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遥感技术与应用  2020, Vol. 35 Issue (3): 694-701    DOI: 10.11873/j.issn.1004-0323.2020.3.0694
遥感应用     
于桥水库蓝藻水华遥感长时序监测研究
岳昂1(),曾庆伟2(),王怀警2,3
1.天津市生态环境监测中心,天津 300191
2.二十一世纪空间技术应用股份有限公司,北京 100096
3.虚拟地理环境教育部重点实验室,南京师范大学地理科学学院,江苏 南京 210023)的
Remote Sensing Long-term Monitoring of Cyanobacterial Blooms in Yuqiao Reservoir
Ang Yue1(),Qingwei Zeng2(),Huaijing Wang2,3
1.Tianjin Eco-Environmental Monitoring Center, Tianjin 300191, China
2.Twenty First Century Aerospace Technology Co. , Ltd, Beijing 100096, China
3.Key Laboratory of Virtual Geographical Environment of Ministry of Education, College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
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摘要:

针对水库富营养化给供水安全带来的严重威胁,利用2008~2017年Landsat时间序列卫星数据,基于归一化差值植被指数(NDVI)与实测水质参数的相关分析结果,运用阈值法动态提取了于桥水库的水华分布范围和程度。通过与自然和人为因子的协同分析,认为气温、降水和人类活动等共同驱动引发了水华爆发,其中人为干预的生态修复工程可抑制或减缓水华爆发,并有效改善水质状况。时间分辨率更高的气象因子数据和卫星遥感数据将更有助于对中小型饮用水水面蓝藻水华驱动力的分析,推动准实时遥感监测预警技术应用。

关键词: Landsat归一化差值植被指数(NDVI)水华遥感监测    
Abstract:

Reservoir eutrophication leads serious threat to water supply safety. This paper apples Landsat time series satellite data from 2008 to 2017 to extract the distribution and degree of water bloom in Yuqiao Reservoir based on a threshold method to the correlation analysis results between Normalized Difference Vegetation Index (NDVI) and measured water quality parameters. Through the collaborative analysis of both natural and artificial factors, the water bloom was jointly drive by temperature, precipitation, and human activities. Among them, the ecological restoration project with human intervention could inhibit or slow down the blooms and effectively improve the water quality. Meteorological and spaceborne remote sensing data with higher temporal resolution will be more conducive the analyze the driver force of cyanobacteria blooms on small and medium-sized drinking water surfaces. Meanwhile, remote sensing data based monitoring and early warning technology could be promoted.

Key words: Landsat    Normalized Difference Vegetation Index(NDVI)    Water Bloom    Remote Sensing    Monitor
收稿日期: 2019-03-27 出版日期: 2020-07-10
ZTFLH:  X87  
基金资助: 天津市科技计划项目“天津水源地水土环境状况及污染风险时空分异与预警”(16YFXTSF00380)
通讯作者: 曾庆伟     E-mail: 576252175@ qq.com;278825327@qq.com
作者简介: 岳昂(1986-),男,天津人,硕士,工程师,主要从事生态环境遥感监测与评估方面的研究。E?mail:576252175@ qq.com
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引用本文:

岳昂,曾庆伟,王怀警. 于桥水库蓝藻水华遥感长时序监测研究[J]. 遥感技术与应用, 2020, 35(3): 694-701.

Ang Yue,Qingwei Zeng,Huaijing Wang. Remote Sensing Long-term Monitoring of Cyanobacterial Blooms in Yuqiao Reservoir. Remote Sensing Technology and Application, 2020, 35(3): 694-701.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.3.0694        http://www.rsta.ac.cn/CN/Y2020/V35/I3/694

图1  研究区地理位置示意图
数据源时相分辨率
Landsat 5 TM2008年8月2日30 m
2009年7月20日
2010年8月1日
2011年9月5日
Landsat 7 ETM2012年7月4日
Landsat 8 OLI2013年7月31日
2014年7月11日
2015年7月14日
2016年7月16日
2017年7月10日
表1  数据列表
样点号总磷(mg/L)总氮(mg/L)叶绿素a(mg/L)NDVI
10.0230.2431.930.351
20.0100.1511.140.110
30.0040.0500.22-0.12
40.0520.5009.130.212
50.2342.78012.00.402
60.0100.0490.19-0.111
70.2324.74012.00.419
80.2234.55010.30.399
90.2244.62010.00.398
100.0350.3319.230.302
表2  实测样地水质参数
参数相关系数(R)
总磷0.748
总氮0.702
叶绿素a0.817
表3  水质参数与NDVI相关性分析
图2  水质参数与NDVI变化关系
图3  2008~2017年于桥水库库区水华等级分布图
年份2008200920102011201220132014201520162017
轻度水华区/km239.1152.6660.9964.3167.0937.635.3663.9344.224.81
中度水华区/km23.721.715.612.530.882.011.672.672.971.59
重度水华区/km23.530.342.291.180.071.392.922.325.404.77
表4  2008~2017年于桥水库水华等级及面积统计表
图4  水华等级面积百分比堆叠柱状图
图5  2008~2017年天津市年均降雨量及年均温变化图
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