于桥水库蓝藻水华遥感长时序监测研究

doi:10.11873/j.issn.1004-0323.2020.3.0694

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 Wang^2,³

^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 或 http://www.rsta.ac.cn/CN/Y2020/V35/I3/694

图1 研究区地理位置示意图

表1 数据列表

表2 实测样地水质参数

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

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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