充了商业生态系统的评价体系。研究发现，第一，互联网独角兽企业的生态圈建设与过去的国际化生态、上下游供应链生态等模式相比，更倾向于选择通过成立新企业而非传统的依托于其他企业的方式来构建商业生态系统，且更强调商业生态系统的独立性，而非依托于传统的供应链；第二，众多互联网独角兽企业选择具有很大共性的“反常”跨界战略作为生态圈构建的战略方向，是因为它们面对着诸多数据环境压力，包括市场信心带来的数据覆盖压力、下游市场屏障带来的基础数据压力、行业壁垒低带来的数据接入压力和机会、用户黏性低带来的数据黏性压力、人口红利和专利法律政策带来的数据规模和数据模仿特点、垄断型市场格局带来的数据竞争压力、业务扩张带来的数据差异化压力、重大突发公共事件带来的数据网络化压力、盈利缺失带来的数据变现压力等；第三，独角兽企业的互联网生态圈具有广泛的环境影响力，应从用户黏性与生态圈数据沉淀、用户数据权与数据价值密度、用户消费习惯与数据价值深度、平台数据创新与数据应用多样化四方面评价其与数字化环境的双向影响。新时代的企业生态越来越成为依托数据的数据网络生态，数字化企业尤其是大规模的独角兽企业在构建生态圈的过程中应充分发挥自身数据优势，构建圈内圈外的数据交换体系，并最大化地利用数据方法和效应提升企业的生态竞争力，找到适合自己的生态位。

关键词：数字经济独角兽企业互联网生态圈数据应用跨界 digital economy unicorn enterprise internet ecosystem data application cross-boundary

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阅读推广线上活动数据分析与应用

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情报资料工作 2019年第4期

作者：黄红梅

文章在界定阅读推广线上活动数据基础上，详细介绍阅读推广线上活动用户数据、图文数据、菜单数据和交互数据的分析指标及其内容；同时围绕用户信息标签、线上活动校正、线上活动导向和线上活动效果可视化探讨数据分析在阅读推广线上活... 详细信息

文章在界定阅读推广线上活动数据基础上，详细介绍阅读推广线上活动用户数据、图文数据、菜单数据和交互数据的分析指标及其内容；同时围绕用户信息标签、线上活动校正、线上活动导向和线上活动效果可视化探讨数据分析在阅读推广线上活动中的直接应用，围绕去中心化和多元化活动链接深入剖析阅读推广线上活动数据的延伸应用。

关键词：阅读推广线上活动数据分析数据应用 reading promotion on-line activities data analysis data application

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