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遥感技术与应用  2020, Vol. 35 Issue (2): 295-301    DOI: 10.11873/j.issn.1004-0323.2020.2.0295
大数据美丽中国专栏     
基于地球大数据的“美丽湖泊” SDG 6.3.2综合评价体系构建
沈明1,2(),丁云生3,段洪涛1()
1.中国科学院南京地理与湖泊研究所 中国科学院流域地理学重点实验室,江苏 南京 210008
2.中国科学院大学,北京 100049
3.安徽省巢湖管理局环境信息中心,安徽 巢湖 238000
Construction of “Beautiful Lakes” Comprehensive Assessment System based on Big Earth Data and SDG 6.3.2
Ming Shen1,2(),Yunsheng Ding3,Hongtao Duan1()
1.Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Environmental Information Centre of Chaohu Lake Management Authority, Chaohu 238000, China
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摘要:

湖泊等水体水质状况直接关系到人类社会的可持续发展。传统的水环境质量评价体系大都基于统计数据和原位测量数据,存在周期过长和时效性差等问题,难以实现大范围、连续地湖泊水环境质量评价。遥感技术的发展为高时空分辨率的湖泊水环境质量评价提供了可能。在总结现有湖泊水环境质量评价体系的基础上,以联合国可持续发展目标(Sustainable Development Goals, SDGs)中指标SDG 6.3.2(环境水质良好的水体比例)为导向,结合统计数据、野外实测数据和卫星遥感数据等地球大数据构建了“美丽湖泊”综合评价体系,以期在联合国可持续发展目标框架下,推进我国湖泊水环境质量综合评价,为美丽中国评价提供技术参考。

关键词: 地球大数据“美丽湖泊”SDG 6.3.2“美丽中国”    
Abstract:

Lake water quality is directly related to the survival and development of human beings and society. Most of the existing assessment systems are based on statistical data and in-situ measurement data. Due to the long cycle and poor timeliness, these assessment systems are hard to achieve large-scale and continuous assessment of lake water environment. The development of remote sensing technology has made it possible to evaluate the quality of lake water environment with high spatial and temporal resolution. Thus, after summarizing the existing lake water environment quality assessment system, a new assessment system called “Beautiful Lakes” comprehensive assessment system was developed. A novel index system based on Big Earth Data (such as statistical data, field measured data and satellite remote sensing data) was first developed and integrates human activities, water quality, biology and hydrology indexes. Then, the threshold of each index was determined and the Percentage Compliance of Water Quality Index (cwq) was calculated. Following UN water, the threshold 80% of cwq was used to classify the water quality, which means if a certain water body is with cwq greater than 80%, the water quality is “good”; otherwise, the water quality is poor. Finally, the Percentage of Water Bodies of Good Quality (WBGQ) was calculated to attain the comprehensive assessment of water quality on a large scale (basin scale or national scale). The new assessment system will promote the comprehensive assessment of lake water environment quality in China under the framework of the UN Sustainable Development Goals and provide a technical reference for the evaluation of beautiful China.

Key words: Big earth data    “Beautiful lakes”    SDG 6.3.2    Beautiful China
收稿日期: 2019-03-05 出版日期: 2020-07-10
ZTFLH:  P237  
基金资助: 中国科学院战略性先导科技专项(A 类)(XDA19040500)
通讯作者: 段洪涛     E-mail: mshen@niglas.ac.cn;htduan@niglas.ac.cn
作者简介: 沈 明(1993-),男,江苏南京人,博士研究生,主要从事湖泊水环境遥感研究。E?mail: mshen@niglas.ac.cn
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引用本文:

沈明,丁云生,段洪涛. 基于地球大数据的“美丽湖泊” SDG 6.3.2综合评价体系构建[J]. 遥感技术与应用, 2020, 35(2): 295-301.

Ming Shen,Yunsheng Ding,Hongtao Duan. Construction of “Beautiful Lakes” Comprehensive Assessment System based on Big Earth Data and SDG 6.3.2. Remote Sensing Technology and Application, 2020, 35(2): 295-301.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.2.0295        http://www.rsta.ac.cn/CN/Y2020/V35/I2/295

类型传感器空间分辨率波段数时间分辨率发射时间叶绿素藻蓝素悬浮物CDOMKd浊度浮叶植被挺水植被沉水植被
高光谱Hyperion30 m6060 d2000~2017SSSSSSPPP
海洋-沿岸MERIS1.2 km/300 m152 d2002~2012HHHHHHPPP
MODIS-A and T1 km131 d1999~至今HSSSSSNNN
MODIS-A and T500 m131 d1999~至今PNSPPPNPP
MODIS-A and T250 m131 d1999~至今PNSNPPNPP
Suomi-VIIRS750 m131 d2011~至今HSHHHHNNN
Suomi-VIIRS375 m131 d2011~至今PNSNPPNPP
OLCI300 m211 d(双星)2016~至今HHHHHHPPP
静止卫星GOCI500 m830 min2010~至今HPHHHHPPP
Himawari-8&9,GOES-R500 m~2 km410 min2014~至今PNHPPSNNN
中-高空间分辨率MSI10 m~60 m1010 d(双星5 d)2015~至今SSSSSSSSS
中空间分辨率Landsat 1-730 m416 d1972~至今PPSPSSSSP
Landsat-830 m516 d2013~至今PSSPSSPPP
表 1  水质监测相关卫星传感器及其监测能力[16]
来源指标
中华人民共和国生态环境部[24,26]pH值、溶解氧、高猛酸盐指数、化学需氧量、五日生化需氧量、氨氮、总磷、铜、锌、氟化物、硒、砷、汞、镉、铬、铅、氰化物、挥发酚、石油类、阴离子表面活性剂、硫化物
欧盟[23]浮游植物组成、丰度和生物量、其他水生植物组成和丰度、底栖无脊椎动物组成和丰度、鱼类动物组成、丰度和年龄结构、流量与流速、换水周期、地下水连通性、水深变化、护窗数量、结构和基质、透明度、热状况、溶解氧、盐度、pH值、营养条件、重要污染物、其他污染物
美国环境保护局[22,28]生物群落状况、溶解氧、温度、电导率、pH值、栖息地评价、流、营养盐、土地利用情况、富营养状况、病原体指标、滋扰植物生长情况、叶绿素、透明度、硝酸盐、盐度、悬浮物、汞、DDT、多氯联苯
联合国水机制[11,29]溶解氧、电导率、总氧化氮、总磷和pH值
水利部太湖流域管理局[30,31]最低旬平均水位满足状况、湖泊水位变幅程度、河湖连通状况、湖泊萎缩状况、湖滨带状况、溶解氧水质状况、耗氧有机污染状况、富营养状况、浮游植物数量、浮游动物生物损失指数、大型水生植物覆盖度、大型底栖无脊椎动物、生物完整性指数、鱼类生物损失指数、水功能区达标指标、水资源开发利用指标、防洪指标、公众满意度指标、岸线开发利用率
巢湖管理局[32]水资源开发利用率、最低生态水位满足程度、植被覆盖度指标、人工干扰程度、湖泊面积萎缩比例、水质污染指数、水华程度、水华规模、鱼类保有指数、浮游植物香浓-威纳指数、浮游植物数量、浮游动物香浓-威纳指数、浮游动物生物损失指数、底栖动物香浓-威纳指数、底栖动物耐污指数、大型水生植物覆盖度变化比例、防洪达标率、综合供水保证率、水功能区达标率、通航水深保证率
表 2  水环境质量评价指标
要素层指标层数据源更新频率与方法阈值确定方法
人类活动要素采砂等级遥感数据晴好天气逐日更新参照状态法、频度分析法
综合营养状态指数各省、市环境监测部门水质监测数据/ 野外实测数据逐月更新规范标准类比法
水质要素透明度遥感数据晴好天气逐日更新参照状态法、频度分析法
叶绿素a浓度遥感数据晴好天气逐日更新参照状态法、频度分析法
藻蓝素浓度遥感数据晴好天气逐日更新参照状态法、频度分析法
悬浮物浓度遥感数据晴好天气逐日更新参照状态法、频度分析法
CDOM遥感数据晴好天气逐日更新参照状态法、频度分析法
总磷各省、市环境监测部门水质监测数据/ 野外实测数据逐月更新规范标准类比法
总氮各省、市环境监测部门水质监测数据/ 野外实测数据逐月更新规范标准类比法
生物要素蓝藻水华面积遥感数据晴好天气逐日更新参照状态法、频度分析法
蓝藻水华风险性遥感数据晴好天气逐日更新参照状态法、频度分析法
蓝藻水华持续时间遥感数据每年更新参照状态法、频度分析法
水生植被面积遥感数据逐月更新参照状态法、频度分析法
草、藻湖区比例遥感数据逐月更新参照状态法、频度分析法
水文要素湖泊面积遥感数据逐月更新参照状态法、频度分析法
换水周期统计数据每年更新规范标准类比法
表 3  “美丽湖泊”综合评价指标体系
图 1  “美丽湖泊”综合评价流程
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