海南省遥感大数据服务平台建设与应用示范
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摘要
空间信息技术产业是国家战略性新兴产业。遥感技术的多元化应用和海量数据使得空间信息技术与大数据时代接轨成为现实。海南省是中国最大的省级经济特区。依靠独特的自然地理环境,海南省目前正在实施国际旅游岛、南海战略和21世纪海上丝绸之路重要战略支点等国家战略;坚持生态立省的原则,海南省也是国家生态文明试验区。2016年立项的海南省重大科技计划——"海南省遥感大数据服务平台建设与应用示范"项目,以天空地一体化的空间科技为切入点,基于遥感、导航、GIS等天空地一体化技术手段,建设以海南遥感大数据云为代表的大数据基础设施和智能化共享服务平台,实现海南省典型行业的空间技术应用示范,满足面向新时期海南省社会经济发展中对空间信息产品的快捷、准确、个性化共享服务需求。项目重点攻克了大规模空间观测数据和信息产品共享中的多项关键技术难题,消除目前空间数据分散和信息孤岛现象,提高空间信息获取的准确性和时效性,实现信息资源共享和高效服务。在天空地一体化遥感大数据服务平台下,项目围绕海岸带、农业、林业、旅游、城市环境等典型行业领域开展应用示范,构建省级典型行业领域应用服务信息系统,提供及时有效的动态监测信息和科学决策,以进一步提升政府部门在资源环境管理方面的能力和水平,实现全省资源、环境、经济、社会协调可持续发展。
Spatial information technology is a strategic and emerging industry worldwide. The diversified application of remote sensing technology and massive data featuring the integration of spatial information technology with the big data era has now become a mere reality.Hainan Province, relying on its unique natural and geographical environment, adheres to ecological development and is the largest provincial-level special economic zone in China. Hainan Province is also currently implementing a national strategy for an international tourism island. The major scientific and technological plan for Hainan Province, which was established in 2016, is a demonstration project for the construction and application of the remote sensing big data service platform in the province. With the large-data infrastructure and intelligent shared service platform represented by the Hainan remote sensing big data cloud, the demonstration of space technology applications in a typical industry in Hainan Province is realized to meet the needs of spatial information products in the socio-economic development in the new era of personalized shared service requirements. The project focuses on numerous key technical problems in the sharing of large-scale space observation data and information products, eliminating the current spatial data dispersion and information of island phenomenon, improving the accuracy and timeliness of spatial information acquisition, and achieving information resource sharing and efficient service. Under the integrated remote sensing big data service platform, application demonstration is conducted in typical industries, such as coastal zone, agriculture, forestry, tourism, urban environment, and provincial-level application-oriented information system for typical industries.The big data service platform envisages to provide timely and effective dynamic monitoring information to scientific decision-making platform to further enhance the ability of the government departments in resource and environmental management and achieve coordinated and sustainable development of resources, environment, economy, and society of the province.
Spatial information technology is a strategic and emerging industry worldwide. The diversified application of remote sensing technology and massive data featuring the integration of spatial information technology with the big data era has now become a mere reality.Hainan Province, relying on its unique natural and geographical environment, adheres to ecological development and is the largest provincial-level special economic zone in China. Hainan Province is also currently implementing a national strategy for an international tourism island. The major scientific and technological plan for Hainan Province, which was established in 2016, is a demonstration project for the construction and application of the remote sensing big data service platform in the province. With the large-data infrastructure and intelligent shared service platform represented by the Hainan remote sensing big data cloud, the demonstration of space technology applications in a typical industry in Hainan Province is realized to meet the needs of spatial information products in the socio-economic development in the new era of personalized shared service requirements. The project focuses on numerous key technical problems in the sharing of large-scale space observation data and information products, eliminating the current spatial data dispersion and information of island phenomenon, improving the accuracy and timeliness of spatial information acquisition, and achieving information resource sharing and efficient service. Under the integrated remote sensing big data service platform, application demonstration is conducted in typical industries, such as coastal zone, agriculture, forestry, tourism, urban environment, and provincial-level application-oriented information system for typical industries.The big data service platform envisages to provide timely and effective dynamic monitoring information to scientific decision-making platform to further enhance the ability of the government departments in resource and environmental management and achieve coordinated and sustainable development of resources, environment, economy, and society of the province.
引文
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Li G Q,Zhang H Y,Zhang L C,Wang Y Y and Tian C Z.2016.Development and trend of Earth observation data sharing.Journal of Remote Sensing,20(5):979-990(李国庆,张红月,张连翀,王媛媛,田传召.2016.地球观测数据共享的发展和趋势.遥感学报,20(5):979-990)[DOI:10.11834/jrs.20166173]
Li X W,Zhang L,Wang L Y and Wan X X.2017.Effects of BOWmodel with affinity propagation and spatial pyramid matching on polarimetric SAR image classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,10(7):3314-3322[DOI:10.1109/JSTARS.2017.2671364]
Meng Q Y,Zhang L L,Sun Z H,Meng F,Wang L and Sun Y X.2017.Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area:a 12-year case study in Beijing,China.Remote Sensing of Environment,204:826-837[DOI:10.1016/j.rse.2017.09.019]
Ren C S,Ye H C,Cui B and Huang W J.2017.Acreage estimation of mango orchards using object-oriented classification and remote sensing.Resources Science,39(8):1584-1591(任传帅,叶回春,崔贝,黄文江.2017.基于面向对象分类的芒果林遥感提取方法研究.资源科学,39(8):1584-1591)[DOI:10.18402/resci.2017.08.14]
Wang H,Zhang L,Bi S and Han R D.2018.Remote sensing monitoring and simulation of city development for Hainan Province.Journal of Applied Sciences,36(5):798-807(王恒,张丽,毕森,韩瑞丹.2018.海南城市发展进程遥感监测分析与模拟.应用科学学报,36(5):798-807)[DOI:10.3969/j.issn.0255-8297.2018.05.007]
Wang Q J,Chen Y and Zhou H Y.2017.Investigation and evaluation of Sanya geological tourism resources.Journal of Hainan Normal University(Natural Science),30(4):443-449(王钦军,陈玉,周红英.2017.三亚市地质旅游资源调查与评价.海南师范大学学报(自然科学版),30(4):443-449)[DOI:10.12051/j.issn.1674-4942.2017.04.017]
Zhang L,Lin H,bin Hashim M,Sutrisno D,Khaing M M,Hossain MS,Lwin Z M,Zhang H S and Zhu L W.2017.Earth observation of resources and environment in coastal zone along the maritime silk road.Bulletin of Chinese Academy of Sciences,32(Z1):26-33(张丽,林珲,bin Hashim M,Sutrisno D,Khaing M M,Hossain MS,Lwin Z M,张鸿生,朱岚巍.2017.空间观测海上丝绸之路沿线海岸带资源环境格局.中国科学院院刊,32(Z1):26-33)
Chen B Q,Xiao X M,Ye H C,Ma J,Doughty R and Li X P.2018.Mapping forest and their spatial-temporal changes from 2007 to2015 in tropical Hainan Island by integrating ALOS/ALOS-2 L-band SAR and Landsat optical images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,11(3):852-867[DOI:10.1109/JSTARS.2018.2795595]
Chen Z C,Zhu X J,Hu Y Q.Research on Cloud Computing Technology Architecture and Data Management.China Atomic Energy Press,2017,July.(陈作聪,朱晓静,胡雅祺.2017.云计算技术架构及数据管理探究.北京:中国原子能出版社)
Guo H D,Wang L Z,Chen F and Liang D.2014.Scientific big data and Digital Earth.Chinese Science Bulletin,59(12):1047-1054(郭华东,王力哲,陈方,梁栋.2014.科学大数据与数字地球.科学通报,59(12):1047-1054)[DOI:10.1007/s11434-014-0645-3]
Jiang W,He G J,Liu H C,Long T F,Wang W,Zheng S Z and Ma XX.2017.Research on China’s land image mosaicking and mapping technology based on GF-1 satellite WFV data.Remote Sensing for Land and Resources,29(4):190-196(江威,何国金,刘慧婵,龙腾飞,王威,郑守住,马肖肖.2017.高分一号卫星WFV影像全国陆地镶嵌与制图技术研究.国土资源遥感,29(4):190-196)[DOI:10.6046/gtzyyg.2017.04.29]
Li G Q and Pang L S.2017.A new age of public-oriented Earth observation development.Scientia Sinica Informationis,47(2):193-206(李国庆,庞禄申.2017.公众化驱动的地球观测发展新时代.中国科学:信息科学,47(2):193-206)[DOI:10.1360/N112016-00127]
Li G Q,Zhang H Y,Zhang L C,Wang Y Y and Tian C Z.2016.Development and trend of Earth observation data sharing.Journal of Remote Sensing,20(5):979-990(李国庆,张红月,张连翀,王媛媛,田传召.2016.地球观测数据共享的发展和趋势.遥感学报,20(5):979-990)[DOI:10.11834/jrs.20166173]
Li X W,Zhang L,Wang L Y and Wan X X.2017.Effects of BOWmodel with affinity propagation and spatial pyramid matching on polarimetric SAR image classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,10(7):3314-3322[DOI:10.1109/JSTARS.2017.2671364]
Meng Q Y,Zhang L L,Sun Z H,Meng F,Wang L and Sun Y X.2017.Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area:a 12-year case study in Beijing,China.Remote Sensing of Environment,204:826-837[DOI:10.1016/j.rse.2017.09.019]
Ren C S,Ye H C,Cui B and Huang W J.2017.Acreage estimation of mango orchards using object-oriented classification and remote sensing.Resources Science,39(8):1584-1591(任传帅,叶回春,崔贝,黄文江.2017.基于面向对象分类的芒果林遥感提取方法研究.资源科学,39(8):1584-1591)[DOI:10.18402/resci.2017.08.14]
Wang H,Zhang L,Bi S and Han R D.2018.Remote sensing monitoring and simulation of city development for Hainan Province.Journal of Applied Sciences,36(5):798-807(王恒,张丽,毕森,韩瑞丹.2018.海南城市发展进程遥感监测分析与模拟.应用科学学报,36(5):798-807)[DOI:10.3969/j.issn.0255-8297.2018.05.007]
Wang Q J,Chen Y and Zhou H Y.2017.Investigation and evaluation of Sanya geological tourism resources.Journal of Hainan Normal University(Natural Science),30(4):443-449(王钦军,陈玉,周红英.2017.三亚市地质旅游资源调查与评价.海南师范大学学报(自然科学版),30(4):443-449)[DOI:10.12051/j.issn.1674-4942.2017.04.017]
Zhang L,Lin H,bin Hashim M,Sutrisno D,Khaing M M,Hossain MS,Lwin Z M,Zhang H S and Zhu L W.2017.Earth observation of resources and environment in coastal zone along the maritime silk road.Bulletin of Chinese Academy of Sciences,32(Z1):26-33(张丽,林珲,bin Hashim M,Sutrisno D,Khaing M M,Hossain MS,Lwin Z M,张鸿生,朱岚巍.2017.空间观测海上丝绸之路沿线海岸带资源环境格局.中国科学院院刊,32(Z1):26-33)
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