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A risk assessment method for remote sensing of cyanobacterial blooms in inland waters

机译:内陆水域近视蓝藻绽放的风险评估方法

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摘要

The widespread occurrence of Cyanobacterial blooms (CABs) in inland waters is a typical and severe challenge for water resources management and environment protection. An accurate and spatially continuous risk assessment of CABs is critical for prediction and preparedness in advance. In this study, a multivariate integrated risk assessment (MIRA) method of CABs in inland waters was proposed. MIRA was simplified with the trophic levels, cyanobacterial and other aquatic plant condition using remote sensing indexes, including the Trophic State Index (TSI), Floating Algae Index (FAI) and Cyanobacteria and Macrophytes Index (CMI). First, the dates of risk assessment were carefully selected based on TSI. Then, we obtained the trophic levels, cyanobacterial, and other aquatic plant condition of water using TSI, CMI and FAI on the selected date, and further scored them pixel by pixel to quantify the risk value. Finally, the risk of CABs in water was accurately assessed based on the pixel risk value. Based on Landsat 8 OLI dataset, MIRA was executed and validated in three different lakes of Wuhan urban agglomeration (WUA) with different trophic states. The results demonstrated that the risk of CABs in Lake LongGan was overall higher than that in Lake LiangZi and Lake FuTou. And the risk of CABs in the east part of Lake LongGan was higher than the other parts. Seasonally, the risk level ranking in Lake LiangZi was the highest in summer, while lowest in winter. However, the seasonal risk ranking was spring, summer, autumn, and winter in Lake LongGan. Based on the comparisons with monthly water quality classification data and results of the existing study, including trophic level, ecology risk, and algal extent, the MIRA method was valuable for accurate and spatially continuous identifying the risk of CABs in inland waters with potential eutrophication trends.
机译:内陆水域的蓝藻盛开(驾驶室)的广泛发生是水资源管理和环境保护的典型和严峻挑战。对驾驶室的准确性和空间连续的风险评估对于预先预测和准备至关重要。在本研究中,提出了内陆水域驾驶室的多元综合风险评估(MIRA)方法。使用遥感指数的营养水平,蓝藻和其他水生植物状况简化了MIRA,包括营养态指数(TSI),浮藻指数(FAI)和Cyanobacteria和Macrophytes指数(CMI)。首先,根据TSI仔细选择风险评估日期。然后,我们在所选日期上使用TSI,CMI和FAI获得营养水平,蓝藻和其他水生植物状况,并进一步将像素的像素进一步缩放,以量化风险值。最后,基于像素风险值准确评估水中驾驶室的风险。基于Landsat 8 Oli DataSet,MIRA在武汉城市集聚(WuA)的三个不同湖泊中执行和验证了不同的营养态。结果表明,龙根湖驾驶室的风险总体上高于梁子湖和德禄湖。龙那湖东部出租车的风险高于其他零件。季节性地,凉席湖的风险水平排名最高,冬季最低。然而,季节性风险排名为龙登湖的春季,夏季,秋季和冬季。基于对每月水质分类数据的比较和现有研究的结果,包括营养级别,生态风险和藻类程度,Mira方法对于准确和空间持续识别内陆水域的风险具有潜在的富营养化趋势。

著录项

  • 来源
    《The Science of the Total Environment》 |2020年第20期|140012.1-140012.14|共14页
  • 作者单位

    State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (UESMARS) Wuhan University Wuhan 430079 China Collaborative Innovation Center of Geospatial Technology Wuhan 430079 China;

    State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (UESMARS) Wuhan University Wuhan 430079 China;

    State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (UESMARS) Wuhan University Wuhan 430079 China;

    State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (UESMARS) Wuhan University Wuhan 430079 China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Landsat 8 OLI; Inland waters; Risk assessment; Cyanobacterial; Aquatic plants; Trophic level;

    机译:Landsat 8 Oli;内陆水域;风险评估;蓝藻;水生植物;营养级别;

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