遥感大数据在水利中的应用及发展
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摘要
首先回顾国内外遥感技术的发展历史;其次总结遥感数据在水利中的应用,尤其是在洪涝灾害监测评估中水体的动态监测、旱情灾害监测中土壤含水量监测、地表水源监测中水体边界和面积的提取及水土保持监测中植被覆盖度等环境因子的提取中的应用;最后结合现代信息技术手段,展望遥感大数据在水利应用中的前景,即形成由水利大数据到知识库,再到知识图谱,最终利用可视化应用服务水利行业的整体应用框架,利用微观宏观知识的融合,可服务于水利科学机理揭示,并提升水利管理的实时性、精准性、科学性和智能性,符合水利信息化的新时代要求。
Firstly,this paper reviews the development of remote sensing technology at home and abroad; Secondly,it summarizes the application of remote sensing data in water conservancy,especially in the dynamic monitoring in flood disaster monitoring and evaluation,the monitoring of soil moisture content in drought disaster monitoring,the extraction of water boundary and water area in water source monitoring,and the extraction of environmental factors such as vegetation coverage in soil and water conservation monitoring.Finally,combined with modern information technology such as knowledge base and knowledge graph,the prospects and applications of remote sensing large data in hydrology research are proposed.It is a whole application framework which is from big data of water conservancy to knowledge base and then to knowledge graph,and using visual applications to serve the water conservancy industry.The integration of micro and macro knowledge is helpful to reveal the mechanism of hydrology and improve the realtime,accuracy,scientificity and intelligence.Hence,it meets the new era requirement of water informatization.
Firstly,this paper reviews the development of remote sensing technology at home and abroad; Secondly,it summarizes the application of remote sensing data in water conservancy,especially in the dynamic monitoring in flood disaster monitoring and evaluation,the monitoring of soil moisture content in drought disaster monitoring,the extraction of water boundary and water area in water source monitoring,and the extraction of environmental factors such as vegetation coverage in soil and water conservation monitoring.Finally,combined with modern information technology such as knowledge base and knowledge graph,the prospects and applications of remote sensing large data in hydrology research are proposed.It is a whole application framework which is from big data of water conservancy to knowledge base and then to knowledge graph,and using visual applications to serve the water conservancy industry.The integration of micro and macro knowledge is helpful to reveal the mechanism of hydrology and improve the realtime,accuracy,scientificity and intelligence.Hence,it meets the new era requirement of water informatization.
引文
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[23]李国瑞,王贵平,冯九梁,等.土壤侵蚀模型研究的现状与发展趋势[J].太原理工大学学报,2003,34(1):99-101.
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[25]SERVENAY A,PRAT C.Erosion extension of induratedvolcanic soils of Mexico by aerial photographs and remote sensing analysis[J].Geodemra,2003(117):367-375.
[26]KERDILES H,GRONDONA M O.NOAA/AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa[J].International Journal of Remote Sensing,1995,16(7):1303-1325.
[27]周月敏.面向小流域管理的水土保持遥感监测方法研究[D].北京:中国科学院研究生院,2005:35-43.
[28]陈军飞,邓梦华,王慧敏.水利大数据研究综述[J].水科学进展,2017,28(4):622-631.
[29]高伟.基于特征知识库的遥感信息提取技术研究[D].武汉:中国地质大学,2010.
[30]张兵.遥感大数据时代与智能信息提取[J].武汉大学学报(信息科学版),2018,43(12):1861-1871.
[31]王渊,彭晨辉,王志强,等.知识图谱在电网全业务统一数据中心的应用[J/OL].计算机工程与应用[2019-03-11].http://kns.cnki.net/kcms/detail/11.2127.TP.20190122.1702.015.html.
[32]葛召华,张中坤,李博.基于知识图谱的水利数据垂直搜索应用[J].山东水利,2018(5):1-2.
[33]王占宏,白穆,李宏建.地理空间大数据服务自然资源调查监测的方向分析[J].地理信息世界,2019,26(1):1-5.
[34]张建龙,解建仓.实行最严格的水资源管理制度的支撑与应用模式研究[J].水利发展研究,2013,13(1):17-20.
[2]潘继亚,金亮.基于MODIS数据的曼谷洪水淹没区信息提取[J].环境科学导刊,2015(3):116-120.
[3]饶品增,蒋卫国,王晓雅,等.基于MODIS数据的洪涝灾害分析研究——以2017年洞庭湖区洪水为[J].灾害学,2019,34(1):203-207.
[4]杨存建,魏一鸣,王思远,等.基于DEM的SAR图像洪水水体的提取[J].自然灾害学报,2002(3):121-125.
[5]汤玲英,刘雯,杨东,等.基于面向对象方法的Sentinel-1A SAR在洪水监测中的应用[J].地球信息科学学报,2018,20(3):377-384.
[6]赵昕.基于MODIS与ASAR数据的洪水淹没信息提取方法研究[D].成都:电子科技大学,2012.
[7]王嘉芃,刘婷,俞志强,等.基于COSMO-SkyMed和SPOT-5的城镇洪水淹没信息快速提取研究[J].遥感技术与应用,2016,31(3):564-571.
[8]王丽涛,王世新,周艺,等.旱情遥感监测研究进展与应用案例分析[J].遥感学报,2011,15(6):1315-1330.
[9]马春锋,王维真,吴月茹,等.基于MODIS数据的黑河流域土壤热惯量反演研究[J].遥感技术与应用,2012,27(2):197-207.
[10]张红卫,陈怀亮,申双和,等.基于表层水分含量指数(SWCI)的土壤干旱遥感监测[J].遥感技术与应用,2008,23(6):624-628.
[11]张长春,王晓燕,邵景力.利用NOAA数据估算黄河三角洲区域蒸散量[J].资源科学,2005(1):86-91.
[12]杨婷,陈秀万,万玮,等.基于光学与被动微波遥感的青藏高原地区土壤水分反演[J].地球物理学报,2017,60(7):2556-2567.
[13]朱鹤.遥感技术在地表水源地水体监测中的应用研究[D].北京:中国水利水电科学研究院,2013:1-13.
[14]KLOIBER S M,BREZONIK P L,MARVIN E B.Application of landsat imagery to regional——scale assessments of lake clarity[J].Water Research,2002,36:4330-4340.
[15]李戈伟.基于遥感和GIS的洪灾检测和评估方法研究[D].北京:中国科学院遥感应用研究所,2002.
[16]李畅游.乌梁素海遥感影像的水体提取方法与分析[J].内蒙古农业大学学报,2004,25(1):1-4.
[17]HARRIS A R,MASON I M.Lake area measurement using AVHRR a case study[J].International Journal of Remote Sensing,1989,10(4-5):885-895.
[18]GUIRGUIS S K,HASSAN H M,EL-RAEY M E,et al.Technical Note Multi-temporal change of Lake Brullus,Egypt,from 1983 to 1991[J].International Journal of Remote Sensing,1996,17(15):2915-2921.
[19]陈文倩,丁建丽,李艳华,等.基于国产GF-1遥感影像的水体提取方法[J].资源科学,2015,37(6):1166-1172.
[20]冯沛华,刘振华,胡月明.基于TM遥感优化模型的地表水体信息提取[J].科技通报,2016,32(8):176-181.
[21]RENARD K G.Predicting soil erosion by walter:A guide to conservation planning with the revised universal loss equation(RUSLE)[M].Washington DC:National Technical Information Service,United States Department of Agric ulture(USDA),1997.
[22]张光辉.土壤侵蚀模型研究现状与展望[J].水科学进展,2002,13(3):389-396.
[23]李国瑞,王贵平,冯九梁,等.土壤侵蚀模型研究的现状与发展趋势[J].太原理工大学学报,2003,34(1):99-101.
[24]METTERNICHT G I,ZINCK J A.Evaluating the information contents of JERS-1SAR and Landsat TM data for discrimination of soil erosion features[J].ISPRS Journal of Photogrammetry&Remote Sensing,1998,53:143-153.
[25]SERVENAY A,PRAT C.Erosion extension of induratedvolcanic soils of Mexico by aerial photographs and remote sensing analysis[J].Geodemra,2003(117):367-375.
[26]KERDILES H,GRONDONA M O.NOAA/AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa[J].International Journal of Remote Sensing,1995,16(7):1303-1325.
[27]周月敏.面向小流域管理的水土保持遥感监测方法研究[D].北京:中国科学院研究生院,2005:35-43.
[28]陈军飞,邓梦华,王慧敏.水利大数据研究综述[J].水科学进展,2017,28(4):622-631.
[29]高伟.基于特征知识库的遥感信息提取技术研究[D].武汉:中国地质大学,2010.
[30]张兵.遥感大数据时代与智能信息提取[J].武汉大学学报(信息科学版),2018,43(12):1861-1871.
[31]王渊,彭晨辉,王志强,等.知识图谱在电网全业务统一数据中心的应用[J/OL].计算机工程与应用[2019-03-11].http://kns.cnki.net/kcms/detail/11.2127.TP.20190122.1702.015.html.
[32]葛召华,张中坤,李博.基于知识图谱的水利数据垂直搜索应用[J].山东水利,2018(5):1-2.
[33]王占宏,白穆,李宏建.地理空间大数据服务自然资源调查监测的方向分析[J].地理信息世界,2019,26(1):1-5.
[34]张建龙,解建仓.实行最严格的水资源管理制度的支撑与应用模式研究[J].水利发展研究,2013,13(1):17-20.